Sentence-ALDi
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AMR-KELEG
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Quick Summary
[](https://github.com/AMR-KELEG/ALDi) [](https://huggingface.co/spaces/AMR-KELEG/ALDi) A BERT-based model fine-tuned to estimate the Arabic Level of Dialectnes...
Code Examples
Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Usagetexttransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AMR-KELEG/Sentence-ALDi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def compute_score(sentence):
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
return min(max(0, logits[0][0].item()), 1)
if __name__ == "__main__":
s1 = "الطقس جيد اليوم"
s2 = "الجو Øلو النهاردة"
print(s1, round(compute_score(s1), 3)) # 0
print(s2, round(compute_score(s2), 3)) #Â 0.951Deploy This Model
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