bert-uncased-intent-classification
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license:apache-2.0
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yeniguno
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Quick Summary
This is a fine-tuned BERT-based model for intent classification, capable of categorizing intents into 82 distinct labels.
Code Examples
How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]How to Get Started with the Modelpythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("yeniguno/bert-uncased-intent-classification")
tokenizer = AutoTokenizer.from_pretrained("yeniguno/bert-uncased-intent-classification")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Play the song, Sam."
prediction = pipe(text)
print(prediction)
# [{'label': 'play_music', 'score': 0.9997674822807312}]Deploy This Model
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