xlm-roberta-ovos-intent-classifier

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license:apache-2.0
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fdemelo
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Quick Summary

XLM-RoBERTa OVOS intent classifier (base-sized model) XLM-RoBERTa model pre-trained on 2.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1780GB+ RAM

Code Examples

Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)
Intended uses & limitationspythontransformers
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
model = AutoModelForSequenceClassification.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
tokenizer = AutoTokenizer.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")
config = AutoConfig.from_pretrained("fdemelo/xlm-roberta-ovos-intent-classifier")

# preprocess dataset
def tokenize_function(examples):
examples["label"] = list(map(lambda x: config.label2id[x], examples["label"]))
return tokenizer(examples["sentence"], padding="max_length", truncation=True)

tokenized_dataset = dataset.map(tokenize_function, batched=True)
prediction = model.predict(tokenized_dataset)

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