Instructions to use chkla/roberta-argument with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chkla/roberta-argument with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chkla/roberta-argument")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chkla/roberta-argument") model = AutoModelForSequenceClassification.from_pretrained("chkla/roberta-argument", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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**Model training**
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**RoBERTArg** was fine-tuned on a $$RoBERTA_{base}$$ pre-trained model using the HuggingFace trainer with the following hyperparameters. The hyperparameters were determined using a hyperparameter search on a 20% validation set.
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training_args = TrainingArguments(
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**Model training**
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**RoBERTArg** was fine-tuned on a $$$RoBERTA_{base}$$$ pre-trained model using the HuggingFace trainer with the following hyperparameters. The hyperparameters were determined using a hyperparameter search on a 20% validation set.
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```
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training_args = TrainingArguments(
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