Instructions to use HuggingFaceFW/fineweb-edu-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceFW/fineweb-edu-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceFW/fineweb-edu-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceFW/fineweb-edu-classifier") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceFW/fineweb-edu-classifier") - Inference
- Notebooks
- Google Colab
- Kaggle
Update src/run_edu_bert.py
Browse files- src/run_edu_bert.py +1 -1
src/run_edu_bert.py
CHANGED
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@@ -39,7 +39,7 @@ def main(args):
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_name", type=str, default="
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parser.add_argument("--dataset_name", type=str, default="HuggingFaceFW/fineweb")
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parser.add_argument("--dataset_config", type=str, default="default")
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parser.add_argument("--output_dataset_name", type=str, default="HuggingFaceFW/fineweb-edu")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_name", type=str, default="HuggingFaceFW/fineweb-edu-classifier")
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parser.add_argument("--dataset_name", type=str, default="HuggingFaceFW/fineweb")
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parser.add_argument("--dataset_config", type=str, default="default")
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parser.add_argument("--output_dataset_name", type=str, default="HuggingFaceFW/fineweb-edu")
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