Token Classification
PyTorch
English
bert
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@@ -71,7 +71,7 @@ displacy.serve(doc, style="ent")
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  We have created a free [Google Colab notebook](https://colab.research.google.com/drive/1IVrTIqIlsARraI6pM-mVdYHIzNAo4Ap1?usp=sharing) to help you explore the library and customize it for your specific use case with ease.
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- ## 📥 Training Data (Unfiltered)
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  The model is trained on synthetic annotations generated by LLaMA-3.1-8B-instruct over the [Pile Uncopyrighted](https://huggingface.co/datasets/monology/pile-uncopyrighted) dataset.
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  The resulting automatically annotated dataset, [PileUncopyrighted-NER-BIO](https://huggingface.co/datasets/disi-unibo-nlp/PileUncopyrighted-NER-BIO), follows the BIO format and was used as the training source for this model.
 
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  We have created a free [Google Colab notebook](https://colab.research.google.com/drive/1IVrTIqIlsARraI6pM-mVdYHIzNAo4Ap1?usp=sharing) to help you explore the library and customize it for your specific use case with ease.
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+ ## 📥 Training Data
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  The model is trained on synthetic annotations generated by LLaMA-3.1-8B-instruct over the [Pile Uncopyrighted](https://huggingface.co/datasets/monology/pile-uncopyrighted) dataset.
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  The resulting automatically annotated dataset, [PileUncopyrighted-NER-BIO](https://huggingface.co/datasets/disi-unibo-nlp/PileUncopyrighted-NER-BIO), follows the BIO format and was used as the training source for this model.