Token Classification
GLiNER
PyTorch
English
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Add arxiv paper

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@@ -14,7 +14,7 @@ This model is a fine-tune of [GLiNER](https://huggingface.co/urchade/gliner_medi
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  ![results table](assets/zero-shot_18_table.png)
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- The underlying dataset, [AskNews-NER-v0](https://huggingface.co/datasets/EmergentMethods/AskNews-NER-v0) was engineered with the objective of diversifying global perspectives by enforcing country/language/topic/temporal diversity. All data used to fine-tune this model was synthetically generated. WizardLM 13B v1.2 was used for translation/summarization of open-web news articles, while Llama3 70b instruct was used for entity extraction. Both the diversification and fine-tuning methods are presented in a [pre-print submitted to NeurIps2024](https://linktoarxiv.org).
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  # Usage
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  ![results table](assets/zero-shot_18_table.png)
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+ The underlying dataset, [AskNews-NER-v0](https://huggingface.co/datasets/EmergentMethods/AskNews-NER-v0) was engineered with the objective of diversifying global perspectives by enforcing country/language/topic/temporal diversity. All data used to fine-tune this model was synthetically generated. WizardLM 13B v1.2 was used for translation/summarization of open-web news articles, while Llama3 70b instruct was used for entity extraction. Both the diversification and fine-tuning methods are presented in a our paper on [ArXiv](https://arxiv.org/abs/2406.10258).
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  # Usage
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