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- model_checkpoint/config.json → config.json +0 -0
- model_checkpoint/merges.txt → merges.txt +0 -0
- model_checkpoint/optimizer.pt → optimizer.pt +0 -0
- model_checkpoint/pytorch_model.bin → pytorch_model.bin +0 -0
- model_checkpoint/rng_state.pth → rng_state.pth +0 -0
- model_checkpoint/scheduler.pt → scheduler.pt +0 -0
- model_checkpoint/special_tokens_map.json → special_tokens_map.json +0 -0
- model_checkpoint/tokenizer.json → tokenizer.json +0 -0
- model_checkpoint/tokenizer_config.json → tokenizer_config.json +0 -0
- model_checkpoint/trainer_state.json → trainer_state.json +0 -0
- model_checkpoint/training_args.bin → training_args.bin +0 -0
- model_checkpoint/vocab.json → vocab.json +0 -0
README.md
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This model is a version of roberta which has been fine tuned for tokenclassification on the [LitBank](https://github.com/dbamman/litbank) dataset.
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license: cc-by-4.0
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license: cc-by-4.0
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### Description
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A `roberta-base` model which has been fine tuned for token classification on the [LitBank](https://github.com/dbamman/litbank) dataset.
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### Intended Use
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This model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from ACE 2005.
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- PER
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- FAC
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- GPE
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- LOC
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- VEH
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- ORG
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Due to the fine-tuning domain, it is expected to work best with literary sentences.
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