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Parent(s):
28045b8
reverting to dataset sd-nlp
Browse files
README.md
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- token classification
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license: agpl-3.0
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datasets:
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- EMBO/sd-
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metrics:
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---
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## Model description
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This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It has then been fine-tuned for token classification on the SourceData [sd-
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## Intended uses & limitations
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## Training data
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The model was trained for token classification using the [EMBO/sd-
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## Training procedure
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- Model fine tuned: EMBL/bio-lm
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- Tokenizer vocab size: 50265
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- Training data: EMBO/sd-
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- Dataset configuration: SMALL_MOL_ROLES
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- Training with 48771 examples.
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- Evaluating on 13801 examples.
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- token classification
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license: agpl-3.0
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datasets:
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- EMBO/sd-nlp
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metrics:
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---
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## Model description
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This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It has then been fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `SMALL_MOL_ROLES` configuration to perform pure context-dependent semantic role classification of bioentities.
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## Intended uses & limitations
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## Training data
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The model was trained for token classification using the [EMBO/sd-nlp dataset](https://huggingface.co/datasets/EMBO/sd-nlp) which includes manually annotated examples.
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## Training procedure
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- Model fine tuned: EMBL/bio-lm
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- Tokenizer vocab size: 50265
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- Training data: EMBO/sd-nlp
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- Dataset configuration: SMALL_MOL_ROLES
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- Training with 48771 examples.
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- Evaluating on 13801 examples.
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