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update model card
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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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## 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 was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/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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The training was run on
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Training code is available at https://github.com/source-data/soda-roberta
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- Model fine-tuned: EMBO/bio-lm
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- Tokenizer vocab size: 50265
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- Training data: EMBO/sd-
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- Dataset configuration: NER
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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-panels
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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 was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-panels) dataset with the `NER` configuration to perform Named Entity Recognition 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-panels dataset](https://huggingface.co/datasets/EMBO/sd-panels) dataset which includes manually annotated examples.
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## Training procedure
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The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs.
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Training code is available at https://github.com/source-data/soda-roberta
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- Model fine-tuned: EMBO/bio-lm
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- Tokenizer vocab size: 50265
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- Training data: EMBO/sd-panels
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- Dataset configuration: NER
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- Training with 48771 examples.
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- Evaluating on 13801 examples.
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