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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-nlp) dataset wit the `NER` task to perform Named Entity Recognition of bioentities.
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  ## Intended uses & limitations
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  #### How to use
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- The intended use of this model is for Named Entity Recognition of biological entitie used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods.
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  To have a quick check of the model:
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  Training code is available at https://github.com/source-data/soda-roberta
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  - Tokenizer vocab size: 50265
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- - Training data: EMBO/sd-nlp NER
 
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  - Training with 48771 examples.
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  - Evaluating on 13801 examples.
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  - Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY
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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-nlp) dataset with the `NER` configuration to perform Named Entity Recognition of bioentities.
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  ## Intended uses & limitations
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  #### How to use
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+ The intended use of this model is for Named Entity Recognition of biological entities used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods.
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  To have a quick check of the model:
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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-nlp
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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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  - Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY