readme: ---
  language: 
  - english
  thumbnail: 
  tags:
  - token classification
  license: 
  datasets:
  - EMBO/sd-panels
  metrics:
  -
  ---
  
  # sd-ner
  
  ## Model description
  
  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) and fine-tuned for token classification on the SourceData [sd-panels](https://huggingface.co/datasets/EMBO/sd-panels) dataset to perform Named Entity Recognition of bioentities. 
  
  
  ## Intended uses & limitations
  
  #### How to use
  
  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.
  
  To have a quick check of the model:
  
  ```python
  from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification
  example = """<s> F. Western blot of input and eluates of Upf1 domains purification in a Nmd4-HA strain. The band with the # might corresponds to a dimer of Upf1-CH, bands marked with a star correspond to residual signal with the anti-HA antibodies (Nmd4). Fragments in the eluate have a smaller size because the protein A part of the tag was removed by digestion with the TEV protease. G6PDH served as a loading control in the input samples </s>"""
  tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
  model = RobertaForTokenClassification.from_pretrained('EMBO/sd-ner')
  ner = pipeline('ner', model, tokenizer=tokenizer)
  res = ner(example)
  for r in res:
      print(r['word'], r['entity'])
  ```
  
  #### Limitations and bias
  
  The model must be used with the `roberta-base` tokenizer.
  
  ## Training data
  
  The model was trained for token classification using the  [EMBO/sd-panels dataset](https://huggingface.co/datasets/EMBO/biolang) wich includes manually annotated examples.
  
  ## Training procedure
  
  The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
  
  Training code is available at https://github.com/source-data/soda-roberta
  
  - Command: `python -m tokcl.train /data/json/sd_panels NER  --num_train_epochs=3.5`
  - Tokenizer vocab size: 50265
  - Training data: EMBO/biolang MLM
  - Training with 31410 examples.
  - Evaluating on 8861 examples.
  - 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
  - Epochs: 3.5
  - `per_device_train_batch_size`: 32
  - `per_device_eval_batch_size`: 32
  - `learning_rate`: 0.0001
  - `weight_decay`: 0.0
  - `adam_beta1`: 0.9
  - `adam_beta2`: 0.999
  - `adam_epsilon`: 1e-08
  - `max_grad_norm`: 1.0
  
  ## Eval results
  
  On test set with `sklearn.metrics`:
  
  ```
                  precision    recall  f1-score   support
  
            CELL       0.77      0.81      0.79      3477
       EXP_ASSAY       0.71      0.70      0.71      7049
        GENEPROD       0.86      0.90      0.88     16140
        ORGANISM       0.80      0.82      0.81      2759
  SMALL_MOLECULE       0.78      0.82      0.80      4446
     SUBCELLULAR       0.71      0.75      0.73      2125
          TISSUE       0.70      0.75      0.73      1971
  
       micro avg       0.79      0.82      0.81     37967
       macro avg       0.76      0.79      0.78     37967
    weighted avg       0.79      0.82      0.81     37967
  ```
  