Sd Ner

🚀 sd-ner

This model is designed for Named Entity Recognition (NER) of bioentities. It leverages a pre - trained RoBERTa base model and fine - tunes it on specific datasets to accurately identify various biological entities.

🚀 Quick Start

How to use

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.

To have a quick check of the model:

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'])

✨ Features

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. 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.

Limitations and bias

The model must be used with the roberta - base tokenizer.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

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'])

📚 Documentation

Training data

The model was trained for token classification using the [EMBO/sd - nlp dataset](https://huggingface.co/datasets/EMBO/sd - nlp) dataset which includes manually annotated examples.

Training procedure

The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs.

Training code is available at https://github.com/source - data/soda - roberta

Eval results

Testing on 7178 examples of test set with sklearn.metrics:

                precision    recall  f1 - score   support

          CELL       0.69      0.81      0.74      5245
     EXP_ASSAY       0.56      0.57      0.56     10067
      GENEPROD       0.77      0.89      0.82     23587
      ORGANISM       0.72      0.82      0.77      3623
SMALL_MOLECULE       0.70      0.80      0.75      6187
   SUBCELLULAR       0.65      0.72      0.69      3700
        TISSUE       0.62      0.73      0.67      3207

     micro avg       0.70      0.79      0.74     55616
     macro avg       0.67      0.77      0.72     55616
  weighted avg       0.70      0.79      0.74     55616

{'test_loss': 0.1830928772687912, 'test_accuracy_score': 0.9334821000160841, 'test_precision': 0.6987463009514112, 'test_recall': 0.789682825086306, 'test_f1': 0.7414366506288511, 'test_runtime': 61.0547, 'test_samples_per_second': 117.567, 'test_steps_per_second': 1.851}

📄 License

The license of this model is agpl - 3.0.