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roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES

This model is a fine-tuned version of StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES on the CRAFT dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2043
  • Precision: 0.8666
  • Recall: 0.8614
  • F1: 0.8639
  • Accuracy: 0.9734

Model description

This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the CRAFT(Colorado Richly Annotated Full Text) Corpus in Spanish (MT translated) and English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I-Chemical.

This model is trained on augmented data created using Entity Replacement. 20% of the entities were replaced using a list of entities for each entity tag obtained from the official ontologies for each entity class. Three datasets (original, augmented, MT translated CRAFT) were concatenated. To improve F1 score the transfer learning was completed in two steps.

Using StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES as a base model, I finetuned once more on the original CRAFT dataset in English.

Biobert --> Augmented CRAFT --> CRAFT ES (MT translated)

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0088 1.0 1360 0.1793 0.8616 0.8487 0.8551 0.9721
0.0046 2.0 2720 0.1925 0.8618 0.8426 0.8521 0.9713
0.0032 3.0 4080 0.1926 0.8558 0.8630 0.8594 0.9725
0.0011 4.0 5440 0.2043 0.8666 0.8614 0.8639 0.9734

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu111
  • Datasets 2.0.0
  • Tokenizers 0.11.6
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