|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT |
|
|
|
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the CRAFT dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1720 |
|
- Precision: 0.8253 |
|
- Recall: 0.8147 |
|
- F1: 0.8200 |
|
- Accuracy: 0.9660 |
|
|
|
## Model description |
|
|
|
This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the [CRAFT](https://github.com/UCDenver-ccp/CRAFT/releases)(Colorado Richly Annotated Full Text) Corpus in English. |
|
Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I-Chemical. |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.1133 | 1.0 | 1360 | 0.1629 | 0.7985 | 0.7782 | 0.7882 | 0.9610 | |
|
| 0.049 | 2.0 | 2720 | 0.1530 | 0.8165 | 0.8084 | 0.8124 | 0.9651 | |
|
| 0.0306 | 3.0 | 4080 | 0.1603 | 0.8198 | 0.8075 | 0.8136 | 0.9650 | |
|
| 0.0158 | 4.0 | 5440 | 0.1720 | 0.8253 | 0.8147 | 0.8200 | 0.9660 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.16.2 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 1.18.3 |
|
- Tokenizers 0.11.6 |
|
|