File size: 2,518 Bytes
e0f3e79 5a2bddd e0f3e79 5a2bddd e0f3e79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES
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_Augmented_ES
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: 0.2224
- Precision: 0.8298
- Recall: 0.8306
- F1: 0.8302
- Accuracy: 0.9659
## 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 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.
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0624 | 1.0 | 4078 | 0.1844 | 0.8002 | 0.7923 | 0.7963 | 0.9607 |
| 0.0284 | 2.0 | 8156 | 0.1937 | 0.8394 | 0.7988 | 0.8186 | 0.9637 |
| 0.0118 | 3.0 | 12234 | 0.2007 | 0.8285 | 0.8232 | 0.8258 | 0.9649 |
| 0.0043 | 4.0 | 16312 | 0.2224 | 0.8298 | 0.8306 | 0.8302 | 0.9659 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|