|
--- |
|
base_model: plncmm/beto-clinical-wl-es |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: beto-clinical-wl-es-ner |
|
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. --> |
|
|
|
# beto-clinical-wl-es-ner |
|
|
|
This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co./plncmm/beto-clinical-wl-es) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3299 |
|
- Precision: 0.8665 |
|
- Recall: 0.9037 |
|
- F1: 0.8847 |
|
- Accuracy: 0.9418 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## 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: 2e-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: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 280 | 0.2544 | 0.8328 | 0.8489 | 0.8408 | 0.9247 | |
|
| 0.3847 | 2.0 | 560 | 0.2645 | 0.8170 | 0.8667 | 0.8411 | 0.9236 | |
|
| 0.3847 | 3.0 | 840 | 0.2372 | 0.8512 | 0.8726 | 0.8617 | 0.9338 | |
|
| 0.1056 | 4.0 | 1120 | 0.2749 | 0.8403 | 0.8963 | 0.8674 | 0.9327 | |
|
| 0.1056 | 5.0 | 1400 | 0.2895 | 0.8557 | 0.9052 | 0.8798 | 0.9354 | |
|
| 0.057 | 6.0 | 1680 | 0.2630 | 0.8707 | 0.9081 | 0.8891 | 0.9408 | |
|
| 0.057 | 7.0 | 1960 | 0.2759 | 0.8614 | 0.9022 | 0.8813 | 0.9418 | |
|
| 0.031 | 8.0 | 2240 | 0.3099 | 0.8689 | 0.9037 | 0.8860 | 0.9408 | |
|
| 0.0222 | 9.0 | 2520 | 0.3506 | 0.8597 | 0.9081 | 0.8833 | 0.9386 | |
|
| 0.0222 | 10.0 | 2800 | 0.2962 | 0.8693 | 0.8963 | 0.8826 | 0.9421 | |
|
| 0.0169 | 11.0 | 3080 | 0.3218 | 0.8709 | 0.8993 | 0.8848 | 0.9432 | |
|
| 0.0169 | 12.0 | 3360 | 0.3459 | 0.8672 | 0.9096 | 0.8879 | 0.9400 | |
|
| 0.0134 | 13.0 | 3640 | 0.3299 | 0.8661 | 0.9007 | 0.8831 | 0.9413 | |
|
| 0.0134 | 14.0 | 3920 | 0.3318 | 0.8707 | 0.9081 | 0.8891 | 0.9429 | |
|
| 0.0126 | 15.0 | 4200 | 0.3299 | 0.8665 | 0.9037 | 0.8847 | 0.9418 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.1 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
