|
--- |
|
license: cc-by-4.0 |
|
base_model: NazaGara/NER-fine-tuned-BETO |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- conll2002 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: NER-finetuning-BETO |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: conll2002 |
|
type: conll2002 |
|
config: es |
|
split: validation |
|
args: es |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.8416742493175614 |
|
- name: Recall |
|
type: recall |
|
value: 0.8501838235294118 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8459076360310929 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.967827919662782 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# NER-finetuning-BETO |
|
|
|
This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2653 |
|
- Precision: 0.8417 |
|
- Recall: 0.8502 |
|
- F1: 0.8459 |
|
- Accuracy: 0.9678 |
|
|
|
## 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: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0507 | 1.0 | 1041 | 0.1448 | 0.8298 | 0.8571 | 0.8432 | 0.9691 | |
|
| 0.0333 | 2.0 | 2082 | 0.1728 | 0.8259 | 0.8481 | 0.8369 | 0.9678 | |
|
| 0.0195 | 3.0 | 3123 | 0.1722 | 0.8392 | 0.8516 | 0.8453 | 0.9693 | |
|
| 0.0147 | 4.0 | 4164 | 0.2037 | 0.8502 | 0.8488 | 0.8495 | 0.9679 | |
|
| 0.011 | 5.0 | 5205 | 0.2041 | 0.8394 | 0.8529 | 0.8461 | 0.9695 | |
|
| 0.0082 | 6.0 | 6246 | 0.2418 | 0.8410 | 0.8401 | 0.8406 | 0.9664 | |
|
| 0.006 | 7.0 | 7287 | 0.2323 | 0.8448 | 0.8552 | 0.8500 | 0.9678 | |
|
| 0.0046 | 8.0 | 8328 | 0.2415 | 0.8411 | 0.8527 | 0.8469 | 0.9691 | |
|
| 0.003 | 9.0 | 9369 | 0.2502 | 0.8402 | 0.8495 | 0.8448 | 0.9677 | |
|
| 0.0022 | 10.0 | 10410 | 0.2653 | 0.8417 | 0.8502 | 0.8459 | 0.9678 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.1 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
