|
---
|
|
library_name: transformers
|
|
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-actualizado
|
|
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: 0.8385333636984742
|
|
- name: Recall
|
|
type: recall
|
|
value: 0.8460477941176471
|
|
- name: F1
|
|
type: f1
|
|
value: 0.8422738190552442
|
|
- name: Accuracy
|
|
type: accuracy
|
|
value: 0.9687422514257377
|
|
---
|
|
|
|
<!-- 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-actualizado
|
|
|
|
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: 0.2642
|
|
- Precision: 0.8385
|
|
- Recall: 0.8460
|
|
- F1: 0.8423
|
|
- Accuracy: 0.9687
|
|
|
|
## 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: 5e-05
|
|
- train_batch_size: 4
|
|
- eval_batch_size: 8
|
|
- seed: 42
|
|
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
|
|
- lr_scheduler_type: linear
|
|
- num_epochs: 10
|
|
|
|
### Training results
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
|
| 0.0778 | 1.0 | 2081 | 0.1603 | 0.8164 | 0.8277 | 0.8220 | 0.9676 |
|
|
| 0.0608 | 2.0 | 4162 | 0.1756 | 0.8114 | 0.8136 | 0.8125 | 0.9640 |
|
|
| 0.0362 | 3.0 | 6243 | 0.1986 | 0.8236 | 0.8226 | 0.8231 | 0.9650 |
|
|
| 0.0277 | 4.0 | 8324 | 0.1904 | 0.8092 | 0.8359 | 0.8223 | 0.9665 |
|
|
| 0.0217 | 5.0 | 10405 | 0.1988 | 0.8137 | 0.8389 | 0.8261 | 0.9658 |
|
|
| 0.0146 | 6.0 | 12486 | 0.2298 | 0.8470 | 0.8536 | 0.8503 | 0.9684 |
|
|
| 0.0091 | 7.0 | 14567 | 0.2509 | 0.8315 | 0.8412 | 0.8363 | 0.9665 |
|
|
| 0.0058 | 8.0 | 16648 | 0.2375 | 0.8274 | 0.8435 | 0.8354 | 0.9688 |
|
|
| 0.0033 | 9.0 | 18729 | 0.2489 | 0.8349 | 0.8421 | 0.8385 | 0.9683 |
|
|
| 0.0023 | 10.0 | 20810 | 0.2642 | 0.8385 | 0.8460 | 0.8423 | 0.9687 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.51.2
|
|
- Pytorch 2.6.0+cu126
|
|
- Datasets 3.5.0
|
|
- Tokenizers 0.21.1
|
|
|
