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---
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: beto-finetuned-ner-cfv
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: 0.8614471581830633
- name: Recall
type: recall
value: 0.8671875
- name: F1
type: f1
value: 0.8643077980075576
- name: Accuracy
type: accuracy
value: 0.9790072369291234
---
<!-- 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-finetuned-ner-cfv
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: 0.1659
- Precision: 0.8614
- Recall: 0.8672
- F1: 0.8643
- Accuracy: 0.9790
## 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: 4e-06
- 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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0272 | 1.0 | 1041 | 0.1062 | 0.8549 | 0.8637 | 0.8593 | 0.9786 |
| 0.0208 | 2.0 | 2082 | 0.1127 | 0.8443 | 0.8596 | 0.8519 | 0.9782 |
| 0.0158 | 3.0 | 3123 | 0.1195 | 0.8545 | 0.8598 | 0.8572 | 0.9787 |
| 0.0129 | 4.0 | 4164 | 0.1332 | 0.8629 | 0.8589 | 0.8609 | 0.9782 |
| 0.0107 | 5.0 | 5205 | 0.1299 | 0.8555 | 0.8635 | 0.8595 | 0.9786 |
| 0.0087 | 6.0 | 6246 | 0.1486 | 0.8564 | 0.8564 | 0.8564 | 0.9782 |
| 0.0085 | 7.0 | 7287 | 0.1583 | 0.8618 | 0.8596 | 0.8607 | 0.9783 |
| 0.0066 | 8.0 | 8328 | 0.1582 | 0.8604 | 0.8580 | 0.8592 | 0.9783 |
| 0.0052 | 9.0 | 9369 | 0.1571 | 0.8554 | 0.8566 | 0.8560 | 0.9781 |
| 0.0054 | 10.0 | 10410 | 0.1604 | 0.8628 | 0.8640 | 0.8634 | 0.9787 |
| 0.004 | 11.0 | 11451 | 0.1584 | 0.8624 | 0.8670 | 0.8647 | 0.9791 |
| 0.0036 | 12.0 | 12492 | 0.1633 | 0.8603 | 0.8658 | 0.8630 | 0.9786 |
| 0.0036 | 13.0 | 13533 | 0.1620 | 0.8628 | 0.8658 | 0.8643 | 0.9790 |
| 0.0032 | 14.0 | 14574 | 0.1645 | 0.8617 | 0.8676 | 0.8647 | 0.9793 |
| 0.0028 | 15.0 | 15615 | 0.1645 | 0.8604 | 0.8670 | 0.8637 | 0.9792 |
| 0.003 | 16.0 | 16656 | 0.1659 | 0.8614 | 0.8672 | 0.8643 | 0.9790 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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