metadata
license: cc-by-4.0
base_model: NazaGara/NER-fine-tuned-BETO
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: NER-finetuned-BETO
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2002
type: conll2002
config: es
split: validation
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.9697960575254153
- name: F1
type: f1
value: 0.9693514387921158
- name: Precision
type: precision
value: 0.9691715895096829
- name: Recall
type: recall
value: 0.9697960575254153
NER-finetuned-BETO
This model is a fine-tuned version of NazaGara/NER-fine-tuned-BETO on the conll2002 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1898
- Accuracy: 0.9698
- F1: 0.9694
- Precision: 0.9692
- Recall: 0.9698
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0509 | 1.0 | 521 | 0.1309 | 0.9700 | 0.9696 | 0.9698 | 0.9700 |
| 0.0292 | 2.0 | 1042 | 0.1618 | 0.9679 | 0.9673 | 0.9670 | 0.9679 |
| 0.0178 | 3.0 | 1563 | 0.1460 | 0.9718 | 0.9712 | 0.9709 | 0.9718 |
| 0.0141 | 4.0 | 2084 | 0.1775 | 0.9689 | 0.9682 | 0.9680 | 0.9689 |
| 0.0091 | 5.0 | 2605 | 0.1815 | 0.9700 | 0.9695 | 0.9693 | 0.9700 |
| 0.007 | 6.0 | 3126 | 0.1898 | 0.9698 | 0.9694 | 0.9692 | 0.9698 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1