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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: 0.865948670944088
- name: Recall
type: recall
value: 0.8683363970588235
- name: F1
type: f1
value: 0.867140890316659
- name: Accuracy
type: accuracy
value: 0.9792528768210419
---
<!-- 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: 0.1667
- Precision: 0.8659
- Recall: 0.8683
- F1: 0.8671
- Accuracy: 0.9793
## 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-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: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0373 | 1.0 | 521 | 0.1002 | 0.8642 | 0.8568 | 0.8605 | 0.9779 |
| 0.0255 | 2.0 | 1042 | 0.1018 | 0.8410 | 0.8555 | 0.8482 | 0.9779 |
| 0.0147 | 3.0 | 1563 | 0.1093 | 0.8654 | 0.8626 | 0.8640 | 0.9789 |
| 0.0107 | 4.0 | 2084 | 0.1277 | 0.8772 | 0.8614 | 0.8692 | 0.9787 |
| 0.0069 | 5.0 | 2605 | 0.1422 | 0.8496 | 0.8529 | 0.8513 | 0.9782 |
| 0.0052 | 6.0 | 3126 | 0.1436 | 0.8511 | 0.8511 | 0.8511 | 0.9775 |
| 0.0039 | 7.0 | 3647 | 0.1515 | 0.8663 | 0.8621 | 0.8642 | 0.9784 |
| 0.0029 | 8.0 | 4168 | 0.1525 | 0.8585 | 0.8617 | 0.8601 | 0.9785 |
| 0.0024 | 9.0 | 4689 | 0.1549 | 0.8635 | 0.8633 | 0.8634 | 0.9784 |
| 0.0021 | 10.0 | 5210 | 0.1643 | 0.8660 | 0.8672 | 0.8666 | 0.9792 |
| 0.0017 | 11.0 | 5731 | 0.1667 | 0.8659 | 0.8683 | 0.8671 | 0.9793 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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