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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: 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
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