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--- |
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license: cc-by-4.0 |
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base_model: NazaGara/NER-fine-tuned-BETO |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2002 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: beto-finetuned-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: conll2002 |
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type: conll2002 |
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config: es |
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split: validation |
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args: es |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8402527075812274 |
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- name: Recall |
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type: recall |
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value: 0.8556985294117647 |
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- name: F1 |
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type: f1 |
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value: 0.8479052823315117 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9701834862385321 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# beto-finetuned-ner |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2248 |
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- Precision: 0.8403 |
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- Recall: 0.8557 |
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- F1: 0.8479 |
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- Accuracy: 0.9702 |
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## Model description |
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Este modelo está basado en BETO, que es un modelo de lenguaje preentrenado para el español similar a BERT. BETO fue entrenado inicialmente en grandes cantidades de texto en español. |
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Posteriormente, este modelo toma la arquitectura y pesos preentrenados de BETO y los ajusta aún más en la tarea específica de Reconocimiento de Entidades Nombradas (NER) utilizando el conjunto de datos conll2002. |
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Este modelo ajustado puede usarse para anotar automáticamente nuevos textos en español, asignando etiquetas de entidad nombradas. |
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## How to Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("JoshuaAAX/beto-finetuned-ner") |
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model = AutoModelForTokenClassification.from_pretrained("JoshuaAAX/beto-finetuned-ner") |
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text = "La Federación nacional de cafeteros de Colombia es una entidad del estado. El primer presidente el Dr Augusto Guerra contó con el aval de la Asociación Colombiana de Aviación." |
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ner_pipeline= pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max") |
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ner_pipeline(text) |
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``` |
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## Training data |
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| Abbreviation | Description | |
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|:-------------:|:-------------:| |
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| O | Outside of NE | |
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| PER | Person’s name | |
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| ORG | Organization | |
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| LOC | Location | |
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| MISC | Miscellaneous | |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0512 | 1.0 | 521 | 0.1314 | 0.8328 | 0.8562 | 0.8443 | 0.9703 | |
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| 0.0305 | 2.0 | 1042 | 0.1549 | 0.8318 | 0.8442 | 0.8380 | 0.9688 | |
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| 0.0193 | 3.0 | 1563 | 0.1498 | 0.8513 | 0.8578 | 0.8545 | 0.9708 | |
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| 0.0148 | 4.0 | 2084 | 0.1810 | 0.8363 | 0.8442 | 0.8403 | 0.9682 | |
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| 0.0112 | 5.0 | 2605 | 0.1904 | 0.8412 | 0.8529 | 0.8470 | 0.9703 | |
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| 0.0078 | 6.0 | 3126 | 0.1831 | 0.8364 | 0.8539 | 0.8450 | 0.9708 | |
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| 0.0058 | 7.0 | 3647 | 0.2060 | 0.8419 | 0.8543 | 0.8481 | 0.9701 | |
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| 0.0049 | 8.0 | 4168 | 0.2111 | 0.8357 | 0.8541 | 0.8448 | 0.9697 | |
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| 0.0037 | 9.0 | 4689 | 0.2255 | 0.8371 | 0.8504 | 0.8437 | 0.9692 | |
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| 0.0031 | 10.0 | 5210 | 0.2248 | 0.8403 | 0.8557 | 0.8479 | 0.9702 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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