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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-base-cased-finetuned-conll2002 |
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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: 0.8607856650585803 |
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- name: Recall |
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type: recall |
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value: 0.8609834558823529 |
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- name: F1 |
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type: f1 |
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value: 0.860884549109707 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9786860155319993 |
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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-base-cased-finetuned-conll2002 |
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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: 0.1281 |
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- Precision: 0.8608 |
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- Recall: 0.8610 |
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- F1: 0.8609 |
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- Accuracy: 0.9787 |
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## Model description |
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The model described here is a fine-tuned version of the BETO (BERT-based Spanish language model) for Named Entity Recognition (NER) tasks, |
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trained on the CoNLL-2002 dataset. BETO is a pre-trained language model specifically designed for the Spanish language, based on the BERT architecture. |
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By fine-tuning BETO on the CoNLL-2002 dataset, the model has been adapted to recognize and classify named entities such as persons, organizations, |
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locations, and other miscellaneous entities within Spanish text. The fine-tuning process involves adjusting the pre-trained model weights to better |
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fit the specific task of NER, thereby improving its performance and accuracy on Spanish text. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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The training was performed using a GPU with 22.5 GB of RAM, 53 GB of system RAM, and 200 GB of disk space. |
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This setup ensured efficient handling of the large dataset and the computational demands of fine-tuning the model. |
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## Training procedure |
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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: 8 |
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- eval_batch_size: 8 |
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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: 3 |
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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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| 0.019 | 1.0 | 1041 | 0.1095 | 0.8581 | 0.8628 | 0.8604 | 0.9790 | |
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| 0.018 | 2.0 | 2082 | 0.1121 | 0.8461 | 0.8589 | 0.8525 | 0.9783 | |
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| 0.0133 | 3.0 | 3123 | 0.1281 | 0.8608 | 0.8610 | 0.8609 | 0.9787 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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