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End of training

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README.md ADDED
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+ ---
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+ library_name: transformers
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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: NER-finetuning-BETO-actualizado
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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: 0.8385333636984742
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+ - name: Recall
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+ type: recall
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+ value: 0.8460477941176471
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+ - name: F1
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+ type: f1
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+ value: 0.8422738190552442
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9687422514257377
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+ ---
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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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+
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+ # NER-finetuning-BETO-actualizado
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+
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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: 0.2642
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+ - Precision: 0.8385
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+ - Recall: 0.8460
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+ - F1: 0.8423
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+ - Accuracy: 0.9687
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0778 | 1.0 | 2081 | 0.1603 | 0.8164 | 0.8277 | 0.8220 | 0.9676 |
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+ | 0.0608 | 2.0 | 4162 | 0.1756 | 0.8114 | 0.8136 | 0.8125 | 0.9640 |
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+ | 0.0362 | 3.0 | 6243 | 0.1986 | 0.8236 | 0.8226 | 0.8231 | 0.9650 |
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+ | 0.0277 | 4.0 | 8324 | 0.1904 | 0.8092 | 0.8359 | 0.8223 | 0.9665 |
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+ | 0.0217 | 5.0 | 10405 | 0.1988 | 0.8137 | 0.8389 | 0.8261 | 0.9658 |
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+ | 0.0146 | 6.0 | 12486 | 0.2298 | 0.8470 | 0.8536 | 0.8503 | 0.9684 |
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+ | 0.0091 | 7.0 | 14567 | 0.2509 | 0.8315 | 0.8412 | 0.8363 | 0.9665 |
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+ | 0.0058 | 8.0 | 16648 | 0.2375 | 0.8274 | 0.8435 | 0.8354 | 0.9688 |
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+ | 0.0033 | 9.0 | 18729 | 0.2489 | 0.8349 | 0.8421 | 0.8385 | 0.9683 |
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+ | 0.0023 | 10.0 | 20810 | 0.2642 | 0.8385 | 0.8460 | 0.8423 | 0.9687 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.51.2
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+ - Pytorch 2.6.0+cu126
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+ - Datasets 3.5.0
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+ - Tokenizers 0.21.1
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