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---
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library_name: transformers
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base_model: dccuchile/albert-base-spanish-finetuned-ner
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tags:
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- generated_from_trainer
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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: albert-base-spanish-finetuned-ner-finetuned-ner
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results: []
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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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# albert-base-spanish-finetuned-ner-finetuned-ner
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This model is a fine-tuned version of [dccuchile/albert-base-spanish-finetuned-ner](https://huggingface.co/dccuchile/albert-base-spanish-finetuned-ner) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3012
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- Precision: 0.8356
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- Recall: 0.8356
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- F1: 0.8356
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- Accuracy: 0.9385
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## Model description
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More information needed
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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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More information needed
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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: 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: 20
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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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| No log | 1.0 | 13 | 1.8849 | 0.0 | 0.0 | 0.0 | 0.5939 |
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| No log | 2.0 | 26 | 1.4600 | 0.0 | 0.0 | 0.0 | 0.6687 |
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| No log | 3.0 | 39 | 1.1449 | 0.0 | 0.0 | 0.0 | 0.6832 |
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| No log | 4.0 | 52 | 0.9138 | 0.2857 | 0.2329 | 0.2566 | 0.8056 |
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| No log | 5.0 | 65 | 0.7441 | 0.4504 | 0.4041 | 0.4260 | 0.8399 |
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| No log | 6.0 | 78 | 0.6292 | 0.5310 | 0.5274 | 0.5292 | 0.875 |
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| No log | 7.0 | 91 | 0.5406 | 0.6786 | 0.6507 | 0.6643 | 0.9041 |
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| No log | 8.0 | 104 | 0.4747 | 0.7397 | 0.7397 | 0.7397 | 0.9259 |
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| No log | 9.0 | 117 | 0.4228 | 0.7945 | 0.7945 | 0.7945 | 0.9306 |
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| No log | 10.0 | 130 | 0.3900 | 0.8333 | 0.8219 | 0.8276 | 0.9332 |
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| No log | 11.0 | 143 | 0.3685 | 0.8392 | 0.8219 | 0.8304 | 0.9339 |
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| No log | 12.0 | 156 | 0.3487 | 0.8333 | 0.8219 | 0.8276 | 0.9339 |
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| No log | 13.0 | 169 | 0.3325 | 0.8219 | 0.8219 | 0.8219 | 0.9339 |
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| No log | 14.0 | 182 | 0.3227 | 0.8472 | 0.8356 | 0.8414 | 0.9339 |
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| No log | 15.0 | 195 | 0.3150 | 0.8531 | 0.8356 | 0.8443 | 0.9358 |
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| No log | 16.0 | 208 | 0.3094 | 0.8345 | 0.8288 | 0.8316 | 0.9358 |
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| No log | 17.0 | 221 | 0.3047 | 0.8414 | 0.8356 | 0.8385 | 0.9378 |
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| No log | 18.0 | 234 | 0.3027 | 0.8356 | 0.8356 | 0.8356 | 0.9385 |
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| No log | 19.0 | 247 | 0.3017 | 0.8414 | 0.8356 | 0.8385 | 0.9385 |
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| No log | 20.0 | 260 | 0.3012 | 0.8356 | 0.8356 | 0.8356 | 0.9385 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu118
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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