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datos-ner

Token Classification
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distilbert
Generated from Trainer
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+ ---
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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: datos-ner
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+ results: []
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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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+ # datos-ner
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+
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+ This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0751
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+ - Precision: 0.9516
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+ - Recall: 0.9219
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+ - F1: 0.9365
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+ - Accuracy: 0.9805
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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: 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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+
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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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+ | No log | 1.0 | 38 | 0.6419 | 0.8947 | 0.2656 | 0.4096 | 0.8357 |
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+ | No log | 2.0 | 76 | 0.2665 | 0.8511 | 0.625 | 0.7207 | 0.9276 |
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+ | No log | 3.0 | 114 | 0.1322 | 0.9508 | 0.9062 | 0.9280 | 0.9749 |
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+ | No log | 4.0 | 152 | 0.0907 | 0.9524 | 0.9375 | 0.9449 | 0.9805 |
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+ | No log | 5.0 | 190 | 0.0760 | 0.9683 | 0.9531 | 0.9606 | 0.9833 |
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+ | No log | 6.0 | 228 | 0.0644 | 0.9531 | 0.9531 | 0.9531 | 0.9861 |
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+ | No log | 7.0 | 266 | 0.0728 | 0.9365 | 0.9219 | 0.9291 | 0.9805 |
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+ | No log | 8.0 | 304 | 0.0690 | 0.9365 | 0.9219 | 0.9291 | 0.9805 |
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+ | No log | 9.0 | 342 | 0.0709 | 0.9365 | 0.9219 | 0.9291 | 0.9805 |
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+ | No log | 10.0 | 380 | 0.0781 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | No log | 11.0 | 418 | 0.0654 | 0.9365 | 0.9219 | 0.9291 | 0.9805 |
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+ | No log | 12.0 | 456 | 0.0746 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | No log | 13.0 | 494 | 0.0721 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | 0.1874 | 14.0 | 532 | 0.0739 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | 0.1874 | 15.0 | 570 | 0.0765 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | 0.1874 | 16.0 | 608 | 0.0777 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | 0.1874 | 17.0 | 646 | 0.0756 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | 0.1874 | 18.0 | 684 | 0.0765 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | 0.1874 | 19.0 | 722 | 0.0758 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+ | 0.1874 | 20.0 | 760 | 0.0751 | 0.9516 | 0.9219 | 0.9365 | 0.9805 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2