Commit
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End of training
Browse files- README.md +26 -31
- model.safetensors +1 -1
- runs/Jun06_04-06-43_7db57397ed39/events.out.tfevents.1717646804.7db57397ed39.435.0 +3 -0
- runs/Jun06_04-07-39_7db57397ed39/events.out.tfevents.1717646862.7db57397ed39.435.1 +3 -0
- runs/Jun06_04-07-39_7db57397ed39/events.out.tfevents.1717648853.7db57397ed39.435.2 +3 -0
- training_args.bin +1 -1
README.md
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metrics:
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- name: Precision
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type: precision
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value: 0.
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- name: Recall
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type: recall
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value: 0.
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- name: F1
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type: f1
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value: 0.
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- name: Accuracy
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type: accuracy
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value: 0.
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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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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.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 4e-
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- train_batch_size:
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- eval_batch_size:
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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:
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### Training results
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| Training Loss | Epoch | Step
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| 0.0036 | 12.0 | 12492 | 0.1633 | 0.8603 | 0.8658 | 0.8630 | 0.9786 |
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| 0.0036 | 13.0 | 13533 | 0.1620 | 0.8628 | 0.8658 | 0.8643 | 0.9790 |
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| 0.0032 | 14.0 | 14574 | 0.1645 | 0.8617 | 0.8676 | 0.8647 | 0.9793 |
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| 0.0028 | 15.0 | 15615 | 0.1645 | 0.8604 | 0.8670 | 0.8637 | 0.9792 |
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| 0.003 | 16.0 | 16656 | 0.1659 | 0.8614 | 0.8672 | 0.8643 | 0.9790 |
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### Framework versions
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metrics:
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- name: Precision
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type: precision
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value: 0.865948670944088
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- name: Recall
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type: recall
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value: 0.8683363970588235
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- name: F1
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type: f1
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value: 0.867140890316659
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- name: Accuracy
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type: accuracy
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value: 0.9792528768210419
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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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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: 0.1667
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- Precision: 0.8659
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- Recall: 0.8683
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- F1: 0.8671
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- Accuracy: 0.9793
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 4e-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: 11
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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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| 0.0373 | 1.0 | 521 | 0.1002 | 0.8642 | 0.8568 | 0.8605 | 0.9779 |
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| 0.0255 | 2.0 | 1042 | 0.1018 | 0.8410 | 0.8555 | 0.8482 | 0.9779 |
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| 0.0147 | 3.0 | 1563 | 0.1093 | 0.8654 | 0.8626 | 0.8640 | 0.9789 |
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| 0.0107 | 4.0 | 2084 | 0.1277 | 0.8772 | 0.8614 | 0.8692 | 0.9787 |
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| 0.0069 | 5.0 | 2605 | 0.1422 | 0.8496 | 0.8529 | 0.8513 | 0.9782 |
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| 0.0052 | 6.0 | 3126 | 0.1436 | 0.8511 | 0.8511 | 0.8511 | 0.9775 |
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| 0.0039 | 7.0 | 3647 | 0.1515 | 0.8663 | 0.8621 | 0.8642 | 0.9784 |
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| 0.0029 | 8.0 | 4168 | 0.1525 | 0.8585 | 0.8617 | 0.8601 | 0.9785 |
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| 0.0024 | 9.0 | 4689 | 0.1549 | 0.8635 | 0.8633 | 0.8634 | 0.9784 |
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| 0.0021 | 10.0 | 5210 | 0.1643 | 0.8660 | 0.8672 | 0.8666 | 0.9792 |
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| 0.0017 | 11.0 | 5731 | 0.1667 | 0.8659 | 0.8683 | 0.8671 | 0.9793 |
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### Framework versions
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model.safetensors
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