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--- |
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license: mit |
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base_model: FacebookAI/roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lener_br |
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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: roberta-base_LeNER-Br |
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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: lener_br |
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type: lener_br |
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config: lener_br |
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split: validation |
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args: lener_br |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.765 |
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- name: Recall |
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type: recall |
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value: 0.8415841584158416 |
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- name: F1 |
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type: f1 |
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value: 0.8014667365112624 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9711736213348917 |
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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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# roberta-base_LeNER-Br |
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the lener_br dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Precision: 0.765 |
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- Recall: 0.8416 |
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- F1: 0.8015 |
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- Accuracy: 0.9712 |
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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: 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: 10 |
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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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| 0.293 | 1.0 | 979 | nan | 0.5758 | 0.7525 | 0.6524 | 0.9542 | |
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| 0.0596 | 2.0 | 1958 | nan | 0.6546 | 0.7987 | 0.7195 | 0.9534 | |
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| 0.0376 | 3.0 | 2937 | nan | 0.7366 | 0.8339 | 0.7822 | 0.9672 | |
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| 0.0256 | 4.0 | 3916 | nan | 0.6975 | 0.8042 | 0.7471 | 0.9627 | |
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| 0.0192 | 5.0 | 4895 | nan | 0.7173 | 0.8317 | 0.7702 | 0.9646 | |
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| 0.013 | 6.0 | 5874 | nan | 0.7271 | 0.8498 | 0.7837 | 0.9605 | |
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| 0.013 | 7.0 | 6853 | nan | 0.7426 | 0.8537 | 0.7943 | 0.9680 | |
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| 0.0064 | 8.0 | 7832 | nan | 0.7493 | 0.8399 | 0.7920 | 0.9702 | |
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| 0.0052 | 9.0 | 8811 | nan | 0.7611 | 0.8273 | 0.7928 | 0.9725 | |
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| 0.0044 | 10.0 | 9790 | nan | 0.765 | 0.8416 | 0.8015 | 0.9712 | |
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### Testing results |
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metrics={'test_loss': 0.08161260932683945, 'test_precision': 0.8342714196372732, 'test_recall': 0.8840291583830351, 'test_f1': 0.8584298584298585, 'test_accuracy': 0.9863512377202157, 'test_runtime': 20.4317, 'test_samples_per_second': 68.032, 'test_steps_per_second': 8.516}) |
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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 2.20.0 |
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- Tokenizers 0.19.1 |
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