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--- |
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license: mit |
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base_model: FacebookAI/xlm-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: xlm-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: 0.8295165394402035 |
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- name: Recall |
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type: recall |
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value: 0.8965896589658966 |
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- name: F1 |
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type: f1 |
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value: 0.8617499339148824 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9714166181062949 |
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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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# xlm-roberta-base_LeNER-Br |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-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: 0.8295 |
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- Recall: 0.8966 |
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- F1: 0.8617 |
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- Accuracy: 0.9714 |
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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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| 0.2394 | 1.0 | 979 | nan | 0.7134 | 0.8614 | 0.7805 | 0.9638 | |
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| 0.0375 | 2.0 | 1958 | nan | 0.8035 | 0.9043 | 0.8509 | 0.9670 | |
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| 0.0256 | 3.0 | 2937 | nan | 0.8026 | 0.8878 | 0.8430 | 0.9761 | |
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| 0.0194 | 4.0 | 3916 | nan | 0.7836 | 0.8861 | 0.8317 | 0.9670 | |
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| 0.015 | 5.0 | 4895 | nan | 0.8061 | 0.8988 | 0.8499 | 0.9691 | |
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| 0.0098 | 6.0 | 5874 | nan | 0.8279 | 0.9076 | 0.8659 | 0.9715 | |
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| 0.0082 | 7.0 | 6853 | nan | 0.8067 | 0.8905 | 0.8465 | 0.9681 | |
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| 0.0042 | 8.0 | 7832 | nan | 0.8233 | 0.9021 | 0.8609 | 0.9737 | |
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| 0.0037 | 9.0 | 8811 | nan | 0.8281 | 0.9010 | 0.8630 | 0.9712 | |
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| 0.0031 | 10.0 | 9790 | nan | 0.8295 | 0.8966 | 0.8617 | 0.9714 | |
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### Testing Results |
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metrics={'test_loss': 0.07461995631456375, 'test_precision': 0.8852040816326531, 'test_recall': 0.9137590520079, 'test_f1': 0.8992549400712667, 'test_accuracy': 0.9883402014967543, 'test_runtime': 13.0766, 'test_samples_per_second': 106.297, 'test_steps_per_second': 13.306}) |
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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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