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--- |
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library_name: transformers |
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license: mit |
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base_model: xaviergillard/xlm-roberta-large-vieille-france |
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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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model-index: |
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- name: xlm-roberta-large-vieille-france-v2 |
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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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# xlm-roberta-large-vieille-france-v2 |
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This model is a fine-tuned version of [xaviergillard/xlm-roberta-large-vieille-france](https://huggingface.co/xaviergillard/xlm-roberta-large-vieille-france) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0960 |
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- Precision: 0.7626 |
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- Recall: 0.8061 |
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- F1: 0.7838 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 54 | 0.0609 | 0.7207 | 0.7966 | 0.7568 | |
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| No log | 2.0 | 108 | 0.0541 | 0.7140 | 0.8054 | 0.7570 | |
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| No log | 3.0 | 162 | 0.0585 | 0.7595 | 0.8083 | 0.7831 | |
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| No log | 4.0 | 216 | 0.0592 | 0.7384 | 0.8361 | 0.7842 | |
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| No log | 5.0 | 270 | 0.0684 | 0.7379 | 0.7827 | 0.7597 | |
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| No log | 6.0 | 324 | 0.0649 | 0.7568 | 0.8193 | 0.7868 | |
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| No log | 7.0 | 378 | 0.0668 | 0.7585 | 0.8113 | 0.7840 | |
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| No log | 8.0 | 432 | 0.0825 | 0.7094 | 0.8142 | 0.7582 | |
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| No log | 9.0 | 486 | 0.0767 | 0.7653 | 0.8252 | 0.7941 | |
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| 0.0406 | 10.0 | 540 | 0.0841 | 0.7621 | 0.8040 | 0.7825 | |
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| 0.0406 | 11.0 | 594 | 0.0850 | 0.7678 | 0.8032 | 0.7851 | |
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| 0.0406 | 12.0 | 648 | 0.0880 | 0.7579 | 0.7944 | 0.7757 | |
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| 0.0406 | 13.0 | 702 | 0.0914 | 0.7619 | 0.8054 | 0.7831 | |
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| 0.0406 | 14.0 | 756 | 0.0950 | 0.7613 | 0.8003 | 0.7803 | |
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| 0.0406 | 15.0 | 810 | 0.0960 | 0.7626 | 0.8061 | 0.7838 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.1.2 |
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- Datasets 3.3.0 |
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- Tokenizers 0.21.0 |
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