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--- |
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license: mit |
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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: pos_final_xlm_fr |
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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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# pos_final_xlm_fr |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1022 |
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- Precision: 0.9744 |
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- Recall: 0.9746 |
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- F1: 0.9745 |
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- Accuracy: 0.9769 |
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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: 256 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 1024 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 40.0 |
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- mixed_precision_training: Native AMP |
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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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| No log | 0.95 | 14 | 3.5537 | 0.0 | 0.0 | 0.0 | 0.0026 | |
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| No log | 1.95 | 28 | 3.4536 | 0.0153 | 0.0024 | 0.0042 | 0.0049 | |
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| No log | 2.95 | 42 | 3.1247 | 0.2395 | 0.1816 | 0.2066 | 0.2843 | |
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| No log | 3.95 | 56 | 2.5988 | 0.4342 | 0.3539 | 0.3900 | 0.4543 | |
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| No log | 4.95 | 70 | 2.0168 | 0.5125 | 0.4086 | 0.4547 | 0.5148 | |
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| No log | 5.95 | 84 | 1.4838 | 0.5959 | 0.5180 | 0.5543 | 0.6086 | |
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| No log | 6.95 | 98 | 0.9300 | 0.7905 | 0.7619 | 0.7759 | 0.7981 | |
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| No log | 7.95 | 112 | 0.4874 | 0.9111 | 0.9078 | 0.9094 | 0.9147 | |
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| No log | 8.95 | 126 | 0.2940 | 0.9372 | 0.9368 | 0.9370 | 0.9396 | |
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| No log | 9.95 | 140 | 0.2086 | 0.9471 | 0.9482 | 0.9476 | 0.9490 | |
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| No log | 10.95 | 154 | 0.1688 | 0.9594 | 0.9610 | 0.9602 | 0.9627 | |
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| No log | 11.95 | 168 | 0.1450 | 0.9624 | 0.9641 | 0.9632 | 0.9659 | |
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| No log | 12.95 | 182 | 0.1334 | 0.9651 | 0.9669 | 0.9660 | 0.9686 | |
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| No log | 13.95 | 196 | 0.1213 | 0.9674 | 0.9685 | 0.9679 | 0.9702 | |
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| No log | 14.95 | 210 | 0.1155 | 0.9684 | 0.9696 | 0.9690 | 0.9718 | |
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| No log | 15.95 | 224 | 0.1093 | 0.9707 | 0.9712 | 0.9709 | 0.9734 | |
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| No log | 16.95 | 238 | 0.1059 | 0.9710 | 0.9716 | 0.9713 | 0.9739 | |
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| No log | 17.95 | 252 | 0.1046 | 0.9711 | 0.9716 | 0.9714 | 0.9740 | |
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| No log | 18.95 | 266 | 0.1014 | 0.9719 | 0.9724 | 0.9722 | 0.9745 | |
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| No log | 19.95 | 280 | 0.1003 | 0.9715 | 0.9722 | 0.9718 | 0.9742 | |
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| No log | 20.95 | 294 | 0.0987 | 0.9724 | 0.9730 | 0.9727 | 0.9751 | |
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| No log | 21.95 | 308 | 0.0971 | 0.9722 | 0.9728 | 0.9725 | 0.9750 | |
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| No log | 22.95 | 322 | 0.0968 | 0.9724 | 0.9735 | 0.9730 | 0.9754 | |
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| No log | 23.95 | 336 | 0.0954 | 0.9728 | 0.9736 | 0.9732 | 0.9756 | |
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| No log | 24.95 | 350 | 0.0967 | 0.9722 | 0.9731 | 0.9727 | 0.9752 | |
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| No log | 25.95 | 364 | 0.0965 | 0.9735 | 0.9744 | 0.9739 | 0.9763 | |
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| No log | 26.95 | 378 | 0.0963 | 0.9725 | 0.9735 | 0.9730 | 0.9757 | |
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| No log | 27.95 | 392 | 0.0972 | 0.9728 | 0.9738 | 0.9733 | 0.9759 | |
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| No log | 28.95 | 406 | 0.0987 | 0.9736 | 0.9745 | 0.9740 | 0.9766 | |
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| No log | 29.95 | 420 | 0.0994 | 0.9737 | 0.9742 | 0.9740 | 0.9764 | |
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| No log | 30.95 | 434 | 0.0985 | 0.9737 | 0.9741 | 0.9739 | 0.9764 | |
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| No log | 31.95 | 448 | 0.1022 | 0.9744 | 0.9746 | 0.9745 | 0.9769 | |
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| No log | 32.95 | 462 | 0.1020 | 0.9740 | 0.9744 | 0.9742 | 0.9767 | |
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| No log | 33.95 | 476 | 0.1055 | 0.9730 | 0.9738 | 0.9734 | 0.9758 | |
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| No log | 34.95 | 490 | 0.1068 | 0.9732 | 0.9742 | 0.9737 | 0.9760 | |
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| 0.6768 | 35.95 | 504 | 0.1085 | 0.9737 | 0.9740 | 0.9739 | 0.9764 | |
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| 0.6768 | 36.95 | 518 | 0.1088 | 0.9735 | 0.9743 | 0.9739 | 0.9764 | |
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| 0.6768 | 37.95 | 532 | 0.1100 | 0.9739 | 0.9744 | 0.9742 | 0.9768 | |
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| 0.6768 | 38.95 | 546 | 0.1107 | 0.9739 | 0.9745 | 0.9742 | 0.9767 | |
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| 0.6768 | 39.95 | 560 | 0.1115 | 0.9740 | 0.9747 | 0.9744 | 0.9769 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.13.2 |
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