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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos_final_xlm_fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pos_final_xlm_fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1022
- Precision: 0.9744
- Recall: 0.9746
- F1: 0.9745
- Accuracy: 0.9769
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.95 | 14 | 3.5537 | 0.0 | 0.0 | 0.0 | 0.0026 |
| No log | 1.95 | 28 | 3.4536 | 0.0153 | 0.0024 | 0.0042 | 0.0049 |
| No log | 2.95 | 42 | 3.1247 | 0.2395 | 0.1816 | 0.2066 | 0.2843 |
| No log | 3.95 | 56 | 2.5988 | 0.4342 | 0.3539 | 0.3900 | 0.4543 |
| No log | 4.95 | 70 | 2.0168 | 0.5125 | 0.4086 | 0.4547 | 0.5148 |
| No log | 5.95 | 84 | 1.4838 | 0.5959 | 0.5180 | 0.5543 | 0.6086 |
| No log | 6.95 | 98 | 0.9300 | 0.7905 | 0.7619 | 0.7759 | 0.7981 |
| No log | 7.95 | 112 | 0.4874 | 0.9111 | 0.9078 | 0.9094 | 0.9147 |
| No log | 8.95 | 126 | 0.2940 | 0.9372 | 0.9368 | 0.9370 | 0.9396 |
| No log | 9.95 | 140 | 0.2086 | 0.9471 | 0.9482 | 0.9476 | 0.9490 |
| No log | 10.95 | 154 | 0.1688 | 0.9594 | 0.9610 | 0.9602 | 0.9627 |
| No log | 11.95 | 168 | 0.1450 | 0.9624 | 0.9641 | 0.9632 | 0.9659 |
| No log | 12.95 | 182 | 0.1334 | 0.9651 | 0.9669 | 0.9660 | 0.9686 |
| No log | 13.95 | 196 | 0.1213 | 0.9674 | 0.9685 | 0.9679 | 0.9702 |
| No log | 14.95 | 210 | 0.1155 | 0.9684 | 0.9696 | 0.9690 | 0.9718 |
| No log | 15.95 | 224 | 0.1093 | 0.9707 | 0.9712 | 0.9709 | 0.9734 |
| No log | 16.95 | 238 | 0.1059 | 0.9710 | 0.9716 | 0.9713 | 0.9739 |
| No log | 17.95 | 252 | 0.1046 | 0.9711 | 0.9716 | 0.9714 | 0.9740 |
| No log | 18.95 | 266 | 0.1014 | 0.9719 | 0.9724 | 0.9722 | 0.9745 |
| No log | 19.95 | 280 | 0.1003 | 0.9715 | 0.9722 | 0.9718 | 0.9742 |
| No log | 20.95 | 294 | 0.0987 | 0.9724 | 0.9730 | 0.9727 | 0.9751 |
| No log | 21.95 | 308 | 0.0971 | 0.9722 | 0.9728 | 0.9725 | 0.9750 |
| No log | 22.95 | 322 | 0.0968 | 0.9724 | 0.9735 | 0.9730 | 0.9754 |
| No log | 23.95 | 336 | 0.0954 | 0.9728 | 0.9736 | 0.9732 | 0.9756 |
| No log | 24.95 | 350 | 0.0967 | 0.9722 | 0.9731 | 0.9727 | 0.9752 |
| No log | 25.95 | 364 | 0.0965 | 0.9735 | 0.9744 | 0.9739 | 0.9763 |
| No log | 26.95 | 378 | 0.0963 | 0.9725 | 0.9735 | 0.9730 | 0.9757 |
| No log | 27.95 | 392 | 0.0972 | 0.9728 | 0.9738 | 0.9733 | 0.9759 |
| No log | 28.95 | 406 | 0.0987 | 0.9736 | 0.9745 | 0.9740 | 0.9766 |
| No log | 29.95 | 420 | 0.0994 | 0.9737 | 0.9742 | 0.9740 | 0.9764 |
| No log | 30.95 | 434 | 0.0985 | 0.9737 | 0.9741 | 0.9739 | 0.9764 |
| No log | 31.95 | 448 | 0.1022 | 0.9744 | 0.9746 | 0.9745 | 0.9769 |
| No log | 32.95 | 462 | 0.1020 | 0.9740 | 0.9744 | 0.9742 | 0.9767 |
| No log | 33.95 | 476 | 0.1055 | 0.9730 | 0.9738 | 0.9734 | 0.9758 |
| No log | 34.95 | 490 | 0.1068 | 0.9732 | 0.9742 | 0.9737 | 0.9760 |
| 0.6768 | 35.95 | 504 | 0.1085 | 0.9737 | 0.9740 | 0.9739 | 0.9764 |
| 0.6768 | 36.95 | 518 | 0.1088 | 0.9735 | 0.9743 | 0.9739 | 0.9764 |
| 0.6768 | 37.95 | 532 | 0.1100 | 0.9739 | 0.9744 | 0.9742 | 0.9768 |
| 0.6768 | 38.95 | 546 | 0.1107 | 0.9739 | 0.9745 | 0.9742 | 0.9767 |
| 0.6768 | 39.95 | 560 | 0.1115 | 0.9740 | 0.9747 | 0.9744 | 0.9769 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.13.2
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