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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos_final_xlm_de
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_de
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: 0.0580
- Precision: 0.9895
- Recall: 0.9894
- F1: 0.9894
- Accuracy: 0.9901
## 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 | 0.99 | 128 | 0.3828 | 0.9159 | 0.9106 | 0.9133 | 0.9196 |
| No log | 1.99 | 256 | 0.0659 | 0.9810 | 0.9812 | 0.9811 | 0.9824 |
| No log | 2.99 | 384 | 0.0447 | 0.9857 | 0.9857 | 0.9857 | 0.9865 |
| 0.7525 | 3.99 | 512 | 0.0388 | 0.9870 | 0.9871 | 0.9871 | 0.9878 |
| 0.7525 | 4.99 | 640 | 0.0373 | 0.9871 | 0.9875 | 0.9873 | 0.9881 |
| 0.7525 | 5.99 | 768 | 0.0354 | 0.9880 | 0.9882 | 0.9881 | 0.9889 |
| 0.7525 | 6.99 | 896 | 0.0350 | 0.9883 | 0.9885 | 0.9884 | 0.9891 |
| 0.0318 | 7.99 | 1024 | 0.0354 | 0.9884 | 0.9886 | 0.9885 | 0.9891 |
| 0.0318 | 8.99 | 1152 | 0.0356 | 0.9888 | 0.9888 | 0.9888 | 0.9894 |
| 0.0318 | 9.99 | 1280 | 0.0367 | 0.9888 | 0.9889 | 0.9888 | 0.9895 |
| 0.0318 | 10.99 | 1408 | 0.0370 | 0.9887 | 0.9888 | 0.9887 | 0.9894 |
| 0.0205 | 11.99 | 1536 | 0.0370 | 0.9889 | 0.9891 | 0.9890 | 0.9896 |
| 0.0205 | 12.99 | 1664 | 0.0388 | 0.9888 | 0.9889 | 0.9888 | 0.9895 |
| 0.0205 | 13.99 | 1792 | 0.0397 | 0.9890 | 0.9891 | 0.9890 | 0.9897 |
| 0.0205 | 14.99 | 1920 | 0.0403 | 0.9891 | 0.9891 | 0.9891 | 0.9897 |
| 0.0146 | 15.99 | 2048 | 0.0413 | 0.9891 | 0.9891 | 0.9891 | 0.9897 |
| 0.0146 | 16.99 | 2176 | 0.0423 | 0.9891 | 0.9891 | 0.9891 | 0.9898 |
| 0.0146 | 17.99 | 2304 | 0.0429 | 0.9891 | 0.9891 | 0.9891 | 0.9897 |
| 0.0146 | 18.99 | 2432 | 0.0443 | 0.9893 | 0.9894 | 0.9893 | 0.9899 |
| 0.0103 | 19.99 | 2560 | 0.0457 | 0.9890 | 0.9889 | 0.9890 | 0.9896 |
| 0.0103 | 20.99 | 2688 | 0.0455 | 0.9891 | 0.9892 | 0.9891 | 0.9898 |
| 0.0103 | 21.99 | 2816 | 0.0468 | 0.9891 | 0.9892 | 0.9891 | 0.9898 |
| 0.0103 | 22.99 | 2944 | 0.0491 | 0.9891 | 0.9892 | 0.9892 | 0.9898 |
| 0.0073 | 23.99 | 3072 | 0.0495 | 0.9894 | 0.9894 | 0.9894 | 0.9900 |
| 0.0073 | 24.99 | 3200 | 0.0503 | 0.9892 | 0.9892 | 0.9892 | 0.9898 |
| 0.0073 | 25.99 | 3328 | 0.0519 | 0.9892 | 0.9892 | 0.9892 | 0.9898 |
| 0.0073 | 26.99 | 3456 | 0.0522 | 0.9892 | 0.9893 | 0.9892 | 0.9899 |
| 0.0052 | 27.99 | 3584 | 0.0526 | 0.9892 | 0.9892 | 0.9892 | 0.9899 |
| 0.0052 | 28.99 | 3712 | 0.0535 | 0.9892 | 0.9892 | 0.9892 | 0.9899 |
| 0.0052 | 29.99 | 3840 | 0.0544 | 0.9894 | 0.9894 | 0.9894 | 0.9900 |
| 0.0052 | 30.99 | 3968 | 0.0548 | 0.9893 | 0.9894 | 0.9894 | 0.9900 |
| 0.0038 | 31.99 | 4096 | 0.0563 | 0.9892 | 0.9892 | 0.9892 | 0.9899 |
| 0.0038 | 32.99 | 4224 | 0.0562 | 0.9894 | 0.9894 | 0.9894 | 0.9900 |
| 0.0038 | 33.99 | 4352 | 0.0577 | 0.9891 | 0.9892 | 0.9892 | 0.9898 |
| 0.0038 | 34.99 | 4480 | 0.0580 | 0.9895 | 0.9894 | 0.9894 | 0.9901 |
| 0.003 | 35.99 | 4608 | 0.0581 | 0.9893 | 0.9894 | 0.9894 | 0.9900 |
| 0.003 | 36.99 | 4736 | 0.0585 | 0.9893 | 0.9893 | 0.9893 | 0.9899 |
| 0.003 | 37.99 | 4864 | 0.0586 | 0.9893 | 0.9894 | 0.9893 | 0.9900 |
| 0.003 | 38.99 | 4992 | 0.0588 | 0.9893 | 0.9894 | 0.9894 | 0.9900 |
| 0.0024 | 39.99 | 5120 | 0.0589 | 0.9894 | 0.9894 | 0.9894 | 0.9900 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.13.2
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