---
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