---
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: multilabel_transfer_learning_transformer
  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. -->

# multilabel_transfer_learning_transformer

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0217
- F1: 0.9924
- Roc Auc: 0.9955
- Accuracy: 0.9887

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.5454        | 1.0   | 136  | 0.4135          | 0.0125 | 0.5030  | 0.0      |
| 0.3917        | 2.0   | 272  | 0.3582          | 0.2939 | 0.5855  | 0.0338   |
| 0.3405        | 3.0   | 408  | 0.3048          | 0.4862 | 0.6649  | 0.0827   |
| 0.2918        | 4.0   | 544  | 0.2753          | 0.5913 | 0.7250  | 0.1278   |
| 0.2531        | 5.0   | 680  | 0.2285          | 0.7261 | 0.8065  | 0.2406   |
| 0.214         | 6.0   | 816  | 0.1971          | 0.7684 | 0.8328  | 0.3233   |
| 0.181         | 7.0   | 952  | 0.1663          | 0.8199 | 0.8624  | 0.4173   |
| 0.1529        | 8.0   | 1088 | 0.1431          | 0.8591 | 0.8905  | 0.4774   |
| 0.1307        | 9.0   | 1224 | 0.1224          | 0.8979 | 0.9260  | 0.6090   |
| 0.1108        | 10.0  | 1360 | 0.1034          | 0.9195 | 0.9329  | 0.6955   |
| 0.0961        | 11.0  | 1496 | 0.0920          | 0.9435 | 0.9553  | 0.7744   |
| 0.0821        | 12.0  | 1632 | 0.0793          | 0.9559 | 0.9627  | 0.8346   |
| 0.0719        | 13.0  | 1768 | 0.0682          | 0.9636 | 0.9732  | 0.8759   |
| 0.0612        | 14.0  | 1904 | 0.0618          | 0.9651 | 0.9760  | 0.8947   |
| 0.0526        | 15.0  | 2040 | 0.0519          | 0.9757 | 0.9796  | 0.9135   |
| 0.0456        | 16.0  | 2176 | 0.0468          | 0.9778 | 0.9835  | 0.9248   |
| 0.0394        | 17.0  | 2312 | 0.0396          | 0.9854 | 0.9885  | 0.9586   |
| 0.0343        | 18.0  | 2448 | 0.0372          | 0.9855 | 0.9911  | 0.9586   |
| 0.0299        | 19.0  | 2584 | 0.0329          | 0.9854 | 0.9885  | 0.9586   |
| 0.0266        | 20.0  | 2720 | 0.0289          | 0.9887 | 0.9932  | 0.9887   |
| 0.0233        | 21.0  | 2856 | 0.0264          | 0.9874 | 0.9919  | 0.9812   |
| 0.0212        | 22.0  | 2992 | 0.0258          | 0.9887 | 0.9932  | 0.9887   |
| 0.02          | 23.0  | 3128 | 0.0242          | 0.9887 | 0.9932  | 0.9887   |
| 0.0177        | 24.0  | 3264 | 0.0217          | 0.9924 | 0.9955  | 0.9887   |
| 0.0162        | 25.0  | 3400 | 0.0200          | 0.9887 | 0.9932  | 0.9887   |
| 0.0146        | 26.0  | 3536 | 0.0201          | 0.9906 | 0.9951  | 0.9887   |
| 0.0136        | 27.0  | 3672 | 0.0192          | 0.9906 | 0.9951  | 0.9887   |
| 0.0127        | 28.0  | 3808 | 0.0169          | 0.9924 | 0.9955  | 0.9887   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1