---
license: mit
base_model: facebook/xlm-v-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMV_data-cl-cardiff_cl_only_alpha
  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. -->

# scenario-TCR-XLMV_data-cl-cardiff_cl_only_alpha

This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1025
- Accuracy: 0.3387
- F1: 0.1932

## 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: 32
- eval_batch_size: 32
- seed: 1123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.09  | 250  | 1.0991          | 0.3164   | 0.3136 |
| 1.0996        | 2.17  | 500  | 1.0987          | 0.3333   | 0.1667 |
| 1.0996        | 3.26  | 750  | 1.0987          | 0.3333   | 0.1667 |
| 1.0993        | 4.35  | 1000 | 1.0988          | 0.3333   | 0.1667 |
| 1.0993        | 5.43  | 1250 | 1.0988          | 0.3333   | 0.1667 |
| 1.0998        | 6.52  | 1500 | 1.0990          | 0.3333   | 0.1667 |
| 1.0998        | 7.61  | 1750 | 1.0987          | 0.3333   | 0.1667 |
| 1.0996        | 8.7   | 2000 | 1.0993          | 0.3333   | 0.1667 |
| 1.0996        | 9.78  | 2250 | 1.0986          | 0.3333   | 0.1667 |
| 1.0998        | 10.87 | 2500 | 1.0990          | 0.3333   | 0.1667 |
| 1.0998        | 11.96 | 2750 | 1.0987          | 0.3333   | 0.1667 |
| 1.0994        | 13.04 | 3000 | 1.0986          | 0.3333   | 0.1667 |
| 1.0994        | 14.13 | 3250 | 1.1003          | 0.3333   | 0.1667 |
| 1.0988        | 15.22 | 3500 | 1.0988          | 0.3333   | 0.1667 |
| 1.0988        | 16.3  | 3750 | 1.0986          | 0.3333   | 0.1667 |
| 1.0991        | 17.39 | 4000 | 1.0986          | 0.3349   | 0.2410 |
| 1.0991        | 18.48 | 4250 | 1.0990          | 0.3333   | 0.1667 |
| 1.0959        | 19.57 | 4500 | 1.0984          | 0.3395   | 0.1945 |
| 1.0959        | 20.65 | 4750 | 1.1009          | 0.3341   | 0.1821 |
| 1.0898        | 21.74 | 5000 | 1.1051          | 0.3364   | 0.1934 |
| 1.0898        | 22.83 | 5250 | 1.1045          | 0.3372   | 0.2101 |
| 1.0851        | 23.91 | 5500 | 1.1036          | 0.3403   | 0.2024 |
| 1.0851        | 25.0  | 5750 | 1.1100          | 0.3426   | 0.2129 |
| 1.0836        | 26.09 | 6000 | 1.1027          | 0.3426   | 0.2045 |
| 1.0836        | 27.17 | 6250 | 1.0999          | 0.3418   | 0.1990 |
| 1.0845        | 28.26 | 6500 | 1.1027          | 0.3380   | 0.1918 |
| 1.0845        | 29.35 | 6750 | 1.1025          | 0.3387   | 0.1932 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3