---
license: mit
base_model: facebook/xlm-v-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMV_data-en-cardiff_eng_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-en-cardiff_eng_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: 1.0987
- Accuracy: 0.3333
- F1: 0.1667

## 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        | 1.03  | 60   | 1.0986          | 0.3333   | 0.1667 |
| No log        | 2.07  | 120  | 1.0986          | 0.3333   | 0.1667 |
| No log        | 3.1   | 180  | 1.0991          | 0.3329   | 0.1665 |
| No log        | 4.14  | 240  | 1.0987          | 0.3333   | 0.1667 |
| No log        | 5.17  | 300  | 1.0988          | 0.3333   | 0.1667 |
| No log        | 6.21  | 360  | 1.0990          | 0.3333   | 0.1667 |
| No log        | 7.24  | 420  | 1.0993          | 0.3333   | 0.1667 |
| No log        | 8.28  | 480  | 1.0987          | 0.3333   | 0.1667 |
| 1.0995        | 9.31  | 540  | 1.0988          | 0.3333   | 0.1667 |
| 1.0995        | 10.34 | 600  | 1.0987          | 0.3333   | 0.1667 |
| 1.0995        | 11.38 | 660  | 1.0988          | 0.3333   | 0.1667 |
| 1.0995        | 12.41 | 720  | 1.0988          | 0.3333   | 0.1667 |
| 1.0995        | 13.45 | 780  | 1.0999          | 0.3333   | 0.1667 |
| 1.0995        | 14.48 | 840  | 1.1007          | 0.3333   | 0.1667 |
| 1.0995        | 15.52 | 900  | 1.0988          | 0.3338   | 0.1685 |
| 1.0995        | 16.55 | 960  | 1.0987          | 0.3333   | 0.1667 |
| 1.1001        | 17.59 | 1020 | 1.0990          | 0.3333   | 0.1667 |
| 1.1001        | 18.62 | 1080 | 1.0989          | 0.3333   | 0.1667 |
| 1.1001        | 19.66 | 1140 | 1.0986          | 0.3333   | 0.1667 |
| 1.1001        | 20.69 | 1200 | 1.0987          | 0.3333   | 0.1667 |
| 1.1001        | 21.72 | 1260 | 1.0989          | 0.3333   | 0.1667 |
| 1.1001        | 22.76 | 1320 | 1.0988          | 0.3333   | 0.1667 |
| 1.1001        | 23.79 | 1380 | 1.0986          | 0.3333   | 0.1667 |
| 1.1001        | 24.83 | 1440 | 1.0986          | 0.3333   | 0.1667 |
| 1.0992        | 25.86 | 1500 | 1.0986          | 0.3333   | 0.1667 |
| 1.0992        | 26.9  | 1560 | 1.0986          | 0.3333   | 0.1667 |
| 1.0992        | 27.93 | 1620 | 1.0987          | 0.3333   | 0.1667 |
| 1.0992        | 28.97 | 1680 | 1.0987          | 0.3333   | 0.1667 |
| 1.0992        | 30.0  | 1740 | 1.0987          | 0.3333   | 0.1667 |


### Framework versions

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