---
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_beta
  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_beta

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: 3.4054
- Accuracy: 0.5467
- F1: 0.5510

## 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: 112233
- 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   | 1.0887          | 0.4449   | 0.3540 |
| No log        | 2.07  | 120  | 1.0211          | 0.4700   | 0.3777 |
| No log        | 3.1   | 180  | 1.0598          | 0.5141   | 0.4790 |
| No log        | 4.14  | 240  | 1.0131          | 0.5644   | 0.5652 |
| No log        | 5.17  | 300  | 1.1073          | 0.5586   | 0.5595 |
| No log        | 6.21  | 360  | 1.3697          | 0.5635   | 0.5542 |
| No log        | 7.24  | 420  | 1.4910          | 0.5379   | 0.5385 |
| No log        | 8.28  | 480  | 1.7325          | 0.5507   | 0.5542 |
| 0.6649        | 9.31  | 540  | 1.8878          | 0.5489   | 0.5505 |
| 0.6649        | 10.34 | 600  | 2.2758          | 0.5309   | 0.5320 |
| 0.6649        | 11.38 | 660  | 2.3053          | 0.5357   | 0.5357 |
| 0.6649        | 12.41 | 720  | 2.3674          | 0.5542   | 0.5574 |
| 0.6649        | 13.45 | 780  | 2.7705          | 0.5309   | 0.5332 |
| 0.6649        | 14.48 | 840  | 2.7515          | 0.5520   | 0.5522 |
| 0.6649        | 15.52 | 900  | 2.9868          | 0.5423   | 0.5447 |
| 0.6649        | 16.55 | 960  | 2.7489          | 0.5582   | 0.5597 |
| 0.1079        | 17.59 | 1020 | 2.8748          | 0.5525   | 0.5560 |
| 0.1079        | 18.62 | 1080 | 3.0165          | 0.5467   | 0.5511 |
| 0.1079        | 19.66 | 1140 | 3.2954          | 0.5340   | 0.5356 |
| 0.1079        | 20.69 | 1200 | 3.1051          | 0.5441   | 0.5488 |
| 0.1079        | 21.72 | 1260 | 3.2199          | 0.5441   | 0.5467 |
| 0.1079        | 22.76 | 1320 | 3.1660          | 0.5454   | 0.5500 |
| 0.1079        | 23.79 | 1380 | 3.2637          | 0.5445   | 0.5474 |
| 0.1079        | 24.83 | 1440 | 3.2934          | 0.5538   | 0.5576 |
| 0.0279        | 25.86 | 1500 | 3.2834          | 0.5476   | 0.5506 |
| 0.0279        | 26.9  | 1560 | 3.3734          | 0.5467   | 0.5507 |
| 0.0279        | 27.93 | 1620 | 3.4145          | 0.5437   | 0.5476 |
| 0.0279        | 28.97 | 1680 | 3.4043          | 0.5454   | 0.5496 |
| 0.0279        | 30.0  | 1740 | 3.4054          | 0.5467   | 0.5510 |


### Framework versions

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