---
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_alpha2
  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_alpha2

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: 3.6119
- Accuracy: 0.5472
- F1: 0.5508

## 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: 24
- 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   | 1.0411          | 0.4652   | 0.4291 |
| No log        | 2.07  | 120  | 1.0685          | 0.5035   | 0.4475 |
| No log        | 3.1   | 180  | 1.1001          | 0.5485   | 0.5474 |
| No log        | 4.14  | 240  | 1.0647          | 0.5516   | 0.5549 |
| No log        | 5.17  | 300  | 1.2458          | 0.5489   | 0.5528 |
| No log        | 6.21  | 360  | 1.2913          | 0.5719   | 0.5717 |
| No log        | 7.24  | 420  | 1.5986          | 0.5437   | 0.5459 |
| No log        | 8.28  | 480  | 1.6908          | 0.5498   | 0.5510 |
| 0.641         | 9.31  | 540  | 1.7310          | 0.5582   | 0.5587 |
| 0.641         | 10.34 | 600  | 1.9959          | 0.5388   | 0.5394 |
| 0.641         | 11.38 | 660  | 2.2660          | 0.5357   | 0.5401 |
| 0.641         | 12.41 | 720  | 2.3724          | 0.5507   | 0.5543 |
| 0.641         | 13.45 | 780  | 2.5843          | 0.5450   | 0.5464 |
| 0.641         | 14.48 | 840  | 2.7003          | 0.5534   | 0.5556 |
| 0.641         | 15.52 | 900  | 2.7255          | 0.5459   | 0.5491 |
| 0.641         | 16.55 | 960  | 2.9127          | 0.5481   | 0.5504 |
| 0.1116        | 17.59 | 1020 | 2.9543          | 0.5432   | 0.5462 |
| 0.1116        | 18.62 | 1080 | 3.0564          | 0.5560   | 0.5591 |
| 0.1116        | 19.66 | 1140 | 3.1501          | 0.5494   | 0.5530 |
| 0.1116        | 20.69 | 1200 | 3.2882          | 0.5467   | 0.5507 |
| 0.1116        | 21.72 | 1260 | 3.3562          | 0.5459   | 0.5496 |
| 0.1116        | 22.76 | 1320 | 3.4030          | 0.5538   | 0.5573 |
| 0.1116        | 23.79 | 1380 | 3.4897          | 0.5489   | 0.5523 |
| 0.1116        | 24.83 | 1440 | 3.5540          | 0.5476   | 0.5508 |
| 0.0147        | 25.86 | 1500 | 3.5772          | 0.5498   | 0.5530 |
| 0.0147        | 26.9  | 1560 | 3.6123          | 0.5481   | 0.5515 |
| 0.0147        | 27.93 | 1620 | 3.5954          | 0.5494   | 0.5529 |
| 0.0147        | 28.97 | 1680 | 3.6081          | 0.5489   | 0.5524 |
| 0.0147        | 30.0  | 1740 | 3.6119          | 0.5472   | 0.5508 |


### Framework versions

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