---
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_gamma2
  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_gamma2

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: 3.4094
- Accuracy: 0.5516
- F1: 0.5553

## 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: 77
- 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   | 1.0398          | 0.4828   | 0.3902 |
| No log        | 2.07  | 120  | 1.1798          | 0.4489   | 0.3679 |
| No log        | 3.1   | 180  | 1.0463          | 0.4868   | 0.4351 |
| No log        | 4.14  | 240  | 1.0244          | 0.5622   | 0.5553 |
| No log        | 5.17  | 300  | 1.0819          | 0.5595   | 0.5478 |
| No log        | 6.21  | 360  | 1.4170          | 0.5410   | 0.5407 |
| No log        | 7.24  | 420  | 1.4249          | 0.5617   | 0.5653 |
| No log        | 8.28  | 480  | 1.6285          | 0.5626   | 0.5627 |
| 0.6824        | 9.31  | 540  | 1.8719          | 0.5494   | 0.5516 |
| 0.6824        | 10.34 | 600  | 1.9037          | 0.5547   | 0.5574 |
| 0.6824        | 11.38 | 660  | 1.7645          | 0.5494   | 0.5516 |
| 0.6824        | 12.41 | 720  | 2.0301          | 0.5437   | 0.5459 |
| 0.6824        | 13.45 | 780  | 2.6619          | 0.5317   | 0.5330 |
| 0.6824        | 14.48 | 840  | 2.5606          | 0.5498   | 0.5520 |
| 0.6824        | 15.52 | 900  | 2.9065          | 0.5326   | 0.5347 |
| 0.6824        | 16.55 | 960  | 2.6860          | 0.5564   | 0.5597 |
| 0.132         | 17.59 | 1020 | 2.9277          | 0.5476   | 0.5495 |
| 0.132         | 18.62 | 1080 | 3.1905          | 0.5441   | 0.5472 |
| 0.132         | 19.66 | 1140 | 2.9974          | 0.5410   | 0.5446 |
| 0.132         | 20.69 | 1200 | 2.8902          | 0.5556   | 0.5575 |
| 0.132         | 21.72 | 1260 | 3.2156          | 0.5401   | 0.5432 |
| 0.132         | 22.76 | 1320 | 3.2772          | 0.5472   | 0.5501 |
| 0.132         | 23.79 | 1380 | 3.2211          | 0.5551   | 0.5569 |
| 0.132         | 24.83 | 1440 | 3.3844          | 0.5423   | 0.5450 |
| 0.0295        | 25.86 | 1500 | 3.3534          | 0.5494   | 0.5531 |
| 0.0295        | 26.9  | 1560 | 3.4030          | 0.5498   | 0.5534 |
| 0.0295        | 27.93 | 1620 | 3.4206          | 0.5511   | 0.5547 |
| 0.0295        | 28.97 | 1680 | 3.4273          | 0.5529   | 0.5565 |
| 0.0295        | 30.0  | 1740 | 3.4094          | 0.5516   | 0.5553 |


### Framework versions

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