---
license: mit
base_model: facebook/xlm-v-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMV-XCOPA-4_data-xcopa_all
  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-XCOPA-4_data-xcopa_all

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: 0.6931
- Accuracy: 0.5017
- F1: 0.4736

## 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: 4824
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.38  | 5    | 0.6931          | 0.4967   | 0.4711 |
| No log        | 0.77  | 10   | 0.6931          | 0.5258   | 0.5133 |
| No log        | 1.15  | 15   | 0.6931          | 0.5433   | 0.5456 |
| No log        | 1.54  | 20   | 0.6931          | 0.5267   | 0.5145 |
| No log        | 1.92  | 25   | 0.6931          | 0.5208   | 0.4987 |
| No log        | 2.31  | 30   | 0.6931          | 0.5183   | 0.4921 |
| No log        | 2.69  | 35   | 0.6931          | 0.5242   | 0.4987 |
| No log        | 3.08  | 40   | 0.6931          | 0.5308   | 0.5151 |
| No log        | 3.46  | 45   | 0.6931          | 0.53     | 0.5138 |
| No log        | 3.85  | 50   | 0.6931          | 0.4833   | 0.4737 |
| No log        | 4.23  | 55   | 0.6931          | 0.51     | 0.4922 |
| No log        | 4.62  | 60   | 0.6931          | 0.5008   | 0.4759 |
| No log        | 5.0   | 65   | 0.6931          | 0.4817   | 0.4591 |
| No log        | 5.38  | 70   | 0.6931          | 0.5133   | 0.4859 |
| No log        | 5.77  | 75   | 0.6932          | 0.4642   | 0.4305 |
| No log        | 6.15  | 80   | 0.6932          | 0.4783   | 0.4360 |
| No log        | 6.54  | 85   | 0.6932          | 0.4658   | 0.4282 |
| No log        | 6.92  | 90   | 0.6932          | 0.455    | 0.4303 |
| No log        | 7.31  | 95   | 0.6932          | 0.4808   | 0.4578 |
| No log        | 7.69  | 100  | 0.6931          | 0.4967   | 0.4784 |
| No log        | 8.08  | 105  | 0.6931          | 0.5208   | 0.5081 |
| No log        | 8.46  | 110  | 0.6931          | 0.555    | 0.5444 |
| No log        | 8.85  | 115  | 0.6931          | 0.5508   | 0.5397 |
| No log        | 9.23  | 120  | 0.6931          | 0.5567   | 0.5430 |
| No log        | 9.62  | 125  | 0.6931          | 0.5458   | 0.5338 |
| No log        | 10.0  | 130  | 0.6931          | 0.5267   | 0.5044 |
| No log        | 10.38 | 135  | 0.6931          | 0.5175   | 0.4961 |
| No log        | 10.77 | 140  | 0.6931          | 0.5325   | 0.5185 |
| No log        | 11.15 | 145  | 0.6931          | 0.5283   | 0.5104 |
| No log        | 11.54 | 150  | 0.6931          | 0.5217   | 0.5    |
| No log        | 11.92 | 155  | 0.6931          | 0.5058   | 0.4812 |
| No log        | 12.31 | 160  | 0.6932          | 0.5      | 0.4792 |
| No log        | 12.69 | 165  | 0.6931          | 0.5017   | 0.4736 |


### Framework versions

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