---
license: mit
base_model: facebook/xlm-v-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMV-XCOPA-5_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-5_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: 0.505
- F1: 0.4396

## 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: 214
- 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    | 0.6937          | 0.5158   | 0.4808 |
| No log        | 0.77  | 10   | 0.6929          | 0.4917   | 0.4777 |
| No log        | 1.15  | 15   | 0.6933          | 0.5025   | 0.4919 |
| No log        | 1.54  | 20   | 0.6945          | 0.5125   | 0.4734 |
| No log        | 1.92  | 25   | 0.6931          | 0.4767   | 0.4442 |
| No log        | 2.31  | 30   | 0.6931          | 0.5192   | 0.4898 |
| No log        | 2.69  | 35   | 0.6931          | 0.5042   | 0.4644 |
| No log        | 3.08  | 40   | 0.6931          | 0.5183   | 0.5085 |
| No log        | 3.46  | 45   | 0.6931          | 0.5083   | 0.4896 |
| No log        | 3.85  | 50   | 0.6931          | 0.5158   | 0.5004 |
| No log        | 4.23  | 55   | 0.6931          | 0.5292   | 0.5031 |
| No log        | 4.62  | 60   | 0.6931          | 0.5233   | 0.5077 |
| No log        | 5.0   | 65   | 0.6931          | 0.5292   | 0.5083 |
| No log        | 5.38  | 70   | 0.6931          | 0.5      | 0.4643 |
| No log        | 5.77  | 75   | 0.6931          | 0.4983   | 0.4837 |
| No log        | 6.15  | 80   | 0.6932          | 0.4708   | 0.4385 |
| No log        | 6.54  | 85   | 0.6932          | 0.475    | 0.4375 |
| No log        | 6.92  | 90   | 0.6932          | 0.4717   | 0.4379 |
| No log        | 7.31  | 95   | 0.6932          | 0.4925   | 0.4518 |
| No log        | 7.69  | 100  | 0.6932          | 0.4758   | 0.4448 |
| No log        | 8.08  | 105  | 0.6932          | 0.4908   | 0.4617 |
| No log        | 8.46  | 110  | 0.6931          | 0.5017   | 0.4613 |
| No log        | 8.85  | 115  | 0.6931          | 0.4875   | 0.4591 |
| No log        | 9.23  | 120  | 0.6931          | 0.4783   | 0.4371 |
| No log        | 9.62  | 125  | 0.6932          | 0.4533   | 0.4164 |
| No log        | 10.0  | 130  | 0.6932          | 0.475    | 0.4335 |
| No log        | 10.38 | 135  | 0.6931          | 0.5242   | 0.4870 |
| No log        | 10.77 | 140  | 0.6931          | 0.5258   | 0.4969 |
| No log        | 11.15 | 145  | 0.6931          | 0.4808   | 0.4452 |
| No log        | 11.54 | 150  | 0.6932          | 0.4408   | 0.3993 |
| No log        | 11.92 | 155  | 0.6932          | 0.4375   | 0.3968 |
| No log        | 12.31 | 160  | 0.6932          | 0.4325   | 0.3925 |
| No log        | 12.69 | 165  | 0.6932          | 0.435    | 0.4084 |
| No log        | 13.08 | 170  | 0.6932          | 0.4825   | 0.4557 |
| No log        | 13.46 | 175  | 0.6931          | 0.4892   | 0.4541 |
| No log        | 13.85 | 180  | 0.6931          | 0.5      | 0.4828 |
| No log        | 14.23 | 185  | 0.6931          | 0.5325   | 0.5109 |
| No log        | 14.62 | 190  | 0.6931          | 0.5367   | 0.5071 |
| No log        | 15.0  | 195  | 0.6932          | 0.47     | 0.4291 |
| No log        | 15.38 | 200  | 0.6932          | 0.4483   | 0.4193 |
| No log        | 15.77 | 205  | 0.6932          | 0.4325   | 0.4052 |
| No log        | 16.15 | 210  | 0.6932          | 0.47     | 0.4411 |
| No log        | 16.54 | 215  | 0.6932          | 0.47     | 0.4441 |
| No log        | 16.92 | 220  | 0.6932          | 0.4567   | 0.4210 |
| No log        | 17.31 | 225  | 0.6932          | 0.4408   | 0.4067 |
| No log        | 17.69 | 230  | 0.6932          | 0.44     | 0.4032 |
| No log        | 18.08 | 235  | 0.6932          | 0.455    | 0.4233 |
| No log        | 18.46 | 240  | 0.6932          | 0.4558   | 0.4297 |
| No log        | 18.85 | 245  | 0.6932          | 0.4542   | 0.4239 |
| No log        | 19.23 | 250  | 0.6932          | 0.4783   | 0.4431 |
| No log        | 19.62 | 255  | 0.6931          | 0.4908   | 0.4664 |
| No log        | 20.0  | 260  | 0.6932          | 0.4825   | 0.4567 |
| No log        | 20.38 | 265  | 0.6932          | 0.4775   | 0.4581 |
| No log        | 20.77 | 270  | 0.6946          | 0.495    | 0.4580 |
| No log        | 21.15 | 275  | 0.6931          | 0.5208   | 0.4806 |
| No log        | 21.54 | 280  | 0.6931          | 0.5      | 0.4652 |
| No log        | 21.92 | 285  | 0.6931          | 0.5067   | 0.4426 |
| No log        | 22.31 | 290  | 0.6931          | 0.4908   | 0.3944 |
| No log        | 22.69 | 295  | 0.6931          | 0.5317   | 0.4458 |
| No log        | 23.08 | 300  | 0.6931          | 0.4933   | 0.4109 |
| No log        | 23.46 | 305  | 0.6931          | 0.5025   | 0.4176 |
| No log        | 23.85 | 310  | 0.6931          | 0.5033   | 0.4601 |
| No log        | 24.23 | 315  | 0.6931          | 0.5017   | 0.4305 |
| No log        | 24.62 | 320  | 0.6931          | 0.4883   | 0.4408 |
| No log        | 25.0  | 325  | 0.6931          | 0.5267   | 0.4799 |
| No log        | 25.38 | 330  | 0.6931          | 0.5083   | 0.4370 |
| No log        | 25.77 | 335  | 0.6931          | 0.505    | 0.4396 |


### Framework versions

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