---
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: 0.5008
- F1: 0.4628

## 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    | 0.6932          | 0.4642   | 0.4462 |
| No log        | 0.77  | 10   | 0.6932          | 0.485    | 0.4560 |
| No log        | 1.15  | 15   | 0.6932          | 0.4992   | 0.4551 |
| No log        | 1.54  | 20   | 0.6932          | 0.4983   | 0.4467 |
| No log        | 1.92  | 25   | 0.6932          | 0.4958   | 0.4444 |
| No log        | 2.31  | 30   | 0.6932          | 0.5075   | 0.4661 |
| No log        | 2.69  | 35   | 0.6931          | 0.5042   | 0.4625 |
| No log        | 3.08  | 40   | 0.6931          | 0.4967   | 0.4597 |
| No log        | 3.46  | 45   | 0.6931          | 0.4908   | 0.4636 |
| No log        | 3.85  | 50   | 0.6932          | 0.5008   | 0.4647 |
| No log        | 4.23  | 55   | 0.6931          | 0.5067   | 0.4752 |
| No log        | 4.62  | 60   | 0.6931          | 0.5033   | 0.4688 |
| No log        | 5.0   | 65   | 0.6932          | 0.4875   | 0.4524 |
| No log        | 5.38  | 70   | 0.6932          | 0.4517   | 0.4156 |
| No log        | 5.77  | 75   | 0.6932          | 0.4667   | 0.4286 |
| No log        | 6.15  | 80   | 0.6932          | 0.4683   | 0.4334 |
| No log        | 6.54  | 85   | 0.6932          | 0.47     | 0.4382 |
| No log        | 6.92  | 90   | 0.6932          | 0.4692   | 0.4437 |
| No log        | 7.31  | 95   | 0.6931          | 0.4967   | 0.4766 |
| No log        | 7.69  | 100  | 0.6931          | 0.53     | 0.5138 |
| No log        | 8.08  | 105  | 0.6931          | 0.4858   | 0.4686 |
| No log        | 8.46  | 110  | 0.6932          | 0.4767   | 0.4452 |
| No log        | 8.85  | 115  | 0.6931          | 0.4617   | 0.4353 |
| No log        | 9.23  | 120  | 0.6931          | 0.4683   | 0.4433 |
| No log        | 9.62  | 125  | 0.6931          | 0.4717   | 0.4429 |
| No log        | 10.0  | 130  | 0.6931          | 0.4858   | 0.4630 |
| No log        | 10.38 | 135  | 0.6931          | 0.4983   | 0.4872 |
| No log        | 10.77 | 140  | 0.6931          | 0.4958   | 0.4771 |
| No log        | 11.15 | 145  | 0.6931          | 0.5108   | 0.4846 |
| No log        | 11.54 | 150  | 0.6931          | 0.5075   | 0.4784 |
| No log        | 11.92 | 155  | 0.6931          | 0.5267   | 0.4883 |
| No log        | 12.31 | 160  | 0.6931          | 0.5142   | 0.4809 |
| No log        | 12.69 | 165  | 0.6931          | 0.5108   | 0.4873 |
| No log        | 13.08 | 170  | 0.6931          | 0.5075   | 0.4829 |
| No log        | 13.46 | 175  | 0.6931          | 0.5042   | 0.4758 |
| No log        | 13.85 | 180  | 0.6931          | 0.4825   | 0.4567 |
| No log        | 14.23 | 185  | 0.6931          | 0.4625   | 0.4337 |
| No log        | 14.62 | 190  | 0.6931          | 0.4783   | 0.4528 |
| No log        | 15.0  | 195  | 0.6931          | 0.4767   | 0.4510 |
| No log        | 15.38 | 200  | 0.6931          | 0.4675   | 0.4370 |
| No log        | 15.77 | 205  | 0.6931          | 0.4675   | 0.4370 |
| No log        | 16.15 | 210  | 0.6931          | 0.465    | 0.4368 |
| No log        | 16.54 | 215  | 0.6931          | 0.4792   | 0.4522 |
| No log        | 16.92 | 220  | 0.6931          | 0.4875   | 0.4591 |
| No log        | 17.31 | 225  | 0.6931          | 0.4892   | 0.4628 |
| No log        | 17.69 | 230  | 0.6931          | 0.4933   | 0.4591 |
| No log        | 18.08 | 235  | 0.6931          | 0.5325   | 0.4978 |
| No log        | 18.46 | 240  | 0.6931          | 0.5192   | 0.4750 |
| No log        | 18.85 | 245  | 0.6931          | 0.5142   | 0.4836 |
| No log        | 19.23 | 250  | 0.6931          | 0.5008   | 0.4628 |


### Framework versions

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