---
license: mit
base_model: facebook/xlm-v-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMV-XCOPA-2_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-2_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: 1.3263
- Accuracy: 0.5417
- F1: 0.5291

## 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: 34
- 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          | 0.5075   | 0.4793 |
| No log        | 0.77  | 10   | 0.6932          | 0.5025   | 0.4749 |
| No log        | 1.15  | 15   | 0.6931          | 0.51     | 0.4878 |
| No log        | 1.54  | 20   | 0.6931          | 0.5192   | 0.4934 |
| No log        | 1.92  | 25   | 0.6931          | 0.51     | 0.4869 |
| No log        | 2.31  | 30   | 0.6931          | 0.5417   | 0.5089 |
| No log        | 2.69  | 35   | 0.6931          | 0.5533   | 0.5323 |
| No log        | 3.08  | 40   | 0.6931          | 0.5542   | 0.5352 |
| No log        | 3.46  | 45   | 0.6931          | 0.55     | 0.5329 |
| No log        | 3.85  | 50   | 0.6931          | 0.5475   | 0.5233 |
| No log        | 4.23  | 55   | 0.6931          | 0.5217   | 0.4884 |
| No log        | 4.62  | 60   | 0.6932          | 0.5283   | 0.5052 |
| No log        | 5.0   | 65   | 0.6931          | 0.5308   | 0.4969 |
| No log        | 5.38  | 70   | 0.6930          | 0.5217   | 0.4929 |
| No log        | 5.77  | 75   | 0.6930          | 0.53     | 0.5138 |
| No log        | 6.15  | 80   | 0.6931          | 0.5417   | 0.5259 |
| No log        | 6.54  | 85   | 0.6931          | 0.5525   | 0.5318 |
| No log        | 6.92  | 90   | 0.6931          | 0.5417   | 0.5054 |
| No log        | 7.31  | 95   | 0.6931          | 0.56     | 0.5319 |
| No log        | 7.69  | 100  | 0.6930          | 0.555    | 0.5291 |
| No log        | 8.08  | 105  | 0.6931          | 0.5192   | 0.4978 |
| No log        | 8.46  | 110  | 0.6931          | 0.5192   | 0.4943 |
| No log        | 8.85  | 115  | 0.6930          | 0.5383   | 0.5249 |
| No log        | 9.23  | 120  | 0.6930          | 0.5375   | 0.5244 |
| No log        | 9.62  | 125  | 0.6930          | 0.54     | 0.5192 |
| No log        | 10.0  | 130  | 0.6930          | 0.5617   | 0.5450 |
| No log        | 10.38 | 135  | 0.6930          | 0.54     | 0.5217 |
| No log        | 10.77 | 140  | 0.6929          | 0.5475   | 0.5241 |
| No log        | 11.15 | 145  | 0.6926          | 0.57     | 0.5559 |
| No log        | 11.54 | 150  | 0.6920          | 0.5658   | 0.5611 |
| No log        | 11.92 | 155  | 0.6896          | 0.5758   | 0.5661 |
| No log        | 12.31 | 160  | 0.6879          | 0.5275   | 0.5074 |
| No log        | 12.69 | 165  | 0.7069          | 0.4792   | 0.4435 |
| No log        | 13.08 | 170  | 0.6925          | 0.5358   | 0.5276 |
| No log        | 13.46 | 175  | 0.6941          | 0.5275   | 0.5082 |
| No log        | 13.85 | 180  | 0.7034          | 0.52     | 0.5052 |
| No log        | 14.23 | 185  | 0.7386          | 0.54     | 0.5208 |
| No log        | 14.62 | 190  | 0.7108          | 0.5242   | 0.4987 |
| No log        | 15.0  | 195  | 0.7418          | 0.5275   | 0.4978 |
| No log        | 15.38 | 200  | 0.8982          | 0.5292   | 0.5074 |
| No log        | 15.77 | 205  | 0.8073          | 0.5525   | 0.5398 |
| No log        | 16.15 | 210  | 1.0723          | 0.5633   | 0.5459 |
| No log        | 16.54 | 215  | 0.8696          | 0.5508   | 0.5293 |
| No log        | 16.92 | 220  | 0.8563          | 0.5533   | 0.5315 |
| No log        | 17.31 | 225  | 1.1253          | 0.5317   | 0.5147 |
| No log        | 17.69 | 230  | 1.0949          | 0.5483   | 0.5279 |
| No log        | 18.08 | 235  | 0.8768          | 0.5592   | 0.5482 |
| No log        | 18.46 | 240  | 1.1880          | 0.5558   | 0.5401 |
| No log        | 18.85 | 245  | 1.1781          | 0.5617   | 0.5434 |
| No log        | 19.23 | 250  | 0.9759          | 0.5508   | 0.5381 |
| No log        | 19.62 | 255  | 1.2402          | 0.5442   | 0.5321 |
| No log        | 20.0  | 260  | 1.2534          | 0.5308   | 0.5048 |
| No log        | 20.38 | 265  | 1.1987          | 0.5567   | 0.5382 |
| No log        | 20.77 | 270  | 1.1627          | 0.5425   | 0.5344 |
| No log        | 21.15 | 275  | 1.2200          | 0.5417   | 0.5133 |
| No log        | 21.54 | 280  | 1.4727          | 0.5342   | 0.5152 |
| No log        | 21.92 | 285  | 1.1766          | 0.55     | 0.5271 |
| No log        | 22.31 | 290  | 1.3099          | 0.5458   | 0.5265 |
| No log        | 22.69 | 295  | 1.3408          | 0.5558   | 0.5361 |
| No log        | 23.08 | 300  | 1.2960          | 0.5458   | 0.5164 |
| No log        | 23.46 | 305  | 1.3263          | 0.5417   | 0.5291 |


### Framework versions

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