---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMR-XCOPA_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-XLMR-XCOPA_data-xcopa_all

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.4558
- F1: 0.4185

## 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: 42
- 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          | 0.49     | 0.4715 |
| No log        | 0.77  | 10   | 0.6932          | 0.4925   | 0.4634 |
| No log        | 1.15  | 15   | 0.6931          | 0.5033   | 0.4669 |
| No log        | 1.54  | 20   | 0.6932          | 0.4683   | 0.4491 |
| No log        | 1.92  | 25   | 0.6932          | 0.4883   | 0.4689 |
| No log        | 2.31  | 30   | 0.6932          | 0.4808   | 0.4716 |
| No log        | 2.69  | 35   | 0.6931          | 0.5058   | 0.4785 |
| No log        | 3.08  | 40   | 0.6932          | 0.4867   | 0.4615 |
| No log        | 3.46  | 45   | 0.6932          | 0.4533   | 0.4090 |
| No log        | 3.85  | 50   | 0.6932          | 0.4592   | 0.4190 |
| No log        | 4.23  | 55   | 0.6931          | 0.5167   | 0.4858 |
| No log        | 4.62  | 60   | 0.6931          | 0.5133   | 0.4832 |
| No log        | 5.0   | 65   | 0.6931          | 0.525    | 0.5128 |
| No log        | 5.38  | 70   | 0.6931          | 0.5158   | 0.5072 |
| No log        | 5.77  | 75   | 0.6931          | 0.5275   | 0.5158 |
| No log        | 6.15  | 80   | 0.6931          | 0.5333   | 0.5302 |
| No log        | 6.54  | 85   | 0.6931          | 0.5075   | 0.4883 |
| No log        | 6.92  | 90   | 0.6931          | 0.5042   | 0.4893 |
| No log        | 7.31  | 95   | 0.6930          | 0.5042   | 0.4979 |
| No log        | 7.69  | 100  | 0.6932          | 0.4883   | 0.4623 |
| No log        | 8.08  | 105  | 0.6932          | 0.4617   | 0.4159 |
| No log        | 8.46  | 110  | 0.6932          | 0.4425   | 0.4085 |
| No log        | 8.85  | 115  | 0.6931          | 0.4992   | 0.4705 |
| No log        | 9.23  | 120  | 0.6931          | 0.4975   | 0.4668 |
| No log        | 9.62  | 125  | 0.6931          | 0.4867   | 0.4510 |
| No log        | 10.0  | 130  | 0.6931          | 0.4817   | 0.4456 |
| No log        | 10.38 | 135  | 0.6931          | 0.4808   | 0.4442 |
| No log        | 10.77 | 140  | 0.6932          | 0.49     | 0.4565 |
| No log        | 11.15 | 145  | 0.6932          | 0.4908   | 0.4579 |
| No log        | 11.54 | 150  | 0.6931          | 0.5067   | 0.4761 |
| No log        | 11.92 | 155  | 0.6931          | 0.5083   | 0.4887 |
| No log        | 12.31 | 160  | 0.6931          | 0.5217   | 0.4974 |
| No log        | 12.69 | 165  | 0.6932          | 0.4967   | 0.4499 |
| No log        | 13.08 | 170  | 0.6931          | 0.5158   | 0.4872 |
| No log        | 13.46 | 175  | 0.6931          | 0.5225   | 0.5065 |
| No log        | 13.85 | 180  | 0.6931          | 0.5475   | 0.5323 |
| No log        | 14.23 | 185  | 0.6931          | 0.5383   | 0.5224 |
| No log        | 14.62 | 190  | 0.6932          | 0.4925   | 0.4558 |
| No log        | 15.0  | 195  | 0.6932          | 0.4783   | 0.4340 |
| No log        | 15.38 | 200  | 0.6931          | 0.5267   | 0.5120 |
| No log        | 15.77 | 205  | 0.6931          | 0.5158   | 0.4890 |
| No log        | 16.15 | 210  | 0.6931          | 0.53     | 0.5179 |
| No log        | 16.54 | 215  | 0.6932          | 0.48     | 0.4497 |
| No log        | 16.92 | 220  | 0.6932          | 0.4558   | 0.4133 |
| No log        | 17.31 | 225  | 0.6932          | 0.4742   | 0.4290 |
| No log        | 17.69 | 230  | 0.6931          | 0.4967   | 0.4838 |
| No log        | 18.08 | 235  | 0.6931          | 0.5325   | 0.5126 |
| No log        | 18.46 | 240  | 0.6931          | 0.5242   | 0.5090 |
| No log        | 18.85 | 245  | 0.6931          | 0.5242   | 0.5013 |
| No log        | 19.23 | 250  | 0.6932          | 0.4683   | 0.4273 |
| No log        | 19.62 | 255  | 0.6931          | 0.5158   | 0.4890 |
| No log        | 20.0  | 260  | 0.6932          | 0.4533   | 0.4225 |
| No log        | 20.38 | 265  | 0.6932          | 0.4658   | 0.4282 |
| No log        | 20.77 | 270  | 0.6932          | 0.4667   | 0.4326 |
| No log        | 21.15 | 275  | 0.6932          | 0.4725   | 0.4403 |
| No log        | 21.54 | 280  | 0.6932          | 0.4667   | 0.4306 |
| No log        | 21.92 | 285  | 0.6932          | 0.4658   | 0.4272 |
| No log        | 22.31 | 290  | 0.6932          | 0.4625   | 0.4256 |
| No log        | 22.69 | 295  | 0.6932          | 0.4617   | 0.4232 |
| No log        | 23.08 | 300  | 0.6932          | 0.465    | 0.4247 |
| No log        | 23.46 | 305  | 0.6932          | 0.455    | 0.4223 |
| No log        | 23.85 | 310  | 0.6931          | 0.4558   | 0.4226 |
| No log        | 24.23 | 315  | 0.6931          | 0.4875   | 0.4638 |
| No log        | 24.62 | 320  | 0.6931          | 0.4642   | 0.4315 |
| No log        | 25.0  | 325  | 0.6931          | 0.4508   | 0.4153 |
| No log        | 25.38 | 330  | 0.6932          | 0.4558   | 0.4185 |


### Framework versions

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