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---
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
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