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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-3_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-3_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: 0.4625
- F1: 0.4277
## 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: 48
- 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 | 0.4725 | 0.4472 |
| No log | 0.77 | 10 | 0.6932 | 0.4892 | 0.4656 |
| No log | 1.15 | 15 | 0.6932 | 0.4867 | 0.45 |
| No log | 1.54 | 20 | 0.6932 | 0.49 | 0.4584 |
| No log | 1.92 | 25 | 0.6931 | 0.51 | 0.4722 |
| No log | 2.31 | 30 | 0.6931 | 0.5042 | 0.4730 |
| No log | 2.69 | 35 | 0.6932 | 0.4825 | 0.4576 |
| No log | 3.08 | 40 | 0.6931 | 0.4767 | 0.4520 |
| No log | 3.46 | 45 | 0.6931 | 0.4842 | 0.4556 |
| No log | 3.85 | 50 | 0.6931 | 0.4883 | 0.4508 |
| No log | 4.23 | 55 | 0.6931 | 0.5392 | 0.5145 |
| No log | 4.62 | 60 | 0.6931 | 0.5508 | 0.5183 |
| No log | 5.0 | 65 | 0.6931 | 0.5392 | 0.5076 |
| No log | 5.38 | 70 | 0.6931 | 0.5567 | 0.5325 |
| No log | 5.77 | 75 | 0.6931 | 0.5642 | 0.5368 |
| No log | 6.15 | 80 | 0.6931 | 0.4483 | 0.4173 |
| No log | 6.54 | 85 | 0.6931 | 0.4358 | 0.4025 |
| No log | 6.92 | 90 | 0.6931 | 0.4492 | 0.4257 |
| No log | 7.31 | 95 | 0.6931 | 0.4442 | 0.4185 |
| No log | 7.69 | 100 | 0.6931 | 0.4492 | 0.4207 |
| No log | 8.08 | 105 | 0.6931 | 0.4575 | 0.4294 |
| No log | 8.46 | 110 | 0.6931 | 0.4592 | 0.4322 |
| No log | 8.85 | 115 | 0.6931 | 0.4583 | 0.4268 |
| No log | 9.23 | 120 | 0.6931 | 0.4567 | 0.4240 |
| No log | 9.62 | 125 | 0.6931 | 0.465 | 0.4309 |
| No log | 10.0 | 130 | 0.6931 | 0.5608 | 0.5239 |
| No log | 10.38 | 135 | 0.6931 | 0.5525 | 0.5244 |
| No log | 10.77 | 140 | 0.6931 | 0.5542 | 0.5253 |
| No log | 11.15 | 145 | 0.6931 | 0.5567 | 0.5284 |
| No log | 11.54 | 150 | 0.6931 | 0.5517 | 0.5247 |
| No log | 11.92 | 155 | 0.6931 | 0.5567 | 0.5325 |
| No log | 12.31 | 160 | 0.6931 | 0.5483 | 0.5271 |
| No log | 12.69 | 165 | 0.6931 | 0.5183 | 0.5009 |
| No log | 13.08 | 170 | 0.6931 | 0.5125 | 0.4891 |
| No log | 13.46 | 175 | 0.6931 | 0.4917 | 0.4696 |
| No log | 13.85 | 180 | 0.6931 | 0.4683 | 0.4462 |
| No log | 14.23 | 185 | 0.6931 | 0.4758 | 0.4507 |
| No log | 14.62 | 190 | 0.6931 | 0.515 | 0.4913 |
| No log | 15.0 | 195 | 0.6931 | 0.5242 | 0.5048 |
| No log | 15.38 | 200 | 0.6931 | 0.5208 | 0.4996 |
| No log | 15.77 | 205 | 0.6931 | 0.4567 | 0.4389 |
| No log | 16.15 | 210 | 0.6931 | 0.4492 | 0.4145 |
| No log | 16.54 | 215 | 0.6931 | 0.465 | 0.4309 |
| No log | 16.92 | 220 | 0.6931 | 0.4633 | 0.4270 |
| No log | 17.31 | 225 | 0.6931 | 0.4625 | 0.4277 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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