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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_data-en-cardiff_eng_only_gamma2
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_data-en-cardiff_eng_only_gamma2
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: 3.4094
- Accuracy: 0.5516
- F1: 0.5553
## 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: 77
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.03 | 60 | 1.0398 | 0.4828 | 0.3902 |
| No log | 2.07 | 120 | 1.1798 | 0.4489 | 0.3679 |
| No log | 3.1 | 180 | 1.0463 | 0.4868 | 0.4351 |
| No log | 4.14 | 240 | 1.0244 | 0.5622 | 0.5553 |
| No log | 5.17 | 300 | 1.0819 | 0.5595 | 0.5478 |
| No log | 6.21 | 360 | 1.4170 | 0.5410 | 0.5407 |
| No log | 7.24 | 420 | 1.4249 | 0.5617 | 0.5653 |
| No log | 8.28 | 480 | 1.6285 | 0.5626 | 0.5627 |
| 0.6824 | 9.31 | 540 | 1.8719 | 0.5494 | 0.5516 |
| 0.6824 | 10.34 | 600 | 1.9037 | 0.5547 | 0.5574 |
| 0.6824 | 11.38 | 660 | 1.7645 | 0.5494 | 0.5516 |
| 0.6824 | 12.41 | 720 | 2.0301 | 0.5437 | 0.5459 |
| 0.6824 | 13.45 | 780 | 2.6619 | 0.5317 | 0.5330 |
| 0.6824 | 14.48 | 840 | 2.5606 | 0.5498 | 0.5520 |
| 0.6824 | 15.52 | 900 | 2.9065 | 0.5326 | 0.5347 |
| 0.6824 | 16.55 | 960 | 2.6860 | 0.5564 | 0.5597 |
| 0.132 | 17.59 | 1020 | 2.9277 | 0.5476 | 0.5495 |
| 0.132 | 18.62 | 1080 | 3.1905 | 0.5441 | 0.5472 |
| 0.132 | 19.66 | 1140 | 2.9974 | 0.5410 | 0.5446 |
| 0.132 | 20.69 | 1200 | 2.8902 | 0.5556 | 0.5575 |
| 0.132 | 21.72 | 1260 | 3.2156 | 0.5401 | 0.5432 |
| 0.132 | 22.76 | 1320 | 3.2772 | 0.5472 | 0.5501 |
| 0.132 | 23.79 | 1380 | 3.2211 | 0.5551 | 0.5569 |
| 0.132 | 24.83 | 1440 | 3.3844 | 0.5423 | 0.5450 |
| 0.0295 | 25.86 | 1500 | 3.3534 | 0.5494 | 0.5531 |
| 0.0295 | 26.9 | 1560 | 3.4030 | 0.5498 | 0.5534 |
| 0.0295 | 27.93 | 1620 | 3.4206 | 0.5511 | 0.5547 |
| 0.0295 | 28.97 | 1680 | 3.4273 | 0.5529 | 0.5565 |
| 0.0295 | 30.0 | 1740 | 3.4094 | 0.5516 | 0.5553 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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