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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_gamma
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_gamma
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: 1.0986
- Accuracy: 0.3333
- F1: 0.1667
## 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: 11423
- 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 | 1.0985 | 0.3333 | 0.1667 |
| No log | 2.07 | 120 | 1.0986 | 0.3333 | 0.1667 |
| No log | 3.1 | 180 | 1.0987 | 0.3333 | 0.1667 |
| No log | 4.14 | 240 | 1.0988 | 0.3333 | 0.1667 |
| No log | 5.17 | 300 | 1.0991 | 0.3333 | 0.1667 |
| No log | 6.21 | 360 | 1.0993 | 0.3333 | 0.1667 |
| No log | 7.24 | 420 | 1.0988 | 0.3333 | 0.1667 |
| No log | 8.28 | 480 | 1.0987 | 0.3333 | 0.1667 |
| 1.1004 | 9.31 | 540 | 1.0990 | 0.3333 | 0.1667 |
| 1.1004 | 10.34 | 600 | 1.0993 | 0.3333 | 0.1667 |
| 1.1004 | 11.38 | 660 | 1.0988 | 0.3333 | 0.1667 |
| 1.1004 | 12.41 | 720 | 1.0987 | 0.3333 | 0.1667 |
| 1.1004 | 13.45 | 780 | 1.0990 | 0.3333 | 0.1667 |
| 1.1004 | 14.48 | 840 | 1.0989 | 0.3333 | 0.1667 |
| 1.1004 | 15.52 | 900 | 1.0987 | 0.3333 | 0.1667 |
| 1.1004 | 16.55 | 960 | 1.0987 | 0.3333 | 0.1667 |
| 1.0996 | 17.59 | 1020 | 1.0989 | 0.3333 | 0.1667 |
| 1.0996 | 18.62 | 1080 | 1.0991 | 0.3333 | 0.1667 |
| 1.0996 | 19.66 | 1140 | 1.0986 | 0.3333 | 0.1667 |
| 1.0996 | 20.69 | 1200 | 1.0987 | 0.3333 | 0.1667 |
| 1.0996 | 21.72 | 1260 | 1.0986 | 0.3333 | 0.1667 |
| 1.0996 | 22.76 | 1320 | 1.0988 | 0.3333 | 0.1667 |
| 1.0996 | 23.79 | 1380 | 1.0989 | 0.3333 | 0.1667 |
| 1.0996 | 24.83 | 1440 | 1.0986 | 0.3333 | 0.1667 |
| 1.0995 | 25.86 | 1500 | 1.0987 | 0.3333 | 0.1667 |
| 1.0995 | 26.9 | 1560 | 1.0986 | 0.3333 | 0.1667 |
| 1.0995 | 27.93 | 1620 | 1.0986 | 0.3333 | 0.1667 |
| 1.0995 | 28.97 | 1680 | 1.0986 | 0.3333 | 0.1667 |
| 1.0995 | 30.0 | 1740 | 1.0986 | 0.3333 | 0.1667 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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