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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-cl-cardiff_cl_only_delta
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-cl-cardiff_cl_only_delta
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: 1.0448
- Accuracy: 0.4838
- F1: 0.4798
## 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: 11213
- 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 | 1.09 | 250 | 1.0988 | 0.3333 | 0.1667 |
| 1.0959 | 2.17 | 500 | 1.0989 | 0.3333 | 0.1667 |
| 1.0959 | 3.26 | 750 | 1.1000 | 0.3333 | 0.1667 |
| 1.0996 | 4.35 | 1000 | 1.1023 | 0.3333 | 0.1667 |
| 1.0996 | 5.43 | 1250 | 1.0990 | 0.3333 | 0.1667 |
| 1.1001 | 6.52 | 1500 | 1.0997 | 0.3333 | 0.1667 |
| 1.1001 | 7.61 | 1750 | 1.0998 | 0.3333 | 0.1667 |
| 1.0992 | 8.7 | 2000 | 1.0988 | 0.3333 | 0.1667 |
| 1.0992 | 9.78 | 2250 | 1.0990 | 0.3333 | 0.1667 |
| 1.0998 | 10.87 | 2500 | 1.0992 | 0.3333 | 0.1667 |
| 1.0998 | 11.96 | 2750 | 1.0996 | 0.3333 | 0.1667 |
| 1.0994 | 13.04 | 3000 | 1.0987 | 0.3333 | 0.1667 |
| 1.0994 | 14.13 | 3250 | 1.0988 | 0.3333 | 0.1667 |
| 1.0993 | 15.22 | 3500 | 1.0993 | 0.3333 | 0.1667 |
| 1.0993 | 16.3 | 3750 | 1.0987 | 0.3333 | 0.1667 |
| 1.0995 | 17.39 | 4000 | 1.0986 | 0.3333 | 0.1667 |
| 1.0995 | 18.48 | 4250 | 1.0989 | 0.3333 | 0.1667 |
| 1.0991 | 19.57 | 4500 | 1.0989 | 0.3333 | 0.1667 |
| 1.0991 | 20.65 | 4750 | 1.0987 | 0.3333 | 0.1667 |
| 1.0994 | 21.74 | 5000 | 1.0987 | 0.3333 | 0.1667 |
| 1.0994 | 22.83 | 5250 | 1.0987 | 0.3333 | 0.1667 |
| 1.0991 | 23.91 | 5500 | 1.0987 | 0.3333 | 0.1667 |
| 1.0991 | 25.0 | 5750 | 1.0986 | 0.3333 | 0.1667 |
| 1.0991 | 26.09 | 6000 | 1.0987 | 0.3333 | 0.1667 |
| 1.0991 | 27.17 | 6250 | 1.0986 | 0.3333 | 0.1667 |
| 1.0946 | 28.26 | 6500 | 1.0796 | 0.4560 | 0.4220 |
| 1.0946 | 29.35 | 6750 | 1.0448 | 0.4838 | 0.4798 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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