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---
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: VogagenRelation
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. -->
# VogagenRelation
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6927
- Accuracy: 0.5203
## 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: 1e-08
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.21 | 100 | 0.6928 | 0.5133 |
| No log | 0.42 | 200 | 0.6928 | 0.5148 |
| No log | 0.62 | 300 | 0.6928 | 0.5156 |
| No log | 0.83 | 400 | 0.6928 | 0.5156 |
| 0.6925 | 1.04 | 500 | 0.6928 | 0.5148 |
| 0.6925 | 1.25 | 600 | 0.6928 | 0.5156 |
| 0.6925 | 1.46 | 700 | 0.6928 | 0.5156 |
| 0.6925 | 1.66 | 800 | 0.6928 | 0.5172 |
| 0.6925 | 1.87 | 900 | 0.6928 | 0.5164 |
| 0.6929 | 2.08 | 1000 | 0.6928 | 0.5156 |
| 0.6929 | 2.29 | 1100 | 0.6928 | 0.5172 |
| 0.6929 | 2.49 | 1200 | 0.6928 | 0.5172 |
| 0.6929 | 2.7 | 1300 | 0.6928 | 0.5164 |
| 0.6929 | 2.91 | 1400 | 0.6928 | 0.5164 |
| 0.6928 | 3.12 | 1500 | 0.6928 | 0.5172 |
| 0.6928 | 3.33 | 1600 | 0.6928 | 0.5195 |
| 0.6928 | 3.53 | 1700 | 0.6928 | 0.5195 |
| 0.6928 | 3.74 | 1800 | 0.6928 | 0.5211 |
| 0.6928 | 3.95 | 1900 | 0.6928 | 0.5179 |
| 0.6927 | 4.16 | 2000 | 0.6928 | 0.5187 |
| 0.6927 | 4.37 | 2100 | 0.6928 | 0.5179 |
| 0.6927 | 4.57 | 2200 | 0.6927 | 0.5179 |
| 0.6927 | 4.78 | 2300 | 0.6927 | 0.5187 |
| 0.6927 | 4.99 | 2400 | 0.6927 | 0.5187 |
| 0.6929 | 5.2 | 2500 | 0.6928 | 0.5179 |
| 0.6929 | 5.41 | 2600 | 0.6928 | 0.5187 |
| 0.6929 | 5.61 | 2700 | 0.6928 | 0.5211 |
| 0.6929 | 5.82 | 2800 | 0.6927 | 0.5187 |
| 0.6929 | 6.03 | 2900 | 0.6927 | 0.5187 |
| 0.692 | 6.24 | 3000 | 0.6927 | 0.5195 |
| 0.692 | 6.44 | 3100 | 0.6927 | 0.5203 |
| 0.692 | 6.65 | 3200 | 0.6927 | 0.5203 |
| 0.692 | 6.86 | 3300 | 0.6927 | 0.5203 |
| 0.692 | 7.07 | 3400 | 0.6927 | 0.5203 |
| 0.6933 | 7.28 | 3500 | 0.6927 | 0.5203 |
| 0.6933 | 7.48 | 3600 | 0.6927 | 0.5203 |
| 0.6933 | 7.69 | 3700 | 0.6927 | 0.5203 |
| 0.6933 | 7.9 | 3800 | 0.6927 | 0.5203 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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