|
--- |
|
license: mit |
|
base_model: camembert-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
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: 0.6952 |
|
- Accuracy: 0.5 |
|
- Precision: 0.5 |
|
- Recall: 1.0 |
|
- F1: 0.6667 |
|
|
|
## 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: 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 | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| No log | 0.21 | 100 | 0.6928 | 0.5 | 0.5 | 1.0 | 0.6667 | |
|
| No log | 0.42 | 200 | 0.7024 | 0.5 | 0.0 | 0.0 | 0.0 | |
|
| No log | 0.62 | 300 | 0.7194 | 0.5 | 0.0 | 0.0 | 0.0 | |
|
| No log | 0.83 | 400 | 0.6944 | 0.4992 | 0.0 | 0.0 | 0.0 | |
|
| 0.6974 | 1.04 | 500 | 0.6973 | 0.5 | 0.5 | 1.0 | 0.6667 | |
|
| 0.6974 | 1.25 | 600 | 0.7078 | 0.5 | 0.5 | 1.0 | 0.6667 | |
|
| 0.6974 | 1.46 | 700 | 0.6945 | 0.5 | 0.5 | 1.0 | 0.6667 | |
|
| 0.6974 | 1.66 | 800 | 0.6942 | 0.5 | 0.5 | 1.0 | 0.6667 | |
|
| 0.6974 | 1.87 | 900 | 0.6945 | 0.5 | 0.5 | 1.0 | 0.6667 | |
|
| 0.6962 | 2.08 | 1000 | 0.6932 | 0.4992 | 0.4 | 0.0031 | 0.0062 | |
|
| 0.6962 | 2.29 | 1100 | 0.6952 | 0.5 | 0.5 | 1.0 | 0.6667 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.34.0 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.14.1 |
|
|
