metadata
base_model: camembert/camembert-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: relatives_labels-cbert_finetuned
results: []
relatives_labels-cbert_finetuned
This model is a fine-tuned version of camembert/camembert-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6900
- Accuracy: 0.5859
- Precision: 0.2929
- Recall: 0.5
- F1: 0.3694
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: 2e-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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 49 | 0.7393 | 0.5859 | 0.2929 | 0.5 | 0.3694 |
| No log | 2.0 | 98 | 0.7367 | 0.5859 | 0.2929 | 0.5 | 0.3694 |
| No log | 3.0 | 147 | 0.6921 | 0.5859 | 0.2929 | 0.5 | 0.3694 |
| No log | 4.0 | 196 | 0.6894 | 0.5859 | 0.2929 | 0.5 | 0.3694 |
| No log | 5.0 | 245 | 0.6900 | 0.5859 | 0.2929 | 0.5 | 0.3694 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1