metadata
license: apache-2.0
base_model: projecte-aina/roberta-base-ca-v2-cased-te
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 2504v4-8ep
results: []
2504v4-8ep
This model is a fine-tuned version of projecte-aina/roberta-base-ca-v2-cased-te on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5711
- Accuracy: 0.8487
- Precision: 0.8523
- Recall: 0.8487
- F1: 0.8484
- Ratio: 0.5504
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 4
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|---|---|---|---|---|---|---|---|---|
| 2.0709 | 0.9870 | 38 | 0.8049 | 0.7059 | 0.7073 | 0.7059 | 0.7054 | 0.4580 |
| 0.7325 | 2.0 | 77 | 0.6190 | 0.8067 | 0.8081 | 0.8067 | 0.8065 | 0.5336 |
| 0.6249 | 2.9870 | 115 | 0.5998 | 0.8109 | 0.8230 | 0.8109 | 0.8091 | 0.5966 |
| 0.5768 | 3.9481 | 152 | 0.5711 | 0.8487 | 0.8523 | 0.8487 | 0.8484 | 0.5504 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1