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
results: []
2504v4
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: 0.6121
- Accuracy: 0.8193
- Precision: 0.8296
- Recall: 0.8193
- F1: 0.8179
- Ratio: 0.5882
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 |
|---|---|---|---|---|---|---|---|---|
| 2.127 | 0.9870 | 38 | 0.8502 | 0.6345 | 0.6397 | 0.6345 | 0.6310 | 0.5966 |
| 0.7538 | 2.0 | 77 | 0.6640 | 0.7689 | 0.7885 | 0.7689 | 0.7649 | 0.6303 |
| 0.6205 | 2.9870 | 115 | 0.6121 | 0.8193 | 0.8296 | 0.8193 | 0.8179 | 0.5882 |
| 0.5664 | 3.9481 | 152 | 0.6239 | 0.8109 | 0.8278 | 0.8109 | 0.8085 | 0.6134 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1