metadata
base_model: finiteautomata/beto-sentiment-analysis
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: beto-sentiment-analysis-finetuned-detests-wandb24
results: []
beto-sentiment-analysis-finetuned-detests-wandb24
This model is a fine-tuned version of finiteautomata/beto-sentiment-analysis on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6204
- Accuracy: 0.8674
- F1-score: 0.7993
- Precision: 0.8225
- Recall: 0.7822
- Auc: 0.7822
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|---|---|---|---|---|---|---|---|---|
| 0.393 | 1.0 | 77 | 0.3365 | 0.8592 | 0.7633 | 0.8424 | 0.7287 | 0.7287 |
| 0.1947 | 2.0 | 154 | 0.3843 | 0.8396 | 0.7845 | 0.7716 | 0.8023 | 0.8023 |
| 0.0597 | 3.0 | 231 | 0.5486 | 0.8740 | 0.8046 | 0.8398 | 0.7814 | 0.7814 |
| 0.0028 | 4.0 | 308 | 0.6204 | 0.8674 | 0.7993 | 0.8225 | 0.7822 | 0.7822 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1