metadata
base_model: finiteautomata/beto-sentiment-analysis
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: beto-sentiment-analysis-finetuned-detests
results: []
beto-sentiment-analysis-finetuned-detests
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: 1.2413
- Accuracy: 0.8396
- F1-score: 0.7695
- Precision: 0.7724
- Recall: 0.7668
- Auc: 0.7668
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc |
|---|---|---|---|---|---|---|---|---|
| 0.3772 | 1.0 | 174 | 0.4358 | 0.8298 | 0.6814 | 0.8246 | 0.6513 | 0.6513 |
| 0.1092 | 2.0 | 348 | 0.4312 | 0.8625 | 0.7925 | 0.8139 | 0.7765 | 0.7765 |
| 0.0955 | 3.0 | 522 | 0.7126 | 0.8412 | 0.7724 | 0.7746 | 0.7704 | 0.7704 |
| 0.0625 | 4.0 | 696 | 0.9681 | 0.8412 | 0.7688 | 0.7757 | 0.7627 | 0.7627 |
| 0.0056 | 5.0 | 870 | 1.1017 | 0.8347 | 0.7567 | 0.7666 | 0.7484 | 0.7484 |
| 0.0018 | 6.0 | 1044 | 1.2244 | 0.8347 | 0.7630 | 0.7651 | 0.7610 | 0.7610 |
| 0.0001 | 7.0 | 1218 | 1.2190 | 0.8412 | 0.7637 | 0.7778 | 0.7526 | 0.7526 |
| 0.0001 | 8.0 | 1392 | 1.2356 | 0.8396 | 0.7645 | 0.7739 | 0.7566 | 0.7566 |
| 0.0001 | 9.0 | 1566 | 1.2332 | 0.8380 | 0.7547 | 0.7746 | 0.7403 | 0.7403 |
| 0.0001 | 10.0 | 1740 | 1.2413 | 0.8396 | 0.7695 | 0.7724 | 0.7668 | 0.7668 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3