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test_trainer
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4916
- Accuracy: 0.659
Model description
This model is a fine-tuned model based on Sentiment Analysis or Text Classification for reviews based on the new 'Threads' app. The reviews dataset can be found on Kaggle.
Intended uses & limitations
Basically it converts the review text into rating points from 1-5(1 being a very bad review and 5 being a very good review)
Training and evaluation data
'Reviews' dataset(Thread) from Kaggle.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 1.0560 | 0.6895 |
| 0.5502 | 2.0 | 500 | 1.3548 | 0.6595 |
| 0.5502 | 3.0 | 750 | 1.4916 | 0.659 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
