metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Sentiment-Analysis-on-Twitter-BCS
results: []
Sentiment-Analysis-on-Twitter-BCS
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1303
- Accuracy: 0.9615
- Precision: 0.7730
- Recall: 0.6384
- F1: 0.6993
- Roc Auc: 0.9701
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 0.211 | 1.0 | 1798 | 0.1622 | 0.9515 | 0.6769 | 0.5893 | 0.6301 | 0.9417 |
| 0.1369 | 2.0 | 3596 | 0.1568 | 0.9568 | 0.7009 | 0.6696 | 0.6849 | 0.9646 |
| 0.1118 | 3.0 | 5394 | 0.1303 | 0.9615 | 0.7730 | 0.6384 | 0.6993 | 0.9701 |
| 0.0887 | 4.0 | 7192 | 0.1532 | 0.9631 | 0.8011 | 0.6295 | 0.7050 | 0.9708 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3