distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1413
- Accuracy: 0.937
- F1: 0.9372
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7628 | 1.0 | 250 | 0.2489 | 0.9155 | 0.9141 |
| 0.2014 | 2.0 | 500 | 0.1716 | 0.928 | 0.9283 |
| 0.1351 | 3.0 | 750 | 0.1456 | 0.937 | 0.9374 |
| 0.1046 | 4.0 | 1000 | 0.1440 | 0.9355 | 0.9349 |
| 0.0877 | 5.0 | 1250 | 0.1413 | 0.937 | 0.9372 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Downloads last month
- 25
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train Ahmed007/distilbert-base-uncased-finetuned-emotion
Evaluation results
- Accuracy on emotionself-reported0.937
- F1 on emotionself-reported0.937
- Accuracy on emotiontest set self-reported0.924
- Precision Macro on emotiontest set self-reported0.881
- Precision Micro on emotiontest set self-reported0.924
- Precision Weighted on emotiontest set self-reported0.925
- Recall Macro on emotiontest set self-reported0.888
- Recall Micro on emotiontest set self-reported0.924
- Recall Weighted on emotiontest set self-reported0.924
- F1 Macro on emotiontest set self-reported0.884