bert-emotion
This model is a fine-tuned version of distilbert-base-cased on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 1.2350
- Precision: 0.7081
- Recall: 0.7094
- Fscore: 0.7082
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|---|---|---|---|---|---|---|
| 0.8442 | 1.0 | 815 | 0.8653 | 0.7642 | 0.6192 | 0.6363 |
| 0.5488 | 2.0 | 1630 | 0.9330 | 0.7116 | 0.6838 | 0.6912 |
| 0.2713 | 3.0 | 2445 | 1.2350 | 0.7081 | 0.7094 | 0.7082 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
- Downloads last month
- 9
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 umangchaudhry/bert-emotion
Evaluation results
- Precision on tweet_evalself-reported0.708
- Recall on tweet_evalself-reported0.709