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distilbert-finetuned-emotion
pt-sk/distilbert-finetuned-emotion
distilbert-finetuned-emotion is a text classification model from pt-sk. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Feb 7, 2024
- Updated
- Apr 14, 2024
- SHA
- 092dc139
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Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Distilbert-finetuned-emotion
Distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet. 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: 0.2195
- Accuracy: 0.9235
- F1: 0.9233
Emotion Labels
- label_0: Sadness
- label_1: Joy
- label_2: Love
- label_3: Anger
- label_4: Fear
- label_5: Surprise
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.8537 | 1.0 | 250 | 0.3235 | 0.897 | 0.8958 |
| 0.2506 | 2.0 | 500 | 0.2195 | 0.9235 | 0.9233 |
Validation metrics
- test_loss : 0.2194512039422989
- test_accuracy : 0.9235
- test_f1 : 0.923296474937779
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Languages
- en
- Created
- Feb 7, 2024
- Updated
- Apr 14, 2024