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distilbert-base-uncased-finetuned-emotion
YONGWOOHUH/distilbert-base-uncased-finetuned-emotion
distilbert-base-uncased-finetuned-emotion is a text classification model from YONGWOOHUH. 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
- Aug 16, 2023
- Updated
- Aug 16, 2023
- SHA
- 2d70ae2a
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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-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: 0.1543
- Accuracy: 0.936
- F1: 0.9362
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 |
|---|---|---|---|---|---|
| 0.1705 | 1.0 | 250 | 0.1681 | 0.9315 | 0.9311 |
| 0.1169 | 2.0 | 500 | 0.1612 | 0.932 | 0.9324 |
| 0.0967 | 3.0 | 750 | 0.1547 | 0.936 | 0.9358 |
| 0.0736 | 4.0 | 1000 | 0.1556 | 0.9375 | 0.9373 |
| 0.0614 | 5.0 | 1250 | 0.1543 | 0.936 | 0.9362 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Aug 16, 2023
- Updated
- Aug 16, 2023