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distilbert-base-uncased-finetuned-emotion
csuazob/distilbert-base-uncased-finetuned-emotion
distilbert-base-uncased-finetuned-emotion is a text classification model from csuazob. 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
- Mar 7, 2023
- Updated
- Mar 13, 2023
- SHA
- 3528c4eb
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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: 0.1461
- 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 |
|---|---|---|---|---|---|
| 0.76 | 1.0 | 250 | 0.2572 | 0.9145 | 0.9129 |
| 0.2029 | 2.0 | 500 | 0.1732 | 0.9305 | 0.9308 |
| 0.1377 | 3.0 | 750 | 0.1506 | 0.937 | 0.9374 |
| 0.1079 | 4.0 | 1000 | 0.1510 | 0.9345 | 0.9340 |
| 0.0893 | 5.0 | 1250 | 0.1461 | 0.937 | 0.9372 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 7, 2023
- Updated
- Mar 13, 2023