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distilbert-base-uncased_emotion_ft_0416
jianhui-su/distilbert-base-uncased_emotion_ft_0416
distilbert-base-uncased_emotion_ft_0416 is a text classification model from jianhui-su. 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
- Dec 6, 2023
- Updated
- Dec 6, 2023
- SHA
- 54e4faa2
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Base models
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_emotion_ft_0416
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: 0.1475
- Accuracy: 0.937
- F1: 0.9372
- Precision: 0.9069
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|---|---|---|---|---|---|---|
| 0.8242 | 1.0 | 250 | 0.2955 | 0.908 | 0.9076 | 0.8764 |
| 0.2219 | 2.0 | 500 | 0.1762 | 0.931 | 0.9311 | 0.9022 |
| 0.1375 | 3.0 | 750 | 0.1560 | 0.933 | 0.9330 | 0.9066 |
| 0.1124 | 4.0 | 1000 | 0.1475 | 0.937 | 0.9372 | 0.9069 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Dec 6, 2023
- Updated
- Dec 6, 2023