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distilbert-base-uncased_emotion_ft_0416
lanchunhui/distilbert-base-uncased_emotion_ft_0416
distilbert-base-uncased_emotion_ft_0416 is a text classification model from lanchunhui. 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
- Apr 16, 2023
- Updated
- Apr 16, 2023
- SHA
- fd9f9fac
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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_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: 0.1714
- Accuracy: 0.9325
- F1: 0.9327
- Precision: 0.9028
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: 128
- eval_batch_size: 128
- 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 |
|---|---|---|---|---|---|---|
| No log | 1.0 | 125 | 0.4267 | 0.877 | 0.8677 | 0.8778 |
| 0.6498 | 2.0 | 250 | 0.2128 | 0.922 | 0.9219 | 0.8975 |
| 0.6498 | 3.0 | 375 | 0.1880 | 0.925 | 0.9258 | 0.8877 |
| 0.1653 | 4.0 | 500 | 0.1714 | 0.9325 | 0.9327 | 0.9028 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.11.0
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Apr 16, 2023
- Updated
- Apr 16, 2023