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distilbert-base-uncased_emotion_ft_2334
pansysy/distilbert-base-uncased_emotion_ft_2334
distilbert-base-uncased_emotion_ft_2334 is a text classification model from pansysy. 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 23, 2023
- Updated
- Aug 23, 2023
- SHA
- abeafa3a
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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_2334
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: 0.1499
- Accuracy: 0.939
- F1: 0.9391
- Precision: 0.9127
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 |
|---|---|---|---|---|---|---|
| 0.8383 | 1.0 | 250 | 0.2832 | 0.9145 | 0.9148 | 0.8813 |
| 0.2128 | 2.0 | 500 | 0.1734 | 0.936 | 0.9365 | 0.9047 |
| 0.1442 | 3.0 | 750 | 0.1605 | 0.9345 | 0.9350 | 0.9029 |
| 0.1126 | 4.0 | 1000 | 0.1499 | 0.939 | 0.9391 | 0.9127 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Aug 23, 2023
- Updated
- Aug 23, 2023