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distilbert-base-uncased_emotion_ft_0520
Zhouzk/distilbert-base-uncased_emotion_ft_0520
distilbert-base-uncased_emotion_ft_0520 is a text classification model from Zhouzk. 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
- May 29, 2023
- Updated
- May 30, 2023
- SHA
- a14bcd02
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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_0520
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: 0.2557
- Accuracy: 0.921
- F1: 0.9210
- Precision: 0.8947
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: 256
- eval_batch_size: 256
- 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 | 63 | 0.9006 | 0.7055 | 0.6410 | 0.4988 |
| No log | 2.0 | 126 | 0.4166 | 0.8805 | 0.8729 | 0.8886 |
| No log | 3.0 | 189 | 0.2844 | 0.9175 | 0.9167 | 0.8987 |
| 0.6518 | 4.0 | 252 | 0.2557 | 0.921 | 0.9210 | 0.8947 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- May 29, 2023
- Updated
- May 30, 2023