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distilbert-base-uncased-finetuned-emotion
Ahmed007/distilbert-base-uncased-finetuned-emotion
distilbert-base-uncased-finetuned-emotion is a text classification model from Ahmed007. 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
- Jul 11, 2022
- Updated
- Aug 20, 2022
- SHA
- 184051ee
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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: 0.1413
- 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 |
|---|---|---|---|---|---|
| 0.7628 | 1.0 | 250 | 0.2489 | 0.9155 | 0.9141 |
| 0.2014 | 2.0 | 500 | 0.1716 | 0.928 | 0.9283 |
| 0.1351 | 3.0 | 750 | 0.1456 | 0.937 | 0.9374 |
| 0.1046 | 4.0 | 1000 | 0.1440 | 0.9355 | 0.9349 |
| 0.0877 | 5.0 | 1250 | 0.1413 | 0.937 | 0.9372 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jul 11, 2022
- Updated
- Aug 20, 2022