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distilbert-base-uncased-finetuned-emotion
Fengkai/distilbert-base-uncased-finetuned-emotion
distilbert-base-uncased-finetuned-emotion is a text classification model from Fengkai. 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
- Mar 2, 2022
- Updated
- Jan 25, 2022
- SHA
- e6243bb5
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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: 0.1495
- Accuracy: 0.9385
- F1: 0.9383
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 |
|---|---|---|---|---|---|
| 0.1739 | 1.0 | 250 | 0.1827 | 0.931 | 0.9302 |
| 0.1176 | 2.0 | 500 | 0.1567 | 0.9325 | 0.9326 |
| 0.0994 | 3.0 | 750 | 0.1555 | 0.9385 | 0.9389 |
| 0.08 | 4.0 | 1000 | 0.1496 | 0.9445 | 0.9443 |
| 0.0654 | 5.0 | 1250 | 0.1495 | 0.9385 | 0.9383 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Jan 25, 2022