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distilbert-base-uncased-finetuned-classification
ThomasLI/distilbert-base-uncased-finetuned-classification
distilbert-base-uncased-finetuned-classification is a text classification model from ThomasLI. 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 1, 2023
- Updated
- Mar 15, 2023
- SHA
- 4e0b273d
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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-classification
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7862
- Accuracy: 0.8
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 200 | 0.4623 | 0.79 |
| No log | 2.0 | 400 | 0.4932 | 0.7812 |
| 0.3924 | 3.0 | 600 | 0.6621 | 0.7913 |
| 0.3924 | 4.0 | 800 | 0.7862 | 0.8 |
| 0.118 | 5.0 | 1000 | 0.8935 | 0.785 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 1, 2023
- Updated
- Mar 15, 2023