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distilbert-base-uncased-finetuned-mrpc
anuj55/distilbert-base-uncased-finetuned-mrpc
distilbert-base-uncased-finetuned-mrpc is a text classification model from anuj55. 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 15, 2022
- Updated
- Jun 30, 2023
- SHA
- 831acaa3
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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-mrpc
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: 0.5682
- Accuracy: 0.7164
- F1: 0.2022
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5789 | 1.0 | 635 | 0.5764 | 0.7055 | 0.0 |
| 0.543 | 2.0 | 1270 | 0.5682 | 0.7164 | 0.2022 |
| 0.4661 | 3.0 | 1905 | 0.6358 | 0.7164 | 0.2812 |
| 0.2338 | 4.0 | 2540 | 0.9347 | 0.6844 | 0.4670 |
| 0.1538 | 5.0 | 3175 | 1.3556 | 0.6758 | 0.4740 |
| 0.1067 | 6.0 | 3810 | 1.6163 | 0.7016 | 0.3322 |
| 0.0928 | 7.0 | 4445 | 2.0786 | 0.6984 | 0.3609 |
| 0.0438 | 8.0 | 5080 | 2.1976 | 0.6945 | 0.4309 |
| 0.0312 | 9.0 | 5715 | 2.1931 | 0.6969 | 0.4209 |
| 0.0311 | 10.0 | 6350 | 2.4030 | 0.6883 | 0.4158 |
| 0.0281 | 11.0 | 6985 | 2.3715 | 0.7148 | 0.3739 |
| 0.0166 | 12.0 | 7620 | 2.6843 | 0.6984 | 0.3390 |
| 0.0167 | 13.0 | 8255 | 2.7291 | 0.6922 | 0.3604 |
| 0.0181 | 14.0 | 8890 | 2.7929 | 0.6906 | 0.3851 |
| 0.0147 | 15.0 | 9525 | 2.8976 | 0.7117 | 0.3303 |
| 0.0103 | 16.0 | 10160 | 3.0229 | 0.6859 | 0.3964 |
| 0.0047 | 17.0 | 10795 | 3.0616 | 0.6836 | 0.3817 |
| 0.0136 | 18.0 | 11430 | 3.0513 | 0.6875 | 0.3730 |
| 0.005 | 19.0 | 12065 | 3.0634 | 0.6930 | 0.3732 |
| 0.0042 | 20.0 | 12700 | 3.0611 | 0.7 | 0.3642 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 1.18.4
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- May 15, 2022
- Updated
- Jun 30, 2023