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distilbert-base-uncased-finetuned-mrpc
abhinav-kumar-thakur/distilbert-base-uncased-finetuned-mrpc
distilbert-base-uncased-finetuned-mrpc is a text classification model from abhinav-kumar-thakur. 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 1, 2022
- Updated
- Jul 1, 2022
- SHA
- 44523229
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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-mrpc
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.5556
- Accuracy: 0.8578
- F1: 0.9007
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 | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 230 | 0.3937 | 0.8113 | 0.8670 |
| No log | 2.0 | 460 | 0.3660 | 0.8480 | 0.8967 |
| 0.4387 | 3.0 | 690 | 0.4298 | 0.8529 | 0.8973 |
| 0.4387 | 4.0 | 920 | 0.5573 | 0.8529 | 0.8990 |
| 0.1832 | 5.0 | 1150 | 0.5556 | 0.8578 | 0.9007 |
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 1, 2022
- Updated
- Jul 1, 2022