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distilbert-base-uncased-finetuned-stsb
keikofujii/distilbert-base-uncased-finetuned-stsb
distilbert-base-uncased-finetuned-stsb is a text classification model from keikofujii. 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
- Sep 22, 2023
- Updated
- Sep 22, 2023
- SHA
- 1f974535
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Base models
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-stsb
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: 0.5563
- Pearson: 0.8708
- Spearmanr: 0.8679
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 | Pearson | Spearmanr |
|---|---|---|---|---|---|
| No log | 1.0 | 360 | 0.6167 | 0.8601 | 0.8577 |
| 1.0026 | 2.0 | 720 | 0.6175 | 0.8670 | 0.8653 |
| 0.3862 | 3.0 | 1080 | 0.6439 | 0.8703 | 0.8675 |
| 0.3862 | 4.0 | 1440 | 0.5563 | 0.8708 | 0.8679 |
| 0.2514 | 5.0 | 1800 | 0.5616 | 0.8694 | 0.8659 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Sep 22, 2023
- Updated
- Sep 22, 2023