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ditilbert-base-uncased-finetuned-clinc
ysige/ditilbert-base-uncased-finetuned-clinc
ditilbert-base-uncased-finetuned-clinc is a text classification model from ysige. 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 2, 2023
- Updated
- Jul 4, 2023
- SHA
- c2019634
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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. -->
ditilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 0.0
- Accuracy: 0.0065
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: 48
- eval_batch_size: 48
- 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 | 318 | 0.0 | 0.0065 |
| 0.0 | 2.0 | 636 | 0.0 | 0.0065 |
| 0.0 | 3.0 | 954 | 0.0 | 0.0065 |
| 0.0 | 4.0 | 1272 | 0.0 | 0.0065 |
| 0.0 | 5.0 | 1590 | 0.0 | 0.0065 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jul 2, 2023
- Updated
- Jul 4, 2023