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distilbert-optimized-uncased-distilled-clinc
alekskusz/distilbert-optimized-uncased-distilled-clinc
distilbert-optimized-uncased-distilled-clinc is a machine learning model from alekskusz. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Oct 16, 2024
- Updated
- Oct 16, 2024
- SHA
- 29efa0ad
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-optimized-uncased-distilled-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: 0.2365
- Accuracy: 0.9532
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: 7.418555873965401e-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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 318 | 0.5953 | 0.9035 |
| 1.6189 | 2.0 | 636 | 0.2753 | 0.9458 |
| 1.6189 | 3.0 | 954 | 0.2527 | 0.95 |
| 0.1343 | 4.0 | 1272 | 0.2386 | 0.9558 |
| 0.0592 | 5.0 | 1590 | 0.2352 | 0.9529 |
| 0.0592 | 6.0 | 1908 | 0.2389 | 0.9526 |
| 0.0443 | 7.0 | 2226 | 0.2365 | 0.9532 |
Framework versions
- Transformers 4.16.2
- Pytorch 2.4.1+cu121
- Datasets 1.16.1
- Tokenizers 0.20.1
- Type
- distilbert
- License
- apache-2.0
- Created
- Oct 16, 2024
- Updated
- Oct 16, 2024