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distilbert-base-uncased-lora-text-classification
shawhin/distilbert-base-uncased-lora-text-classification
distilbert-base-uncased-lora-text-classification is a machine learning model from shawhin. 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. --
Access
Public
Updated Sep 6, 2023
Repo size
30.4 MB
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2
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Sep 6, 2023
- Updated
- Sep 6, 2023
- SHA
- bd902e3b
Base models
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-lora-text-classification
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: 1.0684
- Accuracy: {'accuracy': 0.879}
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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 0.4266 | {'accuracy': 0.87} |
| 0.4232 | 2.0 | 500 | 0.4260 | {'accuracy': 0.88} |
| 0.4232 | 3.0 | 750 | 0.5071 | {'accuracy': 0.885} |
| 0.2213 | 4.0 | 1000 | 0.7424 | {'accuracy': 0.875} |
| 0.2213 | 5.0 | 1250 | 0.7885 | {'accuracy': 0.881} |
| 0.067 | 6.0 | 1500 | 0.9312 | {'accuracy': 0.872} |
| 0.067 | 7.0 | 1750 | 0.9669 | {'accuracy': 0.874} |
| 0.0238 | 8.0 | 2000 | 1.0856 | {'accuracy': 0.874} |
| 0.0238 | 9.0 | 2250 | 1.0637 | {'accuracy': 0.88} |
| 0.0066 | 10.0 | 2500 | 1.0684 | {'accuracy': 0.879} |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.2
- License
- apache-2.0
- Created
- Sep 6, 2023
- Updated
- Sep 6, 2023