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distilbert-base-uncased-lora-text-classification
Phalguna/distilbert-base-uncased-lora-text-classification
distilbert-base-uncased-lora-text-classification is a machine learning model from Phalguna. 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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Public
Updated Nov 28, 2023
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Nov 28, 2023
- Updated
- Nov 28, 2023
- SHA
- 1d8e62a2
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 an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0281
- Accuracy: {'accuracy': 0.888}
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 | 0.3331 | {'accuracy': 0.881} |
| 0.4182 | 2.0 | 500 | 0.4217 | {'accuracy': 0.89} |
| 0.4182 | 3.0 | 750 | 0.5842 | {'accuracy': 0.882} |
| 0.2061 | 4.0 | 1000 | 0.6004 | {'accuracy': 0.891} |
| 0.2061 | 5.0 | 1250 | 0.7716 | {'accuracy': 0.89} |
| 0.0663 | 6.0 | 1500 | 0.9055 | {'accuracy': 0.887} |
| 0.0663 | 7.0 | 1750 | 1.0172 | {'accuracy': 0.88} |
| 0.0307 | 8.0 | 2000 | 1.0077 | {'accuracy': 0.891} |
| 0.0307 | 9.0 | 2250 | 0.9932 | {'accuracy': 0.887} |
| 0.0114 | 10.0 | 2500 | 1.0281 | {'accuracy': 0.888} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- License
- apache-2.0
- Created
- Nov 28, 2023
- Updated
- Nov 28, 2023