Downloads · 30 days
6
55% of all-time downloads
distilbert-base-uncased-lora-text-classification
roopeshperla/distilbert-base-uncased-lora-text-classification
distilbert-base-uncased-lora-text-classification is a machine learning model from roopeshperla. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
11
Public
Repo size
2.5 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.5 MB · 72%
At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Feb 21, 2025
- Updated
- Feb 21, 2025
- SHA
- 2605279a
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: 0.9069
- Accuracy: {'accuracy': 0.909}
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 0.4078 | {'accuracy': 0.87} |
| 0.418 | 2.0 | 500 | 0.3741 | {'accuracy': 0.883} |
| 0.418 | 3.0 | 750 | 0.6762 | {'accuracy': 0.875} |
| 0.1992 | 4.0 | 1000 | 0.5752 | {'accuracy': 0.904} |
| 0.1992 | 5.0 | 1250 | 0.7332 | {'accuracy': 0.898} |
| 0.057 | 6.0 | 1500 | 0.8001 | {'accuracy': 0.901} |
| 0.057 | 7.0 | 1750 | 0.8734 | {'accuracy': 0.905} |
| 0.0275 | 8.0 | 2000 | 0.9458 | {'accuracy': 0.896} |
| 0.0275 | 9.0 | 2250 | 0.9224 | {'accuracy': 0.905} |
| 0.0117 | 10.0 | 2500 | 0.9069 | {'accuracy': 0.909} |
Framework versions
- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
- Library
- peft
- License
- apache-2.0
- Created
- Feb 21, 2025
- Updated
- Feb 21, 2025