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llama3.2-finetuned-newsclassify
YaminiP/llama3.2-finetuned-newsclassify
llama3.2-finetuned-newsclassify is a text classification model from YaminiP. Use it when you need a label for a piece of text. It is set up for peft. The card lists the license as llama3.2.
should probably proofread and complete it, then remove this comment. --
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.json17.3 MB · 56%
At a glance
- Task
- Text Classification
- Library
- peft
- License
- llama3.2
- Access
- Public
- Created
- Oct 29, 2024
- Updated
- Oct 29, 2024
- SHA
- 2bcef841
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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. -->
llama3.2-finetuned-newsclassify
This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the None dataset. It achieves the following results on the evaluation set:
-
Loss: 0.0205
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Balanced Accuracy: 0.992
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Accuracy: 0.992
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F1-score: 0.9920
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Classification-report: precision recall f1-score support
0 1.00 0.96 0.98 50 1 1.00 1.00 1.00 50 2 1.00 1.00 1.00 50 3 1.00 1.00 1.00 50 4 0.96 1.00 0.98 50accuracy 0.99 250 macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Balanced Accuracy | Accuracy | F1-score | Classification-report |
|---|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 157 | 0.0405 | 0.9880 | 0.988 | 0.9880 | precision recall f1-score support |
0 1.00 0.94 0.97 50
1 1.00 1.00 1.00 50
2 1.00 1.00 1.00 50
3 1.00 1.00 1.00 50
4 0.94 1.00 0.97 50
accuracy 0.99 250
macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250 | | 0.0 | 2.0 | 314 | 0.0300 | 0.9880 | 0.988 | 0.9880 | precision recall f1-score support
0 1.00 0.94 0.97 50
1 1.00 1.00 1.00 50
2 1.00 1.00 1.00 50
3 1.00 1.00 1.00 50
4 0.94 1.00 0.97 50
accuracy 0.99 250
macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250 | | 0.0 | 3.0 | 471 | 0.0177 | 0.992 | 0.992 | 0.9920 | precision recall f1-score support
0 1.00 0.96 0.98 50
1 1.00 1.00 1.00 50
2 1.00 1.00 1.00 50
3 1.00 1.00 1.00 50
4 0.96 1.00 0.98 50
accuracy 0.99 250
macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250 | | 0.0 | 4.0 | 628 | 0.0205 | 0.992 | 0.992 | 0.9920 | precision recall f1-score support
0 1.00 0.96 0.98 50
1 1.00 1.00 1.00 50
2 1.00 1.00 1.00 50
3 1.00 1.00 1.00 50
4 0.96 1.00 0.98 50
accuracy 0.99 250
macro avg 0.99 0.99 0.99 250 weighted avg 0.99 0.99 0.99 250 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1
- Task
- Text Classification
- Library
- peft
- License
- llama3.2
- Languages
- en
- Created
- Oct 29, 2024
- Updated
- Oct 29, 2024