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---
base_model: meta-llama/Llama-3.2-1B
library_name: peft
license: llama3.2
metrics:
- accuracy
- f1
- recall
- precision
tags:
- generated_from_trainer
model-index:
- name: llama3.2-finetuned-newsclassify
results: []
language:
- en
pipeline_tag: text-classification
---
<!-- 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](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0205
- Balanced Accuracy: 0.992
- Accuracy: 0.992
- F1-score: 0.9920
- 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 50
accuracy 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 |