| | --- |
| | library_name: peft |
| | tags: |
| | - parquet |
| | - text-classification |
| | datasets: |
| | - ag_news |
| | metrics: |
| | - accuracy |
| | base_model: philschmid/tiny-distilbert-classification |
| | model-index: |
| | - name: philschmid_tiny-distilbert-classification-finetuned-lora-ag_news |
| | results: |
| | - task: |
| | type: text-classification |
| | name: Text Classification |
| | dataset: |
| | name: ag_news |
| | type: ag_news |
| | config: default |
| | split: test |
| | args: default |
| | metrics: |
| | - type: accuracy |
| | value: 0.25 |
| | name: accuracy |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # philschmid_tiny-distilbert-classification-finetuned-lora-ag_news |
| |
|
| | This model is a fine-tuned version of [philschmid/tiny-distilbert-classification](https://huggingface.co/philschmid/tiny-distilbert-classification) on the ag_news dataset. |
| | It achieves the following results on the evaluation set: |
| | - accuracy: 0.25 |
| | |
| | ## 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: 0.0004 |
| | - train_batch_size: 24 |
| | - eval_batch_size: 24 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 4 |
| | |
| | ### Training results |
| | |
| | | accuracy | train_loss | epoch | |
| | |:--------:|:----------:|:-----:| |
| | | 0.25 | None | 0 | |
| | | 0.25 | 1.3863 | 0 | |
| | | 0.25 | 1.3863 | 1 | |
| | | 0.25 | 1.3863 | 2 | |
| | | 0.25 | 1.3863 | 3 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - PEFT 0.8.2 |
| | - Transformers 4.37.2 |
| | - Pytorch 2.2.0 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.2 |
