bert-large-uncased-finetuned-lora-ag_news
This model is a fine-tuned version of bert-large-uncased on the ag_news dataset. It achieves the following results on the evaluation set:
- accuracy: 0.9333
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 |
|---|---|---|
| 0.2497 | None | 0 |
| 0.9228 | 0.3117 | 0 |
| 0.9276 | 0.2198 | 1 |
| 0.9314 | 0.2014 | 2 |
| 0.9333 | 0.1888 | 3 |
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2
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Model tree for TransferGraph/bert-large-uncased-finetuned-lora-ag_news
Base model
google-bert/bert-large-uncasedDataset used to train TransferGraph/bert-large-uncased-finetuned-lora-ag_news
Evaluation results
- accuracy on ag_newstest set self-reported0.933