|
|
--- |
|
|
library_name: peft |
|
|
license: mit |
|
|
base_model: deepset/gbert-base |
|
|
tags: |
|
|
- base_model:adapter:deepset/gbert-base |
|
|
- lora |
|
|
- transformers |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: gbert_success4_lora |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- 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. --> |
|
|
|
|
|
# gbert_success4_lora |
|
|
|
|
|
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the None dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.6821 |
|
|
- Accuracy: 0.5788 |
|
|
- Macro F1: 0.5714 |
|
|
|
|
|
## 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: 2e-05 |
|
|
- train_batch_size: 8 |
|
|
- eval_batch_size: 8 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 2 |
|
|
- total_train_batch_size: 16 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 3 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
|
|
| 0.7478 | 1.0 | 340 | 0.7106 | 0.5729 | 0.5461 | |
|
|
| 0.6929 | 2.0 | 680 | 0.6870 | 0.5773 | 0.5744 | |
|
|
| 0.6908 | 3.0 | 1020 | 0.6821 | 0.5788 | 0.5714 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.17.1 |
|
|
- Transformers 4.56.1 |
|
|
- Pytorch 2.8.0+cu126 |
|
|
- Datasets 4.0.0 |
|
|
- Tokenizers 0.22.0 |
