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library_name: peft
license: gemma
base_model: google/gemma-2-9b
tags:
- base_model:adapter:google/gemma-2-9b
- lora
- transformers
metrics:
- accuracy
- precision
- recall
model-index:
- name: google_gemma-2-9b_StereoDetect_Model
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. -->
# google_gemma-2-9b_StereoDetect_Model
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2997
- Accuracy: 0.9505
- Balanced Accuracy: 0.9501
- F1 Weighted: 0.9508
- F1 Macro: 0.9513
- Precision: 0.9517
- Recall: 0.9505
## 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
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced Accuracy | F1 Weighted | F1 Macro | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:-----------:|:--------:|:---------:|:------:|
| 0.9444 | 1.0 | 760 | 0.3210 | 0.9217 | 0.9227 | 0.9225 | 0.9223 | 0.9239 | 0.9217 |
| 0.2271 | 2.0 | 1520 | 0.2235 | 0.9459 | 0.9459 | 0.9462 | 0.9461 | 0.9467 | 0.9459 |
| 0.1214 | 3.0 | 2280 | 0.2020 | 0.9516 | 0.9513 | 0.9518 | 0.9522 | 0.9525 | 0.9516 |
| 0.0705 | 4.0 | 3040 | 0.2116 | 0.9505 | 0.9499 | 0.9506 | 0.9511 | 0.9512 | 0.9505 |
| 0.0376 | 5.0 | 3800 | 0.2778 | 0.9470 | 0.9469 | 0.9474 | 0.9479 | 0.9480 | 0.9470 |
| 0.025 | 6.0 | 4560 | 0.2502 | 0.9551 | 0.9547 | 0.9554 | 0.9556 | 0.9558 | 0.9551 |
| 0.0099 | 7.0 | 5320 | 0.3018 | 0.9505 | 0.9502 | 0.9508 | 0.9512 | 0.9514 | 0.9505 |
| 0.013 | 8.0 | 6080 | 0.2715 | 0.9482 | 0.9480 | 0.9485 | 0.9490 | 0.9493 | 0.9482 |
| 0.0073 | 9.0 | 6840 | 0.2916 | 0.9516 | 0.9515 | 0.9519 | 0.9522 | 0.9526 | 0.9516 |
| 0.0027 | 10.0 | 7600 | 0.2997 | 0.9505 | 0.9501 | 0.9508 | 0.9513 | 0.9517 | 0.9505 |
### Framework versions
- PEFT 0.19.1
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 4.8.5
- Tokenizers 0.21.4 |