---
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