---
library_name: peft
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-360M
tags:
- base_model:adapter:HuggingFaceTB/SmolLM2-360M
- lora
- transformers
metrics:
- accuracy
- precision
- recall
model-index:
- name: HuggingFaceTB_SmolLM2-360M_StereoDetect_Model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# HuggingFaceTB_SmolLM2-360M_StereoDetect_Model

This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3245
- Accuracy: 0.8906
- Balanced Accuracy: 0.8935
- F1 Weighted: 0.8905
- F1 Macro: 0.8914
- Precision: 0.8919
- Recall: 0.8906

## 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: 0.0001
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced Accuracy | F1 Weighted | F1 Macro | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:-----------:|:--------:|:---------:|:------:|
| 0.7655        | 1.0   | 760  | 0.3760          | 0.8399   | 0.8424            | 0.8371      | 0.8382   | 0.8484    | 0.8399 |
| 0.2651        | 2.0   | 1520 | 0.2842          | 0.8825   | 0.8840            | 0.8815      | 0.8825   | 0.8890    | 0.8825 |
| 0.1856        | 3.0   | 2280 | 0.2940          | 0.8802   | 0.8835            | 0.8795      | 0.8806   | 0.8824    | 0.8802 |
| 0.1307        | 4.0   | 3040 | 0.3245          | 0.8906   | 0.8935            | 0.8905      | 0.8914   | 0.8919    | 0.8906 |
| 0.0938        | 5.0   | 3800 | 0.3328          | 0.8871   | 0.8898            | 0.8871      | 0.8877   | 0.8875    | 0.8871 |


### Framework versions

- PEFT 0.19.1
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 4.8.5
- Tokenizers 0.21.4