---
library_name: peft
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results_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. -->

# results_lora

This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co./albert/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2590
- Accuracy: 0.9014
- F1: 0.9044
- Precision: 0.8925
- Recall: 0.9167

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3298        | 1.0   | 4210  | 0.2669          | 0.8922   | 0.8934 | 0.8995    | 0.8874 |
| 0.236         | 2.0   | 8420  | 0.2702          | 0.9048   | 0.9089 | 0.8865    | 0.9324 |
| 0.1772        | 3.0   | 12630 | 0.2590          | 0.9014   | 0.9044 | 0.8925    | 0.9167 |


### Framework versions

- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1