---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google-bert/bert-base-uncased
metrics:
- accuracy
model-index:
- name: peft_ft_random
  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. -->

# peft_ft_random

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.4980
- Accuracy: -5564.7596

## 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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy    |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|
| No log        | 0.8   | 2    | 10.1306         | 48758.8852  |
| No log        | 2.0   | 5    | 9.1696          | 68666.5148  |
| No log        | 2.8   | 7    | 8.8054          | -22131.0614 |
| 9.5148        | 4.0   | 10   | 8.4980          | -5564.7596  |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1