---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: dbmdz/bert-base-italian-uncased
metrics:
- accuracy
model-index:
- name: bert-LoRA-reminder
  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. -->

# bert-LoRA-reminder

This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https://huggingface.co./dbmdz/bert-base-italian-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2139
- Accuracy: 0.9545

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6677        | 1.0   | 22   | 0.6283          | 0.7955   |
| 0.6524        | 2.0   | 44   | 0.6168          | 0.8409   |
| 0.6299        | 3.0   | 66   | 0.6096          | 0.8182   |
| 0.6258        | 4.0   | 88   | 0.5980          | 0.8636   |
| 0.6206        | 5.0   | 110  | 0.5849          | 0.8409   |
| 0.5685        | 6.0   | 132  | 0.5694          | 0.8636   |
| 0.5896        | 7.0   | 154  | 0.5528          | 0.8864   |
| 0.5636        | 8.0   | 176  | 0.5361          | 0.8636   |
| 0.5681        | 9.0   | 198  | 0.5217          | 0.8864   |
| 0.5575        | 10.0  | 220  | 0.4968          | 0.8864   |
| 0.5097        | 11.0  | 242  | 0.4776          | 0.9091   |
| 0.5001        | 12.0  | 264  | 0.4541          | 0.9091   |
| 0.4712        | 13.0  | 286  | 0.4269          | 0.9318   |
| 0.4462        | 14.0  | 308  | 0.4016          | 0.9318   |
| 0.4255        | 15.0  | 330  | 0.3778          | 0.9545   |
| 0.3943        | 16.0  | 352  | 0.3566          | 0.9545   |
| 0.3889        | 17.0  | 374  | 0.3358          | 0.9545   |
| 0.3845        | 18.0  | 396  | 0.3169          | 0.9545   |
| 0.3397        | 19.0  | 418  | 0.2987          | 0.9545   |
| 0.3677        | 20.0  | 440  | 0.2862          | 0.9545   |
| 0.3271        | 21.0  | 462  | 0.2729          | 0.9545   |
| 0.3495        | 22.0  | 484  | 0.2607          | 0.9545   |
| 0.3057        | 23.0  | 506  | 0.2495          | 0.9545   |
| 0.2621        | 24.0  | 528  | 0.2399          | 0.9545   |
| 0.2911        | 25.0  | 550  | 0.2314          | 0.9545   |
| 0.2685        | 26.0  | 572  | 0.2253          | 0.9545   |
| 0.248         | 27.0  | 594  | 0.2200          | 0.9545   |
| 0.2421        | 28.0  | 616  | 0.2164          | 0.9545   |
| 0.2688        | 29.0  | 638  | 0.2147          | 0.9545   |
| 0.2723        | 30.0  | 660  | 0.2139          | 0.9545   |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1