---
license: mit
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: nlptown/bert-base-multilingual-uncased-sentiment
model-index:
- name: model_IMDB_bert_base_peft
  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. -->

# model_IMDB_bert_base_peft

This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2116
- Accuracy: 0.9224

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2559        | 1.0   | 1563  | 0.2355          | 0.9088   |
| 0.2406        | 2.0   | 3126  | 0.2285          | 0.9134   |
| 0.2322        | 3.0   | 4689  | 0.2185          | 0.9173   |
| 0.2291        | 4.0   | 6252  | 0.2174          | 0.9193   |
| 0.2177        | 5.0   | 7815  | 0.2171          | 0.9186   |
| 0.2218        | 6.0   | 9378  | 0.2154          | 0.9202   |
| 0.2116        | 7.0   | 10941 | 0.2127          | 0.9221   |
| 0.2133        | 8.0   | 12504 | 0.2101          | 0.9225   |
| 0.2076        | 9.0   | 14067 | 0.2125          | 0.9221   |
| 0.2029        | 10.0  | 15630 | 0.2116          | 0.9224   |


### Framework versions

- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2