---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
  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

This model is a fine-tuned version of distilbert-base-uncased on the sentiment140 (https://huggingface.co/datasets/sentiment140) dataset. 
Due to time and computational limits, I <b>used only 10% of the dataset</b> with a data augmentation. Despite minimal training, model's accuracy <b>improved by 59.5%</b> in comparison with non-fine tuned DistilBert LLM!<br>

It achieves the following results on the evaluation set:
- Loss: 0.4732
- Accuracy: 0.7753

<b>Classification Scores:</b><br>
- Fine Tuned DistilBert LLM with LoRa: 
Accuracy = 0.78, Precision = 0.77, Recall = 0.78, F1 Score = 0.78

- Non-Fine Tuned DistilBert LLM: 
Accuracy = 0.49, Precision = 0.45, Recall = 0.14, F1 Score = 0.22

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5092        | 1.0   | 5688  | 0.4979          | 0.7591   |
| 0.4928        | 2.0   | 11376 | 0.4876          | 0.7662   |
| 0.4915        | 3.0   | 17064 | 0.4821          | 0.7711   |
| 0.4787        | 4.0   | 22752 | 0.4779          | 0.7731   |
| 0.4757        | 5.0   | 28440 | 0.4767          | 0.7746   |
| 0.473         | 6.0   | 34128 | 0.4743          | 0.775    |
| 0.4649        | 7.0   | 39816 | 0.4741          | 0.7751   |
| 0.4709        | 8.0   | 45504 | 0.4732          | 0.7753   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2