---
base_model: distilbert-base-uncased
datasets:
- financial_phrasebank
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-lora-financial-sentiment-analysis
  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. -->

# distilbert-base-uncased-lora-financial-sentiment-analysis

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0787
- Accuracy: {'accuracy': 0.9823788546255506}

## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|
| 0.1615        | 1.0   | 510  | 0.0830          | {'accuracy': 0.973568281938326}  |
| 0.1076        | 2.0   | 1020 | 0.0787          | {'accuracy': 0.9823788546255506} |
| 0.0558        | 3.0   | 1530 | 0.1102          | {'accuracy': 0.9647577092511013} |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1