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---
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- finance
- sentiment-analysis
---
# BERT Fine-tuned - Financial Sentiment Analysis Model
<div style="text-align:center;">
<img src="https://huggingface.co/Shaivn/Financial-Sentiment-Analysis/resolve/main/financial-sentiment-analysis-logo.png" alt="logo" style="width:250px;height:250px;">
</div>
This model is a Fine-Tuned version of BERT (bert-base-uncased)
It is designed to classify text into positive, neutral, and negative sentiments. The fine-tuning was performed using the Financial Phrase Bank dataset.
## Results
It achieves the following results on the evaluation set:
* F1 Score: 0.9468
* Validation loss: 0.1860
## Training Data
The dataset consists of 4840 sentences of the financial phrase bank. The dataset was annotated by 16 people with adequate background knowledge of financial markets.
## Training hyperparameters
The following hyperparameters were used during training:
* learning rate : 2e-5
* train_batch_size : 32
* eval_batch_size: 32
* seed: 42
* Optimizer : AdamW
* num_epochs: 3
## Training Results
| **Epoch** | **Validation Loss** | **Accuracy** |
|:----------:|:---------------------:|:-------------:|
|01 | 0.1860 | 0.9468 |
|02 | 0.1756 | 0.9424 |
|03 | 0.1726 | 0.9432 |
This model is a part of my thesis: "A Proposal of a Sentiment Analysis Model for Business Intelligence" |