---
license: mit
tags:
- sentiment
- sentiment-analysis
- financial
- fine-tuned
- fine-tuned-bert
- bert-uncased
---

### Model Overview:
This NLP model is fine-tuned with a focus on analyzing sentiment in financial text and news headlines. It was fine-tuned using the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on the [financial_phrasebank](https://huggingface.co/datasets/financial_phrasebank) and [auditor_sentiment](https://huggingface.co/datasets/FinanceInc/auditor_sentiment) datasets.

**Accuracies:** \
**financial_phrasebank:** 0.993\
**auditor_senitment:** 0.974

### Training Hyperparameters:

**Learning Rate:** 2e-05\
**Train Batch Size:** 16\
**Eval Batch Size:** 16\
**Random Seed:** 42\
**Optimizer:** AdamW-betas(0.9, 0.999)\
**Learning Rate Scheduler:** Linear\
**Number of Epochs:** 6\
**Number of Warmup Steps:** 0.2 * Number of Training Steps

### How To Use:

```
from transformers import pipeline
pipe = pipeline("sentiment-analysis", model="mstafam/fine-tuned-bert-financial-sentimental-analysis")

text = "Example company has seen a 5% increase in revenue this quarter."

print(pipe(text))

[{'label': 'Positive', 'score': 0.9993795156478882}]
```