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---
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}]
``` |