---
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- finance
widget:
- text: "The semiconductor market is seeing an unprecedented growth this year."
- text: "Due to the recent chip shortages, prices for electronics have increased."
- text: "As the AI blooms, major semiconductor manufacturers are ramping up production to meet demand."
- text: "Investors are wary of the semiconductor industry due to market volatility."
---
# Model Name
SFinBERT

## Description
This is part of Dissertaion Project of University of Glasgow MSc Software development Course

Utilizing the power of FinBERT, a model specifically trained for financial sentiment analysis, this tool adapts the foundational knowledge of FinBERT through transfer learning to cater to the semiconductor industry's nuances. 
It's designed to analyze financial news sentiment uniquely tailored to the semiconductor sector, enabling a more precise interpretation of news impacts within this domain. 
Harnessing both financial and semiconductor-specific insights, this sentiment analyzer offers a refined perspective, making it an essential tool for stakeholders, analysts, and enthusiasts in the semiconductor realm.

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Yt99/SFinBERT")
model = AutoModelForSequenceClassification.from_pretrained("Yt99/SFinBERT")

text = "Your example text here."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```

## Acknowledgments
Thanks to my supervisor, family and friends for supporting my work.