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README.md

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+---
+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.