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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: text-classification |
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tags: |
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- finance |
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widget: |
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- text: "The semiconductor market is seeing an unprecedented growth this year." |
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- text: "Due to the recent chip shortages, prices for electronics have increased." |
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- text: "As the AI blooms, major semiconductor manufacturers are ramping up production to meet demand." |
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- text: "Investors are wary of the semiconductor industry due to market volatility." |
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--- |
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# Model Name |
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SFinBERT |
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## Description |
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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. |
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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. |
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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. |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("Yt99/SFinBERT") |
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model = AutoModelForSequenceClassification.from_pretrained("Yt99/SFinBERT") |
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text = "Your example text here." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
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