# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Yt99/SFinBERT")
model = AutoModelForSequenceClassification.from_pretrained("Yt99/SFinBERT")Quick Links
Model Name
SFinBERT
Description
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.
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)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yt99/SFinBERT")