qhanovh-sentiment

QuantHanovh-Sentiment is a BERT model inherited from FinBERT and also enriched by distillation from other advanced LLM like GPT-5, Gemini 3, Opus, etc. And then fine-tuned on 30K+ hybrid-annotation sentences from analyst reports. This repo IS NOT our full repo, it still use original version from Yi Yang. If you need access to full recent fine-tuned repo, contact us.

It works in conjuction with our QuantHanovh QFM (Quant Foundational Model) for Financial Market Quantitative Analysis.

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FinBERT is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.

• Corporate Reports 10-K & 10-Q: 2.5B tokens
• Earnings Call Transcripts: 1.3B tokens
• Analyst Reports: 1.1B tokens

More technical details on FinBERT: Click Link

This released finbert-tone model is the FinBERT model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using FinBERT for financial tone analysis, give it a try.

If you use the model in your academic work, please cite the following paper:

Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." Contemporary Accounting Research (2022).

How to use

You can use this model with Transformers pipeline for sentiment analysis.

from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')

nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)

sentences = ["there is a shortage of capital, and we need extra financing",  
             "growth is strong and we have plenty of liquidity", 
             "there are doubts about our finances", 
             "profits are flat"]
results = nlp(sentences)
print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative