README.md

FinBERT-FOMC

FinBERT-FOMC model, a language model based on enhanced sentiment analysis of FOMC meeting minutes.

FinBERT-FOMC is a FinBERT model fine-tuned on the data used FOMC minutes 2006.1 to 2023.2 with 3535 relabel complex sentences. It is more accurate than the original FinBERT for more complex financial sentences.

Input:
A financial text.

Output:
Positive, Negative, Neutral

How to use

You can use this model with Transformers pipeline for FinBERT-FOMC.

from transformers import BertTokenizer, BertForSequenceClassification, pipeline

finbert = BertForSequenceClassification.from_pretrained('ZiweiChen/FinBERT-FOMC',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('ZiweiChen/FinBERT-FOMC')
finbert_fomc = pipeline("text-classification", model=finbert, tokenizer=tokenizer)

sentences = ["Spending on cars and light trucks increased somewhat in July after a lackluster pace in the second quarter but apparently weakened in August"]
results = finbert_fomc(sentences)
print(results)
# [{'label': 'Negative', 'score': 0.994509756565094}]

Visit https://github.com/Incredible88/FinBERT-FOMC for more details