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

```bash
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