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README.md

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-license: unknown
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+# FinBERT-FOMC
+FinBERT-FOMC is a FinBERT model fine-tuned on the data used FOMC minutes 2006.1 to 2023.2 with 3535 relabel complex 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
+