Update README.md

README.md

@@ -24,9 +24,9 @@ tags:
 - Digital Currency
 ---
 
-# **CBDC-BERT-Sentiment: A Domain-Specific BERT for CBDC-Related Sentiment Analysis**
+# **CBDC-Sentiment: A Domain-Specific BERT for CBDC-Related Sentiment Analysis**
 
-**CBDC-BERT-Sentiment** is a **3-class** (*negative / neutral / positive*) sentence-level classifier built for **Central Bank Digital Currency (CBDC)** communications. It is trained to identify overall sentiment in central-bank style text such as consultations, speeches, reports, and reputable news.
+**CBDC-Sentiment** is a **3-class** (*negative / neutral / positive*) sentence-level BERT-based classifier built for **Central Bank Digital Currency (CBDC)** communications. It is trained to identify overall sentiment in central-bank style text such as consultations, speeches, reports, and reputable news.
 
 **Base Model:** [`bilalzafar/CentralBank-BERT`](https://huggingface.co/bilalzafar/CentralBank-BERT) — **CentralBank-BERT** is a domain-adapted **BERT base (uncased)**, pretrained on **66M+ tokens** across **2M+ sentences** from central-bank speeches published via the **Bank for International Settlements (1996–2024)**. It is optimized for *masked-token prediction* within the specialized domains of **monetary policy, financial regulation, and macroeconomic communication**, enabling better contextual understanding of central-bank discourse and financial narratives.
 
@@ -68,7 +68,7 @@ Note: On the **entire annotated dataset** (in-domain evaluation, no hold-out), t
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 
-model_name = "bilalzafar/cbdc-bert-sentiment"
+model_name = "bilalzafar/cbdc-sentiment"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)