Update README.md

README.md

@@ -13,17 +13,61 @@ metrics:
 tags:
 - finance
 ---
-# Modern-FinBERT : Financial Sentiment Analysis  
+---
+license: apache-2.0
+datasets:
+- takala/financial_phrasebank
+language:
+- en
+metrics:
+- f1
+base_model:
+- answerdotai/ModernBERT-large
+new_version: ProsusAI/finbert
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- finance
+- sentiment
+- financial-sentiment-analysis
+- sentiment-analysis
+widget:
+- text: "Stocks rallied and the British pound gained."
+---
 
-**Modern-FinBERT** is a state-of-the-art NLP model designed for **financial sentiment analysis**. It extends the **ModernBERT** language model by further training it on a vast financial corpus, ensuring domain-specific accuracy. This additional training fine-tunes the model for **sentiment classification** in financial texts.  
+# Modern-FinBERT: Financial Sentiment Analysis  
 
-For fine-tuning, the model leverages the **[Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts)** by Malo et al. (2014), a widely used benchmark dataset in financial NLP.  
+`Modern-FinBERT` is a **pre-trained NLP model** designed for **financial sentiment analysis**. It extends the [`ModernBERT-large`](https://huggingface.co/answerdotai/ModernBERT-large) language model by further training it on a **large financial corpus**, making it highly specialized for **financial text classification**.  
 
-To learn more, refer to the research paper **[FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063)** and our in-depth **[blog post](https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101)** on Medium.  
+For fine-tuning, the model leverages the **[Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts)** by Malo et al. (2014), a widely recognized benchmark dataset for financial sentiment analysis.  
 
 ### Sentiment Labels  
-The model outputs a **softmax distribution** across three sentiment labels:  
+The model generates a **softmax probability distribution** across three sentiment categories:  
 
 - ✅ **Positive**  
 - ❌ **Negative**  
 - ⚖ **Neutral**  
+
+For more technical insights on `ModernBERT`, check out the research paper:  
+🔍 **[ModernBERT Technical Details](https://arxiv.org/abs/2412.13663)**  
+
+# How to use
+You can use this model with Transformers pipeline for sentiment analysis.
+```bash
+pip install -U transformers
+```
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load the pre-trained model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained('beethogedeon/Modern-FinBERT', num_labels=3)
+tokenizer = AutoTokenizer.from_pretrained('answerdotai/ModernBERT')
+
+# Initialize the NLP pipeline
+nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+sentence = "Stocks rallied and the British pound gained."
+
+print(nlp(sentence))
+```