Update README.md

README.md

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-# test test
+---
+license: apache-2.0
+datasets:
+- kritsadaK/EDGAR-CORPUS-Financial-Summarization
+language:
+- en
+metrics:
+- rouge
+base_model:
+- facebook/bart-large-cnn
+---
+# **BART Financial Summarization Model**  
+
+**Model Name:** `kritsadaK/bart-financial-summarization`  
+**Base Model:** `facebook/bart-large-cnn`  
+**Task:** Financial Text Summarization  
+**Dataset:** `kritsadaK/EDGAR-CORPUS-Financial-Summarization`  
+
+**Techniques:**  
+- Fine-tuned using the Hugging Face `Trainer` API  
+- Tokenized with `AutoTokenizer` (max length 1024 for input, 256 for summary)  
+- Optimized with AdamW, learning rate `2e-5`, batch size `2`, `fp16` enabled  
+- Evaluated using ROUGE scores  
+
+**Evaluation Results:**  
+- **Loss:** 1.18  
+- **Runtime:** 18.9 seconds  
+- **Samples per second:** 56.1  
+- **Steps per second:** 28.1  
+- **Epochs:** 3  
+
+**Usage Example (Python):**  
+```python
+from transformers import pipeline
+
+summarizer = pipeline("summarization", model="kritsadaK/bart-financial-summarization")
+text = "Your financial document text here..."
+summary = summarizer(text, max_length=256, min_length=50, do_sample=False)
+print(summary)
+```
+
 
+The **Financial Statements Summary 10K Dataset** was developed as part of the **CSX4210: Natural Language Processing** project at **Assumption University**.
 
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