Update README.md

README.md

@@ -1,56 +1,45 @@
----
-library_name: transformers
-tags:
-- bangla
-- bangla-classifier
-- multiclass-classifier
-- text-classifier
-datasets:
-- SayedShaun/sentigold
-language:
-- bn
-metrics:
-- accuracy
-base_model:
-- csebuetnlp/banglabert
-pipeline_tag: text-classification
----
-
-# Bangla Binary Text Classifier
-
-## Description
-
-This is a **Bangla binary sentiment classification** model, fine-tuned on top of [`csebuetnlp/banglabert`](https://huggingface.co/csebuetnlp/banglabert). The model was trained using the [**SayedShaun/sentigold**](https://huggingface.co/datasets/SayedShaun/sentigold)
+---
+library_name: transformers
+tags:
+- bangla
+- bangla-classifier
+- binary-classifier
+- text-classifier
+datasets:
+- SayedShaun/sentigold
+language:
+- bn
+metrics:
+- accuracy
+base_model:
+- csebuetnlp/banglabert
+pipeline_tag: text-classification
+---
+
+# 🔍 Bangla Binary Text Classifier
+
+## 🧠 Model Description
+
+This is a **Bangla binary sentiment classification** model, fine-tuned on top of [`csebuetnlp/banglabert`](https://huggingface.co/csebuetnlp/banglabert). The model was trained using the [**SayedShaun/sentigold**](https://huggingface.co/datasets/SayedShaun/sentigold) dataset.
 
 ---
 
-## How to Use
+## 📦 How to Use
 
 ```python
 from transformers import pipeline
 
-pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-multiclass")
+pipe = pipeline("text-classification", model="SayedShaun/bangla-classifier-binary")
 
-response = pipe("ডেলিভারি ম্যান খুব যত্ন সহকারে পণ্যটি ডেলিভারি করেছে")
+response = pipe("এটা যে এত খারাপ আগে জানতাম না।")
 print(response)
-# Output: [{'label': 'LABEL_0', 'score': 0.9503920674324036}]
-```
-
-## Tags
-```
-{"SP" :0, "WP": 1, "WN": 2, "SN": 3, "NU": 4}
-
-SP: Strongly Positive
-WP: Weakly Positive
-WN: Weakly Positive Negative
-SN: Strongly Negative
-NU: Neutral
+# Output: [{'label': 'LABEL_0', 'score': 0.9765}]
 ```
 
 ## Result
 | Training Loss | Validation Loss | Accuracy  | Precision | Recall   | F1 Score  |
 |---------------|-----------------|-----------|-----------|----------|-----------|
-| 0.820600      | 0.916846        | 0.646714  | 0.649295  | 0.642749 | 0.643535  |
+| 0.354600      | 0.396599        | 0.825143  | 0.812587  | 0.842483 | 0.827265  |
 
 
 # Source Code