Update README.md

README.md

@@ -1,43 +1,40 @@
 ---
 tags:
-- text-to-image
-- lora
-- diffusers
-- template:diffusion-lora
-widget:
-- text: '-'
-  output:
-    url: images/banner_github.png
+- vision
+- image-classification
+- vit
+- facial-expression
+- emotion
+- sentiment-analysis
+- education
+- student
+- engagement
+- understanding
+- course-evaluation
+- online-learning
+- confusion-detection
 base_model: trpakov/vit-face-expression
-instance_prompt: >-
-  student, education, emotion, engagement, understanding, facial-expression,
-  sentiment-analysis, vision, vit, image-classification, course-evaluation,
-  online-learning, confusion-detection
 license: unlicense
+language:
+- en
+pipeline_tag: image-classification
 ---
-# gowdaman-student-emotion-detection
 
-<Gallery />
+# 🧠 gowdaman-student-emotion-detection
 
-## Model description 
+## Model description
 
-Here&#39;s a clear and professional description you can use for your Hugging Face model card:
-
----
-
-# 🧠 Student Course Understanding Detection (Fine-tuned ViT)
-
-This is a **fine-tuned version of [&#x60;trpakov&#x2F;vit-face-expression&#x60;](https:&#x2F;&#x2F;huggingface.co&#x2F;trpakov&#x2F;vit-face-expression)** specifically adapted for analyzing student engagement and understanding during online or in-person educational sessions.
+This is a **fine-tuned version of [`trpakov/vit-face-expression`](https://huggingface.co/trpakov/vit-face-expression)** specifically adapted for analyzing student engagement and understanding during online or in-person educational sessions.
 
 ## 📌 Model Overview
 
 - **Model Type**: Vision Transformer (ViT)
-- **Base Model**: &#x60;trpakov&#x2F;vit-face-expression&#x60;
+- **Base Model**: `trpakov/vit-face-expression`
 - **Task**: Binary emotion classification for educational sentiment analysis
 - **Input**: Student face image (RGB)
 - **Output Classes**:
-  - &#x60;Understand&#x60;: The student appears to understand the concept.
-  - &#x60;Not Understand&#x60;: The student appears to be confused or not following the lesson.
+  - `Understand`: The student appears to understand the concept.
+  - `Not Understand`: The student appears to be confused or not following the lesson.
 
 ## 🎯 Use Case
 
@@ -51,77 +48,17 @@ It is ideal for:
 ## 🧪 Training Details
 
 - Fine-tuned on a **custom dataset** of student facial expressions captured during course sessions
-- Labels were manually annotated as &#x60;Understand&#x60; or &#x60;Not Understand&#x60; based on visual indicators of comprehension
+- Labels were manually annotated as `Understand` or `Not Understand` based on visual indicators of comprehension
 
 ## 📥 Input Format
 
 - **Image**: A single student facial image (preferably frontal and well-lit)
-- **Size**: Resized automatically to match the input size expected by ViT
+- **Size**: Automatically resized to match the input size expected by the ViT model
 
 ## 📤 Output
 
-&#x60;&#x60;&#x60;json
+```json
 {
-  &quot;label&quot;: &quot;Understand&quot;,
-  &quot;confidence&quot;: 0.87
-}
-&#x60;&#x60;&#x60;
-
-## ⚠️ Limitations
-
-- Performance may vary based on image quality, lighting, and camera angle
-- Cultural and individual facial expression variations are not fully accounted for
-- Not intended for high-stakes decisions without human review
-
-## 📚 Citation
-
-If you use this model in your research or application, please cite the base model:
-
-&#x60;&#x60;&#x60;
-@misc{trpakov_vit_face_expression,
-  author       &#x3D; {Trpakov},
-  title        &#x3D; {ViT Face Expression Recognition},
-  year         &#x3D; {2023},
-  publisher    &#x3D; {Hugging Face},
-  howpublished &#x3D; {\url{https:&#x2F;&#x2F;huggingface.co&#x2F;trpakov&#x2F;vit-face-expression}}
-}
-&#x60;&#x60;&#x60;
-
----
-
-Would you like me to generate a &#x60;README.md&#x60; file for this or help you write a model card JSON metadata?
-
-## Trigger words
-
-You should use `student` to trigger the image generation.
-
-You should use `education` to trigger the image generation.
-
-You should use `emotion` to trigger the image generation.
-
-You should use `engagement` to trigger the image generation.
-
-You should use `understanding` to trigger the image generation.
-
-You should use `facial-expression` to trigger the image generation.
-
-You should use `sentiment-analysis` to trigger the image generation.
-
-You should use `vision` to trigger the image generation.
-
-You should use `vit` to trigger the image generation.
-
-You should use `image-classification` to trigger the image generation.
-
-You should use `course-evaluation` to trigger the image generation.
-
-You should use `online-learning` to trigger the image generation.
-
-You should use `confusion-detection` to trigger the image generation.
-
-
-## Download model
-
-Weights for this model are available in Safetensors format.
-
-[Download](/gowdaman/gowdaman-emotion-detection/tree/main) them in the Files & versions tab.
+  "label": "Understand",
+  "confidence": 0.87
+}