Update README.md

README.md

@@ -1,11 +1,17 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- bert
+- youtube
+- classification
+license: apache-2.0
+language:
+- en
 ---
 
 # Model Card for Model ID
+This is a fine-tuned BERT model that classifies YouTube channels content into categories such as Education, Technology, Entertainment, and more.
 
-<!-- Provide a quick summary of what the model is/does. -->
 
 
 
@@ -17,7 +23,7 @@ tags: []
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** [Jayesh Mehta]
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
@@ -35,55 +41,116 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+<!-- This model can be directly used to classify YouTube video titles and descriptions into predefined categories: Education, Technology, Motivation, Entertainment, and Gaming.
+
+Example use cases:
+
+Automatically tagging videos in content moderation systems
+
+Enabling smart filtering and recommendations
+
+Analyzing category distribution of YouTube channels -->
 
 ### Direct Use
+<!-- python
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+from transformers import BertTokenizer, BertForSequenceClassification
 
-[More Information Needed]
+model = BertForSequenceClassification.from_pretrained("JaySenpai/bert-youtube-model")
+tokenizer = BertTokenizer.from_pretrained("JaySenpai/bert-youtube-model")
+
+inputs = tokenizer("This video is about personal productivity hacks", return_tensors="pt")
+outputs = model(**inputs)
+predicted = outputs.logits.argmax(dim=1).item()```
+
+-->
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model can be integrated into larger systems, such as:
+
+Content management systems
+
+YouTube channel analytics tools
+
+Personalized recommendation engines 
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model is not suitable for long-form text or transcript-level classification.
+
+Should not be used to classify non-YouTube content or languages other than English.
+
+Avoid using it in sensitive decision-making scenarios (e.g., legal, medical).
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Like most models trained on public or scraped data:
+
+The model may carry biases from the underlying data (e.g., overrepresentation of certain video types).
+
+It may misclassify mixed-genre or ambiguous titles (e.g., “Top 10 Gaming Laptops for Students”).
+
+It is sensitive to text length and clarity—very short or vague titles may reduce accuracy.
 
-[More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Use the model as an assistive tool, not a final decision-maker.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Evaluate its performance on your specific data before deploying.
+
+Consider adding user feedback or manual review in production systems.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+from transformers import BertTokenizer, BertForSequenceClassification
+
+model = BertForSequenceClassification.from_pretrained("JaySenpai/bert-model")
+tokenizer = BertTokenizer.from_pretrained("JaySenpai/bert-model")
+
+text = "10 Tips to Grow Your YouTube Channel"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+prediction = outputs.logits.argmax(dim=1).item()
+
+labels = {0: "Education", 1: "Comedy and Humour", 2: "Gaming", 3: "Technology", 4: "Motivation"}
+print("Predicted label:", labels[prediction])
 
-[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Training Data
+The model was fine-tuned using a labeled dataset of YouTube titles and descriptions, mapped to categories:
+
+Education                         
+Travel                            
+Cooking                           
+Gaming                            
+Music                             
+Health and Fitness                
+Finance                          
+Technology                        
+Vlogging                          
+Beauty & Fashion                  
+Digital Marketing                 
+Movies/Series Reviews             
+Comedy and Humour                 
+Podcast                           
+Youtube or Instagram Grow Tips    
+Online Income                     
+ASMR                              
+Business and Marketing            
+News                              
+Motivation         
 
-[More Information Needed]
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
 #### Preprocessing [optional]
 
@@ -92,7 +159,7 @@ Use the code below to get started with the model.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** <!Base model: bert-base-uncased Epochs: 4 Batch size: 16 Learning rate: 2e-5 Optimizer: AdamW -->
 
 #### Speeds, Sizes, Times [optional]
 
@@ -102,15 +169,13 @@ Use the code below to get started with the model.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+The model was evaluated on a held-out validation set of manually labeled YouTube titles and descriptions.
+
 
-[More Information Needed]
 
 #### Factors
 
@@ -120,13 +185,15 @@ Use the code below to get started with the model.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Accuracy: ~97%
 
-[More Information Needed]
+F1-score (macro): ~0.95
 
 ### Results
 
-[More Information Needed]
+The model performed well on clear-cut categories like "Gaming" and "Technology" but showed confusion between "Motivation" and "Education" in edge cases.
+
+
 
 #### Summary
 
@@ -196,4 +263,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+Author: Jayesh Mehta(JaySenpai)
+Hugging Face: @JaySenpai
+