Update model card

README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# HP BERT Intent Classification Model
 
+This model is fine-tuned BERT for classifying different types of queries in the HP documentation context.
 
 ## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-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]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- 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. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+- Base model: bert-base-uncased
+- Task: 3-class classification
+- Classes:
+    - 0: Queries requiring PDF context
+    - 1: Summary-related queries
+    - 2: Metadata-related queries
+
+## Usage
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+class BertInference:
+    def __init__(self, model_path):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_path).to(self.device)
+        self.tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+        self.template = "Question: {} Response: "
+        self.label_map = {
+            0: "query_with_pdf",
+            1: "summarize_pdf",
+            2: "query_metadata"
+        }
+
+    def predict(self, text):
+        # Format the input text
+        formatted_text = self.template.format(text)
+        
+        # Tokenize
+        inputs = self.tokenizer(
+            formatted_text,
+            truncation=True,
+            max_length=512,
+            padding='max_length',
+            return_tensors="pt"
+        ).to(self.device)
+
+        # Get prediction
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+            predictions = torch.softmax(outputs.logits, dim=1)
+            predicted_class = torch.argmax(predictions, dim=1).item()
+            confidence = predictions[0][predicted_class].item()
+
+        return {
+            "predicted_class": self.label_map[predicted_class],
+            "confidence": confidence,
+            "all_probabilities": {
+                self.label_map[i]: prob.item()
+                for i, prob in enumerate(predictions[0])
+            }
+        }
+
+def main():
+    # Initialize the model
+    model_path = "output_dir_decision"  # Path to your saved model
+    inferencer = BertInference(model_path)
+
+    # Example usage
+    test_questions = [
+        "What are the new features in corolla cross?",
+        "What is the summary of the provided pdf?",
+        "The filesize of the pdf is?",
+    ]
+
+    for question in test_questions:
+        result = inferencer.predict(question)
+        print(f"\nQuestion: {question}")
+        print(f"Predicted Class: {result['predicted_class']}")
+        print(f"Confidence: {result['confidence']:.4f}")
+        print("All Probabilities:")
+        for class_name, prob in result['all_probabilities'].items():
+            print(f"  {class_name}: {prob:.4f}")
+
+if __name__ == "__main__":
+    main()
+```