Update README.md

README.md

@@ -5,9 +5,8 @@ library_name: peft
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+This is a Qlora specifically dedicated to the identification of sophism and cognitive bias
+His performance for now is 85%-100% in detecting sophism , and 85%-100% for detectiong cognitive bias
 
 ## Model Details
 
@@ -17,43 +16,221 @@ library_name: peft
 
 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
+- **Developed by:** Arthur Vigier
+  - **Model type:** Qlora
 - **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]
+- **License:** Apache 2.0
+- **Finetuned from model :** mistral-7b-instruct-v0.3-bnb-4bit
 
 ## 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
+It is dedicated to be used by anyone that want to judge public discourse based on the fundational basis of there language and the solidity 
+of it. Using for education and increasing critical think is also a good way to use this tool
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### API
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+PUBLIC API COMING SOON 
+### Direct Use
 
-[More Information Needed]
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import re
+
+class RationalityDebugger:
+    def __init__(self, base_model="mistralai/Mistral-7B-v0.1", lora_model="your-username/rationality-debugger-v1.0"):
+        """
+        Initialize the cognitive bias and logical fallacy detector.
+        
+        Args:
+            base_model: Base model from Hugging Face
+            lora_model: LoRA adapters for rationality analysis
+        """
+        print(f"Loading base model: {base_model}")
+        self.tokenizer = AutoTokenizer.from_pretrained(base_model)
+        
+        # Options for optimized loading
+        model_kwargs = {
+            "torch_dtype": torch.float16,
+            "device_map": "auto",
+            "low_cpu_mem_usage": True
+        }
+        
+        # Try first with 4-bit quantization to save memory
+        try:
+            from transformers import BitsAndBytesConfig
+            quantization_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_compute_dtype=torch.float16,
+                bnb_4bit_use_double_quant=True
+            )
+            model_kwargs["quantization_config"] = quantization_config
+            self.base_model = AutoModelForCausalLM.from_pretrained(base_model, **model_kwargs)
+        except:
+            # Fallback if bitsandbytes is not available
+            print("4-bit quantization not available, using standard loading...")
+            self.base_model = AutoModelForCausalLM.from_pretrained(base_model, **model_kwargs)
+            
+        print(f"Applying LoRA adapters: {lora_model}")
+        self.model = PeftModel.from_pretrained(self.base_model, lora_model)
+        self.model.eval()  # Evaluation mode
+        
+        self.prompt_template = """
+Analyze the following argument and identify any logical fallacies or cognitive biases:
+
+{text}
+
+###OUTPUT FORMAT
+[Argument] Valid/Invalid
+   → If Valid: Type: [ANALYTICAL / INDUCTIVE / ABDUCTIVE]
+[Sophisms] Yes/No
+   → If Yes: Which: [List detected fallacies]
+   → Extract(s): [Provide exact snippet(s)]
+[Biases] Yes/No
+   → If Yes: Which: [List detected biases]
+   → Extract(s): [Provide exact snippet(s)]
+
+[Short explanation]
+"""
+
+    def analyze(self, text, max_new_tokens=200, temperature=0.1):
+        """
+        Analyze text to detect cognitive biases and logical fallacies.
+        
+        Args:
+            text: Text to analyze
+            max_new_tokens: Maximum number of new tokens to generate
+            temperature: Temperature for generation (lower = more deterministic)
+            
+        Returns:
+            dict: Structured analysis result and raw text
+        """
+        prompt = self.prompt_template.format(text=text)
+        
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+        
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                top_p=0.9,
+                do_sample=temperature > 0
+            )
+        
+        # Extract only the generated part (not the prompt)
+        generated_text = self.tokenizer.decode(
+            outputs[0][inputs.input_ids.shape[1]:], 
+            skip_special_tokens=True
+        )
+        
+        # Parse the response to extract the structure
+        result = self._parse_response(generated_text)
+        
+        return {
+            "raw_text": generated_text,
+            "structured": result
+        }
+    
+    def _parse_response(self, text):
+        """Parse the model's response to extract structured information"""
+        result = {
+            "argument_valid": None,
+            "argument_type": None,
+            "has_sophisms": None,
+            "detected_sophisms": [],
+            "has_biases": None,
+            "detected_biases": [],
+            "too_short": False,
+            "explanation": ""
+        }
+        
+        # Simple parsing example - adapt as needed
+        text_lower = text.lower()
+        
+        # Argument validity detection
+        if "valid argument" in text_lower or "[argument] valid" in text_lower:
+            result["argument_valid"] = True
+        elif "invalid argument" in text_lower or "[argument] invalid" in text_lower:
+            result["argument_valid"] = False
+            
+        # Argument type detection
+        for arg_type in ["ANALYTICAL", "INDUCTIVE", "ABDUCTIVE"]:
+            if arg_type.lower() in text_lower:
+                result["argument_type"] = arg_type
+        
+        # Fallacy detection
+        sophism_keywords = ["ad hominem", "straw man", "red herring", "false dilemma", 
+                           "slippery slope", "post hoc", "circular reasoning"]
+        
+        for sophism in sophism_keywords:
+            if sophism in text_lower:
+                result["detected_sophisms"].append(sophism)
+                
+        result["has_sophisms"] = len(result["detected_sophisms"]) > 0
+        
+        # Cognitive bias detection
+        bias_keywords = ["confirmation bias", "availability bias", "anchoring bias", 
+                         "hindsight bias", "halo effect", "dunning-kruger"]
+        
+        for bias in bias_keywords:
+            if bias in text_lower:
+                result["detected_biases"].append(bias)
+        
+        result["has_biases"] = len(result["detected_biases"]) > 0
+        
+        # Explanation
+        explanation_match = re.search(r"\[Short explanation\](.*?)(?=$|\[)", text, re.DOTALL)
+        if explanation_match:
+            result["explanation"] = explanation_match.group(1).strip()
+        else:
+            # If no explanation tag, take the whole text
+            result["explanation"] = text
+            
+        return result
+
+
+# --- Usage example ---
+if __name__ == "__main__":
+    # Create the analyzer
+    analyzer = RationalityDebugger(
+        base_model="mistralai/Mistral-7B-v0.1",
+        lora_model="your-username/rationality-debugger-v1.0"
+    )
+    
+    # Analysis example
+    argument = """
+    All birds can fly. Penguins are birds. Therefore, penguins can fly.
+    """
+    
+    result = analyzer.analyze(argument)
+    
+    # Display raw result
+    print("\n=== RAW ANALYSIS ===")
+    print(result["raw_text"])
+    
+    # Display structured result
+    print("\n=== STRUCTURED ANALYSIS ===")
+    print(f"Valid argument: {result['structured']['argument_valid']}")
+    
+    if result["structured"]["detected_sophisms"]:
+        print("\nDetected fallacies:")
+        for sophism in result["structured"]["detected_sophisms"]:
+            print(f"- {sophism}")
+    
+    if result["structured"]["detected_biases"]:
+        print("\nDetected cognitive biases:")
+        for bias in result["structured"]["detected_biases"]:
+            print(f"- {bias}")
+    
+    print("\nExplanation:")
+    print(result["structured"]["explanation"])
+```
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+It is not intended to harass anyone or being rude
 
 ## Bias, Risks, and Limitations
 
@@ -63,140 +240,14 @@ library_name: peft
 
 ### Recommendations
 
-<!-- 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]
-
-## 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. -->
-
-[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]
-
-[More Information Needed]
-
-
-#### 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 -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## 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. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+He is very efficient to the most common sophism and cognitive bias but for some more niche like bias frequency illusion he can be less efficient.
+He is mainly dedicated to detect sophism and cognitive bias , he can detect valid reasoning but it is not his main purpose
 
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- 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]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+## Model Card Contact
 
-[More Information Needed]
+mail : [email protected]
 
-## Model Card Contact
 
-[More Information Needed]
 ### Framework versions
 
 - PEFT 0.14.0