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README.md

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+# CodeBERTa-Python-DocGen
+
+## Overview
+`CodeBERTa-Python-DocGen` is a RoBERTa-based model fine-tuned for code-related tasks, specifically **Code-to-Text Generation** (docstring synthesis) and **Code-Text Retrieval** (finding relevant code given a natural language query). The model is pre-trained on a large corpus of Python code from public repositories, with a focus on pairing function bodies with high-quality docstrings and descriptive comments.
+
+It excels at understanding the semantic relationship between a Python function's implementation details and its natural language documentation. 
+
+## Model Architecture
+* **Base Model:** RoBERTa (Robustly optimized BERT Pretraining Approach)
+* **Pre-training:** Masked Language Modeling (MLM) on Python source code.
+* **Fine-tuning Task:** Two-fold:
+    1. **Generation:** Conditional text generation where the input is the function body and the target is the docstring.
+    2. **Retrieval:** Learning cross-modal embeddings between the function body and the docstring (using contrastive loss).
+* **Tokenization:** Byte-Pair Encoding (BPE) optimized for code syntax, including special tokens for `<START_CODE>`, `<END_CODE>`, `<START_DOCSTRING>`, and `<END_DOCSTRING>`.
+* **Max Sequence Length:** 512 tokens.
+
+## Intended Use
+* **Automated Docstring Generation:** Creating initial or full documentation summaries for new or existing Python functions.
+* **Code Search Engine:** Ranking and retrieving the most relevant Python function body from a database given a user's natural language search query (e.g., "function to calculate L2 distance").
+* **Code Comment Completion:** Suggesting descriptive inline comments within a function body.
+* **Code-Text Similarity:** Measuring the semantic similarity between arbitrary code snippets and their descriptive summaries.
+
+## Limitations
+* **Hallucination in Docstrings:** While generally coherent, generated docstrings may sometimes misrepresent the actual logic of complex or subtle code due to the generative nature of the model.
+* **Library Scope:** The model performs best on code utilizing common scientific and data science libraries present in the training data (e.g., `numpy`, `pandas`, `sklearn`). Performance can be lower for highly specialized, domain-specific libraries.
+* **Complexity:** The quality of the docstring degrades rapidly for functions exceeding 100 lines of code or having very high cyclomatic complexity.
+
+## Example Code (PyTorch - Text Generation)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "Code/CodeBERTa-Python-DocGen"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Input: A Python function body
+code_input = """
+def calculate_l2_norm(vector_a, vector_b):
+    diff = np.array(vector_a) - np.array(vector_b)
+    return np.sqrt(np.sum(diff ** 2))
+"""
+
+# Prepare the prompt for docstring generation:
+prompt = f"<START_CODE> {code_input} <END_CODE> <START_DOCSTRING>"
+
+input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+# Generate the docstring
+output_ids = model.generate(
+    input_ids,
+    max_length=100,
+    do_sample=True,
+    top_k=50,
+    top_p=0.95,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.encode("<END_DOCSTRING>")[0]
+)
+
+# Decode and clean the output
+generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=False)
+docstring = generated_text.split("<START_DOCSTRING>")[1].split("<END_DOCSTRING>")[0].strip()
+
+print(f"Generated Docstring:\n{docstring}")
+# Expected output: Calculates the L2 (Euclidean) distance between two numerical vectors. 
+# :param vector_a: A list or numpy array. :param vector_b: A list or numpy array. :return: The L2 distance (float).