Update README.md

README.md

@@ -15,27 +15,58 @@ metrics:
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 The model in the provided documentation is called **AI FixCode**. It's a Transformer-based model built on the **CodeT5** architecture, and its purpose is to automatically fix errors in source code. It's an encoder-decoder (sequence-to-sequence) model designed primarily for Python, with future plans for other languages.
 
-\<br\>
+**The following documentation has been improved for clarity, structure, and conciseness, while providing additional technical details for a more professional and informative tone.**
 
 -----
 
-## How AI FixCode Works
+### **Model: AI FixCode**
 
-AI FixCode operates as a **sequence-to-sequence** system, meaning it takes a sequence of code tokens (the buggy code) as input and generates a new sequence of tokens (the corrected code) as output.  During training, the model learns to identify and predict the necessary changes to a given code snippet by mapping buggy code to its correct version. This allows it to address both **syntactic** and **semantic** errors. It's intended to be integrated into development environments or automated pipelines to help with debugging.
+| **License** | **Base Model** | **Tags** | **Datasets** | **Metrics** |
+|:---:|:---:|:---:|:---:|:---:|
+| MIT | Salesforce/codet5p-220m | code-repair, code-generation, text2text-generation, code-correction | nvidia/OpenCodeReasoning, future-technologies/Universal-Transformers-Dataset | BLEU |
 
 \<br\>
 
+**AI FixCode** is a specialized **Transformer-based model** built upon the **CodeT5** architecture for the purpose of **automated source code repair**. Operating as a **sequence-to-sequence encoder-decoder model**, it's designed to accept buggy code as input and generate a corrected version as output. It is currently optimized for **Python** and addresses both **syntactic** and **semantic** errors. This model is ideal for integration into development environments and CI/CD pipelines to streamline debugging.
+
+-----
+
+### **How It Works**
+
+AI FixCode functions as a **sequence-to-sequence (seq2seq) system**, mapping an input sequence of "buggy" code tokens to an output sequence of "fixed" code tokens. During training, the model learns to identify and predict the necessary code transformations by being exposed to a vast number of faulty and corrected code pairs. This process allows it to generalize and correct a wide range of code issues, from minor syntax errors (e.g., missing colons) to more complex logical (semantic) bugs. The model's encoder processes the input code to create a contextual representation, and the decoder uses this representation to generate the corrected code.
+
 -----
 
-## Training and Usage
+### **Training and Usage**
+
+The model was trained on a custom dataset of structured **buggy-to-fixed code pairs**. Each pair is a JSON object with `"input"` for the faulty code and `"output"` for the corrected code. This supervised learning approach allows the model to learn the specific mappings required for code repair.
+
+#### **Usage Example**
+
+The following Python example demonstrates how to use the model with the Hugging Face `transformers` library. The process involves loading the model, tokenizing the input, generating the corrected output, and decoding the result.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# 1. Load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained("path/to/ai-fixcode")
+model = AutoModelForSeq2SeqLM.from_pretrained("path/to/ai-fixcode")
 
-The model was trained on a custom dataset of buggy and fixed code pairs. Each pair in the dataset is structured in a simple JSON format with `"input"` for the faulty code and `"output"` for the corrected code.
+# 2. Tokenize the input code snippet
+buggy_code = """
+def add(x, y)
+    return x + y
+"""
+inputs = tokenizer(buggy_code, return_tensors="pt")
 
-To use the model, you can leverage the Hugging Face `transformers` library in Python. The process involves:
+# 3. Generate the corrected code
+outputs = model.generate(inputs.input_ids, max_length=128)
 
-1.  Loading the tokenizer and the model using `AutoTokenizer` and `AutoModelForSeq2SeqLM`.
-2.  Tokenizing the input code snippet.
-3.  Generating the corrected code using the model's `generate` method.
-4.  Decoding the output tokens back into a readable string.
+# 4. Decode the output tokens back into a string
+corrected_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-The provided example demonstrates fixing a Python function `def add(x, y)` that's missing a colon and proper indentation. The model's output corrects these issues to produce `def add(x, y):\n    return x + y`.
+# The corrected output will be:
+# def add(x, y):
+#     return x + y
+print(corrected_code)
+```