Update README.md

README.md

@@ -1,33 +1,46 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- code
+- bug-fix
+- code-generation
+- code-repair
+- codet5p
+- ai
+- machine-learning
+- deep-learning
+- huggingface
+- finetuned-model
+license: apache-2.0
+datasets:
+- Girinath11/aiml_code_debug_dataset
+metrics:
+- bleu
+base_model:
+- Salesforce/codet5p-220m
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+This is a fine-tuned version of the [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) model, specialized for real-world AI, ML, and Deep Learning code bug-fix tasks.
+The model was trained on 150,000 code pairs (buggy → fixed) extracted from GitHub projects relevant to the AI/ML/GenAI ecosystem. 
+It is optimized for suggesting correct code fixes from faulty code snippets and is highly effective for debugging and auto-correction in AI coding environments.
 
 ## 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]
+- **Developed by:** [Girinath V]
 - **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 type:** [Text-to-text Transformer (Encoder-Decoder)]
+- **Language(s) (NLP):** [Programming (Python, some support for other AI/ML languages]
+- **License:** [Apache 2.0]
+- **Finetuned from model:** [[Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m)]
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+### Model Sources:
 
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
@@ -35,51 +48,70 @@ 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. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+ -Fix real-world AI/ML/GenAI Python code bugs.
+- Debug model training scripts, data pipelines, and inference code.
+- Educational use for learning from code correction.
 
-[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 -->
+- Integrated into code review pipelines.
+- LLM-enhanced IDE plugins for auto-fixing AI-related bugs.
+- Assistant agents in AI-powered coding copilots.
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- General-purpose natural language tasks.
+- Code generation unrelated to AI/ML domains.
+- Use on production code without human review.
+
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Biases
+
+- Model favors AI/ML/GenAI-related Python patterns.
+- Not trained for full-stack or UI/frontend code debugging.
+
+### Limitations
+
+- May not generalize well outside its fine-tuned domain.
+- Struggles with ambiguous or undocumented buggy code.
+
+
 
-[More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Use alongside human review.
+- Combine with static analysis for best results.
 
-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.
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("Girinath11/aiml_code_debug_model")
+model = AutoModelForSeq2SeqLM.from_pretrained("Girinath11/aiml_code_debug_model")
+inputs = tokenizer("buggy: def add(a,b) return a+b", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))
 
-[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. -->
+    -150,000 real-world buggy–fixed Python code pairs.
+
+    -Data collected from GitHub AI/ML repositories.
+
+    -Includes data cleaning, formatting, deduplication.
+
 
-[More Information Needed]
 
 ### Training Procedure