Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 base_model: Salesforce/codegen-350M-mono
 library_name: peft
-license: apache-2.0
+license: mit
 datasets:
 - google/code_x_glue_ct_code_to_text
 language:
@@ -20,15 +20,20 @@ Generate python code from natural language prompts.
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+This model is a fine-tuned variant of Salesforce/codegen-350M-mono, 
+specialized for natural language to code generation in Python. 
+It takes natural language instructions (e.g., “check MySQL database connection”) 
+and generates the corresponding Python code snippet. 
+The model was trained on a curated text-to-code dataset containing diverse 
+programming instructions and function-level examples to improve semantic and syntactic accuracy.
 
 
+- **Developed by:** Akshay Bharadwaj
 
-- **Developed by:** [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:** Transformer-based Causal Language Model
+- **Language(s) (NLP):** English (Prompts) and Python (Code Outputs)
+- **License:** MIT License
+- **Finetuned from model [optional]:** Salesforce/codegen-350M-mono
 
 <!-- ### Model Sources [optional]
 
@@ -46,37 +51,62 @@ Generate python code from natural language prompts.
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+The model can be used for:
+
+* Translating natural language prompts into functional Python code.
+
+* Assisting in code autocompletion or boilerplate generation.
+
+* Supporting educational and prototyping environments.
 
-### Downstream Use [optional]
+### Downstream Use
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+Can be integrated into:
+
+* Developer tools (IDE plugins or assistants).
+
+* Chatbots for code assistance or educational coding tutors.
+
+* LLM pipelines for multi-step reasoning or coding workflows.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+* Generating production-level code without human review.
+
+* Security-critical or real-time applications (e.g., code execution automation).
+
+* Generation of malicious or unsafe code.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+* The model may produce incomplete or syntactically incorrect code for ambiguous prompts.
 
-### Recommendations
+* It can misinterpret vague natural language queries (semantic drift).
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+* Potential bias toward common Python idioms and limited handling of rare libraries or APIs.
 
-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, AutoModelForCausalLM
 
-[More Information Needed]
+model_id = "akshayb/nl-code-gen-python"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+prompt = "write a python function to check mysql database connection"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## Training Details
 
@@ -84,94 +114,27 @@ Use the code below to get started with the model.
 
 <!-- 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]
+The dataset contains paired natural language descriptions 
+and Python function implementations, collected and cleaned 
+from public code repositories and text-to-code benchmarks (e.g., CodeXGLUE). 
+Preprocessing involved deduplication, tokenization, and removal of incomplete code samples.
 
 ## 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. -->
+For Comparison between Base Model and Fine-tuned model, we use the following metrics:
 
-[More Information Needed]
-
-### Results
+| Metric           | Focus                          | Strength                                  |
+| ---------------- | ------------------------------ | ----------------------------------------- |
+| **BLEU**         | Token-level similarity         | Measures fluency and lexical accuracy     |
+| **CodeBLEU**     | Lexical + syntactic + semantic | Captures holistic code quality            |
+| **Exact Match**  | String equality                | Strict correctness measure                |
+| **Syntax Match** | AST structure                  | Validates syntactic and logical integrity |
 
-[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]
 
@@ -179,29 +142,12 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **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]
-
-[More Information Needed]
+@misc{akshay2025nlcodegen,
+  title={Natural Language to Code Generation (Fine-tuned CodeGen-350M)},
+  author={Akshay Bharadwaj},
+  year={2025},
+  howpublished={\url{https://huggingface.co/akshayb/nl-code-gen-python}}
+}
 
-## Model Card Contact
 
-[More Information Needed]
-### Framework versions
- -->
 - PEFT 0.7.2.dev0