Update README.md

README.md

@@ -17,21 +17,18 @@ The goal of the DocuMint model is to generate docstrings that are concise (brief
 
 
 
-- **Developed by:** [More Information Needed]
-- **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]
+- **Developed by:** Bibek Poudel, Adam Cook, Sekou Traore, Shelah Ameli (University of Tennessee, Knoxville)
+- **Model type:** Causal language model fine-tuned for code documentation generation
+- **Language(s) (NLP):** English, Python
+- **License:** MIT
+- **Finetuned from model [optional]:** google/codegemma-2b
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** TODO
+- **Paper [optional]:** TODO
 
 ## Uses
 
@@ -39,66 +36,24 @@ The goal of the DocuMint model is to generate docstrings that are concise (brief
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+The DocuMint model can be used directly to generate high-quality docstrings for Python functions. Given a Python function definition, the model will output a docstring in the format """<generated docstring>""".
 
-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-[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. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
+The training data consists of 100,000 Python functions and their docstrings extracted from popular open-source repositories in the FLOSS ecosystem. Repositories were filtered based on metrics such as number of contributors (> 50), commits (> 5k), stars (> 35k), and forks (> 10k) to focus on well-established and actively maintained projects. 
 
+An abstract syntax tree (AST) based parser was used to extract functions and docstrings. Challenges in the data sampling process included syntactic errors, multi-language repositories, computational expense, repository size discrepancies, and ensuring diversity while avoiding repetition.
 
 #### 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 -->
+The model was fine-tuned using Low-Rank Adaptation (LoRA) for 4 epochs with a batch size of 8 and gradient accumulation steps of 16. The initial learning rate was 2e-4. In total, there were 78,446,592 LoRA parameters and 185,040,896 training tokens. The full hyperparameter configuration is provided in Table 2 of the paper.
 
-#### Speeds, Sizes, Times [optional]
+Fine-tuning was performed using an Intel 12900K CPU, an Nvidia RTX-3090 GPU, and 64 GB RAM. Total fine-tuning time was 48 GPU hours.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
 
 ## Evaluation
 
@@ -106,67 +61,19 @@ Use the code below to get started with the model.
 
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+- **Accuracy:** Measures the coverage of the generated docstring on code elements like input/output variables. Calculated using cosine similarity between the generated and expert docstring embeddings.
 
-[More Information Needed]
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
+- **Conciseness:** Measures the ability to convey information succinctly without verbosity. Calculated as a compression ratio between the compressed and original docstring sizes. 
 
-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
+- **Clarity:** Measures readability using simple, unambiguous language. Calculated using the Flesch-Kincaid readability score.
 
-### Compute Infrastructure
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-[More Information Needed]
 
 #### Hardware
 
-[More Information Needed]
+Single GPU RTX-3090
 
-#### Software
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-[More Information Needed]
 
 ## Citation [optional]
 
@@ -174,29 +81,19 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-[More Information Needed]
+(TODO)
+@misc{poudel2024documint,
+      title={DocuMint: Docstring Generation for Python using Small Language Models}, 
+      author={Bibek Poudel* and Adam Cook* and Sekou Traore* and Shelah Ameli*},
+      year={2024},
+}
 
-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
 
 [More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
-### Framework versions
+- For questions or more information, please contact:
+{bpoudel3,acook46,staore1,oameli}@vols.utk.edu
 
 - PEFT 0.8.2