Update README.md

README.md

@@ -1,199 +1,77 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- NamCyan/tesoro-code
+base_model:
+- Salesforce/codet5p-220m
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Improving the detection of technical debt in Java source code with an enriched dataset
 
 
 ## 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]
-- **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 Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
-
-[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 -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This model is the part of Tesoro project, used for detecting technical debt in source code. More information can be found at [Tesoro HomePage](https://github.com/NamCyan/tesoro.git).
 
-### Recommendations
+- **Developed by:** [Nam Hai Le](https://github.com/NamCyan)
+- **Model type:** Encoder-Decoder-based PLMs
+- **Language(s):** Java
+- **Finetuned from model:** [CodeT5+](https://huggingface.co/Salesforce/codet5p-220m)
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+### Model Sources
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- **Repository:** [Tesoro](https://github.com/NamCyan/tesoro.git)
+- **Paper:** [To be update]
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[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. -->
-
-[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]
-
-## 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. -->
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("NamCyan/codet5p-220m-technical-debt-code-tesoro")
+model = AutoModelForSequenceClassification.from_pretrained("NamCyan/codet5p-220m-technical-debt-code-tesoro")
+```
 
-#### 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. -->
-
-[More Information Needed]
-
-### Results
-
-[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]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**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]
-
-## Model Card Contact
+## Training Details
 
-[More Information Needed]
+- Training Data: The model is finetuned using [tesoro-code](https://huggingface.co/datasets/NamCyan/tesoro-code)
+
+- Infrastructure: Training process is conducted on two NVIDIA A100 GPUs with 80GB of VRAM.
+
+## Leaderboard
+| Model | Model size  | EM            | F1              |
+|:-------------|:-----------|:------------------|:------------------|
+| **Encoder-based PLMs** |
+| [CodeBERT](https://huggingface.co/microsoft/codebert-base)     | 125M       | 38.28             | 43.47             |
+| [UniXCoder](https://huggingface.co/microsoft/unixcoder-base)    | 125M       | 38.12             | 42.58             |
+| [GraphCodeBERT](https://huggingface.co/microsoft/graphcodebert-base)| 125M       | *39.38*          | *44.21*           |
+| [RoBERTa](https://huggingface.co/FacebookAI/roberta-base)      | 125M       | 35.37             | 38.22             |
+| [ALBERT](https://huggingface.co/albert/albert-base-v2)       | 11.8M      | 39.32             | 41.99             |
+| **Encoder-Decoder-based PLMs** |
+| [PLBART](https://huggingface.co/uclanlp/plbart-base)       | 140M       | 36.85             | 39.90             |
+| [Codet5](https://huggingface.co/Salesforce/codet5-base)       | 220M       | 32.66             | 35.41             |
+| [CodeT5+](https://huggingface.co/Salesforce/codet5p-220m)      | 220M       | 37.91             | 41.96             |
+| **Decoder-based PLMs (LLMs)** |
+| [TinyLlama](https://huggingface.co/TinyLlama/TinyLlama_v1.1_math_code)    | 1.03B      | 37.05             | 40.05             |
+| [DeepSeek-Coder](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) | 1.28B    | **42.52**         | **46.19**         |
+| [OpenCodeInterpreter](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-1.3B)       | 1.35B             | 38.16             | 41.76             |
+| [phi-2](https://huggingface.co/microsoft/phi-2)        | 2.78B      | 37.92             | 41.57             |
+| [starcoder2](https://huggingface.co/bigcode/starcoder2-3b)   | 3.03B      | 35.37             | 41.77             |
+| [CodeLlama](https://huggingface.co/codellama/CodeLlama-7b-hf)    | 6.74B      | 34.14             | 38.16             |
+| [Magicoder](https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B)    | 6.74B      | 39.14             | 42.49             |
+
+
+## Citing us
+```bibtex
+@article{nam2024tesoro,
+  title={Improving the detection of technical debt in Java source code with an enriched dataset},
+  author={Hai, Nam Le and Bui, Anh M. T. Bui and Nguyen, Phuong T. and Ruscio, Davide Di and Kazman, Rick},
+  journal={},
+  year={2024}
+}
+```