Update README.md

README.md

@@ -13,28 +13,23 @@ tags: []
 
 ### Model Description
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+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).
 
 - **Developed by:** [Nam Hai Le](https://github.com/NamCyan)
 - **Model type:** Decoder-based PLMs
 - **Language(s):** Java
-- **Finetuned from model [optional]:** [Magicoder](https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B)
+- **Finetuned from model:** [Magicoder](https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B)
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [Tesoro](https://github.com/NamCyan/tesoro.git)
 - **Paper:** [To be update]
 
-## Uses
-
-
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-```
+```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained("NamCyan/Magicoder-S-DS-6.7B-technical-debt-code-tesoro")
@@ -44,14 +39,32 @@ model = AutoModelForSequenceClassification.from_pretrained("NamCyan/Magicoder-S-
 
 ## Training Details
 
-### Training Data
-
-
-## Evaluation
-
-
-#### Summary
-
+- 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. [LoRa](https://arxiv.org/abs/2106.09685) is adopted to train this model.
+
+
+## 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             |
 
 
 **BibTeX:**