Update README.md

README.md

@@ -1,15 +1,19 @@
 ---
 library_name: transformers
+tags:
+- trainer
 datasets:
-- xavierwoon/cestereval
 - xavierwoon/cestertest
+- xavierwoon/cestereval
+base_model:
+- google-bert/bert-base-uncased
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+Cesterrewards is a Bert model that is able to predict the code coverage of Libcester unit test cases.
 
 ## Model Details
 
@@ -17,63 +21,91 @@ datasets:
 
 <!-- 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.
+<!-- 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]
+- **Developed by:** Xavier Woon
+<!-- - **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed] -->
+- **Model type:** Bert
+<!-- - **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed] -->
+- **Finetuned from model [optional]:** google-bert/bert-base-uncased
 
-### Model Sources [optional]
+<!-- ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+Provide the basic links for the model.
 
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed] -->
 
-## Uses
+<!-- ## 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
+<!-- ### Direct Use -->
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-### Downstream Use [optional]
+<!-- ### 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]
+<!-- [More Information Needed] -->
 
-### Out-of-Scope Use
+<!-- ### Out-of-Scope Use -->
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-## Bias, Risks, and Limitations
+<!-- ## Bias, Risks, and Limitations -->
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
-
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Expanding the dataset will help increase the accuracy and robustness of the model, and improve code coverage predictions based on real life scenarios.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```py
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+reward_name = "xavierwoon/cesterrewards"
+reward_model = AutoModelForSequenceClassification.from_pretrained(reward_name)
+tokenizer = AutoTokenizer.from_pretrained(reward_name)
+
+# Change the prompt to sample unit test cases in Libcester format
+prompt = """
+CESTER_TEST(create_stack, test_instance,
+{
+    struct Stack stack;
+    initStack(&stack);
+    cester_assert_equal(stack.top, -1);
+})
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
+# Put the model in evaluation mode
+reward_model.eval()
+
+# Perform inference to get the reward score
+with torch.no_grad():
+    outputs = reward_model(**inputs)
+reward_score = outputs.logits.item()  # Extract the scalar value
+
+print("Expected Code Coverage:", reward_score)
+```
+
+<!-- [More Information Needed] -->
 
 ## Training Details
 
@@ -81,78 +113,70 @@ 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 Data was created based on Data Structures and Algorithm (DSA) codes created using ChatGPT. It would also create corresponding Cester test cases. After testing the code coverage, it was added to the dataset under `score`.
 
 ### 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]
+1. Prompt GPT for sample DSA C code
+2. Prompt GPT for Libcester unit test cases with 100% code coverage
+3. Test generated test cases for code coverage and note down
 
-[More Information Needed]
+<!-- #### Preprocessing [optional]
 
+[More Information Needed]
+ -->
 
-#### Training Hyperparameters
+<!-- #### 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 -->
+<!-- - **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]
+<!-- #### Speeds, Sizes, Times [optional] -->
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-## Evaluation
+<!-- ## Evaluation -->
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
+<!-- ### Testing Data, Factors & Metrics -->
 
-#### Testing Data
+<!-- #### Testing Data -->
 
 <!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-#### Factors
+<!-- #### Factors -->
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-#### Metrics
+<!-- #### Metrics -->
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-### Results
+<!-- ### Results -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-#### Summary
+<!-- #### Summary -->
 
 
 
-## Model Examination [optional]
+<!-- ## 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 -->
+<!-- [More Information Needed] -->
 
-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]
+<!-- ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
@@ -170,11 +194,11 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 [More Information Needed]
 
-## Citation [optional]
+## 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:**
+<!-- **BibTeX:**
 
 [More Information Needed]
 
@@ -182,11 +206,11 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 [More Information Needed]
 
-## Glossary [optional]
+## 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 Needed]
 
 ## More Information [optional]
 
@@ -198,4 +222,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+[More Information Needed] -->