Improve language tag

README.md

@@ -1,232 +1,246 @@
----
-library_name: transformers
-tags:
-- trl
-- sft
-datasets:
-- xavierwoon/cestertrain
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-Cesterqwen is a fine-tuned Qwen2.5-1.5B model that is able to generate Libcester unit test cases in the correct format.
-
-## 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:** Xavier Woon
-<!-- - **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed] -->
-- **Model type:** Qwen
-<!-- - **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed] -->
-- **Finetuned from model [optional]:** Qwen/Qwen2.5-1.5B-Instruct
-
-<!-- ### 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. -->
-
-The model often regenerates the input prompt in the output. This can lead to limited test cases being printed due to truncations based on `max_new_tokens`.
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Expanding the dataset will help increase the accuracy and robustness of the model, and improve code coverage based on real life scenarios.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-```py
-from transformers import AutoModelForCausalLM, Qwen2Tokenizer
-
-model_name = "xavierwoon/cesterqwen"
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = Qwen2Tokenizer.from_pretrained(model_name)
-
-# Paste your own code inside
-code = """
-void add()
-{
-   int a,b,c;
-   printf("\nEnter The Two values:");
-   scanf("%d%d",&a,&b);
-   c=a+b;
-   printf("Addition:%d",c);
-}
-"""
-
-prompt = f"""### Instruction:
-create cester test cases for this function:
-{code}
-
-### Input:
-
-### Response:
-"""
-
-inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
-
-from transformers import TextStreamer
-text_streamer = TextStreamer(tokenizer)
-_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
-```
-
-<!-- [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. -->
-
-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 and ensuring a good code coverage, the prompt and corresponding test cases were added to the dataset.
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-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 robustness and code coverage
-
-<!-- #### 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. -->
-
-<!-- [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. -->
-
-<!-- [More Information Needed] -->
-
-<!-- ### Results -->
-
-<!-- [More Information Needed] -->
-
-<!-- #### Summary -->
-
-
-
-<!-- ## Model Examination [optional] -->
-
-<!-- Relevant interpretability work for the model goes here -->
-
-<!-- [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
-
+---
+library_name: transformers
+tags:
+- trl
+- sft
+datasets:
+- xavierwoon/cestertrain
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+Cesterqwen is a fine-tuned Qwen2.5-1.5B model that is able to generate Libcester unit test cases in the correct format.
+
+## 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:** Xavier Woon
+<!-- - **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed] -->
+- **Model type:** Qwen
+<!-- - **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed] -->
+- **Finetuned from model [optional]:** Qwen/Qwen2.5-1.5B-Instruct
+
+<!-- ### 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. -->
+
+The model often regenerates the input prompt in the output. This can lead to limited test cases being printed due to truncations based on `max_new_tokens`.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Expanding the dataset will help increase the accuracy and robustness of the model, and improve code coverage based on real life scenarios.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```py
+from transformers import AutoModelForCausalLM, Qwen2Tokenizer
+
+model_name = "xavierwoon/cesterqwen"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = Qwen2Tokenizer.from_pretrained(model_name)
+
+# Paste your own code inside
+code = """
+void add()
+{
+   int a,b,c;
+   printf("\nEnter The Two values:");
+   scanf("%d%d",&a,&b);
+   c=a+b;
+   printf("Addition:%d",c);
+}
+"""
+
+prompt = f"""### Instruction:
+create cester test cases for this function:
+{code}
+
+### Input:
+
+### Response:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
+```
+
+<!-- [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. -->
+
+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 and ensuring a good code coverage, the prompt and corresponding test cases were added to the dataset.
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+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 robustness and code coverage
+
+<!-- #### 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. -->
+
+<!-- [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. -->
+
+<!-- [More Information Needed] -->
+
+<!-- ### Results -->
+
+<!-- [More Information Needed] -->
+
+<!-- #### Summary -->
+
+
+
+<!-- ## Model Examination [optional] -->
+
+<!-- Relevant interpretability work for the model goes here -->
+
+<!-- [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
+
 [More Information Needed] -->