xavierwoon
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Update README.md
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README.md
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- sft
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datasets:
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- xavierwoon/cestertrain
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---
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# Model Card for Model ID
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:**
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:**
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:**
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[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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[More Information Needed]
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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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[More Information Needed]
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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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### 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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## 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]
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## Training Details
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<!-- 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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### 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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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### 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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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **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]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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[More Information Needed]
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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 Needed]
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## More Information [optional]
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## Model Card Contact
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[More Information Needed]
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- sft
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datasets:
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- xavierwoon/cestertrain
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base_model:
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- unsloth/mistral-7b-bnb-4bit
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---
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# Model Card for Model ID
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<!-- Provide a longer summary of what this model is. -->
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<!-- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. -->
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- **Developed by:** Xavier Woon
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<!-- - **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed] -->
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- **Model type:** Mistral
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** unsloth/mistral-7b-bnb-4bit
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<!-- ### Model Sources [optional]
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Provide the basic links for the model.
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed] -->
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<!-- ## Uses -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[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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<!-- [More Information Needed] -->
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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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<!-- [More Information Needed] -->
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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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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`.
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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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Expanding the dataset will help increase the accuracy and robustness of the model, and improve code coverage based on real life scenarios.
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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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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "xavierwoon/cestermistral"
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Paste your own code inside
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code = """
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void add()
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{
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int a,b,c;
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printf("\nEnter The Two values:");
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scanf("%d%d",&a,&b);
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c=a+b;
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printf("Addition:%d",c);
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}
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"""
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prompt = f"""### Instruction:
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create cester test cases for this function:
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{code}
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### Input:
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### Response:
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
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from transformers import TextStreamer
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text_streamer = TextStreamer(tokenizer)
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_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
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```
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[More Information Needed]
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## Training Details
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<!-- 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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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.
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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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1. Prompt GPT for sample DSA C code
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2. Prompt GPT for Libcester unit test cases with 100% code coverage
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3. Test generated test cases for robustness and code coverage
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<!-- #### Preprocessing [optional]
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[More Information Needed]
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-->
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<!-- #### Training Hyperparameters -->
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<!-- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> -->
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<!-- #### Speeds, Sizes, Times [optional] -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- [More Information Needed] -->
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<!-- ## Evaluation -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- ### Testing Data, Factors & Metrics -->
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<!-- #### 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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<!-- [More Information Needed] -->
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<!-- #### Metrics -->
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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<!-- [More Information Needed] -->
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<!-- ### Results -->
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<!-- [More Information Needed] -->
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<!-- #### Summary -->
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<!-- ## Model Examination [optional] -->
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<!-- Relevant interpretability work for the model goes here -->
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<!-- [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]
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## Citation [optional] -->
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- **BibTeX:**
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[More Information Needed]
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[More Information Needed]
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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 Needed]
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## More Information [optional]
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## Model Card Contact
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[More Information Needed] -->
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