| | ---
|
| | license: other
|
| | library_name: transformers
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| | license_name: deepseek
|
| | license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL
|
| | pipeline_tag: text-generation
|
| | ---
|
| | # 🤔 SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
|
| |
|
| | > Refer to our GitHub repo [ARiSE-Lab/SemCoder](https://github.com/ARiSE-Lab/SemCoder/) for detailed introduction to SemCoder!
|
| |
|
| | ## Model Details
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| |
|
| |
|
| | Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library.
|
| |
|
| | ```python
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| | from transformers import pipeline
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| | import torch
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| |
|
| | generator = pipeline(
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| | model="semcoder/semcoder_1030",
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| | task="text-generation",
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| | torch_dtype=torch.float16,
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| | device_map="auto",
|
| | )
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| |
|
| | # Generate Code
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| |
|
| | CODEGEN_REQUEST = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable <Code> according to <NL_Description>
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| |
|
| | <NL_Description>
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| | {desc}
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| |
|
| | <Code>
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| | """
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| | desc = """You are tasked with implementing a Python class that simulates a simple version of a "To-Do List" application. The class should have the following functionalities:
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| | 1. Add a new task to the to-do list.
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| | 2. Mark a task as completed.
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| | 3. Display all tasks in the to-do list.
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| | 4. Display only the incomplete tasks in the to-do list.
|
| | """
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| |
|
| | prompt = CODEGEN_REQUEST.format(desc=desc)
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| | result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
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| | code = result[0]["generated_text"].split("```python")[1].split("```")[0]
|
| | print(code)
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| |
|
| | # Understand Code with Monologues
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| |
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| | FWD_MNL_REQUEST = """Simulate the Execution: You are given a Python function and an assertion containing a function input. Complete the assertion containing the execution output corresponding to the given input in [ANSWER] and [/ANSWER] tags.
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| | {code}
|
| | """
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| |
|
| | tests = """
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| | todo_list = ToDoList()
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| | todo_list.add_task("Buy groceries")
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| | todo_list.add_task("Complete assignment")
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| | todo_list.mark_completed("Buy groceries")
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| | assert todo_list.tasks == ???
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| | """
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| | code += tests
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| | prompt = FWD_MNL_REQUEST.format(code=code)
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| | result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
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| | print(result[0]["generated_text"])
|
| | ```
|
| |
|
| | ## Citation
|
| |
|
| | ```bibtex
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| | @article{ding2024semcoder,
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| | title={SemCoder: Training Code Language Models with Comprehensive Semantics},
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| | author={Yangruibo Ding and Jinjun Peng and Marcus J. Min and Gail Kaiser and Junfeng Yang and Baishakhi Ray},
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| | journal={arXiv preprint arXiv:2406.01006},
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| | year={2024}
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| | }
|
| | ```
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| |
|
| | ## Important Note
|
| |
|
| | SemCoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets. SemCoder will not compete with OpenAI's commercial products. |
