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---
license: other
library_name: transformers
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
Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library.
```python
from transformers import pipeline
import torch
generator = pipeline(
model="semcoder/semcoder_s_1030",
task="text-generation",
torch_dtype=torch.float16,
device_map="auto",
)
# Generate Code
CODEGEN_REQUEST = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable <Code> according to <NL_Description>
<NL_Description>
{desc}
<Code>
"""
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:
1. Add a new task to the to-do list.
2. Mark a task as completed.
3. Display all tasks in the to-do list.
4. Display only the incomplete tasks in the to-do list.
"""
prompt = CODEGEN_REQUEST.format(desc=desc)
result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
code = result[0]["generated_text"].split("```python")[1].split("```")[0]
print(code)
# Understand Code with Monologues
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.
{code}
"""
tests = """
todo_list = ToDoList()
todo_list.add_task("Buy groceries")
todo_list.add_task("Complete assignment")
todo_list.mark_completed("Buy groceries")
assert todo_list.tasks == ???
"""
code += tests
prompt = FWD_MNL_REQUEST.format(code=code)
result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
print(result[0]["generated_text"])
```
## Citation
```bibtex
@article{ding2024semcoder,
title={SemCoder: Training Code Language Models with Comprehensive Semantics},
author={Yangruibo Ding and Jinjun Peng and Marcus J. Min and Gail Kaiser and Junfeng Yang and Baishakhi Ray},
journal={arXiv preprint arXiv:2406.01006},
year={2024}
}
```
## 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. |