4ervonec19
/

SimpleTestGenerator

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 tags: []
 ---
+# Model Card for LargeCodeModelGPTBigCode
+## Model Overview
+```LargeCodeModelGPTBigCode``` is a model designed for code test generation and analysis. It is based on [GPTBigCode](https://huggingface.co/docs/transformers/main/en/model_doc/gpt_bigcode) and is specifically tailored for handling and generating tests for code. The model has been trained on a small manually labeled dataset of code and can be used for various tasks related to code analysis and testing.
+<!-- Provide a quick summary of what the model is/does. -->
+### Features:
+* Code test generation.
+* Python code analysis and generation.
+* Uses a pre-trained GPT2 model integrated with Hugging Face.
+## How it Works
+The model is loaded from an external repository, such as Hugging Face, and is initialized using the ```class LargeCodeModelGPTBigCode```. Several parameters can be specified during initialization to configure the model, such as:
+* gpt2_name: The link to the model on Hugging Face
+* prompt_string: An additional wrapper for better understanding of the task by the model
+* params_inference: Inference parameters (used in self.gpt2.generate(**inputs, **inference_params))
+* max_length: The maximum number of tokens in the sequence
+* device: The device to run the model on
+* saved_model_path: Path to the fine-tuned model
+* num_lines: Number of lines (due to "non-terminating" model generation)
+* flag_hugging_face: Flag to enable usage with Hugging Face (default: False)
+* flag_pretrained: Flag to initialize the model with pre-trained weights
+### Model Initialization
+```python
+from inference_gptbigcode import LargeCodeModelGPTBigCode
+gpt2bigcode = "4ervonec19/SimpleTestGenerator"
+CodeModel = LargeCodeModelGPTBigCode(gpt2_name=gpt2bigcode,
+                                    flag_pretrained=True,
+                                    flag_hugging_face=True)
+```
+### Inference Example
+Here’s an example of inference where the model is used to generate tests based on a given code snippet:
+```python
+code_example = '''def equals_zero(a):
+    if a == 0:
+      return True
+    return False'''
+tests_generated = CodeModel.input_inference(code_text=code_example)
+# Result
+print(tests_generated['generated_output'])
+```
+### Output:
+The result will contain the input function and generated tests dict, for example:
+```python
+{'input_function': ('def equals_zero(a):\n    if a == 0:\n      return True\n    return False',),
+ 'generated_output': 'def test_equals_zero():\n    assert equals_zero(0) is True\n    assert equals_zero(1) is False\n    assert equals_zero(0) is True\n    assert equals_zero(1.5) is False'}
+```
+## Model Details
+* Architecture: GPT2
+* Pretraining: Yes, the model uses a pre-trained GPT2 version for test generation and code generation.
+* Framework: PyTorch/HuggingFace
+* License: MIT (or another, depending on the model's license)
+## Limitations
+* The model may not always generate correct or optimal tests, especially for complex or non-standard code fragments.
+* Some understanding of code structure may be required for optimal results.
+* The quality of generated tests depends on the quality of the input code and its context.