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--- |
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language: en |
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tags: |
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- Explain code |
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- Code Summarization |
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- Summarization |
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license: mit |
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--- |
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# Gemini |
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For in-depth understanding of our model and methods, please see our blog [here](https://www.describe-ai.com/gemini) |
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## Model description |
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Gemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explanations that were synthetically generated. Gemini is capable of summarization/explaining short to medium code snippets in: |
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- Python |
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- Javascript (mostly vanilla JS, however, it can handle frameworks like React as well) |
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- Java |
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- Ruby |
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- Go |
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And outputs a description in English. |
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## Intended uses |
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Gemini without any additional fine-tuning is capable of explaining code in a sentence or two and typically performs best in Python and Javascript. We recommend using Gemini for either simple code explanation, documentation or producing more synthetic data to improve its explanations. |
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### How to use |
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You can use this model directly with a pipeline for Text2Text generation, as shown below: |
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```python |
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from transformers import pipeline, set_seed |
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summarizer = pipeline('text2text-generation', model='describeai/gemini') |
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code = "print('hello world!')" |
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response = summarizer(code, max_length=100, num_beams=3) |
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print("Summarized code: " + response[0]['generated_text']) |
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``` |
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Which should yield something along the lines of: |
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``` |
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Summarized code: The following code is greeting the world. |
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``` |
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### Model sizes |
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- Gemini (this repo): 770 Million Parameters |
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- Gemini-Small - 220 Million Parameters |
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### Limitations |
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Typically, Gemini may produce overly simplistic descriptions that don't encompass the entire code snippet. We suspect with more training data, this could be circumvented and will produce better results. |
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### About Us |
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A Describe.ai, we are focused on building Artificial Intelligence systems that can understand language as well as humans. While a long path, we plan to contribute our findings to our API to the Open Source community. |
