Create README.md

README.md

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+---
+language: en
+tags:
+- Explain code
+- Code Summarization
+- Summarization
+
+license: mit
+---
+
+
+# Gemini
+
+For in-depth understanding of our model and methods, please see our blog [here](https://www.describe-ai.com/gemini)
+
+## Model description
+
+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:
+
+- Python
+- Javascript (mostly vanilla JS, however, it can handle frameworks like React as well)
+- Java
+- Ruby
+- Go
+
+And outputs a description in English. 
+
+## Intended uses & limitations
+
+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.
+
+### How to use
+
+You can use this model directly with a pipeline for Text2Text generation, as shown below:
+
+```python
+from transformers import pipeline, set_seed
+
+summarizer = pipeline('text2text-generation', model='describeai/gemini')
+code = "print('hello world!')"
+
+response = "Summarized code: "+ summarizer(code, max_length=100, num_beams=3)
+print(response)
+
+```
+
+Which should yield something along the lines of:
+
+```
+Summarized code: The following code is greeting the world.
+```
+
+### Model sizes
+
+Gemini: 770 Million Parameters
+Gemini-Small (this repo): 220 Million Parameters
+
+
+### Limitations
+
+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.
+
+
+### About Us
+
+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.