mirror of
https://github.com/wassname/Open-Assistant.git
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162 lines
3.8 KiB
Plaintext
162 lines
3.8 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Example Notebook"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[](https://colab.research.google.com/github/LAION-AI/Open-Assistant/blob/example-notebook/notebooks/example/example.ipynb)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# uncomment and run below lines to set up if running in colab\n",
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"# !git clone https://github.com/LAION-AI/Open-Assistant.git\n",
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"# %cd Open-Assistant/notebooks/example\n",
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"# !pip install -r requirements.txt"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Try to add a markdown section to the notebook that explains what the notebook is about and what it does. This will help people understand what the notebook is for and how to use it."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# import required packages\n",
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"import pandas as pd\n",
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"from transformers import pipeline"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Use Headings\n",
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"\n",
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"(it will help with link sharing to specific sections of the notebook)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Make fancy markdown cells if you want."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>row</th>\n",
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" <th>text</th>\n",
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" <th>label</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>some example data</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>some more data</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" row text label\n",
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"0 1 some example data 1\n",
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"1 2 some more data 0"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Do cool stuff here\n",
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"df = pd.read_csv(\"data/data.csv\")\n",
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"df.head()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4 (tags/v3.7.4:e09359112e, Jul 8 2019, 20:34:20) [MSC v.1916 64 bit (AMD64)]"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "25d5c2324055587ceaeef27650c79ce8358ea61d7689f2e0b8ada5d53f85bce4"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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