hamelsmu

name: Binder
on: 
  pull_request:
    types: [opened, reopened]

jobs:
  Create-Binder-Badge:
    runs-on: ubuntu-latest
    steps:

hamelsmu

1. Sally creates this notebook which contain interactive visualizations. She saves the notebook to GitHub.

2. Sally wants to share this report with her colleagues on twitter, preferrably as a blog post. However, she cannot share her notebook via a GitHub link due of the following issues:

   • GitHub doesn't support rendering her interactive visualizations, even when all the code is client side, like the case with Vega-Lite or Altair. (That is why you don't see any visualizations in this notebook on GitHub!)
   • There is no place for colleague to comment on the notebook or provide feedback easily.
   • Sally wants to hide / show code to make the document easier to read, and remove extreanous information. It is impossible to do this without copying her notebook and creating an alternate version.

• Sally needs the ability to hide/show code in collapsable cells in certain

hamelsmu

from nbdev import export2html
from nbdev.export2html import Config, Path, _re_digits

## Monkey Patch Nbdev For Jupyter Blog Posts ##
def _nb2htmlfname(nb_path, dest): 
    if dest is None: dest = Config().doc_path
    return Path(dest)/_re_digits.sub('', nb_path.with_suffix('.md').name)

## apply monkey patch
export2html._nb2htmlfname = _nb2htmlfname

hamelsmu

from vega_datasets import data as vega_data
import pandas as pd
import altair as alt

data = pd.read_json(vega_data.gapminder.url)
data2000 = data.loc[data['year'] == 2000]

chart1 = alt.Chart(data2000).mark_line().encode(
    alt.X('fertility:Q'),

hamelsmu

<link href="https://unpkg.com/@primer/css/dist/primer.css" rel="stylesheet" />

<div class="Toast">
    <span class="Toast-icon">
      {% octicon info %}
    </span>
    <span class="Toast-content">You can add these components, too!</span>
 </div>

hamelsmu

A collapsible section containing markdown

Click to expand!

Heading

1. A numbered
2. list
   • With some
   • Sub bullets

hamelsmu

#!/usr/bin/env python
# coding: utf-8


# This notebook illustrates the use of a utility, `InferenceWrapper.df_to_emb` that can be used to perform inference in bulk.

# - **checkpointed model** (2.29 GB): 
# `https://storage.googleapis.com/issue_label_bot/model/lang_model/models_22zkdqlr/best_22zkdqlr.pth`

hamelsmu

from fastai.text import TextLMDataBunch as lmdb
from fastai.text.transform import Tokenizer
import pandas as pd
from pathlib import Path

small_df = pd.read_csv('https://storage.googleapis.com/issue_label_bot/pre_processed_data/processed_part0000.csv').head(1000)

stokenizer = Tokenizer(pre_rules=[pass_through], n_cpus=30)

spath = Path('lang_model_test/')

hamelsmu

import pandas as pd
from pathlib import Path
from fastai.text import TextLMDataBunch as lmdb, load_data
from fastai.text.transform import Tokenizer

def pass_through(x):
    return x

valid_df = pd.read_csv('https://storage.googleapis.com/issue_label_bot/pre_processed_data/processed_part0000.csv')
train_df = valid_df

hamelsmu

{
  "action": "edited",
  "issue": {
            "html_url": "https://github.com/Codertocat/Hello-World/issues/2",
            "id": 327883527,
            "number": 2,
            "title": "Spelling error in the README file",
            "user": {
                "login": "Codertocat",
                "type": "User",