# hundredblocks/concrete_NLP_tutorial

An NLP workshop about concrete solutions to real problems

Repository: https://github.com/hundredblocks/concrete_NLP_tutorial
Canonical: https://ross.abutalabs.com/products/concrete_nlp_tutorial
Language: Jupyter Notebook
License Family: other
Last push: 2021-01-07T06:26:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3225, "days_push": 2064, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1076, forks 439 (observed 2026-08-28T04:03:29.373644+00:00)

## What it is
A Jupyter Notebook-based NLP workshop from ODSC 2017 covering practical, concrete solutions to real-world natural language processing problems. It uses pretrained word vectors (word2vec via gensim) and a pretrained char-rnn model for Yelp text as teaching materials.

## Use cases
- learn practical NLP techniques from a hands-on tutorial
- understand how to use pretrained word2vec embeddings
- see worked examples of solving real NLP problems
- follow along with an NLP workshop notebook
- learn text classification with word vectors
- explore char-rnn models trained on Yelp data

## When to choose
- you want a hands-on, notebook-driven introduction to applied NLP
- you are preparing for or reviewing workshop-style NLP material
- you want concrete examples using pretrained embeddings and models

## When to avoid
- you need a production-ready NLP library or framework
- you need up-to-date techniques such as transformer-based methods
- you need maintained software with a license and active support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, data-science
- domain: machine-learning, tutorials
- platform: python
- tags: workshop, jupyter-notebook, word2vec, text-classification, educational, natural-language-processing

## Member repositories
- hundredblocks/concrete_NLP_tutorial (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:29.373644+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:53:22.220943+00:00, confidence not recorded.
  - readme: https://github.com/hundredblocks/concrete_NLP_tutorial (fetched 2026-08-28T04:03:29.373644+00:00, sha 6785db7ee4a7)
- Data as of 2026-08-30T08:39:29.467469+00:00.