Register
Login
Resources
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Product
Data Engine LLMs Platform Enterprise
Pricing Explore
Connect to our Discord channel
1a2517aa68
Added a dvc stage to download data from gdrive
5 years ago
8873b385c6
Updated 0.zip.dvc
5 years ago
dfdc2fb905
model_params.json is now a dvc output
5 years ago
1500ed2ed4
Small touches to DVC notebook
5 years ago
4a7abafeac
Straighten out imports for gorenml
5 years ago
b26b03ff09
Adding flask server
5 years ago
19bb546f82
Adding PyCharm files to gitignore
5 years ago
0e68946a89
mac support
5 years ago
8d050f4196
Adding matplotlib to requirements
5 years ago
ab36accf3f
Import fixes in server.py and style_predict.py
5 years ago
Storage Buckets
Data Pipeline
Legend
DVC Managed File
Git Managed File
Metric
Stage File
External File

README.md

You have to be logged in to leave a comment. Sign In

Named-Entity-Recognition Workshop

In this workshop, we would learn how to automatically style ( bold , Italics, etc. ) a word according to context.

We learn styling from html files automatically and apply them to raw text.

This project is used mainly to demonstrate deep-learning implementation of named-entity-recognition (NER) models.

Preparing the environment (locally)

#####Note: in case you are not using Colab

  1. Make sure Python3 is installed.
  2. You can create you virtual environment (recommended) using python3 -m virtualenv ner_ws
  3. To activate your virtual env, run: source ner_ws/bin/activate
  4. Now install all of the requirements: pip3 install -r requirements.txt

Usage

  1. Run style_extract.py to generate training files from html.
  2. Put the .zip file in the data/ folder.
  3. Run style_learn.py to train an NER model.
  4. Run server.py to evaluate your model in the browser.

For more details, contact me at goren.ml .

Tip!

Press p or to see the previous file or, n or to see the next file

About

My attempt at the NLP workshop

Collaborators 1

Comments

Loading...