| explosion/sense2vec |
1,486 |
|
6 |
7 |
almost 3 years ago |
24 |
April 19, 2021 |
20 |
mit |
Python |
| 🦆 Contextually-keyed word vectors |
| dipanjanS/text-analytics-with-python |
1,073 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
apache-2.0 |
Jupyter Notebook |
| Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer. |
| 5hirish/adam_qas |
298 |
|
0 |
0 |
about 6 years ago |
0 |
|
8 |
gpl-3.0 |
Python |
| ADAM - A Question Answering System. Inspired from IBM Watson |
| textpipe/textpipe |
290 |
|
1 |
0 |
almost 5 years ago |
39 |
January 25, 2021 |
24 |
mit |
Python |
| Textpipe: clean and extract metadata from text |
| davidberenstein1957/concise-concepts |
222 |
|
0 |
0 |
almost 3 years ago |
35 |
January 13, 2023 |
6 |
mit |
Python |
| This repository contains an easy and intuitive approach to few-shot NER using most similar expansion over spaCy embeddings. Now with entity scoring. |
| eellak/nlpbuddy |
82 |
|
0 |
0 |
about 7 years ago |
0 |
|
5 |
agpl-3.0 |
HTML |
| A text analysis application for performing common NLP tasks through a web dashboard interface and an API |
| ansegura7/NLP |
77 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
HTML |
| Free hands-on course with the implementation (in Python) and description of several Natural Language Processing (NLP) algorithms and techniques, on several modern platforms and libraries. |
| alisonmitchell/Stock-Prediction |
57 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping. |
| dipanjanS/nlp_workshop_odsc_europe20 |
36 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
gpl-3.0 |
Jupyter Notebook |
| Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models. |
| somanyadav/Youtube-Summariser |
19 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Summarise YouTube videos and save time! |
