alexdyysp/ESIM-pytorch
中国高校计算机大赛--大数据挑战赛
This project helps predict how likely a user is to click on a search result given their search query and the result's title. It takes pairs of search queries and document titles as input and outputs a prediction of whether the user will click on that specific search result. This is useful for search engine developers, content strategists, or anyone managing large-scale text search systems.
No commits in the last 6 months.
Use this if you need to accurately predict click-through rates for search results based on the relationship between a user's query and a document's title.
Not ideal if your task involves different types of text analysis, such as sentiment analysis or document summarization, or if you don't have query-title pairs for click prediction.
Stars
37
Forks
15
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 12, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/alexdyysp/ESIM-pytorch"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
rmovva/HypotheSAEs
HypotheSAEs: hypothesizing interpretable relationships in text datasets using sparse...
interpretml/interpret-text
A library that incorporates state-of-the-art explainers for text-based machine learning models...
fdalvi/NeuroX
A Python library that encapsulates various methods for neuron interpretation and analysis in...
jalammar/ecco
Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations...
MultiplEYE-COST/wg1-experiment-implementation
In this repository we keep the code for the implementation of the eye-tracking experiment for...