IndicoDataSolutions/Enso
Enso: An Open Source Library for Benchmarking Embeddings + Transfer Learning Methods
This tool helps machine learning engineers and data scientists evaluate and compare different natural language processing (NLP) models. It takes unlabeled text data and various pre-trained language models as input, then systematically tests how well these models perform on your specific task when given only a small amount of labeled data. The output is a clear visualization showing which models and approaches are most effective for your project.
No commits in the last 6 months.
Use this if you need to choose the best text embedding or transfer learning method for a new NLP project with limited labeled data.
Not ideal if you're looking for a low-code solution or don't have experience with Python and machine learning workflows.
Stars
96
Forks
12
Language
Python
License
MPL-2.0
Category
Last pushed
Jan 15, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/IndicoDataSolutions/Enso"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Featured in
Higher-rated alternatives
embeddings-benchmark/mteb
MTEB: Massive Text Embedding Benchmark
harmonydata/harmony
The Harmony Python library: a research tool for psychologists to harmonise data and...
yannvgn/laserembeddings
LASER multilingual sentence embeddings as a pip package
embeddings-benchmark/results
Data for the MTEB leaderboard
Hironsan/awesome-embedding-models
A curated list of awesome embedding models tutorials, projects and communities.