Fast_Sentence_Embeddings
jiant
Our great sponsors
| Fast_Sentence_Embeddings | jiant | |
|---|---|---|
| 3 | 2 | |
| 603 | 1,605 | |
| - | 1.0% | |
| 0.0 | 0.0 | |
| about 1 year ago | 10 months ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Fast_Sentence_Embeddings
-
The Illustrated Word2Vec
This is a great guide.
Also - despite the fact that language model embedding [1] are currently the hot rage, good old embedding models are more than good enough for most tasks.
With just a bit of tuning, they're generally as good at many sentence embedding tasks [2], and with good libraries [3] you're getting something like 400k sentence/sec on laptop CPU versus ~4k-15k sentences/sec on a v100 for LM embeddings.
When you should use language model embeddings:
- Multilingual tasks. While some embedding models are multilingual aligned (eg. MUSE [4]), you still need to route the sentence to the correct embedding model file (you need something like langdetect). It's also cumbersome, with one 400mb file per language.
For LM embedding models, many are multilingual aligned right away.
- Tasks that are very context specific or require fine-tuning. For instance, if you're making a RAG system for medical documents, the embedding space is best when it creates larger deviations for the difference between seemingly-related medical words.
This means models with more embedding dimensions, and heavily favors LM models over classic embedding models.
1. sbert.net
2. https://collaborate.princeton.edu/en/publications/a-simple-b...
3. https://github.com/oborchers/Fast_Sentence_Embeddings
4. https://github.com/facebookresearch/MUSE
-
You probably shouldn't use OpenAI's embeddings
You can find some comparisons and evaluation datasets/tasks here: https://www.sbert.net/docs/pretrained_models.html
Generally MiniLM is a good baseline. For faster models you want this library:
https://github.com/oborchers/Fast_Sentence_Embeddings
For higher quality ones, just take the bigger/slower models in the SentenceTransformers library
-
[D] Unsupervised document similarity state of the art
Links: fse: https://github.com/oborchers/Fast_Sentence_Embeddings Sentence-transformers: https://github.com/oborchers/sentence-transformers
jiant
-
Any recommendation for the replacement of the toolkit jiant? [Research] [Discussion]
I am doing research in NLP with the toolkit jiant (https://github.com/nyu-mll/jiant). It is a quite nice and easy-to-use tool. Unfortunately, it stopped being maintained. I wonder is there any other recommendation that I can use to replace it?
-
Looking for a code base to implement multi-task learning in NLP
Jiant should fulfill 1, 2, 4 and 5.
What are some alternatives?
gensim - Topic Modelling for Humans
kiri - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
smaller-labse - Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE
SGDepth - [ECCV 2020] Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance
cso-classifier - Python library that classifies content from scientific papers with the topics of the Computer Science Ontology (CSO).
allennlp - An open-source NLP research library, built on PyTorch.
kgtk - Knowledge Graph Toolkit
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
RecSys_Course_AT_PoliMi - This is the official repository for the Recommender Systems course at Politecnico di Milano.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
sentence-transformers - Sentence Embeddings with BERT & XLNet
PaddleNLP - 👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.