wembedder
Fast_Sentence_Embeddings
| wembedder | Fast_Sentence_Embeddings | |
|---|---|---|
| 1 | 3 | |
| 49 | 603 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 3 years ago | about 1 year ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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wembedder
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[D] Graph embeddings of Wikidata items
I have made Wembedder that is using a simple RDF2Vec model, that you might try. You can download it from https://github.com/fnielsen/wembedder The current pre-trained model running at https://wembedder.toolforge.org is pretty small with only around 600.000 Wikidata items to fit the size of the Toolforge cloud service. It means that the Python programming language is in the model, but not the snake nor Django :/.
Fast_Sentence_Embeddings
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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
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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
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[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
What are some alternatives?
danker - Compute PageRank on >3 billion Wikipedia links on off-the-shelf hardware.
gensim - Topic Modelling for Humans
smaller-labse - Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE
cso-classifier - Python library that classifies content from scientific papers with the topics of the Computer Science Ontology (CSO).
kgtk - Knowledge Graph Toolkit
RecSys_Course_AT_PoliMi - This is the official repository for the Recommender Systems course at Politecnico di Milano.
sentence-transformers - Sentence Embeddings with BERT & XLNet
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs