codequestion
sentence-transformers
codequestion | sentence-transformers | |
---|---|---|
15 | 45 | |
509 | 13,842 | |
0.6% | 2.4% | |
5.5 | 9.2 | |
8 months ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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codequestion
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Introducing the Overflow Offline project
GitHub | Article
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The Overflow Offline Project β Stack Overflow Blog
There was a recent HN Post for codequestion which builds an offline semantic index on the Stack Overflow dumps on archive.org - https://news.ycombinator.com/item?id=33110219
GitHub: https://github.com/neuml/codequestion
Article: https://medium.com/neuml/find-answers-with-codequestion-2-0-...
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[P] Stack Overflow Semantic Search
Release Announcement - https://medium.com/neuml/find-answers-with-codequestion-2-0-50b2cfd8c8fe Release Notes - https://github.com/neuml/codequestion/releases/tag/v2.0.0 GitHub - https://github.com/neuml/codequestion
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Semantic search of Stack Overflow with codequestion
Release Announcement - https://medium.com/neuml/find-answers-with-codequestion-2-0-50b2cfd8c8fe Release Notes - https://github.com/neuml/codequestion/releases/tag/v2.0.0
- Show HN: Semantic search of Stack Overflow with codequestion
sentence-transformers
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External vectorization
txtai is an open-source first system. Given it's own open-source roots, like-minded projects such as sentence-transformers are prioritized during development. But that doesn't mean txtai can't work with Embeddings API services.
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[D] Looking for a better multilingual embedding model
Ok great. My use case is not very specific, but rather general. I am looking for a model that can perform asymmetric semantic search for the languages I mentioned earlier (Urdu, Persian, Arabic etc.). I have also looked into the sentence-transformer training documentation. Do you think it would be a good idea to use the XNLI dataset for fine-tuning? Or maybe you can suggest much better dataset. Furthermore, I am not sure if fine-tuning is suitable for my task. Because my use case is general so I can use already trained model.
- Best pathway for Domain Adaptation with Sentence Transformers?
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Syntactic and Semantic surprisal using a LLM
The task you are looking for is semantic textual similarity. There are a few models and datasets out there that can do this. I'd probably start with the SemEval2017 Task 1 task description and competition entries here and then work outward from there (using something like SemanticScholar or Papers With Code to find newer state of the art works that cite these models if needed). For what it's worth you might find that Sentence Bert (SBERT) gives good vectors for cosine similarity comparison out of the box for this task.
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Mean pooling in BERT
Check out the sentence-transformers implementation. If I don't miss anything they don't exclude CLS when the pooling strategy is set to 'mean'
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I Built an AI Search Engine that can find exact timestamps for anything on Youtube using OpenAI Whisper
Break up transcript into shorter segments and convert segments to a 768 vector array. Use a process known as embedding using our second ML model, UKP Labs BERTβs sentence transformer model.
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Seeking advice on improving NLP search results
Not sure what kind of texts you have, but these models have a max sequence limit of 512 (approx 350 words or so). If you're texts are longer than that, consider splitting them up into chunks or creating a summary and taking an embedding of that. Some clustering algorithm may be the way to go here. Here's a bunch of examples. I use agglomerative for my use case.
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Dev Diary #12 - Finetune model
https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/data_augmentation (Augmented Encoding)
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[R] Customize size of Bio-BERT pre-trained embeddings
For vector representation you can take the mean and then pca to get the size that you want, but if you have time then use sentence transformers to train a vector representation instead.
- SentenceTransformer producing different sentence embedding results in Docker
What are some alternatives?
txtai - π‘ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
transformers - π€ Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
tldrstory - π Semantic search for headlines and story text
onnx - Open standard for machine learning interoperability
tika-python - Tika-Python is a Python binding to the Apache Tikaβ’ REST services allowing Tika to be called natively in the Python community.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
paperai - π π€ Semantic search and workflows for medical/scientific papers
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
freeCodeCamp - freeCodeCamp.org's open-source codebase and curriculum. Learn to code for free.
python_docs
datasets - π€ The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools