HyperTag
sentence-transformers
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HyperTag | sentence-transformers | |
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12 | 45 | |
180 | 13,718 | |
-0.6% | 4.0% | |
4.1 | 9.1 | |
6 days ago | 3 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
HyperTag
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Pitching your early stage startup
I found a related tool a while back that I've wanted to try and integrate, as soon as I get into the habit of making more notes & such:
https://github.com/Ravn-Tech/HyperTag#overview
I guess my ideal tool would be able to recognize the different "contexts" that I'm in, and build a searchable, tagged timeline of my browsing and googling and work history in each of these contexts. Provide the capability to cross-link with notes in an athens/roam-like fashion and it'd be gold
- HyperTag 0.6.3: Knowledge Management for Humans Using Machine Learning and Tags
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Ask HN: What Are You Working On?
https://github.com/SeanPedersen/HyperTag
HyperTag helps humans intuitively express how they think about their files using tags and machine learning. Represent how you think using tags. Find what you look for using semantic search for your text documents (yes, even PDF's) and images. Instead of introducing proprietary file formats like other existing file organization tools, HyperTag just smoothly layers on top of your existing files without any fuss.
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Foam: A personal knowledge management and sharing system for VSCode
Interesting, I just realized I should market my project as a personal knowledge management system as well. Thanks a bunch!
https://github.com/SeanPedersen/HyperTag
- Show HN: HyperTag 0.5.0 β Semantic Search for Images Using Text Queries
- HyperTag 0.5.0 - Semantic Search for Images Using Text Queries powered by OpenAI's CLIP model
- HyperTag 0.5.0 - Semantic Search for Images Using Text Queries using OpenAI's CLIP model
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HyperTag 0.4.3 β Introducing Semantic Search for Text Documents (yes, even PDF)
Github Repo: https://github.com/SeanPedersen/HyperTag
- HyperTag 0.4.3 - Introducing Semantic Search for text documents (yes, even PDF's)
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?
maplibre-gl-js - MapLibre GL JS - Interactive vector tile maps in WebGL2
transformers - π€ Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
rnnoise - Recurrent neural network for audio noise reduction
onnx - Open standard for machine learning interoperability
post-photorec - Tool to auto-organize files recovered by PhotoRec and similar tools.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
procedural-gl-js - Mobile-first 3D mapping engine with emphasis on user experience
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
notenote.link - A Jekyll digital garden template, optimized for integration with Obsidian. It aims to enhance discoverability and help you build a personal knowledge base that can scale with time.
txtai - π‘ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
auto-editor - Auto-Editor: Effort free video editing!
datasets - π€ The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools