ZEzerojames·May 28, 2024Submission
1 points · Open discussionhuggingface
sentence-transformers
Python2020–2024
huggingface / sentence-transformers
sentence-transformers
State-of-the-Art Embeddings, Retrieval, and Reranking
3Distinct people
3Mentions
0Explicit recommenders
19.3KGitHub stars
What to know before exploring
What it is
State-of-the-Art Embeddings, Retrieval, and Reranking
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year3 dated mentions
Observed mentions: 2020-08-25 20:53:24 UTC to 2024-05-28 12:48:11 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
3 sourcesHyperTag 0.4.3 – Introducing Semantic Search for Text Documents (yes, even PDF)
I have added a useful new feature to HyperTag: Semantic search for text documents (including PDF's and epub's). It is powered by DistilBERT (transformer) using the awesome https://github.com/UKPLab/sentence-transformers lib.
It works like this:
Install: $ pip install hypertag
Import text documents: $ hypertag import path/to/directory
Build the index: $ hypertag index
And search: $ hypertag search "your important text query"
Hope any of you find it useful. And always keen on feedback!
8 points · Open discussionPretty cool. The embedding similarity approach makes a lot of sense. I actually this project by experimenting with computing cosine similarities of sentence embeddings [1]. But I wasn't very impressed with out-of-the-box results, and I found it difficult to set a similarity threshold for a match. QA was the second try, and the pretrained models worked better out of the box. I'm wondering if I should revisit the embedding approach now...
[1] https://github.com/UKPLab/sentence-transformers
In Show HN: Shift-Ctrl-F: Search a webpage with natural language and TensorFlowJS