sentence-transformers
yt-dlp
sentence-transformers | yt-dlp | |
---|---|---|
45 | 2,360 | |
13,842 | 71,097 | |
2.4% | 3.2% | |
9.2 | 9.8 | |
3 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | The Unlicense |
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.
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 BERT’s 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
yt-dlp
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FFmpeg 7.0 Released
You can put these options in a config file and they will become the default: https://github.com/yt-dlp/yt-dlp?tab=readme-ov-file#configur...
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Google fights Invidious (a privacy YouTube Front end)
Yep. yt-dlp and youtube-dl
https://github.com/yt-dlp/yt-dlp
https://github.com/ytdl-org/youtube-dl
Will also start to feel the impact. My theory is that we will see a bunch of new video hosting sites as youtube itself attempts to lock down its ecosystem. They haven't paid attention in any adversarial way as far as I can tell.
When they do, it wont be great.
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XZ: A Microcosm of the interactions in Open Source projects
The points you make aren't unreasonable.
It is necessary to establish clear boundaries of what can and can be provided by the maintainers. If not done at an earlier stage of the project, the support burden becomes too much to bear at which point the maintainer transfers ownership, and the project suffers from catastrophic consequences such as the xz backdoor we're talking about here, or other cases where the project mostly stalls and serves as an ego-boosting platform for the new maintainer, as was the case with PhantomJS[6].
This can also happen in your life, where a "friend" sees that you possess a certain skill, and then gradually tries to push an inordinate amount of their personal work related to this field onto you.
Personally, I think it's best to use an approach with extremely clear communication as to what the maintainer can and cannot provide. This can be seen, for example, in yt-dlp[1], where the consumer is clearly informed upfront that not providing detailed information as requested will lead them to block said consumer; or sqlite where their position regarding contributed patches[2] and support[3] is similarly made clear.
Having a shouty BDFL like Torvalds can also help improve code quality[4] and questionable contributions[5], though it is better that the shouty BDFL makes statements that are professional and do not show as much aggression; so for example, "Mauro, shut the fuck up"[7] would become "Mauro, your response is completely unbecoming for a Linux kernel maintainer, and is not in line with the promise of not breaking userspace."
[1] https://github.com/yt-dlp/yt-dlp/issues/new?assignees=&label...
[2] https://www.sqlite.org/copyright.html
[3] https://www.sqlite.org/support.html
[4] https://www.theregister.com/2024/01/29/linux_6_8_rc2/
[5] https://cse.umn.edu/cs/linux-incident
[6] https://github.com/ariya/phantomjs/issues/14541
[7] https://lkml.org/lkml/2012/12/23/75
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Doom Running on a Toothbrush
Or just "yt-dlp "
yt-dlp ( https://github.com/yt-dlp/yt-dlp ) still works pretty well at the current state of Twitter.
- Show HN: I create a free website for download YouTube transcript, subtitle
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Using LangServe to build REST APIs for LangChain Applications
To download audio from YouTube videos, you'll utilize the widely used yt-dlp library, which can be installed using the pip command as follows:
- YouTube-dl has been taken down
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Ask HN: YouTube – how to batch scrape comments and details for 300 videos?
Use: `yt-dlp with --write-comments --no-download --batch-file FILE`
- FILE is a text file with a list of YouTube id's/URL's
- https://superuser.com/a/1732443/4390
- https://github.com/yt-dlp/yt-dlp
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Forget spaceships; I just want my music
> Then youtube-dl wasn't a thing anymore (maybe it is again?)...
yt-dlp is definitely a thing: <https://github.com/yt-dlp/yt-dlp>
What are some alternatives?
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Simple-YouTube-Age-Restriction-Bypass - A simple browser extension to bypass YouTube's age verification, disable content warnings and watch age restricted videos without having to sign in!
onnx - Open standard for machine learning interoperability
tiktok-scraper - TikTok Scraper. Download video posts, collect user/trend/hashtag/music feed metadata, sign URL and etc.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
youtube-dl-gui - A cross platform front-end GUI of the popular youtube-dl written in wxPython.
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
youtube-dl - Command-line program to download videos from YouTube.com and other video sites
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
youtube-dlc - Command-line program to download various media from YouTube.com and other sites
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
youtube-dl - Command-line program to download videos from YouTube.com and other video sites