bclavie

5 ms·

1.
by bclavie 2y ago
Hey! It’s more like comparing apples to apple pie. BGE-M3 is a fine-tuned embedding models. This means that they’ve taken a base language model, which was trained for just language modeling, then applied further fine-tuning to make it usefu
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by bclavie 2y ago
They perform different roles, so they're not directly comparable. Jina V3 is an embedding model, so it's a base model, further fine-tuned specifically for embedding-ish tasks (retrieval, similarity...). This is what we call "
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by bclavie 2y ago
Sentence Transformers ( https://sbert.net/ ), the most used library for embedding models (similarity, retrieval.)
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by bclavie 2y ago
We had a bit of a discussion around it, but I figured that 6 years warranted the prefix, and it's easier to remember in the sea of new acronyms popping up everyday. Besides, PostModernBERT will be there for us for the next generational
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by bclavie 2y ago
Thank you! We're fixing the link.
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by bclavie 2y ago
Hey, Ben here, one of the paper's core author authors. The responses you got were mostly spot on. For (1), it's because BERT has both noticeably fewer parameters, and we're comparing at short context length (in the interest o
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Rerankers: A Lightweight Python Library to Unify Ranking Methods(answer.ai)
4 points by bclavie 2y ago|0 comments
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by bclavie 2y ago
Hey! Thanks for posting this. I'm the author of this post -- please feel free to shout if you've got any questions
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by bclavie 3y ago
Longer Background/Explanation: I’ve been working on RAG problems for quite a while now, and it’s very apparent that solving real-life problems with it is very, very different from the basic tutorials around. There are a million moving
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Show HN: RAGatouille, a simple lib to use&train top retrieval models in RAG apps(github.com)
15 points by bclavie 3y ago|5 comments
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by bclavie 3y ago
Thanks! I agree -- I find it much easier to skim a few paragraphs than to skim through a video when trying to consume information quickly if I'm not sure I want to commit to a full, long vid. Hoping to make it useful enough that it end
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by bclavie 3y ago
Merci! It's early on but I'm quite happy with how the first prototype turned out.
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by bclavie 3y ago
> How do you break down the segments/sections? Is it just fixed time? What happens if there are more than one topic discussed in the segment? Currently it's just a dumb fixed time rule, based on max video length (3 or 5mn segme
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by bclavie 3y ago
Haha merci! Prends ton temps, pour le moment le tout tourne sur des crédits cloud gratuits alors la seule utilité du bouton premium c'est de faire de la lumière. Bonne chance pour ta recherche!
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by bclavie 3y ago
> Impressive, thanks. How could one run something like that on local videos ? It depends how involved you'd want it to be really. You can get a very simple summary using something like Whisper to transcribe a video and having basic
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Show HN: EnfinBref- {GPT3-5|Mistral-7B} YouTube summaries, segment by segment(enfinbref.io)
26 points by bclavie 3y ago|11 comments
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by bclavie 4y ago
Parsing pdfs (and powerpoints) and breaking them into "askable" chunks is definitely something we've been looking into and are keen to roll out. If you'd like to talk more about your use case definitely feel free to chuc
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by bclavie 4y ago
I'm not too familiar with botco.ai, but from what I can see, we're approaching fairly different problems. DocAsker once deployed on an internal documentation (for example) would allow you to query it, for things like "What wa
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by bclavie 4y ago
I've experimented with a few approaches and to be honest, kind of gone with what "felt best" as we're quite artisanal with our testing approach at the moment. We try to always go for logical breakpoints (e.g. never in th
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by bclavie 4y ago
It's kind of like that. You always have the option of fine-tuning, although that quickly gets pricey if you aren't self hosting (e.g. OpenAI bills an order of magnitude higher for serving fine-tuned models) The constraints can be
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by bclavie 4y ago
Sorry about that, I'll dig into the logs -- we had a lot more traffic than we expected overnight so it could be that the backend didn't scale quickly enough and ended up timing out some requests.
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by bclavie 4y ago
This is something I've thought about a lot, as I worked in legaltech for a few years. The main issue here however would be the (lack of) networking and breaking into the legal market, it takes a lot to get the ball rolling even if the
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by bclavie 4y ago
We're working on a similar thing using the Notion API, so far synthetic tests are really encouraging, but we haven't quite tested it on a massive/sprawling corporate doc. Very excited about the prospect though!
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by bclavie 4y ago
Hi HN! I'm the other person working on this with Ankly. This came up as a pet project as we were eager to put some LLM work into production. Currently piggy-backing off APIs, though the results with certain self-hosted models could be