Earlier quoted context omitted.
I can't find any info on whether ModernBERT will handle languages other than English; German, Chinese, Arabic? Any info there would be super helpful.
Probably a multilingual version will be needed, like with BERT and RoBERTa. I should hasten to add for multi language tasks(beyond detection), either simpler methods for tasks like multiple language classification/prediction(e.g. word frequency, BERTopic like approaches or SVMs) or LLMs are generally a better candidate. There are a couple of reasons.. 1) That size (even for the large) is too much for multiple languag…
A Replacement for BERT
71–77 of 77 posts
Re: A Replacement for BERT
#72> encoder-only models add up to over a billion downloads per month, nearly three times more than decoder-only models This is partially because people using decoders aren’t using huggingface at all (they would use an API call) but also because encoders are the unsung heroes of most serious ML applications. If you want to do any ranking, recommendation, RAG, etc it will probably require an encoder. And typically that m…
Re: A Replacement for BERT
#73Hi gang, Jeremy from Answer.AI here. Nice to see this on HN! :) We're very excited about this model release -- it feels like it could be the basis of all kinds of interesting new startups and projects. In fact, the stuff mentioned in the blog post is only the tip of the iceberg. There's a lot of opportunities to fine tune the model in all kinds ways, which I expect will go far beyond what we've managed to achieve in…
Hi Jeremy, I am trying to navigate the space and trying to understand what fits where. Could you shed some lights on what parts of bge-m3 would modernbert overlap with or would this is comparing apples to oranges? https://huggingface.co/BAAI/bge-m3
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 useful for a given application, in this case, retrieval.
ModernBERT is one step back earlier in the pipeline: it’s the language model that application-specific models such as M3 build on.
Re: A Replacement for BERT
#74> encoder-only models add up to over a billion downloads per month, nearly three times more than decoder-only models This is partially because people using decoders aren’t using huggingface at all (they would use an API call) but also because encoders are the unsung heroes of most serious ML applications. If you want to do any ranking, recommendation, RAG, etc it will probably require an encoder. And typically that m…
Encoders are suffering from the curse of all successful AI applications: they work so they are no longer AI. Excited about trying this out, less excited about recalculating a petabyte worth of embedding if it's as good as it looks like it will be. At least I can keep my house warm.
Re: A Replacement for BERT
#75Earlier quoted context omitted.
Encoders are suffering from the curse of all successful AI applications: they work so they are no longer AI. Excited about trying this out, less excited about recalculating a petabyte worth of embedding if it's as good as it looks like it will be. At least I can keep my house warm.
Kinda curious what kind of data you have lying around there and what stack you use to create the embeddings and keep them up to date and how you use then...