cubie
Joined 3 years ago
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152
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22
Member since
Jun 5
Recent submissions
Comment · 8 months ago
I'm a big fan of their work as well, good shout.
Comment · 1 year ago
That's awesome to hear! It's been growing a lot in the background, still useful as ever, especially for retrieval/semantic search.
Comment · 1 year ago
Looks very solid; I'm excited for finetuned variants for retrieval and reranking.
Comment · 1 year ago
Spot on
Comment · 1 year ago
Not yet - these are base models, or "foundational models". They're great for molding into different use cases via finetuning, better than common models like BERT, RoBERTa, etc. in fact, but like those models, these ModernBERT checkpoints can only do one thing: mask filling.
For other tasks, such as retrieval, we still need people to finetune them for it. The ModernBERT documentation has some scripts for finetuning with Sentence Transformers and PyLate for retrieval: https://huggingface.co/docs/transformers/main/en/model_doc/m... But people still need to make and release these models. I have high hopes for them.
Comment · 1 year ago
Beyond what the others have said about 1) ModernBERT-base being 149M parameters vs BERT-base's 110M and 2) most LLMs being decoder-only models, also consider that alternating attention (local vs global) only starts helping once you're processing longer texts. With short texts, local attention is equivalent to global attention.
I'm not sure what length was used in the picture, but GLUE is mostly pretty short text.
Comment · 1 year ago
On a very high level, for NLP:
1. an encoder takes an input (e.g. text), and turns it into a numerical representation (e.g. an embedding).
2. a decoder takes an input (e.g. text), and then extends the text.
(There's also encoder-decoders, but I won't go into those)
These two simple definitions immediately give information on how they can be used. Decoders are at the heart of text generation models, whereas encoders return embeddings with which you can do further computations. For example, if your encoder model is finetuned for it, the embeddings can be fed through another linear layer to give you classes (e.g. token classification like NER, or sequence classification for full texts). Or the embeddings can be compared with cosine similarity to determine the similarity of questions and answers. This is at the core of information retrieval/search (see https://sbert.net/). Such similarity between embeddings can also be used for clustering, etc.
In my humble opinion (but it's perhaps a dated opinion), (encoder-)decoders are for when your output is text (chatbots, summarization, translation), and encoders are for when your output is literally anything else. Embeddings are your toolbox, you can shape them into anything, and encoders are the wonderful providers of these embeddings.