11–20 of 67 posts
SpaCy is criminally underrated. I expect to see it experience a new wave of growth as folks new to AI start to realize all of the language tooling they need to build more reliable "traditional" ML pipelines. API surface is designed well and it's still actively maintained almost 10 years after it initially went public.
Is there any use case for "traditional" NLP in the age of LLMs?
Its annotation tooling was so far ahead. It is still crazy to me that so much of the value in the data annotation space went to Scale AI vs tools like SpaCy that enabled annotation at scale in the enterprise.
What are the key differences from other NLP Python libraries?
A friend, who also has a background in NLP, was asking me the other day "Is there still even a need for traditional NLP in the age of LLMs?" This is one of the under-discussed areas of LLMs imho. For anything that would have have required either word2vec embeddings of a tf-idf representation (classification tasks, sentiment analysis, etc) there are rare exceptions where it wouldn't just be better to start with a sema…
I have stopped trying to use LLMs for this project and switched to discriminative models (Logistic Regression with TFIDF or Embeddings), which are both more computationally efficient and more debuggable. I'm not entirely sure why, but for anything with many possible answers, or to which there is some subjectivity, I have not had success with LLMs simply due to inconsistency of responses.
For VERY obvious tasks like: "is this store a restaurant or not?" I have definitely had success, so YMMV.
Earlier quoted context omitted.
Is there any use case for "traditional" NLP in the age of LLMs?
Most definitely! LLMs are amazing tools for generating synthetic datasets that can be used alongside traditional NLP to train things like decision trees with libraries like cat/xgboost. I have a search background so learning to rank is always top of mind for me, but there other places like sentiment analysis, intent detection, and topic classification where it's great too.
I’ve been a user of SpaCy since 2016. I haven’t touched it in years and I just picked it up again to develop a new metric for RAG using part of speech coverage. The API is one of the best ever, and really set the bar high for language tooling. I’m glad it’s still around and getting updates. I had a bit of trouble integrating it with uv, but nothing too bad. Thanks to the explosion team for making such an amazing proj…
What are the key differences from other NLP Python libraries?
A friend, who also has a background in NLP, was asking me the other day "Is there still even a need for traditional NLP in the age of LLMs?" This is one of the under-discussed areas of LLMs imho. For anything that would have have required either word2vec embeddings of a tf-idf representation (classification tasks, sentiment analysis, etc) there are rare exceptions where it wouldn't just be better to start with a sema…
I have been working on text classification tasks at work, and I have found that for my particular use-case, LLMs are not performing well at all. I have spent a few thousand dollars trying, and I have tried everything from few-shot to asking simple binary yes/no questions, and I have had mixed success. I have stopped trying to use LLMs for this project and switched to discriminative models (Logistic Regression with TF…
I've found encoder only models to be vastly better for anything that doesn't require natural language responses and the majority of them are small enough that _pretraining_ a model for each task costs a few hundred dollars.
A friend, who also has a background in NLP, was asking me the other day "Is there still even a need for traditional NLP in the age of LLMs?" This is one of the under-discussed areas of LLMs imho. For anything that would have have required either word2vec embeddings of a tf-idf representation (classification tasks, sentiment analysis, etc) there are rare exceptions where it wouldn't just be better to start with a sema…
Earlier quoted context omitted.
I have been working on text classification tasks at work, and I have found that for my particular use-case, LLMs are not performing well at all. I have spent a few thousand dollars trying, and I have tried everything from few-shot to asking simple binary yes/no questions, and I have had mixed success. I have stopped trying to use LLMs for this project and switched to discriminative models (Logistic Regression with TF…
When you say llms do you mean decoder only models, gpt et al, or encoder only models, bert et al? I've found encoder only models to be vastly better for anything that doesn't require natural language responses and the majority of them are small enough that _pretraining_ a model for each task costs a few hundred dollars.