SpaCy: Industrial-Strength Natural Language Processing (NLP) in Python

by marklit • 11 months ago

135 64 11 months ago

giantg2• 11 months ago

What are the key differences from other NLP Python libraries?

jihadjihad• 11 months ago

Speed (the C in spaCy). A decade ago it was hard to find anything actually production grade for NLP, most packages had an academic bent or were useful for prototyping. SpaCy really changed the game by being able to run performant NLP on standard hardware.

esafak• 11 months ago

nltk was slow.

EagnaIonat• 11 months ago

nltk was never intended for production, it was for built for teaching.

bratao• 11 months ago

I'm really curious about the history of spaCy. From my PoV: it grew a lot during the pandemic era, hiring a lot of employees. I remember something about raising money for the first time. It was very competitive in NLP tasks. Now it seems that it has scaled back considerably, with a dramatic reduction in employees and a total slowdown of the project. The v4 version looks postponed. It isn't competitive in many tasks anymore (for tasks such as NER, I get better results by fine-tuning a BERT model), and the transformer integration is confusing.

binarymax4• 11 months ago

I’ve had success with fine tuning their transformer model. The issue was that there was only one of them per language, compared to huggingface where you have a choice of many of quality variants that best align with your domain and data.

The SpaCy API is just so nice. I love the ease of iterating over sentences, spans, and tokens and having the enrichment right there. Pipelines are super easy, and patterns are fantastic. It’s just a different use case than BERT.

cantdutchthis• 11 months ago

former employee here, Matt wrote a blogpost with pretty much all of the details here: https://honnibal.dev/blog/back-to-our-roots

microtonal• 11 months ago

:wave:

Also: https://explosion.ai/blog/back-to-our-roots-company-update

(Interesting tidbit: I got hired by Explosion after a HN comment on model distillation :))

binarymax4• 11 months ago

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 project and keeping it going all these years.

To the new “AI” people in the room: checkout SpaCy, and see how well it works and how fast it chews through text. You might find yourself in a situation where you don’t need to send your data to OpenAI for some small things.

Edit: I almost forgot to add this little nugget of history: one of Huggingfaces first projects was a SpaCy extension for conference resolution. Built before their breakthrough with transformers https://github.com/huggingface/neuralcoref

jehejej• 11 months ago

*coreference resolution.

ok_dad• 11 months ago

What’s great about the API that you enjoy and do you have anything you hate about it?

I’m writing a small library at work for some NLP tasks and I haven’t got a whole lot of experience in writing libraries for NLP, so I’m interested in what would make my library the best for the user.

binarymax4• 11 months ago

The thing about spaCys API is that it perfectly aligns with how NLP worked at the time with actual programming paradigms and allows you to be very pythonic. For example, you can use list comprehension to get all the nouns from a document in a one liner.

These days NLP is quite different, because we look for outcomes rather than iterating over tokens.

What does your NLP library need to do? The way I design APIs is I write the calling code that I want to exist, and then I write the API to make it work. Here’s an example I’ve worked on for LLM integration. I just wanted to be able to get simple answers from an LLM and cast the answer to a type: https://www.npmjs.com/package/llm-primitives

skeptrune3• 11 months ago

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.

chpatrick2• 11 months ago

Is there any use case for "traditional" NLP in the age of LLMs?

skeptrune3• 11 months ago

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.

binarymax4• 11 months ago

Some low hanging fruit: SpaCy makes an amazing chunking tool for preprocessing text for LLMs.

lyu072823• 11 months ago

I used to work a lot with those pipelines, I think the truth is that LLMs (and LLM embeddings) have surpassed pretty much all traditional NLP. I guess if speed is more important than accuracy? but even then, like with small embedded LLMs they still outperform "traditional NLP" on pretty much every task probably. So it doesn't make a lot of sense to not use it nowadays.

roadside_picnic2• 11 months ago

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 semantic embedding from an LLM.

For NER and similar data extraction tasks, the only advantage of traditional approaches is going to be speed, but my experience in practice is that accuracy is often much more important than speed. Again, I'm not sure why not start with an LLM in these cases.

There are still a few remaining use cases (PoS tagging comes to mind), but honestly, if I have a traditional NLP task today, I'm pretty sure I'm going to start with an LLM as my baseline.

coder6810• 11 months ago

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 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.

noosphr• 11 months ago

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.

meander_water• 11 months ago

Are your categories fixed? If so you could constrain the output using enums in structured outputs.

re: inconsistencies in output, OpenAI provide a seed and system_fingerprint options to (mostly) produce deterministic output.

IanCal5• 11 months ago

It depends on a lot of things but to add to your possible setups you can potentially improve results by using simpler systems for first answers and falling back afterwards.

For example:

If contains cafe and not internet/cyber/etc -> restaurant

No -> (tfidf) -> yes, no, unsure

unsure -> embeddings -> yes, no, unsure

unsure -> llm -> yes, no, unsure

unsure -> human queue ->...

siddheshgunjal2• 11 months ago

At my work, we still prefer to use distilbert for text classification. It almost always does well with a little bit of fine tuning. In very rare cases, we use LLMs/Agentic setup when the task involves refering both images and text and the same time.

leobg2• 11 months ago

If I have 1,000 labeled examples for a classification task, I’ll expand that into a training dataset using augmentation, and then finetune a small model like RoBERTa. It’s fast, cheap, accurate — and predictable.

Others have had success with SetFit as the training framework and Ettin as the base model.

littlekey• 11 months ago

Doesn't that mean having to go back to manually labeling examples? That can be a big hurdle compared to just zero-few shotting some stuff into the LLM prompt. Unless there's something I'm misunderstanding about your approach. Or maybe it's possible to do an unsupervised clustering step on the vectors to get the labeled categories that you can then pass to the supervised classification model. Though I guess that would depend on how strictly defined the target categories are for the use case in question.

potatoman22• 11 months ago

The SpaCy creator has a good blog post on this https://explosion.ai/blog/against-llm-maximalism

btown• 11 months ago

I'd go a step beyond this (excellent) post and posit that one incredibly valuable characteristic of traditional NLP is that it is largely immune to prompt injection attacks.

Especially as LLMs continue to be better tuned to follow instructions that are intentionally colocated and intermingled with data in user messages, it becomes difficult to build systems that can provide real guarantees that "we'll follow your prompt, but not prompts that are in the data you provided."

But no amount of text appended to an input document, no matter how persuasive, can cause an NLP pipeline to change how it interprets the remainder of the document, or to leak its own system instructions, or anything of that nature. "Ignore the above prompt" is just a sentence that doesn't seem like positive or on-topic sentiment to an NLP classifier, and that's it.

There's an even broader discussion to be had about the relative reliability of NLP pipelines, outside of a security perspective. As always, it's important to pick the right tools for the job, and the SpaCy article linked in the parent puts this quite well.

Xmd5a• 11 months ago

https://www.quantamagazine.org/when-chatgpt-broke-an-entire-...

nine_k2• 11 months ago

How about expense? LLMs do dramatically more computations doing simple tasks, and only run on relatively exotic, expensive hardware. You have to trust an LLM provider, and keep paying them.

If a traditional NLP solution can run under your control, and tackle the task at hand, it can be plainly much cheaper at scale.

lyu072823• 11 months ago

thats absurd, there are thousands of open-source LLMs you can run yourself of all shapes and sizes

patrickhogan1• 11 months ago

SpaCy was my go to library for NER before GPT 3+. It was 10x better than regex (though you could also include regex within your pipelines.

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.

renegat0x0• 11 months ago

I use spacy in my raspberry pi project. I am not sure I want to use LLM for analyzing words in it.

joshdavham• 11 months ago

I’ve been using SpaCy for many of my projects for 5 years now. The library has incredible ergonomics and allows you to reuse the same API across languages as different as French and Japanese! I also appreciate that they allow you to install different model sizes (I usually go with small).

robotswantdata• 11 months ago

SpaCy is the OG, nothing but praise for the devs. Built a lot of very powerful legal apps with it pre GPT , very useful today for NER where you want something “small”, fast and reliable.

Used it again recently and the dev experience is 1000x that of wrangling LLMs.

ur-whale• 11 months ago

At the risk of asking a naive question ... why would anyone still do traditional NLP today?

nutjob2• 11 months ago

There is a need for good and easy NLP "structured" interfaces to traditional structured data software. This is a gaping hole in the tech right now. Most other NLP tasks can be handled by ML approaches but this one is a poor fit for those. I'm sure LLM true believers will disagree.

apprentice7• 11 months ago

Could you give a couple specific examples? I'm trying to get into traditional NLP but everything I find is AI related and I don't know if it's worth going the traditional route long-term.

jftuga• 11 months ago

I recently wrote an open source Python module to deidentify people's names and gender specific pronouns. It uses spaCy's Named Entity Recognition (NER) capabilities combined with custom pronoun handling. See the screenshot in the README.md file.

* https://github.com/jftuga/deidentification

* https://pypi.org/project/text-deidentification/

bobosha• 11 months ago

SpaCy is awesome - we have used it in a number of enterprise-grade applications and found it to hold up well.

erikqu• 11 months ago

I figured this project died post-chatgpt, I <3 spacy, learned a ton on this platform back in the day

SpaCy: Industrial-Strength Natural Language Processing (NLP) in Python

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