Best way to do Named Entity Recognition in 2024 with GliNER and spaCy - Zero Shot NER

It’s amazing

gliner-spacy is awesome 💯

Thanks for the video. One question, Is it possible to make a few shot in addition to zero shot with GliNER (without finetuning)

Really cool! Can you make a video on how to further train the LatinCy model? I have a ton of additions to the lemma fixer custom component and I've noticed a few recurring patterns I want to fix generally

Great explanation! Can we use gliner to extract medicinal plants scientific name and their medicinal effects?

Whether NER cannot be achieved using prompt Engg + LLM...? Can you educate on this..

Great video! What would you do to extract hard skills and soft skills from a resume and job description?

I am thinking entity rulers from spacy and match it but I was wondering what you were thinking. Thanks!

is there anything like this, but for text classification? e.g.: I have a list of labels (topics) and a list of texts. And it has to tell me what topics are mentioned in which text

I had better results on GLiNER then on OpenAI 3.5 on zero-shot. A lot of False Positive. But at least we have what to filter later, and good is that it works very fast on low CPU needs. Still waiting for few-shot learning example, sure it will help a lot. Anyone tested domain-knowledge way of doing staff?

Hi, great video!

You've mentioned "... if you don't have training data". I am assuming that you mean that annotated data is not required, and instead the model relies on unsupervised approach.

If this is correct, than for specialized texts it must rely on embedding training?


Thanks!

Best video as always. Thanks!

this is very cool! Whats the benefit of using gliner-spacy over just using gliner by itsself?

im starting to use Space for entity extraction from the content of my competitors on the serp for a keyword (I work on SEO) but the entities that extracts are very very weird, leaving behind some more important (I use it in Spanish). Gliner might help?

does it work on live streaming call?

Regarding your example with Auschwitz: how exactly did it learn that Auschwitz belongs to concentration_camp type? Is it because your example sentence happened to say exactly that or is that just a coincidence?

Are there any resources for finetuning GLiNER? The repo for GLiNER is giving me bugs when I attempt to finetune

Great solution! Thank you!

Can we use those as a backend model for flutter app?

Hi thanks for the informative video! Let's say, like in your book, you had a list of concentration camps that you wanted to feed to the model to improve its accuracy. How would do that? Or would you not do it and just use a more conventional spaCy pipeline?

Great video. The only problem is that gliner is not easy to implement in production such as in a remote server or a huggingface endpoint. Has anyone able to make this work?

Didn't work for me until I added the following four lines of code at the top (below other imports):

from huggingface_hub import login
login(token="hf_INSERT_YOUR_TOKEN_HERE")# replace with your token
import os
os.environ['HF_HUB_DISABLE_SYMLINKS'] = 'true'# Disable symlinks for Hugging Face caching

as I didn't already have "urchade/gliner_base" downloaded on my machine and huggingface wont download without login, for me at least. and the terminal in VSCode in not administrator. Hopefully this helps someone at least get it working.

From all examples you could pick, you come up with this .. ?

excellent! appreciate the simple, intuitive wrapper. the chunk_size config was clutch.

Thanks for the solution :)

Make your videos less bright. Shining like the sun over here with dark mode on.

thank you for being honest that this is not enough this is just a start point

A dumb question, this model works only for english texts, right?