Urchade Zaratiana
Urchade Zaratiana
for finetuning ? if your labels are fixed just add them to `label`. If not, negative entities are samples in batch
Hi @SergheiDinu , I am not sure to understand. Could you please state it differently ?
Hi, I think that gliner-spacy (https://github.com/theirstory/gliner-spacy?ref=bramadams.dev) integrate a chunking function Cc @wjbmattingly
I am not sure to understand Is it for training or inference?
Is entity types in the correct format? It should be a list of string Actually I do not suggest setting entity types during training for better generalization
So, you want to fix the label during training, for supervised fine-tuning ? The solution for this is to add the key `"label"` to each training samples (i.e in addition...
It already include in batch negative sampling
The solution is fine-tuning the model on your specialized domain. You can for instance generate synthetic data for that
Even with small data it should work. How many is it exactly ? I have read someone finetuning with 20-30 samples getting strong performance in his domain
The problem is not from the GLiNER side I think