I am using spacy for NER in multiple languages. Where can I find a list of all supported named entity labels supported in spacy ner models? Can't find it in the docs. More on github.com

One option is Stanford NER, which is a named entity recognition tool developed by Stanford University. It uses a CRF (conditional random field) model trained on a large dataset of named entities, and it's relatively fast and accurate. Stanford NER also has pre-trained models available for various languages, so you could use one of these models or train your own model on a custom dataset. Another option is spaCy... More on reddit.com

Hi, NER is basically a token level text classification problem, which can be considered to be similar to semantic segmentation in vision tasks, which is pixel level classification. To prepare the dataset, first you need to have a fixed number of labels, like any other classification problem, and each word should be labelled (an label for all words doesn't have an entity). Please ensure no words are left unlabelled. Once you have this dataset, you can try these, based on your dataset aswell: as mentioned in other comments, few-shot learning with LLMs using spacy custom NER model ( Ref: https://medium.com/@mjghadge9007/building-your-own-custom-named-entity-recognition-ner-model-with-spacy-v3-a-step-by-step-guide-15c7dcb1c416 ) BERT token level classifier (Ref: https://huggingface.co/docs/transformers/en/tasks/token_classification ) An RNN or LSTM classifier with some dense embedded features (glove, word2vec etc), and a prediction layer at each time step after the stack of (if multi-layer) RNNs I would suggest you try the 4th one only if you have enough time, otherwise invest more on preparing a good enough custom dataset and work on any of the first 3. More on reddit.com

You can try our zero-shot and few-shot NER library which can use GPT to perform predictions. https://github.com/plncmm/llmner More on reddit.com