NERmembert
Find named entities in French texts using NERmemBERT models
CamemBERT models finetuned on the NER task (3 or 4 entities) + the datasets used (420,000 or 385,000 rows respectively) + a Space demo
Find named entities in French texts using NERmemBERT models
Note Blog posts about this model: EN: https://blog.vaniila.ai/en/NER_en/ FR: https://blog.vaniila.ai/NER/
Note Blog posts about this model: EN: https://blog.vaniila.ai/en/NER_en/ FR: https://blog.vaniila.ai/NER/
Note Blog posts about this model: EN: https://blog.vaniila.ai/en/NER_en/ FR: https://blog.vaniila.ai/NER/
Note Blog posts about this model: EN: https://blog.vaniila.ai/en/NER_en/ FR: https://blog.vaniila.ai/NER/
Note French NER dataset containing around 420,000 lines for 3 entities (PER, LOC, ORG). This dataset was used to train CATIE's NERmembert-base-3entities.
Note Blog posts about this model: EN: https://blog.vaniila.ai/en/NER_en/ FR: https://blog.vaniila.ai/NER/
Note Blog posts about this model: EN: https://blog.vaniila.ai/en/NER_en/ FR: https://blog.vaniila.ai/NER/
Note Blog posts about this model: EN: https://blog.vaniila.ai/en/NER_en/ FR: https://blog.vaniila.ai/NER/
Note Blog posts about this model: EN: https://blog.vaniila.ai/en/NER_en/ FR: https://blog.vaniila.ai/NER/
Note French NER dataset containing around 385,000 lines for 4 entities (PER, LOC, ORG, MISC). This dataset was used to train CATIE's NERmembert-base-4entities and NERmembert-large-4entities.