m3hrdadfi/albert-fa-base-v2-ner

The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects . It was trained over 3.9M documents, 73M sentences, and 1.3B words . The task aims to extract named entities in the text, such as names and labels with appropriate NER classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with IOB format . In this format, tokens that are not part of an entity are tagged as ”O” The ”B”tag corresponds to the first word of an object, and the ”I” tag corresponds to rest of the,