#### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Holako/NER_model_holako") model = AutoModelForTokenClassification.from_pretrained("Holako/NER_model_holako") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "اسمي احمد" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ======= #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data Language|Dataset -|- Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)