Instructions to use CAMeL-Lab/bert-base-arabic-camelbert-mix-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAMeL-Lab/bert-base-arabic-camelbert-mix-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CAMeL-Lab/bert-base-arabic-camelbert-mix-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-mix-ner") model = AutoModelForTokenClassification.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-mix-ner") - Inference
- Notebooks
- Google Colab
- Kaggle
Problem: Result from the NER prediction
from camel_tools.ner import NERecognizer
s="المزمنة الأمراض"
print(s)
from transformers import pipeline
ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-ner')
prediction=ner(s)
the result of the prediction is []
Can anyone help?
from camel_tools.ner import NERecognizer
s="المزمنة الأمراض"
print(s)
from transformers import pipeline
ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-ner')
prediction=ner(s)the result of the prediction is []
Can anyone help?
This is very late, but I don't think there are any named entities in the text. The text literally says "chronic diseases". There are no named entities here (e.g. people, organizations, places, etc).