Instructions to use QCRI/bert-base-cased-sem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QCRI/bert-base-cased-sem with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="QCRI/bert-base-cased-sem")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("QCRI/bert-base-cased-sem") model = AutoModelForTokenClassification.from_pretrained("QCRI/bert-base-cased-sem") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("QCRI/bert-base-cased-sem")
model = AutoModelForTokenClassification.from_pretrained("QCRI/bert-base-cased-sem")Quick Links
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="QCRI/bert-base-cased-sem")