Instructions to use QCRI/bert-base-cased-chunking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QCRI/bert-base-cased-chunking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="QCRI/bert-base-cased-chunking")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("QCRI/bert-base-cased-chunking") model = AutoModelForTokenClassification.from_pretrained("QCRI/bert-base-cased-chunking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5320d43efb2e6a2024fcf437b1c1c12cad5f10c5954278bbe811b7de0576c709
- Size of remote file:
- 431 MB
- SHA256:
- 35e8d6c5f8e92dce15138c4a9fcf4d86b211358067bd29a69559f4b66aeb39db
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