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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba15f7804272720f6499d875768b4b9d03bb7847b8a4c28251971a4c9cfee6c2
- Size of remote file:
- 2.42 kB
- SHA256:
- 62f0f4ebbdacd9978f9696254fc02cc9a274041d5d53cef7e2469444cfbd2488
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.