Instructions to use nlpso/m0_flat_ner_ocr_cmbert_io with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpso/m0_flat_ner_ocr_cmbert_io with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nlpso/m0_flat_ner_ocr_cmbert_io")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nlpso/m0_flat_ner_ocr_cmbert_io") model = AutoModelForTokenClassification.from_pretrained("nlpso/m0_flat_ner_ocr_cmbert_io", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nlpso/m0_flat_ner_ocr_cmbert_io: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/nlpso/m0_flat_ner_ocr_cmbert_io/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nlpso/m0_flat_ner_ocr_cmbert_io/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nlpso/m0_flat_ner_ocr_cmbert_io/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 3de03c2139830f8341fb28d0d4353635903019f7846b20c87eecc2e7e96e0405
- Size of remote file:
- 440 MB
- SHA256:
- be4d9fe1800b112be60bbd532d6b178aaa8948952e476dfd70ac3446b774f526
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