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 training_args.bin from nlpso/m0_flat_ner_ocr_cmbert_io: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/nlpso/m0_flat_ner_ocr_cmbert_io/resolve/main/training_args.bin
- Command line
-
hf download hf://nlpso/m0_flat_ner_ocr_cmbert_io/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nlpso/m0_flat_ner_ocr_cmbert_io/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- 0205e35e5d8880173e3f95d8c7b03eb833cb56234a9693c05deacefd534a7921
- Size of remote file:
- 3.5 kB
- SHA256:
- d8decc10feab607704e246b8336e35d634cf0dc1065defecb21111669a5ae243
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