Instructions to use nlpso/m0_flat_ner_ocr_ptrn_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_ptrn_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_ptrn_cmbert_io")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nlpso/m0_flat_ner_ocr_ptrn_cmbert_io") model = AutoModelForTokenClassification.from_pretrained("nlpso/m0_flat_ner_ocr_ptrn_cmbert_io", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from nlpso/m0_flat_ner_ocr_ptrn_cmbert_io: direct link, hf CLI and curl.
- Browser
- Download file 880 MB
-
https://huggingface.co/nlpso/m0_flat_ner_ocr_ptrn_cmbert_io/resolve/main/optimizer.pt
- Command line
-
hf download hf://nlpso/m0_flat_ner_ocr_ptrn_cmbert_io/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/nlpso/m0_flat_ner_ocr_ptrn_cmbert_io/resolve/main/optimizer.pt
880 MB
- Xet hash:
- 0535ae9add294f039c74d15b7d6a5eeb44da479e825816a199d194c74294e5db
- Size of remote file:
- 880 MB
- SHA256:
- 79a02b8e51543c87476b4eec6bde1416e69cc3a325c6b9a92157288b4911bcff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.