Instructions to use nkn002/bert_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nkn002/bert_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nkn002/bert_extractor")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nkn002/bert_extractor") model = AutoModelForTokenClassification.from_pretrained("nkn002/bert_extractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nkn002/bert_extractor: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/nkn002/bert_extractor/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nkn002/bert_extractor/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nkn002/bert_extractor/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 2936f0fa2525988e6337f4b55b756ae577e9037914ef4c4ef2fae5d8ea604d48
- Size of remote file:
- 436 MB
- SHA256:
- 391f766e3cbda9bda6d7a2cd7255def74a7d9cbb335cd82b5f7ea229def6c112
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