Instructions to use Natfike/NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Natfike/NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Natfike/NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Natfike/NER") model = AutoModelForTokenClassification.from_pretrained("Natfike/NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Natfike/NER: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/Natfike/NER/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Natfike/NER/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Natfike/NER/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- b8f31e8aaa4fa7af28d96c5db0e24f2ee622e40868ecc690b00ff1bcb23193e2
- Size of remote file:
- 437 MB
- SHA256:
- b028375ee70d861dfb2c2eb0f3cafdb893c9a20f8a6f74b02f387bcd4b76d669
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