Instructions to use argv947059/example-based-ner-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use argv947059/example-based-ner-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="argv947059/example-based-ner-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("argv947059/example-based-ner-bert") model = AutoModel.from_pretrained("argv947059/example-based-ner-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from argv947059/example-based-ner-bert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/argv947059/example-based-ner-bert/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://argv947059/example-based-ner-bert@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/argv947059/example-based-ner-bert/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- c77ce5775203fde5f55a565dce86a77f20b2471287d623e4f6b808b0f380d67a
- Size of remote file:
- 438 MB
- SHA256:
- cf69ac3c23de8285d2d25a838090a4201d1c7a63acdf2f3f29b5f3ea867a1a9c
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