Instructions to use dexay/fNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexay/fNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dexay/fNER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dexay/fNER") model = AutoModelForTokenClassification.from_pretrained("dexay/fNER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dexay/fNER: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/dexay/fNER/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://dexay/fNER@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dexay/fNER/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.45 GB
- Xet hash:
- 6bb3f83c8f6667eae256637e8213dc1f2c1e38838580d9682e9d6afcf0ca657f
- Size of remote file:
- 1.45 GB
- SHA256:
- e106645d364e99253f96016c8636c3e5cea1e12dd334df11b70ba1892fef76af
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