Instructions to use ken11/bert-japanese-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ken11/bert-japanese-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ken11/bert-japanese-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ken11/bert-japanese-ner") model = AutoModelForTokenClassification.from_pretrained("ken11/bert-japanese-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ken11/bert-japanese-ner: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/ken11/bert-japanese-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ken11/bert-japanese-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ken11/bert-japanese-ner/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 36a8f7dfc99b86f52050200f908fd264a40e2125e169c728c99c92df1f09f464
- Size of remote file:
- 1.34 GB
- SHA256:
- b680319dd738b803d7cb2bc0843dfe23bb65aa5076de7468dbfeaf7615d9b4dd
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