Instructions to use EffyLi/bert-base-uncased-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EffyLi/bert-base-uncased-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EffyLi/bert-base-uncased-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EffyLi/bert-base-uncased-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("EffyLi/bert-base-uncased-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EffyLi/bert-base-uncased-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/EffyLi/bert-base-uncased-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EffyLi/bert-base-uncased-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EffyLi/bert-base-uncased-finetuned-ner/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 0f02947cbb62477190406349d782891bc63b21c3da4ec19a8ea2397793dee8ff
- Size of remote file:
- 436 MB
- SHA256:
- 41918bf0935a6adbfb2f1753f1e5106809da9067a085184d32772a334970d286
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