Instructions to use lingkai/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lingkai/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lingkai/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lingkai/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("lingkai/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lingkai/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/lingkai/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://lingkai/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lingkai/bert-finetuned-ner/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- f1cc074645b62a0f052bedafba70aa6a67e7b7d8f5fe16b6a36c1dc25d1d2c29
- Size of remote file:
- 5.2 kB
- SHA256:
- ca84ab8e07e7ac78ff0e87cc8bbe330c801527ad4fabc0b9504de8d14aacf97f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.