Instructions to use slawguy/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slawguy/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="slawguy/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("slawguy/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("slawguy/bert-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b03adcae71f0b37126ef6a3f7f4a43ba6a63fae1d0f898f6ed21e01a1b4d655
- Size of remote file:
- 5.27 kB
- SHA256:
- 6b4929030716fdd1eda57963485a7b7916266cf5e1ae976acacf94cd399476c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.