Instructions to use Jmolano/bert-finetuned-ner-accelerate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jmolano/bert-finetuned-ner-accelerate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jmolano/bert-finetuned-ner-accelerate")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jmolano/bert-finetuned-ner-accelerate") model = AutoModelForTokenClassification.from_pretrained("Jmolano/bert-finetuned-ner-accelerate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e648e32924955cba500c0c31063f20c6d9ad259066c38dd1690d04d16d04ae72
- Size of remote file:
- 431 MB
- SHA256:
- 56393d2a722a20dbb66307d066ddfce47c5407dc87578d44953cddd01f9ba7c7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.