Instructions to use ML-GOD/deberta-finetuned-ner-microsoft-disaster-cleaned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ML-GOD/deberta-finetuned-ner-microsoft-disaster-cleaned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ML-GOD/deberta-finetuned-ner-microsoft-disaster-cleaned")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ML-GOD/deberta-finetuned-ner-microsoft-disaster-cleaned") model = AutoModelForTokenClassification.from_pretrained("ML-GOD/deberta-finetuned-ner-microsoft-disaster-cleaned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 263518efaf4978b3156c2739553b138bbb6ad50424b500877c00a3f2c4bf2ac0
- Size of remote file:
- 555 MB
- SHA256:
- d753f250bb8afb8ee5f36358f6e6fb108653bcd679de28d5aba55a07a52b762b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.