Instructions to use covalenthq/cryptoNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use covalenthq/cryptoNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="covalenthq/cryptoNER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("covalenthq/cryptoNER") model = AutoModelForTokenClassification.from_pretrained("covalenthq/cryptoNER") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 195c8b86ab7ef39eea80ddcfcdc46ce1bcdb467d4cb761156d4f07938baa5edc
- Size of remote file:
- 17.1 MB
- SHA256:
- 003498ab0695fbf35aedc8bbbae1e20a6033a1b3914e49c6d2fbe1d54d7d93b9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.