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:
- bb93672e492704cbcda4544e2a5a0ba38e74637e2e25fdd34a8a7b4bb5b06270
- Size of remote file:
- 1.11 GB
- SHA256:
- eb0199204864bfbf2afb23b12d566328fcff0b3ca07b369d101f15bd12d91c6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.