Instructions to use tartuNLP/EstBERT_NER_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tartuNLP/EstBERT_NER_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tartuNLP/EstBERT_NER_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tartuNLP/EstBERT_NER_v2") model = AutoModelForTokenClassification.from_pretrained("tartuNLP/EstBERT_NER_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d06020a7a801c5115d4b7cf7f11e4a8726d5eefbc531d2725205416031e76754
- Size of remote file:
- 496 MB
- SHA256:
- 1efa9376aaf96a78946c6b5ca5d66cc6c237f0cebccf75182536b989f8babc5d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.