Instructions to use tartuNLP/EstBERT_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tartuNLP/EstBERT_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tartuNLP/EstBERT_NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tartuNLP/EstBERT_NER") model = AutoModelForTokenClassification.from_pretrained("tartuNLP/EstBERT_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from tartuNLP/EstBERT_NER: direct link, hf CLI and curl.
- Browser
- Download file 495 MB
-
https://huggingface.co/tartuNLP/EstBERT_NER/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://tartuNLP/EstBERT_NER/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/tartuNLP/EstBERT_NER/resolve/main/flax_model.msgpack
495 MB
- Xet hash:
- 58ddc13274a04c11a2cf878678fb2764c91333a1762d54b1b0f0ee98aa46afb4
- Size of remote file:
- 495 MB
- SHA256:
- fd1c850baf305a17a47bc36ec6f7f31161446d9c43afda0c065755663fd22b6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.