Instructions to use tartuNLP/EstBERT_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tartuNLP/EstBERT_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tartuNLP/EstBERT_512")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tartuNLP/EstBERT_512") model = AutoModelForMaskedLM.from_pretrained("tartuNLP/EstBERT_512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from tartuNLP/EstBERT_512: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/tartuNLP/EstBERT_512/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://tartuNLP/EstBERT_512@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/tartuNLP/EstBERT_512/resolve/refs%2Fpr%2F1/flax_model.msgpack
498 MB
- Xet hash:
- e0273cdde1edf19c1494d8c7162f0ed85599656bfa5cdd6ac0efd3edc1a47a39
- Size of remote file:
- 498 MB
- SHA256:
- 2d04bf895bb465a0ff4fc97643f819a1bf251b80c3ac126b21d166d106b833b5
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