Instructions to use maximuspowers/tinybert_unscrambler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maximuspowers/tinybert_unscrambler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maximuspowers/tinybert_unscrambler")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maximuspowers/tinybert_unscrambler") model = AutoModelForSequenceClassification.from_pretrained("maximuspowers/tinybert_unscrambler", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from maximuspowers/tinybert_unscrambler: direct link, hf CLI and curl.
- Browser
- Download file 57.4 MB
-
https://huggingface.co/maximuspowers/tinybert_unscrambler/resolve/main/model.safetensors
- Command line
-
hf download hf://maximuspowers/tinybert_unscrambler/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/maximuspowers/tinybert_unscrambler/resolve/main/model.safetensors
57.4 MB
- Xet hash:
- b253fbc281398e4e89dd6b1b5a57760073e108718685f54a8f21fb0558452571
- Size of remote file:
- 57.4 MB
- SHA256:
- a2050ed7de09ea7c64c6bbc781d452033e2667ce681133cd5af1a57a5a722489
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.