Instructions to use Jeevesh8/multiberts_seed_0_ft_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/multiberts_seed_0_ft_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/multiberts_seed_0_ft_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/multiberts_seed_0_ft_2") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/multiberts_seed_0_ft_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jeevesh8/multiberts_seed_0_ft_2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Jeevesh8/multiberts_seed_0_ft_2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jeevesh8/multiberts_seed_0_ft_2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jeevesh8/multiberts_seed_0_ft_2/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- b08fb8073b10f887d55cce0dcac6aeeee3ef67a3b930cabdd305973e27f135cb
- Size of remote file:
- 438 MB
- SHA256:
- 56604e3a6e0c09563ce21c056e0eb9a06219c01bc01e9e08c2a81635ddf91723
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.