Instructions to use Jeevesh8/multiberts_seed_6_ft_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/multiberts_seed_6_ft_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/multiberts_seed_6_ft_4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/multiberts_seed_6_ft_4") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/multiberts_seed_6_ft_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jeevesh8/multiberts_seed_6_ft_4: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Jeevesh8/multiberts_seed_6_ft_4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jeevesh8/multiberts_seed_6_ft_4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jeevesh8/multiberts_seed_6_ft_4/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 24feef68fd9f2e672c7da2dced60b0e1c5016628f1dc8c20ed9b3f96400aef9b
- Size of remote file:
- 438 MB
- SHA256:
- faf23b2da29e725ef796012cadcaaef85bfee5d7c21cc7fee8687b91ea6a01fe
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