Instructions to use prajjwal1/bert-medium-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-medium-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/bert-medium-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-medium-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/bert-medium-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from prajjwal1/bert-medium-mnli: direct link, hf CLI and curl.
- Browser
- Download file 166 MB
-
https://huggingface.co/prajjwal1/bert-medium-mnli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://prajjwal1/bert-medium-mnli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/prajjwal1/bert-medium-mnli/resolve/main/flax_model.msgpack
166 MB
- Xet hash:
- 9aaaaa14bf9cecc0383d00ff1a463f5ddc780fa522f7f213d0edc9221c28938a
- Size of remote file:
- 166 MB
- SHA256:
- 477e5d2529c4a8264cc55fbaff8c316cb733afc5c16fae5de899d7cde203d0e8
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