Instructions to use prajjwal1/roberta-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/roberta-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/roberta-base-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/roberta-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/roberta-base-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from prajjwal1/roberta-base-mnli: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/prajjwal1/roberta-base-mnli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://prajjwal1/roberta-base-mnli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/prajjwal1/roberta-base-mnli/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 8c65629db531b7853778582780c19c1acec61f3ec95a8f53acd0f60dfee56248
- Size of remote file:
- 499 MB
- SHA256:
- a15d1a1c3c9eaccdf25412c8f02955163dc72011b6fcd6b61ca89b56970f8ee7
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