Instructions to use prajjwal1/bert-small-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-small-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/bert-small-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-small-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/bert-small-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from prajjwal1/bert-small-mnli: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/prajjwal1/bert-small-mnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://prajjwal1/bert-small-mnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/prajjwal1/bert-small-mnli/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 4f6a774ad40fe8032d3fad5b4fa02d6d1ff97d0b886dcb0631e43b6e315cb14f
- Size of remote file:
- 115 MB
- SHA256:
- eacd6a2f9c930270ad52317a8789cdb64071a9b53383478177dc27b4385f0161
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