Instructions to use prajjwal1/bert-tiny-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-tiny-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/bert-tiny-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-tiny-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/bert-tiny-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from prajjwal1/bert-tiny-mnli: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/prajjwal1/bert-tiny-mnli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://prajjwal1/bert-tiny-mnli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/prajjwal1/bert-tiny-mnli/resolve/main/flax_model.msgpack
17.5 MB
- Xet hash:
- 8af117a4e40f7ae4de2de99ebdb43cb5ce30535c0d67c3422d47e07a2ee1273b
- Size of remote file:
- 17.5 MB
- SHA256:
- 6acc54f7396e02e5b3221253a7898cac7a4ce6082be5b03558423889f0c878e4
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