Instructions to use textattack/bert-base-uncased-QNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-QNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-QNLI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-QNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-QNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from textattack/bert-base-uncased-QNLI: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-QNLI/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://textattack/bert-base-uncased-QNLI/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/textattack/bert-base-uncased-QNLI/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- c6728a26cb3dcad7ea719bb5d34ff54f022800942048eee9a437dbea32181e42
- Size of remote file:
- 438 MB
- SHA256:
- f717840ecf7eaadb924e2789c796875c837a45a543996fe48b19548ca56b70d5
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