Instructions to use Sayan01/tiny-bert-qnli-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/tiny-bert-qnli-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/tiny-bert-qnli-distilled")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/tiny-bert-qnli-distilled") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/tiny-bert-qnli-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sayan01/tiny-bert-qnli-distilled: direct link, hf CLI and curl.
- Browser
- Download file 57.4 MB
-
https://huggingface.co/Sayan01/tiny-bert-qnli-distilled/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sayan01/tiny-bert-qnli-distilled/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sayan01/tiny-bert-qnli-distilled/resolve/main/pytorch_model.bin
57.4 MB
- Xet hash:
- 2bcf327e80b7318020ab8b50520f903863bfd3c10f4f9122ac7b0d782f2cbb8f
- Size of remote file:
- 57.4 MB
- SHA256:
- 6c9a542683ee69a8038afaa01f67e855787282dc4f01164427f55aeb983fb3ef
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