Instructions to use Mahmoud8/squeezebert-squeezebert-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud8/squeezebert-squeezebert-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud8/squeezebert-squeezebert-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/squeezebert-squeezebert-uncased") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/squeezebert-squeezebert-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mahmoud8/squeezebert-squeezebert-uncased: direct link, hf CLI and curl.
- Browser
- Download file 204 MB
-
https://huggingface.co/Mahmoud8/squeezebert-squeezebert-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mahmoud8/squeezebert-squeezebert-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mahmoud8/squeezebert-squeezebert-uncased/resolve/main/pytorch_model.bin
204 MB
- Xet hash:
- e27f8cec2fd391fb124910ffd0cfa6a7a0f7ff132fa86fde57f646f6cb34997f
- Size of remote file:
- 204 MB
- SHA256:
- 2705f78ee33b2e69aa31c4ee9321cdfec6cdd657447fe7976b7c713aabfcb46d
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