Instructions to use S2312dal/M1_cross with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use S2312dal/M1_cross with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="S2312dal/M1_cross")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("S2312dal/M1_cross") model = AutoModelForSequenceClassification.from_pretrained("S2312dal/M1_cross", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from S2312dal/M1_cross: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/S2312dal/M1_cross/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://S2312dal/M1_cross/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/S2312dal/M1_cross/resolve/main/pytorch_model.bin
46.8 MB
- Xet hash:
- 91eb1d17d40a3e726b5c9ff994ec6017258beacc90b156941147d3183a6b50f1
- Size of remote file:
- 46.8 MB
- SHA256:
- 9655da5fe70289239553e3d0805b0a501b1815663eeb5837102d1e32e38337f4
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