Instructions to use erfangc/test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erfangc/test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="erfangc/test1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("erfangc/test1") model = AutoModelForSequenceClassification.from_pretrained("erfangc/test1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from erfangc/test1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/erfangc/test1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://erfangc/test1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/erfangc/test1/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 554467a4e10f1d256a5ab9df4434a96efdc9ea8fe1f4f6ae70946212b1cc2c62
- Size of remote file:
- 438 MB
- SHA256:
- d9f0e81d5b271f3300141d949d41cfd3c4140bdf43deed8c02a1917714502bc5
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