Instructions to use sagipend/exp2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagipend/exp2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sagipend/exp2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sagipend/exp2") model = AutoModelForSequenceClassification.from_pretrained("sagipend/exp2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sagipend/exp2: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/sagipend/exp2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sagipend/exp2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sagipend/exp2/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 9317eadd10d4c9064383c697766f481053bfc4c4b296e4a2425bee131b61282c
- Size of remote file:
- 499 MB
- SHA256:
- 104d9e93733bc2cf7fbbd5a3ca33a4d566c88c8ef841ea937a500e3be12bacb8
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