Instructions to use liamcripwell/sle-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liamcripwell/sle-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liamcripwell/sle-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liamcripwell/sle-base") model = AutoModelForSequenceClassification.from_pretrained("liamcripwell/sle-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from liamcripwell/sle-base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/liamcripwell/sle-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://liamcripwell/sle-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/liamcripwell/sle-base/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 3636f0d42cd360baf4f8c59497e74e7b598e47c0d68c2891a1769228d5de9d3f
- Size of remote file:
- 499 MB
- SHA256:
- bba7c76a7b80ad18c61f648a7378b070a81e13257c4246eabf2e191fbb345d80
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.