Instructions to use simonmun/COHA1880s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simonmun/COHA1880s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="simonmun/COHA1880s")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("simonmun/COHA1880s") model = AutoModelForMaskedLM.from_pretrained("simonmun/COHA1880s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from simonmun/COHA1880s: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/simonmun/COHA1880s/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://simonmun/COHA1880s@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/simonmun/COHA1880s/resolve/refs%2Fpr%2F1/pytorch_model.bin
334 MB
- Xet hash:
- 3206ba25928d1ec3698d6ab5ec52e6966ff2269c90111b31018bb41791922feb
- Size of remote file:
- 334 MB
- SHA256:
- 668f3b109d14c44cc2c1a115ff357f31b7c4261d8a3cc9b14a1c029000ec57e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.