Instructions to use simonmun/COHA1840s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simonmun/COHA1840s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="simonmun/COHA1840s")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("simonmun/COHA1840s") model = AutoModelForMaskedLM.from_pretrained("simonmun/COHA1840s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from simonmun/COHA1840s: direct link, hf CLI and curl.
- Browser
- Download file 1.18 kB
-
https://huggingface.co/simonmun/COHA1840s/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://simonmun/COHA1840s@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/simonmun/COHA1840s/resolve/refs%2Fpr%2F1/training_args.bin
1.18 kB
- Xet hash:
- 8b24f1a97e21943ada13f0469757a27b1e175c3789edd2a89d652259cc945c08
- Size of remote file:
- 1.18 kB
- SHA256:
- 7a9002d60279cd5f20d3e5976f3288dad63aa925eb4beb5222cef54215660868
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.