Instructions to use simonmun/COHA1970s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simonmun/COHA1970s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="simonmun/COHA1970s")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("simonmun/COHA1970s") model = AutoModelForMaskedLM.from_pretrained("simonmun/COHA1970s", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from simonmun/COHA1970s: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/simonmun/COHA1970s/resolve/main/training_args.bin
- Command line
-
hf download hf://simonmun/COHA1970s/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/simonmun/COHA1970s/resolve/main/training_args.bin
1.78 kB
- Xet hash:
- 03aa525503b2d1c868a40686293c2a440ca2dda44e7d6e59d48548f6f4feb09a
- Size of remote file:
- 1.78 kB
- SHA256:
- 0f99c3540093d86269f2b105b31a5d71d357da90d21d442e16dbb4c9e1acf1d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.