Instructions to use Jinchen/bert-base-cased-wikitext2-test-mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jinchen/bert-base-cased-wikitext2-test-mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jinchen/bert-base-cased-wikitext2-test-mlm")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jinchen/bert-base-cased-wikitext2-test-mlm") model = AutoModelForMaskedLM.from_pretrained("Jinchen/bert-base-cased-wikitext2-test-mlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jinchen/bert-base-cased-wikitext2-test-mlm: direct link, hf CLI and curl.
- Browser
- Download file 217 MB
-
https://huggingface.co/Jinchen/bert-base-cased-wikitext2-test-mlm/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jinchen/bert-base-cased-wikitext2-test-mlm/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jinchen/bert-base-cased-wikitext2-test-mlm/resolve/main/pytorch_model.bin
217 MB
- Xet hash:
- dbab2c3597afab8be4b91c0415cf5bc6d71f259358488ce3cfb25094fa59b9eb
- Size of remote file:
- 217 MB
- SHA256:
- a32969031ce0c7e9cf4f2551de3e92caca477fd1e4244f06ed3a8fe8878e661e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.