Instructions to use btk/output_bert_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use btk/output_bert_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="btk/output_bert_uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("btk/output_bert_uncased") model = AutoModelForMaskedLM.from_pretrained("btk/output_bert_uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from btk/output_bert_uncased: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/btk/output_bert_uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://btk/output_bert_uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/btk/output_bert_uncased/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 768a67e6bab74d93195438fd8492bcd8ccaf2187cbbbc99df46ad8e5bda24906
- Size of remote file:
- 440 MB
- SHA256:
- 300ad809d140bbb5206bd32201d55d01864228a52f1036f26d9396f1457b0d97
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.