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 flax_model.msgpack from btk/output_bert_uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/btk/output_bert_uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://btk/output_bert_uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/btk/output_bert_uncased/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- ed9512d112466587e6d8d732b5bf08815612eaee8ee55c187080a11075a48ddd
- Size of remote file:
- 438 MB
- SHA256:
- 1336d325e821364c0ebb56813f7dcc1574e75f4c74ad59e79a378126a84e47c3
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