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 training_args.bin from btk/output_bert_uncased: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
-
https://huggingface.co/btk/output_bert_uncased/resolve/main/training_args.bin
- Command line
-
hf download hf://btk/output_bert_uncased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/btk/output_bert_uncased/resolve/main/training_args.bin
1.04 kB
- Xet hash:
- 99bd66b1765ff798fa20b0a7d78aee31d8afdb01b3553ffa20bb6ff36acf7fd6
- Size of remote file:
- 1.04 kB
- SHA256:
- b0bc019e71b4d58cd6f6acae256187b28e5ae3f9c12f054804f37c1f7ceb38df
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