Instructions to use NasimB/bert-truncate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-truncate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-truncate")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-truncate") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-truncate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NasimB/bert-truncate: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/NasimB/bert-truncate/resolve/main/training_args.bin
- Command line
-
hf download hf://NasimB/bert-truncate/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NasimB/bert-truncate/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 31d3c854f15c803cb3ddf342b1cda5195fa23c179a1ea05041414c43b92e4536
- Size of remote file:
- 3.44 kB
- SHA256:
- f752b39b25224df7990a7df8f0e03c991335dc0f0328e3931bc859d683b364bb
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