Instructions to use NasimB/bert-concat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-concat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-concat")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-concat") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-concat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NasimB/bert-concat: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/NasimB/bert-concat/resolve/main/training_args.bin
- Command line
-
hf download hf://NasimB/bert-concat/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NasimB/bert-concat/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 90fe65acd5a6a6bfd87a893886c3767414c959f3ea22107fafe0380b5e23af23
- Size of remote file:
- 3.44 kB
- SHA256:
- adcdd326024558e1a12d5d9c42e309250db8ceda89b820a30ee0f44ea8c9a26e
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