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 pytorch_model.bin from NasimB/bert-concat: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/NasimB/bert-concat/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NasimB/bert-concat/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NasimB/bert-concat/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 353084caa73484011eb0e39737bf39240a911e3848fc59fa2d65313c71540010
- Size of remote file:
- 437 MB
- SHA256:
- 093f42208b9b00c54b66ad7404c536c4a11b252330a2209ec10b817166b9ad3c
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