Instructions to use NasimB/bert-concat-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-concat-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-concat-3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-concat-3") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-concat-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NasimB/bert-concat-3: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/NasimB/bert-concat-3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NasimB/bert-concat-3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NasimB/bert-concat-3/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 8a8ca015628b78663ff97203e920653e5571703ff623494058ce4016e2fa89b0
- Size of remote file:
- 438 MB
- SHA256:
- 923672b6a62c67993cfdaf36dd0dbb4e8ae119a2df3104e027d1c192c3379f04
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