Instructions to use NasimB/bert-dp-second with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-dp-second with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-dp-second")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-dp-second") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-dp-second", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NasimB/bert-dp-second: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/NasimB/bert-dp-second/resolve/main/training_args.bin
- Command line
-
hf download hf://NasimB/bert-dp-second/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/NasimB/bert-dp-second/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 98ba32b649641e9ae0c432a83ca0aa087108e8684dabbfd2ba7f86cb4b86208e
- Size of remote file:
- 3.44 kB
- SHA256:
- ea4bc0ea3fe449ecab9be67ac09a3c8a5fbce1aa5028f5e2060c9ebad1bdbdfe
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