Instructions to use NasimB/bert-dp-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-dp-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-dp-4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-dp-4") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-dp-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NasimB/bert-dp-4: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/NasimB/bert-dp-4/resolve/main/training_args.bin
- Command line
-
hf download hf://NasimB/bert-dp-4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NasimB/bert-dp-4/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 9179b0ee1724ebd1596a58e1de04ed9a2846e656a1e1132c8c625b60e468efdc
- Size of remote file:
- 3.44 kB
- SHA256:
- 85961ffeecf40d32cfed2ca80efdd22e4f9ebeefb37480dfa9b6da874418101d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.