Instructions to use NasimB/bert-concat-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-concat-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-concat-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-concat-2") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-concat-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NasimB/bert-concat-2: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/NasimB/bert-concat-2/resolve/main/training_args.bin
- Command line
-
hf download hf://NasimB/bert-concat-2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NasimB/bert-concat-2/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- de383959d7e2c6f43a254e1dff771b5b4fb6ba10c2385908207a81db476ba8d4
- Size of remote file:
- 3.44 kB
- SHA256:
- 175cac50924175428057cabf21decd3121958ddd51bf4c4d462ad16c9314609b
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