Instructions to use BEE-spoke-data/bert-plus-L8-4096-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BEE-spoke-data/bert-plus-L8-4096-v1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BEE-spoke-data/bert-plus-L8-4096-v1.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/bert-plus-L8-4096-v1.0") model = AutoModelForMaskedLM.from_pretrained("BEE-spoke-data/bert-plus-L8-4096-v1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BEE-spoke-data/bert-plus-L8-4096-v1.0: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/BEE-spoke-data/bert-plus-L8-4096-v1.0/resolve/main/training_args.bin
- Command line
-
hf download hf://BEE-spoke-data/bert-plus-L8-4096-v1.0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BEE-spoke-data/bert-plus-L8-4096-v1.0/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 1ff6810f8dcfdf8f7ae902e6042bf07267f386510b68f08fa2c818ac9f02f64a
- Size of remote file:
- 4.92 kB
- SHA256:
- 7cd1e46c219da670f4ca325cabcaa68d77a6ac557f068ec90ffc6e6e23d7af29
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