Instructions to use BayesTensor/modernbert_seeker1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BayesTensor/modernbert_seeker1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BayesTensor/modernbert_seeker1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BayesTensor/modernbert_seeker1") model = AutoModelForSequenceClassification.from_pretrained("BayesTensor/modernbert_seeker1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BayesTensor/modernbert_seeker1: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/BayesTensor/modernbert_seeker1/resolve/main/training_args.bin
- Command line
-
hf download hf://BayesTensor/modernbert_seeker1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BayesTensor/modernbert_seeker1/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 9aedd58e3500d6a7d32bff8d522d99943ddddc561249a509b6231ee03722dab8
- Size of remote file:
- 5.43 kB
- SHA256:
- b1894affb275710623fc2571745c2dd34c9d362334bbf4e73ae7ff2ecc2cd7c5
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