Instructions to use BayesTensor/modernbert_seeker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BayesTensor/modernbert_seeker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BayesTensor/modernbert_seeker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BayesTensor/modernbert_seeker") model = AutoModelForSequenceClassification.from_pretrained("BayesTensor/modernbert_seeker") - Notebooks
- Google Colab
- Kaggle
Rename last-checkpoint/scheduler.pt to scheduler.pt
Browse files
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optimizer.pt filter=lfs diff=lfs merge=lfs -text
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rng_state.pth filter=lfs diff=lfs merge=lfs -text
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training_args.bin filter=lfs diff=lfs merge=lfs -text
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optimizer.pt filter=lfs diff=lfs merge=lfs -text
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rng_state.pth filter=lfs diff=lfs merge=lfs -text
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training_args.bin filter=lfs diff=lfs merge=lfs -text
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scheduler.pt filter=lfs diff=lfs merge=lfs -text
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last-checkpoint/scheduler.pt → scheduler.pt
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