Fill-Mask
Transformers
Safetensors
PyTorch
modernbert
entity-infilling
text-summarization
masked-modeling
Eval Results (legacy)
Instructions to use Glazkov/sum-entity-infilling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Glazkov/sum-entity-infilling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Glazkov/sum-entity-infilling")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Glazkov/sum-entity-infilling") model = AutoModelForMaskedLM.from_pretrained("Glazkov/sum-entity-infilling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload ModernBERT entity infilling model - 2025-10-17 09:42:43
Browse files- metadata.json +15 -0
metadata.json
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{
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"model_type": "modernbert",
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"task_type": "entity-infilling",
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"base_model": "answerdotai/ModernBERT-base",
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"training_date": "2025-10-17T09:42:43.957362",
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"framework": "pytorch",
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"library": "transformers",
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"tags": [
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"modernbert",
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"entity-infilling",
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"text-summarization",
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"masked-modeling",
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"pytorch"
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]
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}
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