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
| { | |
| "model_type": "modernbert", | |
| "task_type": "entity-infilling", | |
| "base_model": "answerdotai/ModernBERT-base", | |
| "training_date": "2025-10-17T10:17:41.770927", | |
| "framework": "pytorch", | |
| "library": "transformers", | |
| "tags": [ | |
| "modernbert", | |
| "entity-infilling", | |
| "text-summarization", | |
| "masked-modeling", | |
| "pytorch" | |
| ] | |
| } |