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
File size: 340 Bytes
c485a10 a0f608c c485a10 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"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"
]
} |