Fill-Mask
Transformers
PyTorch
English
roberta
NER
named entity recognition
RE
relation extraction
entity mention detection
EMD
coreference resolution
Instructions to use aiola/roberta-base-corener with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aiola/roberta-base-corener with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aiola/roberta-base-corener")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aiola/roberta-base-corener") model = AutoModelForMaskedLM.from_pretrained("aiola/roberta-base-corener") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e865405fc11c52836d80a962c348ddc1d4a81a7955bb629be2cc8390f3f676a9
- Size of remote file:
- 525 MB
- SHA256:
- 9df63fe215a409b7ce7189c5c5f80304f22059887936eb4b8027c871147bf10c
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