Token Classification
Transformers
Safetensors
English
roberta
ner
named-entity-recognition
Eval Results (legacy)
Instructions to use jayant-yadav/roberta-base-multinerd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayant-yadav/roberta-base-multinerd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jayant-yadav/roberta-base-multinerd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jayant-yadav/roberta-base-multinerd") model = AutoModelForTokenClassification.from_pretrained("jayant-yadav/roberta-base-multinerd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cad66960992df0e4fe4b928af8a615142d709ea8f3ac7ece2c5b5e7429f72a3
- Size of remote file:
- 993 MB
- SHA256:
- bef93d8b7b99af7a81f742706115933b17eb2ca891f7577b9870e08b71499502
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