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
Commit ·
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Parent(s): dbd3d69
updated dataset type
Browse files
README.md
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- name: robert-base on MultiNERD by Jayant Yadav
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results:
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- task:
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type:
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name: Named Entity Recognition
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dataset:
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type:
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name:
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split: test
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revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25
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metrics:
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- name: robert-base on MultiNERD by Jayant Yadav
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results:
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- task:
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type: named-entity-recognition
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name: Named Entity Recognition
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dataset:
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type: multinerd-a-multilingual-multi-genre-and-fine
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name: Babelscape/multinerd
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split: test
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revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25
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metrics:
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