Token Classification
Transformers
Safetensors
deberta-v2
ner
named-entity-recognition
multinerd
mdeberta-v3
mdeberta
deberta
lora
multilingual
multi-domain-ner
information-extraction
nlp
sequence-labeling
bio-tagging
entity-extraction
transformer
Instructions to use Rishabh157/multinerd-multilingual-ner-mdeberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rishabh157/multinerd-multilingual-ner-mdeberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Rishabh157/multinerd-multilingual-ner-mdeberta")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Rishabh157/multinerd-multilingual-ner-mdeberta") model = AutoModelForTokenClassification.from_pretrained("Rishabh157/multinerd-multilingual-ner-mdeberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0375e512e55b8b086fb2f4b8a458ea7e4dac187a7d4862a4dd66a3718ad66a4b
- Size of remote file:
- 16 MB
- SHA256:
- 17f480544dfe3d4286205dfa15cf35ae1a176ce33383ed4e7286923b441a98b7
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