Token Classification
Transformers
Safetensors
deberta-v2
ner
named-entity-recognition
universal-ner
unified-ner
mdeberta-v3
mdeberta
deberta
lora
multilingual
fine-grained-ner
cross-lingual
information-extraction
nlp
sequence-labeling
bio-tagging
entity-extraction
text-mining
deep-learning
transformer
wikiann
multinerd
multiconer
multiconer-2023
Instructions to use Rishabh157/unified-multilingual-ner-mdeberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rishabh157/unified-multilingual-ner-mdeberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Rishabh157/unified-multilingual-ner-mdeberta")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Rishabh157/unified-multilingual-ner-mdeberta") model = AutoModelForTokenClassification.from_pretrained("Rishabh157/unified-multilingual-ner-mdeberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Welcome to the community
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