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
| { | |
| "B-ANIM": 1, | |
| "B-AerospaceManufacturer": 2, | |
| "B-AnatomicalStructure": 3, | |
| "B-ArtWork": 4, | |
| "B-Artist": 5, | |
| "B-Athlete": 6, | |
| "B-BIO": 7, | |
| "B-CEL": 8, | |
| "B-CarManufacturer": 9, | |
| "B-Cleric": 10, | |
| "B-Clothing": 11, | |
| "B-DIS": 12, | |
| "B-Disease": 13, | |
| "B-Drink": 14, | |
| "B-EVE": 15, | |
| "B-FOOD": 16, | |
| "B-Facility": 17, | |
| "B-Food": 18, | |
| "B-HumanSettlement": 19, | |
| "B-INST": 20, | |
| "B-LOC": 21, | |
| "B-MEDIA": 22, | |
| "B-MYTH": 23, | |
| "B-MedicalProcedure": 24, | |
| "B-Medication/Vaccine": 25, | |
| "B-MusicalGRP": 26, | |
| "B-MusicalWork": 27, | |
| "B-ORG": 28, | |
| "B-OtherLOC": 29, | |
| "B-OtherPER": 30, | |
| "B-OtherPROD": 31, | |
| "B-PER": 32, | |
| "B-PLANT": 33, | |
| "B-Politician": 34, | |
| "B-PrivateCorp": 35, | |
| "B-PublicCorp": 36, | |
| "B-Scientist": 37, | |
| "B-Software": 38, | |
| "B-SportsGRP": 39, | |
| "B-SportsManager": 40, | |
| "B-Station": 41, | |
| "B-Symptom": 42, | |
| "B-TIME": 43, | |
| "B-VEHI": 44, | |
| "B-Vehicle": 45, | |
| "B-VisualWork": 46, | |
| "B-WrittenWork": 47, | |
| "I-ANIM": 48, | |
| "I-AerospaceManufacturer": 49, | |
| "I-AnatomicalStructure": 50, | |
| "I-ArtWork": 51, | |
| "I-Artist": 52, | |
| "I-Athlete": 53, | |
| "I-BIO": 54, | |
| "I-CEL": 55, | |
| "I-CarManufacturer": 56, | |
| "I-Cleric": 57, | |
| "I-Clothing": 58, | |
| "I-DIS": 59, | |
| "I-Disease": 60, | |
| "I-Drink": 61, | |
| "I-EVE": 62, | |
| "I-FOOD": 63, | |
| "I-Facility": 64, | |
| "I-Food": 65, | |
| "I-HumanSettlement": 66, | |
| "I-INST": 67, | |
| "I-LOC": 68, | |
| "I-MEDIA": 69, | |
| "I-MYTH": 70, | |
| "I-MedicalProcedure": 71, | |
| "I-Medication/Vaccine": 72, | |
| "I-MusicalGRP": 73, | |
| "I-MusicalWork": 74, | |
| "I-ORG": 75, | |
| "I-OtherLOC": 76, | |
| "I-OtherPER": 77, | |
| "I-OtherPROD": 78, | |
| "I-PER": 79, | |
| "I-PLANT": 80, | |
| "I-Politician": 81, | |
| "I-PrivateCorp": 82, | |
| "I-PublicCorp": 83, | |
| "I-Scientist": 84, | |
| "I-Software": 85, | |
| "I-SportsGRP": 86, | |
| "I-SportsManager": 87, | |
| "I-Station": 88, | |
| "I-Symptom": 89, | |
| "I-TIME": 90, | |
| "I-VEHI": 91, | |
| "I-Vehicle": 92, | |
| "I-VisualWork": 93, | |
| "I-WrittenWork": 94, | |
| "O": 0 | |
| } |