Token Classification
Transformers
Safetensors
English
Dutch
German
xlm-roberta
named-entity-recognition
legal
multilingual
Instructions to use lblod/multilingual-ner-abb-improved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lblod/multilingual-ner-abb-improved with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lblod/multilingual-ner-abb-improved")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lblod/multilingual-ner-abb-improved") model = AutoModelForTokenClassification.from_pretrained("lblod/multilingual-ner-abb-improved", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 833 Bytes
707e3ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"label_list": [
"O",
"B-ADMINISTRATIVE_BODY",
"I-ADMINISTRATIVE_BODY",
"B-DATE",
"I-DATE",
"B-LEGAL_GROUND",
"I-LEGAL_GROUND",
"B-LOCATION",
"I-LOCATION",
"B-MANDATARY",
"I-MANDATARY"
],
"label2id": {
"O": 0,
"B-ADMINISTRATIVE_BODY": 1,
"I-ADMINISTRATIVE_BODY": 2,
"B-DATE": 3,
"I-DATE": 4,
"B-LEGAL_GROUND": 5,
"I-LEGAL_GROUND": 6,
"B-LOCATION": 7,
"I-LOCATION": 8,
"B-MANDATARY": 9,
"I-MANDATARY": 10
},
"id2label": {
"0": "O",
"1": "B-ADMINISTRATIVE_BODY",
"2": "I-ADMINISTRATIVE_BODY",
"3": "B-DATE",
"4": "I-DATE",
"5": "B-LEGAL_GROUND",
"6": "I-LEGAL_GROUND",
"7": "B-LOCATION",
"8": "I-LOCATION",
"9": "B-MANDATARY",
"10": "I-MANDATARY"
}
} |