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
| { | |
| "architectures": [ | |
| "DebertaV2ForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 1, | |
| "dtype": "float16", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-ANIM", | |
| "2": "B-BIO", | |
| "3": "B-CEL", | |
| "4": "B-DIS", | |
| "5": "B-EVE", | |
| "6": "B-FOOD", | |
| "7": "B-INST", | |
| "8": "B-LOC", | |
| "9": "B-MEDIA", | |
| "10": "B-MYTH", | |
| "11": "B-ORG", | |
| "12": "B-PER", | |
| "13": "B-PLANT", | |
| "14": "B-TIME", | |
| "15": "B-VEHI", | |
| "16": "I-ANIM", | |
| "17": "I-BIO", | |
| "18": "I-CEL", | |
| "19": "I-DIS", | |
| "20": "I-EVE", | |
| "21": "I-FOOD", | |
| "22": "I-INST", | |
| "23": "I-LOC", | |
| "24": "I-MEDIA", | |
| "25": "I-MYTH", | |
| "26": "I-ORG", | |
| "27": "I-PER", | |
| "28": "I-PLANT", | |
| "29": "I-TIME", | |
| "30": "I-VEHI" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-ANIM": 1, | |
| "B-BIO": 2, | |
| "B-CEL": 3, | |
| "B-DIS": 4, | |
| "B-EVE": 5, | |
| "B-FOOD": 6, | |
| "B-INST": 7, | |
| "B-LOC": 8, | |
| "B-MEDIA": 9, | |
| "B-MYTH": 10, | |
| "B-ORG": 11, | |
| "B-PER": 12, | |
| "B-PLANT": 13, | |
| "B-TIME": 14, | |
| "B-VEHI": 15, | |
| "I-ANIM": 16, | |
| "I-BIO": 17, | |
| "I-CEL": 18, | |
| "I-DIS": 19, | |
| "I-EVE": 20, | |
| "I-FOOD": 21, | |
| "I-INST": 22, | |
| "I-LOC": 23, | |
| "I-MEDIA": 24, | |
| "I-MYTH": 25, | |
| "I-ORG": 26, | |
| "I-PER": 27, | |
| "I-PLANT": 28, | |
| "I-TIME": 29, | |
| "I-VEHI": 30, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-07, | |
| "legacy": true, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta-v2", | |
| "norm_rel_ebd": "layer_norm", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0.0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 768, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 0, | |
| "vocab_size": 251000 | |
| } | |