Token Classification
Transformers
Safetensors
Arabic
bert
nested-ner
multi-label
arabic
wojood
konooz
knowledgegrapheval-2026
arabicnlp-2026
adaptner
Eval Results (legacy)
Instructions to use NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested") model = AutoModelForTokenClassification.from_pretrained("NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download repro_bundle.json from NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested: direct link, hf CLI and curl.
- Browser
- Download file 3.22 kB
-
https://huggingface.co/NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested/resolve/main/repro_bundle.json
- Command line
-
hf download hf://NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested/repro_bundle.json
-
curl -L -o repro_bundle.json https://huggingface.co/NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested/resolve/main/repro_bundle.json
3.22 kB
| { | |
| "exp_id": "EXP-002", | |
| "config": { | |
| "exp_id": "EXP-002", | |
| "run_name": "marbertv2_wojood_nested", | |
| "model_name": "UBC-NLP/MARBERTv2", | |
| "drive_root": "/content/drive/MyDrive/NAMAA Drive /shared_tasks/KnowledgeGraphEval/dataset", | |
| "data_variant": "nested", | |
| "text_col": "token", | |
| "tag_col": "tags", | |
| "sent_col": "global_sentence_id", | |
| "order_col": "token_position", | |
| "domain_col": "Sub_corpus", | |
| "tag_split_regex": "[\\s|,;+]+", | |
| "output_root": "/content/drive/MyDrive/NAMAA Drive /shared_tasks/KnowledgeGraphEval/02_models", | |
| "hf_username": "FatimahEmadEldin", | |
| "hf_private": true, | |
| "seed": 42, | |
| "max_length": 256, | |
| "num_train_epochs": 8, | |
| "learning_rate": 3e-05, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.1, | |
| "pred_threshold": 0.5, | |
| "per_device_train_batch_size": 32, | |
| "per_device_eval_batch_size": 64, | |
| "gradient_accumulation_steps": 1, | |
| "bf16": true, | |
| "tf32": true, | |
| "gradient_checkpointing": false, | |
| "dataloader_num_workers": 2, | |
| "group_by_length": false, | |
| "save_total_limit": 2, | |
| "early_stopping_patience": 3 | |
| }, | |
| "data_paths": { | |
| "train": "/content/drive/MyDrive/NAMAA Drive /shared_tasks/KnowledgeGraphEval/dataset/Wojood/Wojood1_1_nested/train.csv", | |
| "dev": "/content/drive/MyDrive/NAMAA Drive /shared_tasks/KnowledgeGraphEval/dataset/Wojood/Wojood1_1_nested/val.csv", | |
| "test": "/content/drive/MyDrive/NAMAA Drive /shared_tasks/KnowledgeGraphEval/dataset/Wojood/Wojood1_1_nested/test.csv" | |
| }, | |
| "n_train_sentences": 23125, | |
| "n_dev_sentences": 3304, | |
| "labels": [ | |
| "B-CARDINAL", | |
| "B-CURR", | |
| "B-DATE", | |
| "B-EVENT", | |
| "B-FAC", | |
| "B-GPE", | |
| "B-LANGUAGE", | |
| "B-LAW", | |
| "B-LOC", | |
| "B-MONEY", | |
| "B-NORP", | |
| "B-OCC", | |
| "B-ORDINAL", | |
| "B-ORG", | |
| "B-PERCENT", | |
| "B-PERS", | |
| "B-PRODUCT", | |
| "B-QUANTITY", | |
| "B-TIME", | |
| "B-UNIT", | |
| "B-WEBSITE", | |
| "I-CARDINAL", | |
| "I-CURR", | |
| "I-DATE", | |
| "I-EVENT", | |
| "I-FAC", | |
| "I-GPE", | |
| "I-LANGUAGE", | |
| "I-LAW", | |
| "I-LOC", | |
| "I-MONEY", | |
| "I-NORP", | |
| "I-OCC", | |
| "I-ORDINAL", | |
| "I-ORG", | |
| "I-PERCENT", | |
| "I-PERS", | |
| "I-PRODUCT", | |
| "I-QUANTITY", | |
| "I-TIME", | |
| "I-UNIT", | |
| "I-WEBSITE" | |
| ], | |
| "label2id": { | |
| "B-CARDINAL": 0, | |
| "B-CURR": 1, | |
| "B-DATE": 2, | |
| "B-EVENT": 3, | |
| "B-FAC": 4, | |
| "B-GPE": 5, | |
| "B-LANGUAGE": 6, | |
| "B-LAW": 7, | |
| "B-LOC": 8, | |
| "B-MONEY": 9, | |
| "B-NORP": 10, | |
| "B-OCC": 11, | |
| "B-ORDINAL": 12, | |
| "B-ORG": 13, | |
| "B-PERCENT": 14, | |
| "B-PERS": 15, | |
| "B-PRODUCT": 16, | |
| "B-QUANTITY": 17, | |
| "B-TIME": 18, | |
| "B-UNIT": 19, | |
| "B-WEBSITE": 20, | |
| "I-CARDINAL": 21, | |
| "I-CURR": 22, | |
| "I-DATE": 23, | |
| "I-EVENT": 24, | |
| "I-FAC": 25, | |
| "I-GPE": 26, | |
| "I-LANGUAGE": 27, | |
| "I-LAW": 28, | |
| "I-LOC": 29, | |
| "I-MONEY": 30, | |
| "I-NORP": 31, | |
| "I-OCC": 32, | |
| "I-ORDINAL": 33, | |
| "I-ORG": 34, | |
| "I-PERCENT": 35, | |
| "I-PERS": 36, | |
| "I-PRODUCT": 37, | |
| "I-QUANTITY": 38, | |
| "I-TIME": 39, | |
| "I-UNIT": 40, | |
| "I-WEBSITE": 41 | |
| }, | |
| "task": "nested-multilabel-ner", | |
| "python": "3.12.13", | |
| "versions": { | |
| "torch": "2.11.0+cu128", | |
| "transformers": "5.10.2", | |
| "datasets": "4.0.0", | |
| "cuda": "12.8", | |
| "gpu": "Tesla T4" | |
| } | |
| } |