Ahmed Cader commited on
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v1: 103-language WikiANN + honorific-augmentation fine-tune

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: numind/NuNER-multilingual-v0.1
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+ pipeline_tag: token-classification
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+ tags:
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+ - ner
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+ - multilingual
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+ - onnx
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+ - transformers.js
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+ ---
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+
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+ # InputShield NER — multilingual names & places
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+
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+ Token-classification model (PER / LOC / ORG) powering
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+ [InputShield](https://inputshield.app)'s on-device name detection. Runs
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+ in the browser via transformers.js + ONNX Runtime; the quantized weights
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+ (`onnx/model_quantized.onnx`, ~178 MB) are what ships.
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+
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+ ## Coverage
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+
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+ Fine-tuned on 103 languages; **87 are certified** by a two-tier benchmark:
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+ realistic-sentence recall with a zero-false-positive corpus (71 languages),
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+ plus held-out WikiANN validation at PER F1 ≥ 0.80 (84 languages).
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+
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+ ## Lineage
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+
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+ - **Backbone:** [numind/NuNER-multilingual-v0.1](https://huggingface.co/numind/NuNER-multilingual-v0.1) (MIT)
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+ - **Training data:** WikiANN (Wikipedia-derived, PAN-X annotations), 501k
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+ sentences across 103 languages, plus a small synthetic supplement of
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+ conversational name shapes (honorific patterns) that Wikipedia-derived
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+ data lacks.
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+
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+ ## Recommended inference floors
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+
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+ Span mean confidence, per entity: PER ≥ 0.55, LOC ≥ 0.9 (precision-first;
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+ calibrated against the benchmark corpus above).
config.json ADDED
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "O",
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+ "1": "B-PER",
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+ "2": "I-PER",
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+ "3": "B-ORG",
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+ "4": "I-ORG",
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+ "5": "B-LOC",
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+ "B-ORG": 3,
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+ "O": 0
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "num_hidden_layers": 12,
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tokenizer.json ADDED
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vocab.txt ADDED
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