Instructions to use riidact/ner-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use riidact/ner-multilingual with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'riidact/ner-multilingual');
Ahmed Cader commited on
v1: 103-language WikiANN + honorific-augmentation fine-tune
Browse files- README.md +36 -0
- config.json +48 -0
- onnx/model.onnx +3 -0
- onnx/model_quantized.onnx +3 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +60 -0
- vocab.txt +0 -0
README.md
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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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# InputShield NER — multilingual names & places
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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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## Coverage
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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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## Lineage
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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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## Recommended inference floors
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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).
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config.json
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{
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"directionality": "bidi",
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"dtype": "float32",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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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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"6": "I-LOC"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"B-LOC": 5,
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"B-ORG": 3,
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"B-PER": 1,
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"I-LOC": 6,
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"I-ORG": 4,
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"I-PER": 2,
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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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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"transformers_version": "4.57.6",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 119547
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}
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onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:0ec040144b5a101f525cadf6eca8738bfd58a2f2f3ea79201b8cb58f72b512d8
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size 709282748
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onnx/model_quantized.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:2bd415f1e43f7021b02eda0853b0a0a71815b2c20bffc1750a1dbf6eeda508c4
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size 178021445
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special_tokens_map.json
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{
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"max_length": 128,
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"stride": 0,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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}
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vocab.txt
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