Add OpenNER model, card, NOTICE (Apache-2.0, FlowX.AI)

.gitattributes

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 *.zst filter=lfs diff=lfs merge=lfs -text
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+onnx/model.onnx.data filter=lfs diff=lfs merge=lfs -text

NOTICE

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+FlowX OpenNER
+Copyright 2026 FlowX.AI
+
+This product includes software and models developed at FlowX.AI (https://flowx.ai).
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use these
+files except in compliance with the License. You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0

README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-classification
+base_model: answerdotai/ModernBERT-base
+tags:
+  - ner
+  - on-device
+  - privacy
+  - flowx
+  - openner
+  - logistics
+  - de-identification
+  - text-classification
+metrics:
+  - f1
+---
+
+# HSCodeClassify
+
+**HSCodeClassify** is a small, on-device logistics text classifier from the FlowX **OpenNER** family. Developed by **FlowX.AI**. Runs 100% on-premise / air-gapped, so no data leaves your boundary.
+
+## What it does
+- **Task:** text-classification
+- **Base model:** `answerdotai/ModernBERT-base`
+- **Classes (20):** 03 Fish & seafood, 08 Edible fruit & nuts, 09 Coffee, tea & spices, 22 Beverages & spirits, 30 Pharmaceuticals, 39 Plastics & articles, 40 Rubber & articles, 44 Wood & articles, 48 Paper & paperboard, 61 Apparel, knitted, 62 Apparel, not knitted, 64 Footwear, 72 Iron & steel, 73 Articles of iron/steel, 84 Machinery & mechanical, 85 Electrical machinery, 87 Vehicles & parts, 90 Optical/medical instr., 94 Furniture & bedding, 95 Toys, games & sports
+- **Held-out F1:** 1.0000
+- **Runtime:** CPU, Apple Silicon, one GPU, or browser/edge via ONNX (INT8). ~100-160 ms/doc on CPU.
+
+## Why a small model
+Fine-tuned encoders match or beat frontier LLMs on structured, convention-bound extraction, at a fraction of the latency and cost, with **zero data egress**. Identifiers are validated by checksum (IBAN mod-97, card Luhn, ISIN/LEI, container ISO-6346, VIN, national IDs), a correctness guarantee general LLMs lack. See the FlowX OpenNER benchmark for measured results.
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tok = AutoTokenizer.from_pretrained("flowxai/hscodeclassify")
+model = AutoModelForSequenceClassification.from_pretrained("flowxai/hscodeclassify")
+```
+
+## License & attribution
+Licensed under the **Apache License 2.0**. Copyright 2026 **FlowX.AI** (https://flowx.ai). See the `NOTICE` file. Trained on synthetic, checksum-validated data.
+
+_Part of the FlowX OpenNER model family. Synthetic-data F1 reflects an in-distribution synthetic distribution; validate on real documents before production use._

config.json

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+{
+  "architectures": [
+    "ModernBertForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "id2label": {
+    "0": "03 Fish & seafood",
+    "1": "08 Edible fruit & nuts",
+    "2": "09 Coffee, tea & spices",
+    "3": "22 Beverages & spirits",
+    "4": "30 Pharmaceuticals",
+    "5": "39 Plastics & articles",
+    "6": "40 Rubber & articles",
+    "7": "44 Wood & articles",
+    "8": "48 Paper & paperboard",
+    "9": "61 Apparel, knitted",
+    "10": "62 Apparel, not knitted",
+    "11": "64 Footwear",
+    "12": "72 Iron & steel",
+    "13": "73 Articles of iron/steel",
+    "14": "84 Machinery & mechanical",
+    "15": "85 Electrical machinery",
+    "16": "87 Vehicles & parts",
+    "17": "90 Optical/medical instr.",
+    "18": "94 Furniture & bedding",
+    "19": "95 Toys, games & sports"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "label2id": {
+    "03 Fish & seafood": 0,
+    "08 Edible fruit & nuts": 1,
+    "09 Coffee, tea & spices": 2,
+    "22 Beverages & spirits": 3,
+    "30 Pharmaceuticals": 4,
+    "39 Plastics & articles": 5,
+    "40 Rubber & articles": 6,
+    "44 Wood & articles": 7,
+    "48 Paper & paperboard": 8,
+    "61 Apparel, knitted": 9,
+    "62 Apparel, not knitted": 10,
+    "64 Footwear": 11,
+    "72 Iron & steel": 12,
+    "73 Articles of iron/steel": 13,
+    "84 Machinery & mechanical": 14,
+    "85 Electrical machinery": 15,
+    "87 Vehicles & parts": 16,
+    "90 Optical/medical instr.": 17,
+    "94 Furniture & bedding": 18,
+    "95 Toys, games & sports": 19
+  },
+  "layer_norm_eps": 1e-05,
+  "layer_types": [
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "local_attention": 128,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 160000.0,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 10000.0,
+      "rope_type": "default"
+    }
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.14.1",
+  "use_cache": false,
+  "vocab_size": 50368
+}

metrics.json

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+{
+  "test_loss": 2.498298954378697e-06,
+  "test_accuracy": 1.0,
+  "test_macro_f1": 1.0,
+  "test_f1": 1.0,
+  "test_runtime": 3.4699,
+  "test_samples_per_second": 1152.769,
+  "test_steps_per_second": 36.024,
+  "epoch": 3.0
+}

model.safetensors

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onnx/model.int8.onnx

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onnx/model.onnx.data

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}

training_args.bin

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