Upload bert-base-NER ONNX model

README.md

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+---
+library_name: onnx
+tags:
+  - bert
+  - ner
+  - named-entity-recognition
+  - token-classification
+  - conll2003
+  - onnx
+  - inference4j
+license: mit
+pipeline_tag: token-classification
+---
+
+# BERT Base NER — ONNX
+
+ONNX export of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER), a BERT model fine-tuned on CoNLL-2003 for Named Entity Recognition. Identifies persons, organizations, locations, and miscellaneous entities in text using IOB2 tagging.
+
+Mirrored for use with [inference4j](https://github.com/inference4j/inference4j), an inference-only AI library for Java.
+
+## Original Source
+
+- **Repository:** [dslim (ONNX by Xenova)](https://huggingface.co/dslim/bert-base-NER)
+- **License:** mit
+
+## Usage with inference4j
+
+```java
+try (BertNerRecognizer ner = BertNerRecognizer.builder()
+        .modelId("inference4j/bert-base-NER")
+        .build()) {
+    List<NamedEntity> entities = ner.recognize("John works at Google in London.");
+    for (NamedEntity e : entities) {
+        System.out.printf("%s (%s)%n", e.text(), e.label());
+    }
+}
+```
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Architecture | BERT Base (12 layers, 768 hidden, 110M params) |
+| Task | Named Entity Recognition (IOB2 tagging) |
+| Labels | O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC |
+| Training data | CoNLL-2003 |
+| F1 score | 91.3 |
+| Max sequence length | 512 |
+| Tokenizer | WordPiece (cased) |
+| Original framework | PyTorch (HuggingFace Transformers) |
+
+## License
+
+This model is licensed under the [MIT License](https://opensource.org/licenses/MIT). Original model by [dslim](https://huggingface.co/dslim/bert-base-NER), ONNX export by [Xenova](https://huggingface.co/Xenova).

config.json

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+{
+  "_name_or_path": "dslim/bert-base-NER",
+  "_num_labels": 9,
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-MISC",
+    "2": "I-MISC",
+    "3": "B-PER",
+    "4": "I-PER",
+    "5": "B-ORG",
+    "6": "I-ORG",
+    "7": "B-LOC",
+    "8": "I-LOC"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-LOC": 7,
+    "B-MISC": 1,
+    "B-ORG": 5,
+    "B-PER": 3,
+    "I-LOC": 8,
+    "I-MISC": 2,
+    "I-ORG": 6,
+    "I-PER": 4,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.29.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

model.onnx

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+oid sha256:9784f160524c01c12145872ed76c3361b0f90285b484678ad92b2c30be57b408
+size 431172606