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.gitattributes

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README.md

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+---
+language:
+- ar
+license: apache-2.0
+base_model: thejosango/nuha-ajp-mlm
+tags:
+- bert
+- text-classification
+- hate-speech
+- gender-based-violence
+- arabic
+- trinary-classification
+- onnx
+- pilot
+datasets:
+- thejosango/nuha-ajp-dataset
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: nuha-ajp-trinary-onnx
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Jordanian NUHA Dataset
+      type: thejosango/nuha-ajp-dataset
+      config: methodology
+      split: validation
+    metrics:
+    - type: f1
+      value: 0.5363
+      name: F1
+    - type: precision
+      value: 0.6660
+      name: Precision
+    - type: recall
+      value: 0.5188
+      name: Recall
+---
+
+# nuha-ajp-trinary-onnx
+
+## Model Summary
+
+`nuha-ajp-trinary-onnx` is a **lightweight, ONNX-optimised** Arabic text classifier that categorises Jordanian social media comments into three classes based on the NUHA methodology for online gender-based violence (OGBV). It fine-tunes [`nuha-ajp-mlm`](https://huggingface.co/thejosango/nuha-ajp-mlm) — a domain-adapted Arabic BERT — with a reduced 4-layer architecture for efficient CPU inference, and is exported to ONNX. It shares the same classification task and labels as [`nuha-ajp-trinary`](https://huggingface.co/thejosango/nuha-ajp-trinary) but is optimised for production deployment. This is the model powering the NUHA analysis platform.
+
+| Label | Meaning |
+|---|---|
+| `Not Online Violence` | Comments that are not hate speech |
+| `Offensive Language` | Hate speech characterised by irony or sarcasm |
+| `Gender Based Violence` | Direct hate speech targeting gender — the primary focus of NUHA |
+
+This model was developed as part of a **pilot proof-of-concept** for the NUHA project by the [Jordan Open Source Association (JOSA)](https://josa.ngo).
+
+For the full-depth (12-layer) version of this classifier, see [`nuha-ajp-trinary`](https://huggingface.co/thejosango/nuha-ajp-trinary).
+
+## Uses
+
+### Direct Use
+
+```python
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoTokenizer, pipeline
+
+model = ORTModelForSequenceClassification.from_pretrained("thejosango/nuha-ajp-trinary-onnx")
+tokenizer = AutoTokenizer.from_pretrained("thejosango/nuha-ajp-trinary-onnx")
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+result = classifier("اخرسي يا غبية")
+print(result)
+# [{'label': 'Gender Based Violence', 'score': ...}]
+```
+
+For batch inference:
+
+```python
+comments = ["يعطيكم العافية", "أنتِ ساحرة", "اخرسي يا غبية"]
+results = classifier(comments)
+for comment, result in zip(comments, results):
+    print(f"{result['label']} ({result['score']:.2f}): {comment}")
+```
+
+### Using the PyTorch Version
+
+If you need the full PyTorch model (for fine-tuning or non-ONNX inference), use [`nuha-ajp-trinary`](https://huggingface.co/thejosango/nuha-ajp-trinary) directly.
+
+### Out-of-Scope Use
+
+- **Other Arabic dialects**: The model was trained primarily on Jordanian Arabic. Performance on Egyptian, Gulf, or Modern Standard Arabic is not validated.
+- **Other hate speech targets**: NUHA is calibrated for online gender-based violence. It is not designed to detect hate speech targeting race, religion, or other demographics.
+- **High-stakes automated decisions**: Given the moderate performance (F1 ≈ 0.54) and pilot nature of this work, the model should not be used as the sole decision-maker in content moderation systems without human review.
+
+## Preprocessing
+
+At inference time, apply the following normalisation to input text before passing it to the model:
+
+1. URLs replaced with `[رابط]` token
+2. @mentions replaced with `[مستخدم]` token
+3. Email addresses replaced with `[بريد]` token
+4. Numbers removed
+5. Punctuation removed
+6. Arabic diacritics (harakat) removed
+7. Whitespace normalised
+
+## Evaluation Results
+
+Evaluated on the validation split of [`thejosango/nuha-ajp-dataset`](https://huggingface.co/datasets/thejosango/nuha-ajp-dataset) (methodology configuration):
+
+| Metric | Value |
+|---|---|
+| F1 (macro) | 0.5363 |
+| Precision | 0.6660 |
+| Recall | 0.5188 |
+
+See [`nuha-ajp-trinary`](https://huggingface.co/thejosango/nuha-ajp-trinary) for full training details and evaluation discussion.
+
+---
+
+*This model was developed as part of an initial pilot study. Performance metrics reflect the complexity of the task and the proof-of-concept nature of this system.*

config.json

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+{
+  "_name_or_path": "thejosango/nuha-mlm",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Not Online Violence",
+    "1": "Offensive Language",
+    "2": "Gender Based Violence"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Gender Based Violence": 2,
+    "Not Online Violence": 0,
+    "Offensive Language": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.32.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 64000
+}

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": [
+    "[بريد]",
+    "[مستخدم]",
+    "[رابط]"
+  ],
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]",
+  "use_fast": true
+}