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Co-authored-by: yazansh <yazansh@users.noreply.huggingface.co>

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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
+- pilot
+datasets:
+- thejosango/nuha-ajp-dataset
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: nuha-ajp-trinary
+  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` is an 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 — and outputs one of:
+
+| 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). It is the production model behind the NUHA analysis platform.
+
+A **lightweight, ONNX-optimised** 4-layer classifier trained on the same task is available at [`thejosango/nuha-ajp-trinary-onnx`](https://huggingface.co/thejosango/nuha-ajp-trinary-onnx).
+
+For a simpler binary classifier (hate speech / non-hate speech), see [`nuha-ajp-binary`](https://huggingface.co/thejosango/nuha-ajp-binary).
+
+## Uses
+
+### Direct Use
+
+Classifying Arabic social media comments for online gender-based violence, particularly for Jordanian Arabic content from Facebook and X (Twitter).
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="thejosango/nuha-ajp-trinary",
+    tokenizer="thejosango/nuha-ajp-trinary",
+)
+
+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 ONNX Version
+
+For faster CPU inference, use the ONNX export:
+
+```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)
+```
+
+### 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.
+
+## Bias, Risks, and Limitations
+
+- **Pilot annotation quality**: Training labels were produced in an exploratory annotation effort with variable inter-annotator agreement. The model inherits noise from that process, which is reflected in the moderate F1 score.
+- **Three-class difficulty**: Distinguishing `Offensive Language` from `Gender Based Violence` is a genuinely difficult subtask. The `Offensive Language` class is small (≈2% of training data) and the model may struggle with it.
+- **Colloquial Arabic only**: The aggressive text cleaning (Arabic-only filtering) means the model has never seen URLs, numbers, punctuation, or Latin-script text.
+- **Imbalanced classes**: The training data is dominated by `Not Online Violence` (≈59%), with `Offensive Language` being very sparse (≈2%). Data augmentation was applied but class imbalance remains a factor.
+
+## Training Details
+
+### Training Data
+
+Fine-tuned on the `methodology` configuration of [`thejosango/nuha-ajp-dataset`](https://huggingface.co/datasets/thejosango/nuha-ajp-dataset), which applies the three-class NUHA categorisation scheme to the original annotations.
+
+### Preprocessing
+
+At training and inference time, the following normalisation is applied to input text (in addition to the dataset-level Arabic-only filtering):
+
+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
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Base model | thejosango/nuha-ajp-mlm |
+| Hidden layers | 12 (full depth) |
+| Learning rate | 5e-5 |
+| LR schedule | Constant |
+| Batch size | 64 |
+| Epochs | 5 |
+| Weight decay | 0.0 |
+| Label smoothing | 0.1 |
+| Weighted loss | No |
+| Data augmentation | Yes (contextual word substitution, ratio 0.75) |
+| Framework | Transformers 4.32.1, PyTorch 2.0.1 |
+
+### Evaluation Results
+
+Evaluated on the validation split of `thejosango/nuha-ajp-dataset` (methodology configuration):
+
+| Metric | Value |
+|---|---|
+| F1 (macro) | 0.5363 |
+| Precision | 0.6660 |
+| Recall | 0.5188 |
+| Loss | 0.7126 |
+
+The lower recall relative to precision suggests the model is conservative — it tends to under-predict `Gender Based Violence` rather than over-predict it. This reflects both the difficulty of the three-class task and the limited size of the pilot training corpus.
+
+---
+
+*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": 12,
+  "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
+}

config.toml

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+[experiment]
+name = "multiclass-15"
+type = "multiclass"
+
+
+[dataset]
+path = "thejosango/nuha-dataset"
+dataset_revision = "main"
+augment_ratio = 0.75
+undersampling_strategy = false 
+
+
+[model]
+pretrained_model_name_or_path = "thejosango/nuha-mlm"
+revision = "ce20f497544665775129f9ff5b3cd2a3e350dce8"
+#num_hidden_layers = 6
+
+[training]
+num_train_epochs = 5
+warmup_steps = 0
+lr_scheduler_type = "constant"
+learning_rate = 5e-5
+per_device_train_batch_size = 64
+per_device_eval_batch_size = 64
+gradient_accumulation_steps = 1
+weight_decay = 0.00
+label_smoothing_factor = 0.1
+weighted_loss = false
+early_stopping_patience = 5
+early_stopping_threshold = 0.005

pytorch_model.bin

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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
+}

training_args.bin

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