Initial model upload with metrics and example usage

README.md

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+
+---
+language: ru
+license: mit
+tags:
+- toxicity
+- multilabel
+- russian
+- multitask
+- rubert
+- profanity
+- threat
+- illegal
+metrics:
+- f1
+- precision
+- recall
+- auc
+pipeline_tag: text-classification
+---
+
+# Multi-Task Toxicity Classifier
+
+## Описание
+
+Модель для одновременного обнаружения трёх типов токсичности в русскоязычных текстах:
+- **Profanity** (ненормативная лексика)
+- **Threat** (угрозы)
+- **Illegal** (запросы на нарушение закона)
+
+Модель основана на энкодере `cointegrated/rubert-tiny2` с тремя независимыми классификационными головами.
+
+## Метрики на валидационной выборке
+
+| Класс | Threshold | Precision | Recall | F1-Score |
+|-------|-----------|-----------|--------|----------|
+| Profanity | 0.63 | 0.9048 | 0.9429 | 0.9235 |
+| Threat | 0.74 | 0.7462 | 0.8097 | 0.7766 |
+| Illegal | 0.89 | 0.6038 | 0.6598 | 0.6305 |
+
+**Macro F1-Score:** 0.7769
+
+
+## Использование
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import json
+
+# Загрузка модели и токенизатора
+model = AutoModel.from_pretrained("qquarkq/multitask-toxicity-classifier")
+tokenizer = AutoTokenizer.from_pretrained("qquarkq/multitask-toxicity-classifier")
+
+# Загрузка конфигурации с порогами
+with open("config.json", "r") as f:
+    config = json.load(f)
+thresholds = config["thresholds"]
+
+def predict_toxicity(text):
+    encoding = tokenizer(
+        text,
+        truncation=True,
+        padding='max_length',
+        max_length=128,
+        return_tensors='pt'
+    )
+    
+    with torch.no_grad():
+        profanity_logits, threat_logits, illegal_logits = model(
+            encoding['input_ids'], 
+            encoding['attention_mask']
+        )
+    
+    profanity_prob = torch.sigmoid(profanity_logits).item()
+    threat_prob = torch.sigmoid(threat_logits).item()
+    illegal_prob = torch.sigmoid(illegal_logits).item()
+
+    profanity_pred = int(profanity_prob >= thresholds['profanity'])
+    threat_pred = int(threat_prob >= thresholds['threat'])
+    illegal_pred = int(illegal_prob >= thresholds['illegal'])
+    return {
+        'profanity': profanity_prob,
+        'threat': threat_prob,
+        'illegal': illegal_prob
+    }
+
+# Пример использования
+text = "Ты полный идиот!"
+result = predict_toxicity(text)
+print(result)
+````
+Датасет
+Модель обучена на датасете [qquarkq/russian-toxic-multilabel-comments](https://huggingface.co/datasets/qquarkq/russian-toxic-multilabel-comments)

config.json

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+{
+  "model_name": "cointegrated/rubert-tiny2",
+  "num_classes": 3,
+  "task_names": [
+    "profanity",
+    "threat",
+    "illegal"
+  ],
+  "thresholds": {
+    "profanity": 0.6299999999999997,
+    "threat": 0.7399999999999997,
+    "illegal": 0.8899999999999996
+  }
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7f6f595c0887588c5075680dd7de573482c45e1cbe37bdd9dac3ef6215a44a76
+size 116803262

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 2048,
+  "never_split": null,
+  "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]"
+}