Upload Production V3 multi-label model

README.md

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+---
+language: bn
+tags:
+- hate-speech-detection
+- bangla
+- multilabel-classification
+- banglabert
+license: mit
+datasets:
+- custom
+metrics:
+- f1
+- precision
+- recall
+---
+
+# BanglaBERT Hate Speech Detection - Production V3
+
+Production V3 multi-label hate speech detection model for Bangla text.
+
+## Model Architecture
+
+- **Base Model**: BanglaBERT (sagorsarker/bangla-bert-base)
+- **Architecture**: Advanced Dual-Head with Label-Aware Attention
+- **Task**: Multi-label classification
+- **Labels**: 7 categories (vulgar, hate, religious, threat, troll, insult, safe)
+
+## Features
+
+- **Label-Aware Attention**: Each label has specialized attention mechanism
+- **Multi-Scale Feature Extraction**: 3 convolutional scales (kernel 3, 5, 7)
+- **Label Co-occurrence Module**: Captures inter-label relationships
+- **Per-Label Threshold Optimization**: Individual thresholds for each label
+- **Safe Label Exclusivity**: Intelligent conflict resolution
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer
+import pickle
+
+# Load model and tokenizer
+model_path = "tamim65/banglabert-hate-speech-prod-v3"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+# Load full model (custom architecture)
+model = torch.load(f"{model_path}/full_model.pt", map_location='cpu')
+model.eval()
+
+# Load metadata and thresholds
+with open(f"{model_path}/metadata.pkl", 'rb') as f:
+    metadata = pickle.load(f)
+with open(f"{model_path}/optimal_thresholds.pkl", 'rb') as f:
+    optimal_thresholds = pickle.load(f)
+
+# Predict
+def predict(text):
+    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=128)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.sigmoid(outputs).cpu().numpy()[0]
+    
+    # Apply thresholds
+    predictions = {}
+    for i, label in enumerate(metadata['label_names']):
+        predictions[label] = {
+            'probability': float(probs[i]),
+            'predicted': bool(probs[i] >= optimal_thresholds[i])
+        }
+    
+    return predictions
+
+# Example
+text = "আপনার মন্তব্য খুব সুন্দর"
+result = predict(text)
+print(result)
+```
+
+## Performance
+
+- Optimized thresholds for each label
+- Handles multi-label scenarios effectively
+- Safe label exclusivity prevents false positives
+
+## Training Details
+
+- **Optimizer**: AdamW
+- **Learning Rate**: 2e-5
+- **Batch Size**: 16
+- **Epochs**: 10
+- **Loss**: Binary Cross Entropy with Logits
+
+## Files
+
+- `full_model.pt`: Complete model with custom architecture
+- `model.safetensors`: Model weights (safetensors format)
+- `config.json`: Model configuration
+- `tokenizer.json`, `vocab.txt`: Tokenizer files
+- `metadata.pkl`: Label names and metadata
+- `optimal_thresholds.pkl`: Per-label threshold values
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{banglabert-hate-speech-v3,
+  author = {Tamim},
+  title = {BanglaBERT Hate Speech Detection - Production V3},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/tamim65/banglabert-hate-speech-prod-v3}}
+}
+```
+
+## License
+
+MIT License

config.json

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+{
+  "architectures": [
+    "ElectraModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "embedding_size": 768,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

full_model.pt

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+size 525235522

metadata.pkl

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model.safetensors

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+size 440129200

optimal_thresholds.pkl

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+size 296

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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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": false,
+  "tokenizer_class": "ElectraTokenizer",
+  "unk_token": "[UNK]"
+}