JayShah07
/

tinybert-dual-classifier

@@ -1,199 +1,147 @@
 ---
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 ---
+language: en
+license: apache-2.0
+tags:
+- text-classification
+- multi-label-classification
+- tinybert
+- pytorch
+datasets:
+- JayShah07/multi_label_reporting
+metrics:
+- accuracy
+- f1
+widget:
+- text: "Show me my current holdings"
+- text: "What are my capital gains for this year?"
+- text: "Give me monthly scheme-wise returns"
 ---
+# TinyBERT Dual Classifier for Investment Reporting
+This model is a fine-tuned TinyBERT with two classification heads for multi-label classification of investment reporting queries.
+## Model Description
+- **Base Model**: TinyBERT (huawei-noah/TinyBERT_General_4L_312D)
+- **Parameters**: ~14-15M
+- **Architecture**: Single encoder with two independent classification heads
+- **Task**: Multi-label classification (Module + Date)
+## Labels
+**Module Labels (6 classes)**:
+- holdings
+- capital_gains
+- scheme_wise_returns
+- investment_account_wise_returns
+- portfolio_update
+- None_module
+**Date Labels (7 classes)**:
+- Current Year
+- Previous Year
+- Daily
+- Monthly
+- Weekly
+- Yearly
+- None_date
+## Performance
+**Test Set Results**:
+- Module Accuracy: 1.0000
+- Module F1 Score: 1.0000
+- Date Accuracy: 1.0000
+- Date F1 Score: 1.0000
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn as nn
+# Define model class
+class TinyBERTDualClassifier(nn.Module):
+    def __init__(self, num_module_labels, num_date_labels, dropout_rate=0.1):
+        super(TinyBERTDualClassifier, self).__init__()
+        self.encoder = AutoModel.from_pretrained("JayShah07/tinybert-dual-classifier")
+        self.hidden_size = self.encoder.config.hidden_size
+        self.dropout = nn.Dropout(p=dropout_rate)
+        self.module_classifier = nn.Linear(self.hidden_size, num_module_labels)
+        self.date_classifier = nn.Linear(self.hidden_size, num_date_labels)
+    def forward(self, input_ids, attention_mask):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        cls_output = outputs.last_hidden_state[:, 0, :]
+        cls_output = self.dropout(cls_output)
+        module_logits = self.module_classifier(cls_output)
+        date_logits = self.date_classifier(cls_output)
+        return module_logits, date_logits
+# Load model
+classifier_config = torch.hub.load_state_dict_from_url(
+    f"https://huggingface.co/JayShah07/tinybert-dual-classifier/resolve/main/classifier_heads.pt"
+)
+model = TinyBERTDualClassifier(
+    num_module_labels=6,
+    num_date_labels=7
+)
+model.module_classifier.load_state_dict(classifier_config['module_classifier'])
+model.date_classifier.load_state_dict(classifier_config['date_classifier'])
+tokenizer = AutoTokenizer.from_pretrained("JayShah07/tinybert-dual-classifier")
+# Inference
+model.eval()
+text = "Show my holdings for this month"
+inputs = tokenizer(text, return_tensors='pt', padding='max_length',
+                   truncation=True, max_length=128)
+with torch.no_grad():
+    module_logits, date_logits = model(inputs['input_ids'], inputs['attention_mask'])
+    module_pred = torch.argmax(module_logits, dim=1).item()
+    date_pred = torch.argmax(date_logits, dim=1).item()
+module_labels = ['holdings', 'capital_gains', 'scheme_wise_returns', 'investment_account_wise_returns', 'portfolio_update', 'None_module']
+date_labels = ['Current Year', 'Previous Year', 'Daily', 'Monthly', 'Weekly', 'Yearly', 'None_date']
+print(f"Module: {module_labels[module_pred]}")
+print(f"Date: {date_labels[date_pred]}")
+```
 ## Training Details
+- **Dataset**: JayShah07/multi_label_reporting
+- **Training Samples**: 3097
+- **Validation Samples**: 387
+- **Test Samples**: 388
+- **Epochs**: 10
+- **Batch Size**: 16
+- **Learning Rate**: 2e-05
+- **Optimizer**: AdamW
+- **Loss Function**: CrossEntropyLoss (separate for each head)
+## Latency
+Average inference latency on sample queries (mean ± std):
+- See notebook for detailed latency analysis
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{tinybert-dual-classifier,
+  author = {Jay Shah},
+  title = {TinyBERT Dual Classifier for Investment Reporting},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/JayShah07/tinybert-dual-classifier}}
+}
+```
+## License
+Apache 2.0