Upload README.md

README.md

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+# Multi-Task NER + Intent + Language Model
+
+This model performs three tasks simultaneously:
+1. **Named Entity Recognition (NER)**: Extracts entities from B2B transaction descriptions
+2. **Intent Classification**: Classifies transaction intent/purpose
+3. **Language Detection**: Detects language (English, Russian, Uzbek Latin/Cyrillic, Mixed)
+
+## Model Details
+- Base model: `google-bert/bert-base-multilingual-uncased`
+- Architecture: Enhanced multi-task model with BiLSTM for NER, attention pooling for classification
+- Training: Optimized for realistic B2B transaction descriptions
+
+## Supported Languages
+- English (en)
+- Russian (ru)
+- Uzbek Latin (uz_latn)
+- Uzbek Cyrillic (uz_cyrl)
+- Mixed language text
+
+## Usage
+
+```python
+import torch
+import torch.nn as nn
+import numpy as np
+import json
+from transformers import AutoTokenizer, AutoModel
+from huggingface_hub import hf_hub_download
+
+# Download model files
+model_id = "primel/aibanov"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Download label mappings
+mappings_file = hf_hub_download(repo_id=model_id, filename="label_mappings.json")
+with open(mappings_file, "r") as f:
+    label_mappings = json.load(f)
+
+id2tag = {int(k): v for k, v in label_mappings["id2tag"].items()}
+id2intent = {int(k): v for k, v in label_mappings["id2intent"].items()}
+id2lang = {int(k): v for k, v in label_mappings["id2lang"].items()}
+
+# Define model architecture (same as training)
+class EnhancedMultiTaskModel(nn.Module):
+    # ... (copy the model class from training script)
+    pass
+
+# Load model
+base_bert = "google-bert/bert-base-multilingual-uncased"
+model = EnhancedMultiTaskModel(
+    model_name=base_bert,
+    num_ner_labels=len(label_mappings["tag2id"]),
+    num_intent_labels=len(label_mappings["intent2id"]),
+    num_lang_labels=len(label_mappings["lang2id"]),
+    dropout=0.15
+)
+
+# Load trained weights
+weights_file = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin")
+state_dict = torch.load(weights_file, map_location='cpu')
+model.load_state_dict(state_dict)
+model.eval()
+
+# Inference
+text = "Оплата 100% за товары согласно договору №123 от 15.01.2025г ИНН 987654321"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=192)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Process outputs
+ner_logits = outputs['ner_logits'][0].numpy()
+intent_logits = outputs['intent_logits'][0].numpy()
+lang_logits = outputs['lang_logits'][0].numpy()
+
+# Get predictions
+intent_id = np.argmax(intent_logits)
+intent = id2intent[intent_id]
+print(f"Intent: {intent}")
+
+lang_id = np.argmax(lang_logits)
+language = id2lang[lang_id]
+print(f"Language: {language}")
+```
+
+## License
+Apache 2.0
+
+## Citation
+```bibtex
+@misc{aibanov2025,
+  author = {primel},
+  title = {Multi-Task NER Intent Language Model},
+  year = {2025},
+  url = {https://huggingface.co/primel/aibanov}
+}
+```