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--- |
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library_name: transformers |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: tajik-classifier |
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results: [] |
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datasets: |
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- mteb/banking77 |
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language: |
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- tg |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# tajik-banking-intent-classifier |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) trained on a Tajik-translated version of the [Banking77](https://huggingface.co/datasets/mteb/banking77) dataset. |
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The dataset contains customer service queries related to banking, classified into 77 different intent categories. |
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## 🧾 Model description |
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* **Base model**: XLM-RoBERTa Base |
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* **Language**: Tajik (tg) |
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* **Task**: Text classification (intent recognition) |
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* **Number of classes**: 77 |
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<p>The model is designed to classify banking-related queries into one of 77 categories such as card_payment, atm_support, balance, lost_or_stolen_card, etc. It is useful for building customer support bots or virtual assistants that operate in the Tajik language.</p> |
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## ✅ Intended uses |
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* Banking customer support chatbots for Tajik-speaking users |
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* Voice or text-based virtual assistants in the finance domain |
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* Automated ticket or query routing in Tajik financial services |
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## ⚠️ Limitations |
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* The model may not generalize well to non-banking topics |
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* Classification performance depends on the quality and accuracy of the dataset translation |
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## 📚 Training and evaluation data |
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* **Dataset**: Banking77 dataset translated from English to Tajik |
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* **Size**: ~13,000 examples across 77 intent classes |
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* **Source**: Original banking77 English dataset, translated via machine translation |
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## ⚙️ Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.52.4 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
