tajik-banking-intent-classifier

This model is a fine-tuned version of xlm-roberta-base trained on a Tajik-translated version of the Banking77 dataset. The dataset contains customer service queries related to banking, classified into 77 different intent categories.

🧾 Model description

• Base model: XLM-RoBERTa Base
• Language: Tajik (tg)
• Task: Text classification (intent recognition)
• Number of classes: 77

  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.

✅ Intended uses

• Banking customer support chatbots for Tajik-speaking users
• Voice or text-based virtual assistants in the finance domain
• Automated ticket or query routing in Tajik financial services

⚠️ Limitations

• The model may not generalize well to non-banking topics
• Classification performance depends on the quality and accuracy of the dataset translation

📚 Training and evaluation data

• Dataset: Banking77 dataset translated from English to Tajik
• Size: ~13,000 examples across 77 intent classes
• Source: Original banking77 English dataset, translated via machine translation

⚙️ Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1