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

f1rdavs/tajik-banking-intent-classifier

tajik-banking-intent-classifier