---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: tajik-classifier
  results: []
datasets:
- mteb/banking77
language:
- tg
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# tajik-banking-intent-classifier

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.
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
<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>

## ✅ 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