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---
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: label
dtype: string
- name: label_text
dtype: string
- name: text
dtype: string
- name: lang
dtype: string
splits:
- name: train
num_bytes: 1056214
num_examples: 11514
- name: test
num_bytes: 271925
num_examples: 2974
- name: validation
num_bytes: 185181
num_examples: 2033
download_size: 519296
dataset_size: 1513320
- config_name: intent_with_options
features:
- name: id
dtype: string
- name: label
dtype: string
- name: label_text
dtype: string
- name: text
dtype: string
- name: lang
dtype: string
- name: options
list:
- name: label
dtype: string
- name: label_text
dtype: string
splits:
- name: train
num_bytes: 3305760
num_examples: 2974
download_size: 2966668
dataset_size: 3305760
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
- config_name: intent_with_options
data_files:
- split: train
path: intent_with_options/train-*
---
# Turkish Intent Routing Dataset
This dataset is based on the Turkish subset of [`mteb/amazon_massive_intent`](https://huggingface.co/datasets/mteb/amazon_massive_intent).
## `intent_with_options` Configuration
The `intent_with_options` configuration was created for multiple-choice intent routing evaluation.
- Added Turkish descriptions (`label_text`) for each intent.
- Used [`alibayram/embeddingmagibu-200m`](https://huggingface.co/alibayram/embeddingmagibu-200m) to calculate semantic similarity between user utterances and intent descriptions.
- Selected the 9 most similar incorrect intents as distractors for each example.
- Included the correct intent, resulting in 10 options per example.
- Randomly shuffled the options
## Usage
```python
from datasets import load_dataset
dataset = load_dataset(
"canbingol/amazon_massive_intent_tr",
"intent_with_options"
)
```
This configuration is intended for evaluating how well models distinguish between semantically similar intents in Turkish.