Datasets:
File size: 2,612 Bytes
a0deb73 b4de310 a0deb73 b4de310 a0deb73 b4de310 d4e61dd a0deb73 229371f 450fe81 229371f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
---
dataset_info:
- config_name: default
features:
- name: utterance
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 924830
num_examples: 11514
download_size: 347436
dataset_size: 924830
- config_name: intents
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: tags
sequence: 'null'
- name: regexp_full_match
sequence: 'null'
- name: regexp_partial_match
sequence: 'null'
- name: description
dtype: 'null'
splits:
- name: intents
num_bytes: 2266
num_examples: 60
download_size: 3945
dataset_size: 2266
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: intents
data_files:
- split: intents
path: intents/intents-*
task_categories:
- text-classification
language:
- ru
---
# Russian massive
This is a text classification dataset. It is intended for machine learning research and experimentation.
This dataset is obtained via formatting another publicly available data to be compatible with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html).
## Usage
It is intended to be used with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
```python
from autointent import Dataset
massive_ru = Dataset.from_datasets("AutoIntent/massive_ru")
```
## Source
This dataset is taken from `mteb/amazon_massive_intent` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
```python
from datasets import load_dataset
def convert_massive(massive_train):
intent_names = sorted(massive_train.unique("label"))
name_to_id = dict(zip(intent_names, range(len(intent_names)), strict=False))
n_classes = len(intent_names)
classwise_utterance_records = [[] for _ in range(n_classes)]
intents = [
{
"id": i,
"name": name,
}
for i, name in enumerate(intent_names)
]
for batch in massive_train.iter(batch_size=16, drop_last_batch=False):
for txt, name in zip(batch["text"], batch["label"], strict=False):
intent_id = name_to_id[name]
target_list = classwise_utterance_records[intent_id]
target_list.append({"utterance": txt, "label": intent_id})
utterances = [rec for lst in classwise_utterance_records for rec in lst]
return Dataset.from_dict({"intents": intents, "train": utterances})
massive = load_dataset("mteb/amazon_massive_intent", "ru")
massive_converted = convert_massive(massive["train"])
``` |