Datasets:
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---
dataset_info:
- config_name: default
features:
- name: utterance
dtype: string
- name: label
sequence: int64
splits:
- name: train
num_bytes: 1074443.008463079
num_examples: 12384
- name: test
num_bytes: 268523.991536921
num_examples: 3095
download_size: 300800
dataset_size: 1342967.0
- config_name: intents
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: tags
sequence: 'null'
- name: regex_full_match
sequence: 'null'
- name: regex_partial_match
sequence: 'null'
- name: description
dtype: 'null'
splits:
- name: intents
num_bytes: 207
num_examples: 7
download_size: 2996
dataset_size: 207
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- config_name: intents
data_files:
- split: intents
path: intents/intents-*
task_categories:
- text-classification
language:
- en
---
# dstc3
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
dstc3 = Dataset.from_hub("AutoIntent/dstc3")
```
## Source
This dataset is taken from `marcel-gohsen/dstc3` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
```python
import datasets
from autointent import Dataset
from autointent.context.data_handler import split_dataset
def extract_intent_info(ds: datasets.Dataset) -> list[str]:
ds = ds.filter(lambda example: example["transcript"] != "")
intent_names = sorted(
set(name for intents in ds["intent"] for name in intents)
)
intent_names.remove("reqmore")
ds.filter(lambda example: "reqmore" in example["intent"])
return intent_names
def parse(ds: datasets.Dataset, intent_names: list[str]):
def transform(example: dict):
return {
"utterance": example["transcript"],
"label": [int(name in example["intent"]) for name in intent_names],
}
return ds.map(
transform, remove_columns=ds.features.keys()
)
def calc_fractions(ds: datasets.Dataset, intent_names: list[str]) -> list[float]:
res = [0] * len(intent_names)
for sample in ds:
for i, indicator in enumerate(sample["label"]):
res[i] += indicator
for i in range(len(intent_names)):
res[i] /= len(ds)
return res
def remove_low_resource_classes(ds: datasets.Dataset, intent_names: list[str], fraction_thresh: float = 0.01) -> tuple[list[dict], list[str]]:
remove_or_not = [(frac < fraction_thresh) for frac in calc_fractions(ds, intent_names)]
intent_names = [name for i, name in enumerate(intent_names) if not remove_or_not[i]]
res = []
for sample in ds:
if sum(sample["label"]) == 1 and remove_or_not[sample["label"].index(1)]:
continue
sample["label"] = [
indicator for indicator, low_resource in
zip(sample["label"], remove_or_not, strict=True) if not low_resource
]
res.append(sample)
return res, intent_names
def remove_oos(ds: datasets.Dataset):
return ds.filter(lambda sample: sum(sample["label"]) != 0)
if __name__ == "__main__":
dstc3 = datasets.load_dataset("marcel-gohsen/dstc3")
intent_names = extract_intent_info(dstc3["test"])
parsed = parse(dstc3["test"], intent_names)
filtered, intent_names = remove_low_resource_classes(remove_oos(parsed), intent_names)
intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
dstc_final = Dataset.from_dict({"intents": intents, "train": filtered})
dstc_final["train"], dstc_final["test"] = split_dataset(
dstc_final, split="train", test_size=0.2, random_seed=42
)
```
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