Datasets:
File size: 1,903 Bytes
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---
dataset_info:
features:
- name: labels
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: task
dtype: string
splits:
- name: train
num_bytes: 185352754.0
num_examples: 878967
- name: test
num_bytes: 1775890.0
num_examples: 9400
- name: validation
num_bytes: 1817480.0
num_examples: 9400
download_size: 104413879
dataset_size: 188946124.0
license: other
task_categories:
- zero-shot-classification
- text-classification
task_ids:
- natural-language-inference
multilinguality:
- multilingual
---
[mtasksource](https://github.com/sileod/tasksource) classification tasks recasted as natural language inference.
This dataset is intended to improve label understanding in [zero-shot classification HF pipelines](https://huggingface.co/docs/transformers/main/main_classes/pipelines#transformers.ZeroShotClassificationPipeline
).
Inputs that are text pairs are separated by a newline (\n).
```python
from transformers import pipeline
classifier = pipeline(model="sileod/mdeberta-v3-base-tasksource-nli")
classifier(
"I have a problem with my iphone that needs to be resolved asap!!",
candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
)
```
[mdeberta-v3-base-tasksource-nli](https://huggingface.co/sileod/mdeberta-v3-base-tasksource-nli) will include `label-nli` in its training mix (a relatively small portion, to keep the model general, but note that nli models work for label-like zero shot classification without specific supervision (https://aclanthology.org/D19-1404.pdf).
```
@article{sileo2023tasksource,
title={tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation},
author={Sileo, Damien},
year={2023}
}
``` |