| license: apache-2.0 | |
| task_categories: | |
| - zero-shot-classification | |
| - text-classification | |
| task_ids: | |
| - natural-language-inference | |
| language: | |
| - en | |
| dataset_info: | |
| features: | |
| - name: labels | |
| dtype: int64 | |
| - name: premise | |
| dtype: string | |
| - name: hypothesis | |
| dtype: string | |
| - name: task | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 551422306.0 | |
| num_examples: 1090333 | |
| - name: validation | |
| num_bytes: 10824484.0 | |
| num_examples: 14419 | |
| - name: test | |
| num_bytes: 9739005.0 | |
| num_examples: 14680 | |
| download_size: 302500802 | |
| dataset_size: 571985795.0 | |
| [tasksource](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/deberta-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"], | |
| ) | |
| ``` | |
| [deberta-v3-base-tasksource-nli](https://huggingface.co/sileod/deberta-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} | |
| } | |
| ``` |