| | --- |
| | license: apache-2.0 |
| | tags: |
| | - mdeberta-v3-base |
| | - text-classification |
| | - nli |
| | - natural-language-inference |
| | - multilingual |
| | - multitask |
| | - multi-task |
| | - pipeline |
| | - extreme-multi-task |
| | - extreme-mtl |
| | - tasksource |
| | - zero-shot |
| | - rlhf |
| | datasets: |
| | - xnli |
| | - metaeval/xnli |
| | - americas_nli |
| | - MoritzLaurer/multilingual-NLI-26lang-2mil7 |
| | - stsb_multi_mt |
| | - paws-x |
| | - miam |
| | - strombergnlp/x-stance |
| | - tyqiangz/multilingual-sentiments |
| | - metaeval/universal-joy |
| | - amazon_reviews_multi |
| | - cardiffnlp/tweet_sentiment_multilingual |
| | - strombergnlp/offenseval_2020 |
| | - offenseval_dravidian |
| | - nedjmaou/MLMA_hate_speech |
| | - xglue |
| | - ylacombe/xsum_factuality |
| | - metaeval/x-fact |
| | - pasinit/xlwic |
| | - tasksource/oasst1_dense_flat |
| | - papluca/language-identification |
| | - wili_2018 |
| | - exams |
| | - xcsr |
| | - xcopa |
| | - juletxara/xstory_cloze |
| | - Anthropic/hh-rlhf |
| | - universal_dependencies |
| | - tasksource/oasst1_pairwise_rlhf_reward |
| | - OpenAssistant/oasst1 |
| | language: |
| | - multilingual |
| | - zh |
| | - ja |
| | - ar |
| | - ko |
| | - de |
| | - fr |
| | - es |
| | - pt |
| | - hi |
| | - id |
| | - it |
| | - tr |
| | - ru |
| | - bn |
| | - ur |
| | - mr |
| | - ta |
| | - vi |
| | - fa |
| | - pl |
| | - uk |
| | - nl |
| | - sv |
| | - he |
| | - sw |
| | - ps |
| | pipeline_tag: zero-shot-classification |
| | --- |
| | |
| | # Model Card for mDeBERTa-v3-base-tasksource-nli |
| |
|
| | Multilingual [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) with 30k steps multi-task training on [mtasksource](https://github.com/sileod/tasksource/blob/main/mtasks.md) |
| | This model can be used as a stable starting-point for further fine-tuning, or directly in zero-shot NLI model or a zero-shot pipeline. |
| | In addition, you can use the provided [adapters](https://huggingface.co/sileod/mdeberta-v3-base-tasksource-adapters) to directly load a model for hundreds of tasks. |
| | ```python |
| | !pip install tasknet, tasksource -q |
| | import tasknet as tn |
| | pipe=tn.load_pipeline( |
| | 'sileod/mdeberta-v3-base-tasksource-nli', |
| | 'miam/dihana') |
| | pipe(['si','como esta?']) |
| | ``` |
| |
|
| | For more details, see [deberta-v3-base-tasksource-nli](https://huggingface.co/sileod/deberta-v3-base-tasksource-nli) and replace tasksource by mtasksource. |
| |
|
| | # Software |
| | https://github.com/sileod/tasksource/ |
| | https://github.com/sileod/tasknet/ |
| |
|
| | # Contact and citation |
| | For help integrating tasksource into your experiments, please contact [damien.sileo@inria.fr](mailto:damien.sileo@inria.fr). |
| |
|
| | For more details, refer to this [article:](https://arxiv.org/abs/2301.05948) |
| | ```bib |
| | @article{sileo2023tasksource, |
| | title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation}, |
| | author={Sileo, Damien}, |
| | url= {https://arxiv.org/abs/2301.05948}, |
| | journal={arXiv preprint arXiv:2301.05948}, |
| | year={2023} |
| | } |
| | ``` |
