
# 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}
}
```
{"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"], "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"], "pipeline_tag": "zero-shot-classification"}
transformers pytorch safetensors deberta-v2 text-classification mdeberta-v3-base nli natural-language-inference multilingual multitask multi-task pipeline extreme-multi-task extreme-mtl tasksource zero-shot rlhf zero-shot-classification 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 dataset:xnli dataset:metaeval/xnli dataset:americas_nli dataset:MoritzLaurer/multilingual-NLI-26lang-2mil7 dataset:stsb_multi_mt dataset:paws-x dataset:miam dataset:strombergnlp/x-stance dataset:tyqiangz/multilingual-sentiments dataset:metaeval/universal-joy dataset:amazon_reviews_multi dataset:cardiffnlp/tweet_sentiment_multilingual dataset:strombergnlp/offenseval_2020 dataset:offenseval_dravidian dataset:nedjmaou/MLMA_hate_speech dataset:xglue dataset:ylacombe/xsum_factuality dataset:metaeval/x-fact dataset:pasinit/xlwic dataset:tasksource/oasst1_dense_flat dataset:papluca/language-identification dataset:wili_2018 dataset:exams dataset:xcsr dataset:xcopa dataset:juletxara/xstory_cloze dataset:Anthropic/hh-rlhf dataset:universal_dependencies dataset:tasksource/oasst1_pairwise_rlhf_reward dataset:OpenAssistant/oasst1 arxiv:2301.05948 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #mdeberta-v3-base #nli #natural-language-inference #multilingual #multitask #multi-task #pipeline #extreme-multi-task #extreme-mtl #tasksource #zero-shot #rlhf #zero-shot-classification #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 #dataset-xnli #dataset-metaeval/xnli #dataset-americas_nli #dataset-MoritzLaurer/multilingual-NLI-26lang-2mil7 #dataset-stsb_multi_mt #dataset-paws-x #dataset-miam #dataset-strombergnlp/x-stance #dataset-tyqiangz/multilingual-sentiments #dataset-metaeval/universal-joy #dataset-amazon_reviews_multi #dataset-cardiffnlp/tweet_sentiment_multilingual #dataset-strombergnlp/offenseval_2020 #dataset-offenseval_dravidian #dataset-nedjmaou/MLMA_hate_speech #dataset-xglue #dataset-ylacombe/xsum_factuality #dataset-metaeval/x-fact #dataset-pasinit/xlwic #dataset-tasksource/oasst1_dense_flat #dataset-papluca/language-identification #dataset-wili_2018 #dataset-exams #dataset-xcsr #dataset-xcopa #dataset-juletxara/xstory_cloze #dataset-Anthropic/hh-rlhf #dataset-universal_dependencies #dataset-tasksource/oasst1_pairwise_rlhf_reward #dataset-OpenAssistant/oasst1 #arxiv-2301.05948 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Model Card for mDeBERTa-v3-base-tasksource-nli

Multilingual mdeberta-v3-base with 30k steps multi-task training on mtasksource
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 to directly load a model for hundreds of tasks. 


For more details, see deberta-v3-base-tasksource-nli and replace tasksource by mtasksource.

# Software
URL
URL

# Contact and citation
For help integrating tasksource into your experiments, please contact URL@URL.

For more details, refer to this article: 

# Model Card for mDeBERTa-v3-base-tasksource-nli

Multilingual mdeberta-v3-base with 30k steps multi-task training on mtasksource
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 to directly load a model for hundreds of tasks. 


For more details, see deberta-v3-base-tasksource-nli and replace tasksource by mtasksource. # Software
URL
URL # Contact and citation
For help integrating tasksource into your experiments, please contact URL@URL.

For more details, refer to this article:
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #mdeberta-v3-base #nli #natural-language-inference #multilingual #multitask #multi-task #pipeline #extreme-multi-task #extreme-mtl #tasksource #zero-shot #rlhf #zero-shot-classification #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 #dataset-xnli #dataset-metaeval/xnli #dataset-americas_nli #dataset-MoritzLaurer/multilingual-NLI-26lang-2mil7 #dataset-stsb_multi_mt #dataset-paws-x #dataset-miam #dataset-strombergnlp/x-stance #dataset-tyqiangz/multilingual-sentiments #dataset-metaeval/universal-joy #dataset-amazon_reviews_multi #dataset-cardiffnlp/tweet_sentiment_multilingual #dataset-strombergnlp/offenseval_2020 #dataset-offenseval_dravidian #dataset-nedjmaou/MLMA_hate_speech #dataset-xglue #dataset-ylacombe/xsum_factuality #dataset-metaeval/x-fact #dataset-pasinit/xlwic #dataset-tasksource/oasst1_dense_flat #dataset-papluca/language-identification #dataset-wili_2018 #dataset-exams #dataset-xcsr #dataset-xcopa #dataset-juletxara/xstory_cloze #dataset-Anthropic/hh-rlhf #dataset-universal_dependencies #dataset-tasksource/oasst1_pairwise_rlhf_reward #dataset-OpenAssistant/oasst1 #arxiv-2301.05948 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Model Card for mDeBERTa-v3-base-tasksource-nli

Multilingual mdeberta-v3-base with 30k steps multi-task training on mtasksource
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 to directly load a model for hundreds of tasks. 


For more details, see deberta-v3-base-tasksource-nli and replace tasksource by mtasksource. # Software
URL
URL # Contact and citation
For help integrating tasksource into your experiments, please contact URL@URL.

For more details, refer to this article:
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #mdeberta-v3-base #nli #natural-language-inference #multilingual #multitask #multi-task #pipeline #extreme-multi-task #extreme-mtl #tasksource #zero-shot #rlhf #zero-shot-classification #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 #dataset-xnli #dataset-metaeval/xnli #dataset-americas_nli #dataset-MoritzLaurer/multilingual-NLI-26lang-2mil7 #dataset-stsb_multi_mt #dataset-paws-x #dataset-miam #dataset-strombergnlp/x-stance #dataset-tyqiangz/multilingual-sentiments #dataset-metaeval/universal-joy #dataset-amazon_reviews_multi #dataset-cardiffnlp/tweet_sentiment_multilingual #dataset-strombergnlp/offenseval_2020 #dataset-offenseval_dravidian #dataset-nedjmaou/MLMA_hate_speech #dataset-xglue #dataset-ylacombe/xsum_factuality #dataset-metaeval/x-fact #dataset-pasinit/xlwic #dataset-tasksource/oasst1_dense_flat #dataset-papluca/language-identification #dataset-wili_2018 #dataset-exams #dataset-xcsr #dataset-xcopa #dataset-juletxara/xstory_cloze #dataset-Anthropic/hh-rlhf #dataset-universal_dependencies #dataset-tasksource/oasst1_pairwise_rlhf_reward #dataset-OpenAssistant/oasst1 #arxiv-2301.05948 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Model Card for mDeBERTa-v3-base-tasksource-nli

Multilingual mdeberta-v3-base with 30k steps multi-task training on mtasksource
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 to directly load a model for hundreds of tasks. 


For more details, see deberta-v3-base-tasksource-nli and replace tasksource by mtasksource.# Software
URL
URL# Contact and citation
For help integrating tasksource into your experiments, please contact URL@URL.

For more details, refer to this article: