
# Description
This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters. 
- https://huggingface.co/UKP-SQuARE/MADE_HotpotQA_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_TriviaQA_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_SQuAD_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_SearchQA_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_NewsQA_Adapter
- https://huggingface.co/UKP-SQuARE/MADE_NaturalQuestions_Adapter

The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is https://github.com/princeton-nlp/MADE



# Evaluation Results
Friedman et al. (2021) reported the following results:

- SQuAD v1.1: 92.4
- HotoptQA: 81.5
- TriviaQA: 80.5
- NewsQA: 72.1
- SearchQA: 85.8
- NaturalQuestions: 80.9
- Avg: 82.2


Please refer to the original publication for more information.

# Citation
Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
{"language": ["en"], "license": "mit", "library_name": "adapter-transformers", "datasets": ["mrqa"], "metrics": ["squad"], "pipeline_tag": "question-answering"}
adapter-transformers pytorch roberta question-answering en dataset:mrqa license:mit region:us
TAGS
#adapter-transformers #pytorch #roberta #question-answering #en #dataset-mrqa #license-mit #region-us 


# Description
This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters. 
- URL
- URL
- URL
- URL
- URL
- URL

The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is URL



# Evaluation Results
Friedman et al. (2021) reported the following results:

- SQuAD v1.1: 92.4
- HotoptQA: 81.5
- TriviaQA: 80.5
- NewsQA: 72.1
- SearchQA: 85.8
- NaturalQuestions: 80.9
- Avg: 82.2


Please refer to the original publication for more information.

Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
# Description
This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters. 
- URL
- URL
- URL
- URL
- URL
- URL

The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is URL # Evaluation Results
Friedman et al. (2021) reported the following results:

- SQuAD v1.1: 92.4
- HotoptQA: 81.5
- TriviaQA: 80.5
- NewsQA: 72.1
- SearchQA: 85.8
- NaturalQuestions: 80.9
- Avg: 82.2


Please refer to the original publication for more information.

Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
TAGS
#adapter-transformers #pytorch #roberta #question-answering #en #dataset-mrqa #license-mit #region-us 
 # Description
This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters. 
- URL
- URL
- URL
- URL
- URL
- URL

The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is URL # Evaluation Results
Friedman et al. (2021) reported the following results:

- SQuAD v1.1: 92.4
- HotoptQA: 81.5
- TriviaQA: 80.5
- NewsQA: 72.1
- SearchQA: 85.8
- NaturalQuestions: 80.9
- Avg: 82.2


Please refer to the original publication for more information.

Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
TAGS
#adapter-transformers #pytorch #roberta #question-answering #en #dataset-mrqa #license-mit #region-us 
# Description
This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters. 
- URL
- URL
- URL
- URL
- URL
- URL

The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is URL# Evaluation Results
Friedman et al. (2021) reported the following results:

- SQuAD v1.1: 92.4
- HotoptQA: 81.5
- TriviaQA: 80.5
- NewsQA: 72.1
- SearchQA: 85.8
- NaturalQuestions: 80.9
- Avg: 82.2


Please refer to the original publication for more information.

Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)