
# Description
This is the single-dataset adapter for the TriviaQA partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the `roberta-base` encoder.



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

# Usage
This model contains the same weights as https://huggingface.co/princeton-nlp/MADE/resolve/main/single_dataset_adapters/TriviaQA/model.pt. The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:

```
from transformers import RobertaForQuestionAnswering, RobertaTokenizerFast

model = RobertaForQuestionAnswering.from_pretrained("roberta-base")
model.load_adapter("UKP-SQuARE/TriviaQA_Adapter_RoBERTa",  source="hf")
model.set_active_adapters("TriviaQA")

tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')

pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
pipe({"question": "What is the capital of Germany?",  "context": "The capital of Germany is Berlin."})
```

Note you need the adapter-transformers library https://adapterhub.ml

# Evaluation
Friedman et al. report an F1 score of **79.6 on TriviaQA**.

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 roberta question-answering en dataset:mrqa license:mit region:us
TAGS
#adapter-transformers #roberta #question-answering #en #dataset-mrqa #license-mit #region-us 


# Description
This is the single-dataset adapter for the TriviaQA partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the 'roberta-base' encoder.



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

# Usage
This model contains the same weights as URL The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:



Note you need the adapter-transformers library URL

# Evaluation
Friedman et al. report an F1 score of 79.6 on TriviaQA.

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 single-dataset adapter for the TriviaQA partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the 'roberta-base' encoder.



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 # Usage
This model contains the same weights as URL The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:



Note you need the adapter-transformers library URL # Evaluation
Friedman et al. report an F1 score of 79.6 on TriviaQA.

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 #roberta #question-answering #en #dataset-mrqa #license-mit #region-us 
 # Description
This is the single-dataset adapter for the TriviaQA partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the 'roberta-base' encoder.



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 # Usage
This model contains the same weights as URL The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:



Note you need the adapter-transformers library URL # Evaluation
Friedman et al. report an F1 score of 79.6 on TriviaQA.

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 #roberta #question-answering #en #dataset-mrqa #license-mit #region-us 
# Description
This is the single-dataset adapter for the TriviaQA partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the 'roberta-base' encoder.



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# Usage
This model contains the same weights as URL The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:



Note you need the adapter-transformers library URL# Evaluation
Friedman et al. report an F1 score of 79.6 on TriviaQA.

Please refer to the original publication for more information.

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