**Language model:** bert-base-cased **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering. **Language model:** bert-large **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
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View details ↗**Language model:** deepset/roberta-base-squad2-distilled **Language:** English **Training data:** SQuAD 2.0 training set **Eval data:** SQuAD 2.0 dev set **Infrastructure**: 1x V100 GPU **Published**: Apr 21st, 2021 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. **Language model:** deberta-v3-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x NVIDIA A10G Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
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View details ↗This is the deberta-v3-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. **Language model:** deberta-v3-large **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x NVIDIA A10G Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
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View details ↗**Language model:** electra-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x Tesla v100 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model and show that it outperforms its predecessors. **Paper:** here **Architecture:** BERT base **Language:** German See also: deepset/gbert-base deepset/gbert-large deepset/gelectra-base deepset/gelectra-large deepset/gelectra-base-generator deepset/gelectra-large-generator
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View details ↗Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our paper, we outline the steps taken to train our model and show that it outperforms its predecessors. See also: deepset/gbert-base deepset/gbert-large deepset/gelectra-base deepset/gelectra-large deepset/gelectra-base-generator deepset/gelectra-large-generator **Branden Chan:** branden.chan@deepset.ai **Stefan Schweter:** stefan@schweter.eu **Timo Möller:** timo.moeller@deepset.ai
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View details ↗**Language model:** gelectra-base-germanquad **Language:** German **Training data:** GermanQuAD train set (~ 12MB) **Eval data:** GermanQuAD test set (~ 5MB) **Infrastructure**: 1x V100 GPU **Code:** See an example extractive QA pipeline built with Haystack **Published**: Apr 21st, 2021 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗**Language model:** gelectra-large-germanquad **Language:** German **Training data:** GermanQuAD train set (~ 12MB) **Eval data:** GermanQuAD test set (~ 5MB) **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x V100 GPU **Published**: Apr 21st, 2021 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗**Language model:** microsoft/MiniLM-L12-H384-uncased **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 1x Tesla v100 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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View details ↗This is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. We have also released a distilled version of this model called deepset/tinyroberta-squad2. It has a comparable prediction quality and runs at twice the speed of deepset/roberta-base-squad2. **Language model:** roberta-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 4x Tesla v100 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack:
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View details ↗**Language model:** deepset/roberta-base-squad2-distilled **Language:** English **Training data:** SQuAD 2.0 training set **Eval data:** SQuAD 2.0 dev set **Code:** See an example extractive QA pipeline built with Haystack **Infrastructure**: 4x V100 GPU **Published**: Dec 8th, 2021 Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial.
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