Multilingual XLM RoBERTa base for Extractive QA on various languages Overview Language model: xlm roberta base Language: Multilingual Downstream task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 dev set German MLQA German XQuAD Code: See an example extractive QA pipeline built with Haystack Infrastructure : 4x Tesla v100 Hyperparameters Corresponding experiment logs in mlflow: link Usage In 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. In Transformers Performance Evaluated on the SQuAD 2.0 dev set with the official eval script. Evaluated on German MLQA: test context de question de.json "exact": 33.67279167589108 "f1": 44.34437105434842 "total": 4517 Evaluated on German XQuAD: xquad.de.json "exact": 48.739495798319325 "f1": 62.552615701071495 "total": 1190 Authors Branden Chan: branden.chan [at] deepset.ai Timo Möller: timo.moeller [a…
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