--- language: multilingual license: cc-by-4.0 tags: - question-answering datasets: - squad_v2 model-index: - name: deepset/xlm-roberta-large-squad2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - type: exact_match value: 81.8281 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzVhZDE2NTg5NmUwOWRkMmI2MGUxYjFlZjIzNmMyNDQ2MDY2MDNhYzE0ZjY5YTkyY2U4ODc3ODFiZjQxZWQ2YSIsInZlcnNpb24iOjF9.f_rN3WPMAdv-OBPz0T7N7lOxYz9f1nEr_P-vwKhi3jNdRKp_JTy18MYR9eyJM2riKHC6_ge-8XwfyrUf51DSDA - type: f1 value: 84.8886 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGE5MWJmZGUxMGMwNWFhYzVhZjQwZGEwOWQ4N2Q2Yjg5NzdjNDFiNDhiYTQ1Y2E5ZWJkOTFhYmI1Y2Q2ZGYwOCIsInZlcnNpb24iOjF9.TIdH-tOx3kEMDs5wK1r6iwZqqSjNGlBrpawrsE917j1F3UFJVnQ7wJwaj0OIgmC4iw8OQeLZL56ucBcLApa-AQ --- # Multilingual XLM-RoBERTa large for Extractive QA on various languages ## Overview **Language model:** xlm-roberta-large **Language:** Multilingual **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD dev set - German MLQA - German XQuAD **Training run:** [MLFlow link](https://public-mlflow.deepset.ai/#/experiments/124/runs/3a540e3f3ecf4dd98eae8fc6d457ff20) **Code:** See [an example extractive QA pipeline built with Haystack](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "xlm-roberta-large" max_seq_len = 256 learning_rate = 1e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## 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](https://github.com/deepset-ai/haystack/): ```python # After running pip install haystack-ai "transformers[torch,sentencepiece]" from haystack import Document from haystack.components.readers import ExtractiveReader docs = [ Document(content="Python is a popular programming language"), Document(content="python ist eine beliebte Programmiersprache"), ] reader = ExtractiveReader(model="deepset/xlm-roberta-large-squad2") reader.warm_up() question = "What is a popular programming language?" result = reader.run(query=question, documents=docs) # {'answers': [ExtractedAnswer(query='What is a popular programming language?', score=0.5740374326705933, data='python', document=Document(id=..., content: '...'), context=None, document_offset=ExtractedAnswer.Span(start=0, end=6),...)]} ``` For a complete example with an extractive question answering pipeline that scales over many documents, check out the [corresponding Haystack tutorial](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline). ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/xlm-roberta-large-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Performance Evaluated on the SQuAD 2.0 English dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.45759285774446, "f1": 83.79259828925511, "total": 11873, "HasAns_exact": 71.96356275303644, "HasAns_f1": 80.6460053117963, "HasAns_total": 5928, "NoAns_exact": 86.93019343986543, "NoAns_f1": 86.93019343986543, "NoAns_total": 5945 ``` Evaluated on German [MLQA: test-context-de-question-de.json](https://github.com/facebookresearch/MLQA) ``` "exact": 49.34691166703564, "f1": 66.15582561674236, "total": 4517, ``` Evaluated on German [XQuAD: xquad.de.json](https://github.com/deepmind/xquad) ``` "exact": 61.51260504201681, "f1": 78.80206098332569, "total": 1190, ``` ## Usage ### In Haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/xlm-roberta-large-squad2") # or reader = TransformersReader(model="deepset/xlm-roberta-large-squad2",tokenizer="deepset/xlm-roberta-large-squad2") ``` ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/xlm-roberta-large-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors **Branden Chan:** branden.chan@deepset.ai **Timo Möller:** timo.moeller@deepset.ai **Malte Pietsch:** malte.pietsch@deepset.ai **Tanay Soni:** tanay.soni@deepset.ai ## About us
[deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/). Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co./deepset/tinyroberta-squad2) - [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co./mixedbread-ai/deepset-mxbai-embed-de-large-v1) - [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio) ## Get in touch and join the Haystack community

For more info on Haystack, visit our GitHub repo and Documentation. We also have a Discord community open to everyone!

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