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--- |
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language: multilingual |
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license: cc-by-4.0 |
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tags: |
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- question-answering |
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datasets: |
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- squad_v2 |
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model-index: |
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- name: deepset/xlm-roberta-large-squad2 |
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results: |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: squad_v2 |
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type: squad_v2 |
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config: squad_v2 |
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split: validation |
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metrics: |
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- type: exact_match |
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value: 81.8281 |
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name: Exact Match |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzVhZDE2NTg5NmUwOWRkMmI2MGUxYjFlZjIzNmMyNDQ2MDY2MDNhYzE0ZjY5YTkyY2U4ODc3ODFiZjQxZWQ2YSIsInZlcnNpb24iOjF9.f_rN3WPMAdv-OBPz0T7N7lOxYz9f1nEr_P-vwKhi3jNdRKp_JTy18MYR9eyJM2riKHC6_ge-8XwfyrUf51DSDA |
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- type: f1 |
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value: 84.8886 |
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name: F1 |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGE5MWJmZGUxMGMwNWFhYzVhZjQwZGEwOWQ4N2Q2Yjg5NzdjNDFiNDhiYTQ1Y2E5ZWJkOTFhYmI1Y2Q2ZGYwOCIsInZlcnNpb24iOjF9.TIdH-tOx3kEMDs5wK1r6iwZqqSjNGlBrpawrsE917j1F3UFJVnQ7wJwaj0OIgmC4iw8OQeLZL56ucBcLApa-AQ |
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--- |
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# Multilingual XLM-RoBERTa large for QA on various languages |
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## Overview |
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**Language model:** xlm-roberta-large |
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**Language:** Multilingual |
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**Downstream-task:** Extractive QA |
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**Training data:** SQuAD 2.0 |
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**Eval data:** SQuAD dev set - German MLQA - German XQuAD |
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**Training run:** [MLFlow link](https://public-mlflow.deepset.ai/#/experiments/124/runs/3a540e3f3ecf4dd98eae8fc6d457ff20) |
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**Infrastructure**: 4x Tesla v100 |
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## Hyperparameters |
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``` |
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batch_size = 32 |
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n_epochs = 3 |
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base_LM_model = "xlm-roberta-large" |
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max_seq_len = 256 |
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learning_rate = 1e-5 |
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lr_schedule = LinearWarmup |
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warmup_proportion = 0.2 |
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doc_stride=128 |
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max_query_length=64 |
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``` |
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## Performance |
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Evaluated on the SQuAD 2.0 English dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). |
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``` |
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"exact": 79.45759285774446, |
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"f1": 83.79259828925511, |
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"total": 11873, |
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"HasAns_exact": 71.96356275303644, |
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"HasAns_f1": 80.6460053117963, |
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"HasAns_total": 5928, |
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"NoAns_exact": 86.93019343986543, |
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"NoAns_f1": 86.93019343986543, |
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"NoAns_total": 5945 |
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``` |
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Evaluated on German [MLQA: test-context-de-question-de.json](https://github.com/facebookresearch/MLQA) |
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``` |
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"exact": 49.34691166703564, |
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"f1": 66.15582561674236, |
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"total": 4517, |
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``` |
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Evaluated on German [XQuAD: xquad.de.json](https://github.com/deepmind/xquad) |
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``` |
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"exact": 61.51260504201681, |
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"f1": 78.80206098332569, |
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"total": 1190, |
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``` |
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## Usage |
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### In Haystack |
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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/): |
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```python |
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reader = FARMReader(model_name_or_path="deepset/xlm-roberta-large-squad2") |
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# or |
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reader = TransformersReader(model="deepset/xlm-roberta-large-squad2",tokenizer="deepset/xlm-roberta-large-squad2") |
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``` |
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### In Transformers |
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```python |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
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model_name = "deepset/xlm-roberta-large-squad2" |
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# a) Get predictions |
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
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QA_input = { |
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'question': 'Why is model conversion important?', |
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'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' |
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} |
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res = nlp(QA_input) |
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# b) Load model & tokenizer |
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model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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``` |
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## Authors |
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**Branden Chan:** branden.chan@deepset.ai |
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**Timo Möller:** timo.moeller@deepset.ai |
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**Malte Pietsch:** malte.pietsch@deepset.ai |
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**Tanay Soni:** tanay.soni@deepset.ai |
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## About us |
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<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> |
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
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<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> |
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</div> |
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
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<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> |
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</div> |
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</div> |
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[deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. |
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Some of our other work: |
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- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) |
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- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) |
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- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) |
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## Get in touch and join the Haystack community |
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<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. |
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We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> |
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[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) |
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By the way: [we're hiring!](http://www.deepset.ai/jobs) |
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