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--- |
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language: multilingual |
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datasets: |
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- squad_v2 |
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license: mit |
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thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg |
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tags: |
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- exbert |
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--- |
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# deepset/xlm-roberta-base-squad2-distilled |
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- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model. |
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## Overview |
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**Language model:** deepset/xlm-roberta-base-squad2-distilled |
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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 2.0 |
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**Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) |
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**Infrastructure**: 1x Tesla v100 |
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## Hyperparameters |
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``` |
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batch_size = 56 |
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n_epochs = 4 |
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max_seq_len = 384 |
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learning_rate = 3e-5 |
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lr_schedule = LinearWarmup |
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embeds_dropout_prob = 0.1 |
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temperature = 3 |
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distillation_loss_weight = 0.75 |
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``` |
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## Usage |
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### In Haystack |
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Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model 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-base-squad2-distilled") |
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# or |
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reader = TransformersReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled",tokenizer="deepset/xlm-roberta-base-squad2-distilled") |
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``` |
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For a complete example of ``deepset/xlm-roberta-base-squad2-distilled`` being used for [question answering], check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) |
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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-base-squad2-distilled" |
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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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## Performance |
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Evaluated on the SQuAD 2.0 dev set |
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``` |
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"exact": 74.06721131980123% |
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"f1": 76.39919553344667% |
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``` |
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## Authors |
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**Timo Möller:** timo.moeller@deepset.ai |
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**Julian Risch:** julian.risch@deepset.ai |
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**Malte Pietsch:** malte.pietsch@deepset.ai |
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**Michel Bartels:** michel.bartels@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://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/join">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/join) | [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) |
