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--- |
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license: cc-by-4.0 |
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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-base-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: 74.0354 |
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name: Exact Match |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWMxNWQ2ODJkNWIzZGQwOWI4OTZjYjU3ZDVjZGQzMjI5MzljNjliZTY4Mzk4YTk4OTMzZWYxZjUxYmZhYTBhZSIsInZlcnNpb24iOjF9.eEeFYYJ30BfJDd-JYfI1kjlxJrRF6OFtj2GnkTCOO4kqX31inFy8ptDWusVlLFsUphm4dNWfTKXC5e-gytLBDA |
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- type: f1 |
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value: 77.1833 |
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name: F1 |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjg4MjNkOTA4Y2I5OGFlYTk1NWZjMWFlNjI5M2Y0NGZhMThhN2M4YmY2Y2RhZjcwYzU0MGNjN2RkZDljZmJmNiIsInZlcnNpb24iOjF9.TX42YMXpH4e0qu7cC4ARDlZWSkd55dwwyeyFXmOlXERNnEicDuFBCsy8WHLaqQCLUkzODJ22Hw4zhv81rwnlAQ |
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--- |
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# Multilingual XLM-RoBERTa base for QA on various languages |
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## Overview |
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**Language model:** xlm-roberta-base |
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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 dev set - German MLQA - German XQuAD |
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**Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) |
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**Infrastructure**: 4x Tesla v100 |
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## Hyperparameters |
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``` |
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batch_size = 22*4 |
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n_epochs = 2 |
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max_seq_len=256, |
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doc_stride=128, |
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learning_rate=2e-5, |
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``` |
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Corresponding experiment logs in mlflow: [link](https://public-mlflow.deepset.ai/#/experiments/2/runs/b25ec75e07614accb3f1ce03d43dbe08) |
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## Performance |
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Evaluated on the SQuAD 2.0 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": 73.91560683904657 |
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"f1": 77.14103746689592 |
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``` |
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Evaluated on German MLQA: test-context-de-question-de.json |
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"exact": 33.67279167589108 |
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"f1": 44.34437105434842 |
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"total": 4517 |
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Evaluated on German XQuAD: xquad.de.json |
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"exact": 48.739495798319325 |
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"f1": 62.552615701071495 |
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"total": 1190 |
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## Usage |
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### In Transformers |
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```python |
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from transformers.pipelines import pipeline |
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from transformers.modeling_auto import AutoModelForQuestionAnswering |
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from transformers.tokenization_auto import AutoTokenizer |
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model_name = "deepset/xlm-roberta-base-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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### In FARM |
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```python |
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from farm.modeling.adaptive_model import AdaptiveModel |
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from farm.modeling.tokenization import Tokenizer |
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from farm.infer import Inferencer |
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model_name = "deepset/xlm-roberta-base-squad2" |
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# a) Get predictions |
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nlp = Inferencer.load(model_name, task_type="question_answering") |
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QA_input = [{"questions": ["Why is model conversion important?"], |
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"text": "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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res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True) |
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# b) Load model & tokenizer |
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model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering") |
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tokenizer = Tokenizer.load(model_name) |
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``` |
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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-base-squad2") |
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# or |
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reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/xlm-roberta-base-squad2") |
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``` |
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## Authors |
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Branden Chan: `branden.chan [at] deepset.ai` |
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Timo M枚ller: `timo.moeller [at] deepset.ai` |
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Malte Pietsch: `malte.pietsch [at] deepset.ai` |
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Tanay Soni: `tanay.soni [at] deepset.ai` |
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## About us |
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We bring NLP to the industry via open source! |
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Our focus: Industry specific language models & large scale QA systems. |
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Some of our work: |
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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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- [FARM](https://github.com/deepset-ai/FARM) |
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- [Haystack](https://github.com/deepset-ai/haystack/) |
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Get in touch: |
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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) |
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