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license: cc-by-4.0 |
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# roberta-base-squad2 for QA on COVID-19 |
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## Overview |
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**Language model:** deepset/roberta-base-squad2 |
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**Language:** English |
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**Downstream-task:** Extractive QA |
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**Training data:** [SQuAD-style CORD-19 annotations from 23rd April](https://github.com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_covidQA.json) |
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**Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering_crossvalidation.py) in [FARM](https://github.com/deepset-ai/FARM) |
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**Infrastructure**: Tesla v100 |
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## Hyperparameters |
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``` |
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batch_size = 24 |
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n_epochs = 3 |
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base_LM_model = "deepset/roberta-base-squad2" |
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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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warmup_proportion = 0.1 |
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doc_stride = 128 |
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xval_folds = 5 |
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dev_split = 0 |
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no_ans_boost = -100 |
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``` |
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--- |
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license: cc-by-4.0 |
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--- |
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## Performance |
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5-fold cross-validation on the data set led to the following results: |
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**Single EM-Scores:** [0.222, 0.123, 0.234, 0.159, 0.158] |
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**Single F1-Scores:** [0.476, 0.493, 0.599, 0.461, 0.465] |
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**Single top\\_3\\_recall Scores:** [0.827, 0.776, 0.860, 0.771, 0.777] |
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**XVAL EM:** 0.17890995260663506 |
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**XVAL f1:** 0.49925444207319924 |
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**XVAL top\\_3\\_recall:** 0.8021327014218009 |
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This model is the model obtained from the **third** fold of the cross-validation. |
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## Usage |
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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/roberta-base-squad2-covid" |
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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/roberta-base-squad2-covid" |
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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/roberta-base-squad2-covid") |
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# or |
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reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2-covid") |
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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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Bogdan Kosti膰: `bogdan.kostic [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/) | [Slack](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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