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README.md

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-license: cc-by-4.0
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-
-# roberta-base-squad2 for QA on COVID-19
-
-## Overview
-**Language model:** deepset/roberta-base-squad2  
-**Language:** English  
-**Downstream-task:** Extractive QA  
-**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)  
-**Code:**  See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering_crossvalidation.py) in [FARM](https://github.com/deepset-ai/FARM)  
-**Infrastructure**: Tesla v100
-
-## Hyperparameters
-```
-batch_size = 24
-n_epochs = 3
-base_LM_model = "deepset/roberta-base-squad2"
-max_seq_len = 384
-learning_rate = 3e-5
-lr_schedule = LinearWarmup
-warmup_proportion = 0.1
-doc_stride = 128
-xval_folds = 5
-dev_split = 0
-no_ans_boost = -100
-```
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-license: cc-by-4.0
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-
-## Performance
-5-fold cross-validation on the data set led to the following results:  
-
-**Single EM-Scores:**   [0.222, 0.123, 0.234, 0.159, 0.158]  
-**Single F1-Scores:**   [0.476, 0.493, 0.599, 0.461, 0.465]  
-**Single top\\_3\\_recall Scores:**   [0.827, 0.776, 0.860, 0.771, 0.777]  
-**XVAL EM:**   0.17890995260663506  
-**XVAL f1:**   0.49925444207319924  
-**XVAL top\\_3\\_recall:**   0.8021327014218009
-
-This model is the model obtained from the **third** fold of the cross-validation.
-
-## Usage
-
-### In Transformers
-```python
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
-
-
-model_name = "deepset/roberta-base-squad2-covid"
-
-# 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)
-```
-
-### In FARM
-```python
-from farm.modeling.adaptive_model import AdaptiveModel
-from farm.modeling.tokenization import Tokenizer
-from farm.infer import Inferencer
-
-model_name = "deepset/roberta-base-squad2-covid"
-
-# a) Get predictions
-nlp = Inferencer.load(model_name, task_type="question_answering")
-QA_input = [{"questions": ["Why is model conversion important?"],
-             "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
-res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)
-
-# b) Load model & tokenizer
-model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
-tokenizer = Tokenizer.load(model_name)
-```
-
-### 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/roberta-base-squad2-covid")
-# or 
-reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2-covid")
-```
-
-## Authors
-Branden Chan: `branden.chan [at] deepset.ai`  
-Timo Möller: `timo.moeller [at] deepset.ai`  
-Malte Pietsch: `malte.pietsch [at] deepset.ai`  
-Tanay Soni: `tanay.soni [at] deepset.ai`  
-Bogdan Kostić: `bogdan.kostic [at] deepset.ai`  
-
-## About us
-![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo)
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our work: 
-- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-- [Haystack](https://github.com/deepset-ai/haystack/)
-
-Get in touch:
-[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)
-
-By the way: [we're hiring!](http://www.deepset.ai/jobs)