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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
+```
+
+## 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://raw.githubusercontent.com/deepset-ai/FARM/master/docs/img/deepset_logo.png)
+
+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)
+- [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/) | [Website](https://deepset.ai)
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