Create README.md

README.md

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+---
+language: en
+datasets:
+- squad_v2
+- covid_qa_deepset
+license: cc-by-4.0
+---
+
+# minilm-uncased-squad2 for QA on COVID-19
+
+## Overview
+**Language model:** deepset/minilm-uncased-squad2  
+**Language:** English  
+**Downstream-task:** Extractive QA  
+**Training data:** [SQuAD-style COV-19 QA](https://github.com/deepset-ai/COVID-QA/blob/master/data/question-answering/COVID-QA.json)  
+**Infrastructure**: A4000
+
+Initially fine-tuned for https://github.com/CDCapobianco/COVID-Question-Answering-REST-API
+## Hyperparameters
+```
+batch_size = 24
+n_epochs = 3
+base_LM_model = "deepset/minilm-uncased-squad2"
+max_seq_len = 384
+learning_rate = 3e-5
+lr_schedule = LinearWarmup
+warmup_proportion = 0.1
+doc_stride = 128
+dev_split = 0
+x_val_splits = 5
+no_ans_boost = -100
+```
+---
+license: cc-by-4.0
+---
+
+## Performance
+
+**Single EM-Scores:**   [0.7441, 0.7938, 0.6666, 0.6576, 0.6445]  
+**Single F1-Scores:**   [0.8261, 0.8748, 0.8188, 0.7633, 0.7935]  
+**Single top\\_3\\_recall Scores:**   [0.827, 0.776, 0.860, 0.771, 0.777]  
+**XVAL EM:**   0.7013 
+**XVAL f1:**   0.8153  
+
+## Usage
+
+### 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="Frizio/minilm-uncased-squad2-covidqa")
+```
+
+### In Transformers
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+
+model_name = "Frizio/minilm-uncased-squad2-covidqa"
+
+# 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)
+```