Create README.md (#5108)

* Create README.md

* Update model_cards/a-ware/roberta-large-squad-classification/README.md

Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/a-ware/roberta-large-squad-classification/README.md

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+---
+datasets:
+- squad_v2
+---
+
+# Roberta-LARGE finetuned on SQuADv2
+
+This is roberta-large model finetuned on SQuADv2 dataset for question answering answerability classification
+
+## Model details
+This model is simply an Sequenceclassification model with two inputs (context and question) in a list.
+The result is either [1] for answerable or [0] if it is not answerable.
+It was trained over 4 epochs on squadv2 dataset and can be used to filter out which context is good to give into the QA model to avoid bad answers.
+
+## Model training
+This model was trained with following parameters using simpletransformers wrapper:
+```
+train_args = {
+    'learning_rate': 1e-5,
+    'max_seq_length': 512,
+    'overwrite_output_dir': True,
+    'reprocess_input_data': False,
+    'train_batch_size': 4,
+    'num_train_epochs': 4,
+    'gradient_accumulation_steps': 2,
+    'no_cache': True,
+    'use_cached_eval_features': False,
+    'save_model_every_epoch': False,
+    'output_dir': "bart-squadv2",
+    'eval_batch_size': 8,
+    'fp16_opt_level': 'O2',
+    }
+```
+
+## Results
+```{"accuracy": 90.48%}```
+## Model in Action  🚀
+```python3
+from simpletransformers.classification import ClassificationModel
+
+model = ClassificationModel('roberta', 'a-ware/roberta-large-squadv2', num_labels=2, args=train_args)
+
+predictions, raw_outputs = model.predict([["my dog is an year old. he loves to go into the rain", "how old is my dog ?"]])
+print(predictions)
+==> [1]
+```
+
+> Created with ❤️ by A-ware UG [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/aware-ai)