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README.md

@@ -31,17 +31,16 @@ model-index:
       verified: true
       verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFkMmI2ODM0NmY5NGNkNmUxYWViOWYxZDNkY2EzYWFmOWI4N2VhYzY5MGEzMTVhOTU4Zjc4YWViOGNjOWJjMCIsInZlcnNpb24iOjF9.fexrU1icJK5_MiifBtZWkeUvpmFISqBLDXSQJ8E6UnrRof-7cU0s4tX_dIsauHWtUpIHMPZCf5dlMWQKXZuAAA
 ---
+# algmon-base for QA
 
-# roberta-base for QA 
-
-This is the best model for QA [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. 
-
+This is the base model for QA [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
 
 ## Overview
-**Language model:** roberta-base  
-**Language:** English  
-**Downstream-task:** Extractive QA  
-**Training data:** SQuAD 2.0  
+
+**Language model:** roberta-base
+**Language:** English
+**Downstream-task:** Extractive QA
+**Training data:** SQuAD 2.0
 **Eval data:** SQuAD 2.0
 **Infrastructure**: 4x Tesla v100
 
@@ -57,20 +56,24 @@ lr_schedule = LinearWarmup
 warmup_proportion = 0.2
 doc_stride=128
 max_query_length=64
-``` 
+```
 
 ## Usage
 
 ### In Haystack
+
 Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
+
 ```python
 reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
 # or 
 reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
 ```
+
 For a complete example of ``roberta-base-squad2`` being used for  Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)
 
 ### In Transformers
+
 ```python
 from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
 
@@ -90,6 +93,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 ```
 
 ## Performance
+
 Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
 
 ```