Create Readme

README.md

@@ -0,0 +1,35 @@
+## Albert Transformer on SQuAD-v2
+
+Training is done on the [SQuAD_v2](https://rajpurkar.github.io/SQuAD-explorer/) dataset. The model can be accessed via HuggingFace:
+
+## Model Specifications
+
+We have used the following parameters:
+
+- num_train_epochs=0.25,              
+- per_device_train_batch_size=5,    
+- per_device_eval_batch_size=10,  
+- warmup_steps=100,                
+- weight_decay=0.01,               
+
+## Usage Specifications
+
+```python
+from transformers import AutoTokenizer,AutoModelForQuestionAnswering
+from transformers import pipeline
+model=AutoModelForQuestionAnswering.from_pretrained('abhilash1910/albert-squad-v2')
+tokenizer=AutoTokenizer.from_pretrained('abhilash1910/albert-squad-v2')
+nlp_QA=pipeline('question-answering',model=model,tokenizer=tokenizer)
+QA_inp={
+    'question': 'How many parameters does Bert large have?',
+    'context': 'Bert large is really big... it has 24 layers, for a total of 340M parameters.Altogether it is 1.34 GB so expect it to take a couple minutes to download to your Colab instance.'
+}
+result=nlp_QA(QA_inp)
+result
+```
+## Result
+
+The result is:
+
+
+{'answer': '340M', 'end': 65, 'score': 0.14847151935100555, 'start': 61}