refactor readme and test_endpoint

.gitignore

@@ -2,9 +2,10 @@ __pycache__
 *.ipynb
 *.pdf
 
-test_endpoint.py
 test_handler_local.py
 
 setup
 upload_to_hf
 requirements.txt
+hf_token.py
+

README.md

@@ -4,8 +4,17 @@ license: apache-2.0
 datasets:
 - bookcorpus
 - wikipedia
+tags:
+  - endpoints-template
+library_name: generic
+model-index:
+  - name: bert-large-uncased-whole-word-masking-finetuned-squad
+    results: []
+pipeline_tag: other
 ---
 
+# DEPLOYED @: https://ciy95hpzki22rqvf.us-east-1.aws.endpoints.huggingface.cloud
+
 # BERT large model (uncased) whole word masking finetuned on SQuAD
 
 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
@@ -137,15 +146,14 @@ exact_match = 86.91
 
 
 
-# HF endpoint deployment errors
+# Error Log
 
-1
-```
+```json
 {'error': 'Body needs to provide a inputs key, recieved: b\'{"question":"What is my name?","context":"My name is Clara and I live in Berkeley."}\''}
 ```
 
-2
-```
+
+```json
 {'error': 'Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper__index_select)'}
 ```
 

test_endpoint.py

@@ -0,0 +1,38 @@
+import requests
+from hf_token import HF_TOKEN
+
+def query(token: str, url: str, payload: dict):
+    '''
+    returns:: (dict) :: 
+    {
+        "score": 0.9873963594436646,
+        "start": 34,
+        "end": 40,
+        "answer": "Berlin"
+    }
+    '''
+    headers = {
+        "Authorization": f"Bearer {token}",
+        "Content-Type": "application/json"
+    }
+    response = requests.post(url, headers=headers, json=payload)
+    return response.json()
+
+
+if __name__ == "__main__":
+    url = 'https://ciy95hpzki22rqvf.us-east-1.aws.endpoints.huggingface.cloud'
+    context_bert_abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
+    input_ = {
+        "inputs": {
+            "question": "What does the 'B' in BERT stand for?",
+            "context": context_bert_abstract
+        }
+    }
+    output = query(
+        HF_TOKEN,
+        url, 
+        input_
+    )
+    print(output)
+
+