| 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) | |