Created BERT Text Summarization API deployed on AWS ECR
Following Hugging face model was used : mrm8488/t5-base-finetuned-summarize-news 🚀
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu
To load and save the model -
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
To Invoke the model from Hugging face -
import requests
API_URL = "https://api-inference.huggingface.co/models/mrm8488/t5-base-finetuned-summarize-news"
headers = {"Authorization": f"Bearer {API_TOKEN}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "The answer to the universe is",
})
The API is deployed and REST POST call was 200 OK
