Bart summarizer

examples/pytorch/text-summarizer/README.md

@@ -1 +1,3 @@
 Please refer to the [tutorial](https://cortex.dev/iris-classifier) to see how to deploy an example with Cortex.
+
+Please refer [here](https://sshleifer.github.io/blog_v2/jupyter/2020/03/12/bart.html) to learn more about BART.

examples/pytorch/text-summarizer/cortex.yaml

@@ -5,4 +5,6 @@
     type: python
     path: predictor.py
   compute:
+    cpu: 1
+    gpu: 1  # This is optional, since the API will run on CPUs (albeit slower)
     mem: 4G

examples/pytorch/text-summarizer/predictor.py

@@ -1,11 +1,16 @@
 # WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
 
-from summarizer import Summarizer
+import torch
+from transformers import pipeline
 
 
 class PythonPredictor:
     def __init__(self, config):
-        self.model = Summarizer()
+        device = 0 if torch.cuda.is_available() else -1
+        self.summarizer = pipeline(task="summarization", device=device)
 
     def predict(self, payload):
-        return self.model(payload["text"])
+        summary = self.summarizer(
+            payload["text"], num_beams=4, length_penalty=2.0, max_length=142, no_repeat_ngram_size=3
+        )
+        return summary[0]["summary_text"]

examples/pytorch/text-summarizer/requirements.txt

@@ -1,3 +1,2 @@
 transformers
-spacy==2.1.3
-bert-extractive-summarizer==0.3.*
+torch

examples/pytorch/text-summarizer/sample.json

@@ -1,3 +1,3 @@
 {
-  "text": "Machine learnng (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics."
+  "text": "Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics."
 }