Create app.py

app.py

@@ -0,0 +1,17 @@
+from IPython.display import Image
+Image(filename='Self_Attention.png')
+
+# Code to ignore warnings
+from transformers.utils import logging
+logging.set_verbosity_error()
+# Code 1 - Use a pipeline as a high-level helper
+from transformers import pipeline
+# Code 2 - create a summarizer object
+summarizer = pipeline(task="summarization", model="facebook/bart-large-cnn")
+text = """BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder.
+BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
+BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
+This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs."""
+ Summarize the text. the length here is in tokens
+summary = summarizer(text, min_length=10, max_length=100)
+summary