| from IPython.display import Image |
| Image(filename='Self_Attention.png') |
|
|
| |
| from transformers.utils import logging |
| logging.set_verbosity_error() |
| |
| from transformers import pipeline |
| |
| 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 |
