Engineering

After the distributed representation has been obtained, the After the distributed representation is obtained, machine learning can be used to classify it.

Models that can be used include

• Decision Tree
• SVM Support Vector Machine
• NN Neural Networks

and others.

SVM is included in NN in a broad sense.

In this section, we will use the decision tree method.

Bagging

• Image of majority voting with multiple decision trees
• Simple theory
  • Decision trees are highly explainable and are a classic machine learning model.
  • Computational load is light compared to deep learning
    • Depends on the size of the model
• Not much explainability
  • Do we want to analyze each of the multiple decision trees?

``py from sklearn.ensemble import BaggingClassifier from sklearn.tree import DecisionTreeClassifier

• BART is a model for document summarization
• Derived from the same transformer as BERT
• Unlike BERT, it has an encoder-decoder structure
  • This is because it is intended for sentence generation

This page shows the steps to run a tutorial on BART.

Procedure

1. install transformers

Run ``sh pip install transformers

Run summary

2. Run the summary
from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig

model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')

ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')

# Generate Summary
summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
```

On 2021/01/18, the output was MyMy friends.

Interesting.

## Where I got stuck.
Error when the version of pytorch is different from the one specified in transformers.

pip install -U torch

.

We have successfully trained a model to automatically generate titles from news texts using a machine translation model based on deep learning.

Preliminaries

In the past, I was involved in a project to automatically generate titles from manuscripts for online news. In the past, I was involved in a project to automatically generate titles from manuscripts for online news.

In order to tackle this project, I was looking into existing methods.