bert extractive summarizer
Small Bug
Repo ini adalah generalisasi repo kuliah-summarizer. Alat ini menggunakan perpustakaan Huggingface Pytorch Transformers untuk menjalankan ringkasan ekstraktif. Ini bekerja dengan terlebih dahulu menanamkan kalimat, kemudian menjalankan algoritma pengelompokan, menemukan kalimat yang paling dekat dengan centroid cluster. Perpustakaan ini juga menggunakan teknik coreference, memanfaatkan https://github.com/huggingface/neuralcoref Library untuk menyelesaikan kata -kata dalam ringkasan yang membutuhkan lebih banyak konteks. Keserakahan perpustakaan NeuralCoref dapat diubah di kelas Coreferenchandler.
Pada versi terbaru dari Bert-Extractive-Summarizer, secara default, CUDA digunakan jika GPU tersedia.
Kertas: https://arxiv.org/abs/1906.04165
Distill Bert Summarisasi Demo
pip install bert-extractive-summarizer from summarizer import Summarizer
body = 'Text body that you want to summarize with BERT'
body2 = 'Something else you want to summarize with BERT'
model = Summarizer ()
model ( body )
model ( body2 )Jumlah kalimat dapat dipasok sebagai rasio atau bilangan bulat. Contoh disediakan di bawah ini.
from summarizer import Summarizer
body = 'Text body that you want to summarize with BERT'
model = Summarizer ()
result = model ( body , ratio = 0.2 ) # Specified with ratio
result = model ( body , num_sentences = 3 ) # Will return 3 sentences Anda juga dapat menggabungkan embedding Summarizer untuk pengelompokan. Contoh sederhana ada di bawah ini.
from summarizer import Summarizer
body = 'Text body that you want to summarize with BERT'
model = Summarizer ( 'distilbert-base-uncased' , hidden = [ - 1 , - 2 ], hidden_concat = True )
result = model ( body , num_sentences = 3 )Seseorang dapat menggunakan kalimat Bert dengan Bert-Extractive-Summarizer dengan versi terbaru. Ini didasarkan dari kertas di sini: https://arxiv.org/abs/1908.10084, dan perpustakaan di sini: https://www.sbert.net/. Untuk memulai, pertama instal Sbert:
pip install -U sentence-transformers
Maka contoh sederhana adalah sebagai berikut:
from summarizer . sbert import SBertSummarizer
body = 'Text body that you want to summarize with BERT'
model = SBertSummarizer ( 'paraphrase-MiniLM-L6-v2' )
result = model ( body , num_sentences = 3 )Perlu dicatat bahwa semua fitur yang dapat Anda lakukan dengan kelas Summarizer utama, Anda juga dapat melakukannya dengan Sbert.
Anda juga dapat mengambil embedding dari ringkasan. Contoh di bawah ini:
from summarizer import Summarizer
body = 'Text body that you want to summarize with BERT'
model = Summarizer ()
result = model . run_embeddings ( body , ratio = 0.2 ) # Specified with ratio.
result = model . run_embeddings ( body , num_sentences = 3 ) # Will return (3, N) embedding numpy matrix.
result = model . run_embeddings ( body , num_sentences = 3 , aggregate = 'mean' ) # Will return Mean aggregate over embeddings. Pertama pastikan Anda telah menginstal NeuralCoref dan Spacy. Perlu dicatat bahwa NeuralCoref tidak bekerja dengan Spacy> 0.2.1.
pip install spacy
pip install transformers # > 4.0.0
pip install neuralcoref
python -m spacy download en_core_web_mdKemudian untuk menggunakan coreference, jalankan yang berikut:
from summarizer import Summarizer
from summarizer . text_processors . coreference_handler import CoreferenceHandler
handler = CoreferenceHandler ( greedyness = .4 )
# How coreference works:
# >>>handler.process('''My sister has a dog. She loves him.''', min_length=2)
# ['My sister has a dog.', 'My sister loves a dog.']
body = 'Text body that you want to summarize with BERT'
body2 = 'Something else you want to summarize with BERT'
model = Summarizer ( sentence_handler = handler )
model ( body )
model ( body2 ) from transformers import *
# Load model, model config and tokenizer via Transformers
custom_config = AutoConfig . from_pretrained ( 'allenai/scibert_scivocab_uncased' )
custom_config . output_hidden_states = True
custom_tokenizer = AutoTokenizer . from_pretrained ( 'allenai/scibert_scivocab_uncased' )
custom_model = AutoModel . from_pretrained ( 'allenai/scibert_scivocab_uncased' , config = custom_config )
from summarizer import Summarizer
body = 'Text body that you want to summarize with BERT'
body2 = 'Something else you want to summarize with BERT'
model = Summarizer ( custom_model = custom_model , custom_tokenizer = custom_tokenizer )
model ( body )
model ( body2 ) from summarizer import Summarizer
body = '''
The Chrysler Building, the famous art deco New York skyscraper, will be sold for a small fraction of its previous sales price.
The deal, first reported by The Real Deal, was for $150 million, according to a source familiar with the deal.
Mubadala, an Abu Dhabi investment fund, purchased 90% of the building for $800 million in 2008.
Real estate firm Tishman Speyer had owned the other 10%.
The buyer is RFR Holding, a New York real estate company.
Officials with Tishman and RFR did not immediately respond to a request for comments.
It's unclear when the deal will close.
The building sold fairly quickly after being publicly placed on the market only two months ago.
The sale was handled by CBRE Group.
The incentive to sell the building at such a huge loss was due to the soaring rent the owners pay to Cooper Union, a New York college, for the land under the building.
The rent is rising from $7.75 million last year to $32.5 million this year to $41 million in 2028.
Meantime, rents in the building itself are not rising nearly that fast.
While the building is an iconic landmark in the New York skyline, it is competing against newer office towers with large floor-to-ceiling windows and all the modern amenities.
Still the building is among the best known in the city, even to people who have never been to New York.
It is famous for its triangle-shaped, vaulted windows worked into the stylized crown, along with its distinctive eagle gargoyles near the top.
It has been featured prominently in many films, including Men in Black 3, Spider-Man, Armageddon, Two Weeks Notice and Independence Day.
The previous sale took place just before the 2008 financial meltdown led to a plunge in real estate prices.
Still there have been a number of high profile skyscrapers purchased for top dollar in recent years, including the Waldorf Astoria hotel, which Chinese firm Anbang Insurance purchased in 2016 for nearly $2 billion, and the Willis Tower in Chicago, which was formerly known as Sears Tower, once the world's tallest.
Blackstone Group (BX) bought it for $1.3 billion 2015.
The Chrysler Building was the headquarters of the American automaker until 1953, but it was named for and owned by Chrysler chief Walter Chrysler, not the company itself.
Walter Chrysler had set out to build the tallest building in the world, a competition at that time with another Manhattan skyscraper under construction at 40 Wall Street at the south end of Manhattan. He kept secret the plans for the spire that would grace the top of the building, building it inside the structure and out of view of the public until 40 Wall Street was complete.
Once the competitor could rise no higher, the spire of the Chrysler building was raised into view, giving it the title.
'''
model = Summarizer ()
result = model ( body , min_length = 60 )
full = '' . join ( result )
print ( full )
"""
The Chrysler Building, the famous art deco New York skyscraper, will be sold for a small fraction of its previous sales price.
The building sold fairly quickly after being publicly placed on the market only two months ago.
The incentive to sell the building at such a huge loss was due to the soaring rent the owners pay to Cooper Union, a New York college, for the land under the building.'
Still the building is among the best known in the city, even to people who have never been to New York.
"""Pada Bert-Extractive-Summarizer versi 0.7.1, Anda juga dapat menghitung siku untuk menentukan cluster optimal. Di bawah ini menunjukkan contoh contoh cara mengambil daftar inersia.
from summarizer import Summarizer
body = 'Your Text here.'
model = Summarizer ()
res = model . calculate_elbow ( body , k_max = 10 )
print ( res )Anda juga dapat menemukan jumlah kalimat yang optimal dengan siku menggunakan algoritma berikut.
from summarizer import Summarizer
body = 'Your Text here.'
model = Summarizer ()
res = model . calculate_optimal_k ( body , k_max = 10 )
print ( res ) model = Summarizer(
model: This gets used by the hugging face bert library to load the model, you can supply a custom trained model here
custom_model: If you have a pre-trained model, you can add the model class here.
custom_tokenizer: If you have a custom tokenizer, you can add the tokenizer here.
hidden: Needs to be negative, but allows you to pick which layer you want the embeddings to come from.
reduce_option: It can be 'mean', 'median', or 'max'. This reduces the embedding layer for pooling.
sentence_handler: The handler to process sentences. If want to use coreference, instantiate and pass CoreferenceHandler instance
)
model(
body: str # The string body that you want to summarize
ratio: float # The ratio of sentences that you want for the final summary
min_length: int # Parameter to specify to remove sentences that are less than 40 characters
max_length: int # Parameter to specify to remove sentences greater than the max length,
num_sentences: Number of sentences to use. Overrides ratio if supplied.
)
Ada layanan flask yang disediakan dan dockerfile yang sesuai. Menjalankan layanan itu sederhana, dan dapat dilakukan melalui Makefile dengan dua perintah:
make docker-service-build
make docker-service-run
Ini akan menggunakan model Bert-Base-Incased, yang memiliki representasi kecil. Docker Run juga menerima berbagai argumen untuk model khusus dan berbeda. Ini dapat dilakukan melalui perintah seperti:
docker build -t summary-service -f Dockerfile.service ./
docker run --rm -it -p 5000:5000 summary-service:latest -model bert-large-uncased
Argumen lain juga dapat diteruskan ke server. Di bawah ini mencakup daftar argumen yang tersedia.
Setelah layanan berjalan, Anda dapat membuat perintah peringkasan di http://localhost:5000/summarize titik akhir. Titik akhir ini menerima input teks/polos yang mewakili teks yang ingin Anda ringkas. Parameter juga dapat disahkan sebagai argumen permintaan. Argumen yang diterima adalah:
Contoh permintaan adalah sebagai berikut:
POST http://localhost:5000/summarize?ratio=0.1
Content-type: text/plain
Body:
The Chrysler Building, the famous art deco New York skyscraper, will be sold for a small fraction of its previous sales price.
The deal, first reported by The Real Deal, was for $150 million, according to a source familiar with the deal.
Mubadala, an Abu Dhabi investment fund, purchased 90% of the building for $800 million in 2008.
Real estate firm Tishman Speyer had owned the other 10%.
The buyer is RFR Holding, a New York real estate company.
Officials with Tishman and RFR did not immediately respond to a request for comments.
It's unclear when the deal will close.
The building sold fairly quickly after being publicly placed on the market only two months ago.
The sale was handled by CBRE Group.
The incentive to sell the building at such a huge loss was due to the soaring rent the owners pay to Cooper Union, a New York college, for the land under the building.
The rent is rising from $7.75 million last year to $32.5 million this year to $41 million in 2028.
Meantime, rents in the building itself are not rising nearly that fast.
While the building is an iconic landmark in the New York skyline, it is competing against newer office towers with large floor-to-ceiling windows and all the modern amenities.
Still the building is among the best known in the city, even to people who have never been to New York.
It is famous for its triangle-shaped, vaulted windows worked into the stylized crown, along with its distinctive eagle gargoyles near the top.
It has been featured prominently in many films, including Men in Black 3, Spider-Man, Armageddon, Two Weeks Notice and Independence Day.
The previous sale took place just before the 2008 financial meltdown led to a plunge in real estate prices.
Still there have been a number of high profile skyscrapers purchased for top dollar in recent years, including the Waldorf Astoria hotel, which Chinese firm Anbang Insurance purchased in 2016 for nearly $2 billion, and the Willis Tower in Chicago, which was formerly known as Sears Tower, once the world's tallest.
Blackstone Group (BX) bought it for $1.3 billion 2015.
The Chrysler Building was the headquarters of the American automaker until 1953, but it was named for and owned by Chrysler chief Walter Chrysler, not the company itself.
Walter Chrysler had set out to build the tallest building in the world, a competition at that time with another Manhattan skyscraper under construction at 40 Wall Street at the south end of Manhattan. He kept secret the plans for the spire that would grace the top of the building, building it inside the structure and out of view of the public until 40 Wall Street was complete.
Once the competitor could rise no higher, the spire of the Chrysler building was raised into view, giving it the title.
Response:
{
"summary": "The Chrysler Building, the famous art deco New York skyscraper, will be sold for a small fraction of its previous sales price. The buyer is RFR Holding, a New York real estate company. The incentive to sell the building at such a huge loss was due to the soaring rent the owners pay to Cooper Union, a New York college, for the land under the building."
}
