How to Produce Quality Titles & Meta Descriptions Automatically via @hamletbatista

First, let’s install the library in a new Google Colab notebook.

Make sure to select the GPU runtime.

!pip install transformers

Next, let’s add this the pipeline code.

from transformers import pipeline# use bart in pytorchbart_summarizer = pipeline("summarization")

This is the example text we will summarize.

TEXT_TO_SUMMARIZE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the 2010 marriage license application, according to court documents. Prosecutors said the marriages were part of an immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages. Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted. The case was referred to the Bronx District Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18."""

Here is the summary code and resulting summary:

summary = bart_summarizer(TEXT_TO_SUMMARIZE, min_length=50, max_length=250)

print(summary) #Output: [{'summary_text': 'Liana Barrientos has been married 10 times, sometimes within two weeks of each other. Prosecutors say the marriages were part of an immigration scam. She is believed to still be married to four men, and at one time, she was married to eight at once.'}]

I specified the generated summary should not have less than 50 characters and at most 250.

This is very useful to control the type of generation: titles or meta descriptions.

Now, look at the quality of the generated summary and we only typed a few lines of Python code.

Super cool!

'Liana Barrientos has been married 10 times, sometimes within two weeks of each other. Prosecutors say the marriages were part of an immigration scam. She is believed to still be married to four men, and at one time, she was married to eight at once.'print(len(summary[0]["summary_text"]))#Output: 249

Google’s T5

Another state of the art model is Text-to-Text Transfer Transformer, or T5.

One impressive accomplishment of the model is that its performance got really close to the human level baseline in the SuperGLUE leaderboard.

t5_summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base")summary = t5_summarizer(TEXT_TO_SUMMARIZE, min_length=50, max_length=250)

[{'summary_text': 'in total, barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002 . she is believed to still be married to four men, and at one time, she was married to eight men at once .'}]

t5_summarizer_larger = pipeline("summarization", model="t5-large", tokenizer="t5-large")

[{'summary_text': 'Liana barrientos has been married 10 times . nine of her marriages occurred between 1999 and 2002 . she is believed to still be married to four men, and at one time, she was married to eight men at once .'}]

!gcloud auth login --no-launch-browser

!gcloud config set project project-name

%%writefile main.pydef hello_get(request): """HTTP Cloud Function. Args: request (flask.Request): The request object. Returns: The response text, or any set of values that can be turned into a Response object using `make_response` . """ return 'Hello World!'

!gcloud functions deploy hello_get --runtime python37 --trigger-http --allow-unauthenticated

availableMemoryMb: 256entryPoint: hello_gethttpsTrigger:url: https://xxx.cloudfunctions.net/hello_getingressSettings: ALLOW_ALLlabels:deployment-tool: cli-gcloudname: projects/xxx/locations/us-central1/functions/hello_getruntime: python37serviceAccountEmail: xxxxsourceUploadUrl: xxxxstatus: ACTIVEtimeout: 60supdateTime: '2020-04-06T16:33:03.951Z'versionId: '8'

%%writefile requirements.txttransformers==2.0.7

%%writefile requirements.txthttps://download.pytorch.org/whl/cpu/torch-1.0.1.post2-cp37-cp37m-linux_x86_64.whltransformers==2.0.7

from transformers import pipelinenlp_token_class = Nonedef ner_get(request): global nlp_token_class #run once if nlp_token_class is None: nlp_token_class = pipeline('ner') result = nlp_token_class('Hugging Face is a French company based in New-York.') return result

[{'entity': 'I-ORG', 'score': 0.9970937967300415, 'word': 'Hu'},{'entity': 'I-ORG', 'score': 0.9345749020576477, 'word': '##gging'},{'entity': 'I-ORG', 'score': 0.9787060022354126, 'word': 'Face'},{'entity': 'I-MISC', 'score': 0.9981995820999146, 'word': 'French'},{'entity': 'I-LOC', 'score': 0.9983047246932983, 'word': 'New'},{'entity': 'I-LOC', 'score': 0.8913459181785583, 'word': '-'},{'entity': 'I-LOC', 'score': 0.9979523420333862, 'word': 'York'}]

!gcloud functions deploy ner_get --memory 2GiB --timeout 540 --runtime python37 --trigger-http --allow-unauthenticated

%%writefile main.pyfrom transformers import pipelinenlp_t5 = Nonedef t5_get(request): global nlp_t5 #run once if nlp_t5 is None: nlp_t5 = pipeline('summarization', model="t5-small", tokenizer="t5-small") TEXT_TO_SUMMARIZE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York... """ result = nlp_t5(TEXT_TO_SUMMARIZE) return result[0]["summary_text"]

!gcloud functions deploy t5_get --memory 2GiB --timeout 540 --runtime python37 --trigger-http --allow-unauthenticated

%%writefile main.pyfrom transformers import pipelinenlp_t5 = Nonedef t5_post(request): global nlp_t5 #run once if nlp_t5 is None: #small model to avoid memory issue nlp_t5 = pipeline('summarization', model="t5-small", tokenizer="t5-small") #Get text to summarize from POST request content_type = request.headers['content-type'] if content_type == 'application/x-www-form-urlencoded': text = request.form.get('text') result = nlp_t5(text) return result[0]["summary_text"] else: raise ValueError("Unknown content type: {}".format(content_type)) return "Failure"

import requestsurl = "https://us-central1-seo-sheets.cloudfunctions.net/hello_post"data = {"text": text[:100]}requests.post(url, data).text

function getSummary(text){ payload = `text=${text}`; payload = encodeURI(payload); console.log(payload); var url = "https://xxx.cloudfunctions.net/t5_post"; var options = { "method" : "POST", "contentType" : "application/x-www-form-urlencoded", "payload" : payload, 'muteHttpExceptions': true }; var response = UrlFetchApp.fetch(url, options); var result = response.getContentText(); console.log(result); return result; }

=IMPORTXML("https://en.wikipedia.org/wiki/Moon_landing", "//div/text()")

=TEXTJOIN(" ", TRUE, IMPORTXML("https://en.wikipedia.org/wiki/Moon_landing", "//div/text()"))

api_key = "xxx" # Get your own API key from this URL https://www.bing.com/webmaster/home/apiimport requestsdef submit_to_bing(request): global api_key api_url=f"https://ssl.bing.com/webmaster/api.svc/json/SubmitUrlbatch?apikey={api_key}" print(api_url) #Replace for your site url_list = [ "https://www.domain.com/page1.html", "https://www.domain.com/page2.html"] data = { "siteUrl": "http://www.domain.com", "urlList": url_list } r = requests.post(api_url, json=data) if r.status_code == 200: return r.json() else: return r.status_code

{'d': None}