pipeline-from-transformers.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from transformers import pipeline\n\n","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:20:51.055903Z","iopub.execute_input":"2021-07-10T22:20:51.056367Z","iopub.status.idle":"2021-07-10T22:20:51.061957Z","shell.execute_reply.started":"2021-07-10T22:20:51.056327Z","shell.execute_reply":"2021-07-10T22:20:51.060771Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"# sentiment analysis figures out the sentiment of the text, either positive or negative\nclassifier = pipeline(\"sentiment-analysis\")\nsentences = [\"This sentence may or may not contain hateful words\",\n            \"Why do people love to be happy?\",\n            \"I was delighted to hear the news from Sue\"\n           ]\nclassifier(sentences)","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:20:51.072730Z","iopub.execute_input":"2021-07-10T22:20:51.073131Z","iopub.status.idle":"2021-07-10T22:20:57.321613Z","shell.execute_reply.started":"2021-07-10T22:20:51.073097Z","shell.execute_reply":"2021-07-10T22:20:57.320175Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"[{'label': 'NEGATIVE', 'score': 0.9986680746078491},\n {'label': 'POSITIVE', 'score': 0.8809037208557129},\n {'label': 'POSITIVE', 'score': 0.9997554421424866}]"},"metadata":{}}]},{"cell_type":"code","source":"# \"ner\" or Named Entity Recognition extracts the names in a sentence\nclassifier = pipeline(\"ner\")\nclassifier(sentences)","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:20:57.323951Z","iopub.execute_input":"2021-07-10T22:20:57.324654Z","iopub.status.idle":"2021-07-10T22:21:16.882486Z","shell.execute_reply.started":"2021-07-10T22:20:57.324601Z","shell.execute_reply":"2021-07-10T22:21:16.881316Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"[[],\n [],\n [{'word': 'Sue',\n   'score': 0.5739144682884216,\n   'entity': 'I-PER',\n   'index': 9,\n   'start': 38,\n   'end': 41}]]"},"metadata":{}}]},{"cell_type":"code","source":"# feature-extraction returns the values of the hidden state vectors\nclassifier = pipeline(\"feature-extraction\")\nclassifier(\"This is a sentence\")","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:21:16.884815Z","iopub.execute_input":"2021-07-10T22:21:16.885276Z","iopub.status.idle":"2021-07-10T22:21:22.303832Z","shell.execute_reply.started":"2021-07-10T22:21:16.885228Z","shell.execute_reply":"2021-07-10T22:21:22.302762Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"[[[0.3747391402721405,\n   0.1733936071395874,\n   0.046104393899440765,\n   -0.2869178056716919,\n   -0.11920185387134552,\n   0.00827059056609869,\n   0.14172138273715973,\n   -0.17540328204631805,\n   0.06862988322973251,\n   -1.2979260683059692,\n   -0.5133810043334961,\n   0.15344153344631195,\n   -0.17688831686973572,\n   0.1551496982574463,\n   -0.6731306314468384,\n   0.024050800129771233,\n   0.47294268012046814,\n   0.1116955429315567,\n   -0.20341162383556366,\n   -0.40866464376449585,\n   0.12004714459180832,\n   -0.05907208472490311,\n   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-0.5316590070724487,\n   -1.145119071006775,\n   -1.6101995706558228,\n   1.194374442100525,\n   0.06732901185750961,\n   0.576306939125061,\n   -1.238459587097168,\n   -0.9050214290618896,\n   -0.9079774022102356,\n   1.2183516025543213,\n   0.43341097235679626,\n   0.0887889713048935,\n   -0.342774361371994,\n   -0.5033283233642578,\n   -0.6892620325088501,\n   -0.10593952238559723,\n   0.07275600731372833,\n   -0.39732369780540466,\n   0.5654837489128113,\n   1.086553692817688,\n   -0.25725850462913513]]]"},"metadata":{}}]},{"cell_type":"code","source":"# fill-mask fills a mask (LOL)\nclassifier = pipeline(\"fill-mask\")\nclassifier(\"Lionel Messi plays in <mask>\")","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:21:22.305705Z","iopub.execute_input":"2021-07-10T22:21:22.306307Z","iopub.status.idle":"2021-07-10T22:21:30.571906Z","shell.execute_reply.started":"2021-07-10T22:21:22.306261Z","shell.execute_reply":"2021-07-10T22:21:30.571017Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"[{'sequence': 'Lionel Messi plays in Barcelona',\n  'score': 0.3254920542240143,\n  'token': 4612,\n  'token_str': ' Barcelona'},\n {'sequence': 'Lionel Messi plays in Argentina',\n  'score': 0.1448180079460144,\n  'token': 5244,\n  'token_str': ' Argentina'},\n {'sequence': 'Lionel Messi plays in Brazil',\n  'score': 0.036608848720788956,\n  'token': 2910,\n  'token_str': ' Brazil'},\n {'sequence': 'Lionel Messi plays in Spain',\n  'score': 0.035852424800395966,\n  'token': 2809,\n  'token_str': ' Spain'},\n {'sequence': 'Lionel Messi plays in Chile',\n  'score': 0.024896424263715744,\n  'token': 9614,\n  'token_str': ' Chile'}]"},"metadata":{}}]},{"cell_type":"code","source":"# question-answering extracts an answer from a given passage\nclassifier = pipeline(\"question-answering\")\ncontext = r\"\"\"Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a\n question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune\n a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.\n \"\"\"\noutput = classifier(question=\"What is extractive question answering?\",context = context)\nprint(output)\noutput = classifier(question=\"What is a good example of a question answering dataset\",context = context)\nprint(output)","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:21:30.573396Z","iopub.execute_input":"2021-07-10T22:21:30.573910Z","iopub.status.idle":"2021-07-10T22:21:38.483189Z","shell.execute_reply.started":"2021-07-10T22:21:30.573873Z","shell.execute_reply":"2021-07-10T22:21:38.481813Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"{'score': 0.6177279353141785, 'start': 33, 'end': 94, 'answer': 'the task of extracting an answer from a text given a question'}\n{'score': 0.5547497272491455, 'start': 147, 'end': 160, 'answer': 'SQuAD dataset'}\n","output_type":"stream"}]},{"cell_type":"code","source":"# summarization briefs a passage into a summary\nclassifier = pipeline(\"summarization\")\narticle = \"\"\" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.\n... A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.\n... 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.\n... 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.\n... 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\n... 2010 marriage license application, according to court documents.\n... Prosecutors said the marriages were part of an immigration scam.\n... On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.\n... 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\n... Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.\n... 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.\n... Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.\n... Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.\n... The case was referred to the Bronx District Attorney\\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\\'s\n... Investigation Division. Seven of the men are from so-called \"red-flagged\" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.\n... Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.\n... If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.\n... \"\"\"\n\nclassifier(article, max_length=100, min_length=20, do_sample = False)","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:21:38.485081Z","iopub.execute_input":"2021-07-10T22:21:38.485548Z","iopub.status.idle":"2021-07-10T22:22:12.112372Z","shell.execute_reply.started":"2021-07-10T22:21:38.485497Z","shell.execute_reply":"2021-07-10T22:22:12.111264Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"[{'summary_text': ' Liana 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, prosecutors say .'}]"},"metadata":{}}]},{"cell_type":"code","source":"# text-generation generates sentences in incomplete strings\n# any models can be used from the Hub by specifying the model in the parameters\nclassifier = pipeline('text-generation', model = 'distilgpt2')\nclassifier(\"In this course we will learn how to\")","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:23:43.323365Z","iopub.execute_input":"2021-07-10T22:23:43.323935Z","iopub.status.idle":"2021-07-10T22:23:52.399880Z","shell.execute_reply.started":"2021-07-10T22:23:43.323900Z","shell.execute_reply":"2021-07-10T22:23:52.399036Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stderr","text":"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","output_type":"stream"},{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"[{'generated_text': 'In this course we will learn how to properly write code. And we can’t just write everything we need. Instead, let’s write everything in Java. Let’s say that we have to change the code to the class'}]"},"metadata":{}}]},{"cell_type":"code","source":"# translation does what the word suggests, translates from one language to another\nclassifier = pipeline('translation_en_to_de')\nprint(classifier(\"What happened to the pickles in the jar?\", max_length=30))\nprint(classifier(\"How do you turn potatoes into fries?\", max_length=30))\n\nclassifier = pipeline('translation_en_to_fr')\nprint(classifier(\"What happened to the pickles in the jar?\", max_length=30))\nprint(classifier(\"How do you turn potatoes into fries?\", max_length=30))","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:22:40.398934Z","iopub.execute_input":"2021-07-10T22:22:40.399306Z","iopub.status.idle":"2021-07-10T22:23:10.732135Z","shell.execute_reply.started":"2021-07-10T22:22:40.399271Z","shell.execute_reply":"2021-07-10T22:23:10.731165Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"[{'translation_text': 'Was ist mit den Keksen im Glas passiert?'}]\n[{'translation_text': 'Wie kann man Kartoffeln in Pommes Frites umwandeln?'}]\n[{'translation_text': \"Qu'est-ce qui s'est passé avec les pickles dans le pot?\"}]\n[{'translation_text': 'Comment transformer les pommes de terre en frites?'}]\n","output_type":"stream"}]},{"cell_type":"code","source":"# zero shot classification helps to specify which labels to use for the classification for strings with no labels\nclassifier = pipeline('zero-shot-classification')\nclassifier(\"This sentence is about the Transformers library\", candidate_labels=[\"education\",\"politics\",\"sports\"])","metadata":{"execution":{"iopub.status.busy":"2021-07-10T22:23:10.733875Z","iopub.execute_input":"2021-07-10T22:23:10.734537Z","iopub.status.idle":"2021-07-10T22:23:38.448315Z","shell.execute_reply.started":"2021-07-10T22:23:10.734485Z","shell.execute_reply":"2021-07-10T22:23:38.447503Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"{'sequence': 'This sentence is about the Transformers library',\n 'labels': ['education', 'sports', 'politics'],\n 'scores': [0.45379918813705444, 0.33417248725891113, 0.21202830970287323]}"},"metadata":{}}]}]}