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You can imagine an open book question similar to an exam where you are allowed to bring in text documents or cheat sheets that help you answer questions in an exam. These questions can be asked in natural human language and answered in just 1 line with NLU!. With the latest NLU release and Google’s T5 you can answer general knowledge-based questions given no context and in addition answer questions on text databases. T5 Open and Closed Book question answering tutorial

Open book and Closed book question answering with Google’s T5 T5 Open and Closed Book question answering notebook.

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Every T5 Task example notebook to see how to use every T5 Task.Summarize text into a shorter representation. Predict for an ambiguous pronoun in a sentence what it is referring to. The T5 model is trained on various datasets for 17 different tasks which fall into 8 categories.Įvery T5 Task with explanation: Task NameĬlassify if a sentence is grammatically correctĬlassify whether a statement can be deducted from a sentenceĬlassify for a hypothesis and premise whether they contradict or imply each other or neither of both (3 class).Ĭlassify whether a pair of sentences is a rephrasing of each other (semantically equivalent)Ĭlassify whether the answer to a question can be deducted from an answer candidate.Ĭlassify whether a pair of questions is a rephrasing of each other (semantically equivalent)Ĭlassify the sentiment of a sentence as positive or negativeĬlassify the sentiment of a sentence on a scale from 1 to 5 (21 Sentiment classes)Ĭlassify for a premise and a hypothesis whether they contradict each other or not (binary).Ĭlassify for a question, premise, and 2 choices which choice the correct choice is (binary).Ĭlassify for a question, a paragraph of text, and an answer candidate, if the answer is correct (binary),Ĭlassify for a pair of sentences and a disambiguous word if the word has the same meaning in both sentences.

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The language references must be ISO language codes nlu.load('.translate.') You can translate between more than 192 Language pairs with the Marian Models. You need to specify the language your data is in as start_language and the language you want to translate to as target_language.

NEW: Introducing DocumentNormalizer component for cleaning content from HTML or XML documents, applying either data cleansing using an arbitrary number of custom regular expressions either data extraction following the different parameters.

NEW: Introducing WordSegmenter model for word segmentation of languages without any rule-based tokenization such as Chinese, Japanese, or Korean.

The new LanguageDetectorDL is faster, more accurate, and supports up to 375 languages

NEW: Introducing brand new and refactored language detection and identification models.

NEW: Introducing T5Transformer annotator for Text-To-Text Transfer Transformer (Google T5) models to achieve state-of-the-art results on multiple NLP tasks such as Translation, Summarization, Question Answering, Sentence Similarity, and so on.

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Marian is an efficient, free Neural Machine Translation framework mainly being developed by the Microsoft Translator team (646+ pertained models & pipelines in 192+ languages)

NEW: Introducing MarianTransformer annotator for machine translation based on MarianNMT models.

720+ new models, you can find an overview of all NLU models here and further documentation in the models’ hub.

In addition to this, NLU 1.1 comes with 9 new notebooks showcasing training classifiers for various review and sentiment datasets and 7 notebooks for the new features and models. NLU 1.1 has over 720+ new pertained models and pipelines while extending the support of multi-lingual models to 192+ languages such as Chinese, Japanese, Korean, Arabic, Persian, Urdu, and Hebrew. You can now achieve state-of-the-art results with Sequence2Sequence transformers on problems like text summarization, question answering, translation between 192+ languages, and extract Named Entity in various Right to Left written languages like Arabic, Persian, Urdu, and languages that require segmentation like Koreas, Japanese, Chinese, and many more in 1 line of code! These new features are possible because of the integration of Google’s T5 models and Microsoft’s Marian models transformers. We are incredibly excited to release NLU 1.1! This release integrates the 720+ new models from the latest Spark-NLP 2.7 + releases.