text-to-text-transfer-transformer | Transfer Learning with a Unified Text | Machine Learning library

 by   google-research Python Version: v0.4.0 License: Apache-2.0

kandi X-RAY | text-to-text-transfer-transformer Summary

kandi X-RAY | text-to-text-transfer-transformer Summary

text-to-text-transfer-transformer is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. text-to-text-transfer-transformer has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install text-to-text-transfer-transformer' or download it from GitHub, PyPI.

Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

            kandi-support Support

              text-to-text-transfer-transformer has a medium active ecosystem.
              It has 5257 star(s) with 705 fork(s). There are 105 watchers for this library.
              It had no major release in the last 12 months.
              There are 56 open issues and 344 have been closed. On average issues are closed in 19 days. There are 48 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of text-to-text-transfer-transformer is v0.4.0

            kandi-Quality Quality

              text-to-text-transfer-transformer has 0 bugs and 0 code smells.

            kandi-Security Security

              text-to-text-transfer-transformer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              text-to-text-transfer-transformer code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              text-to-text-transfer-transformer is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              text-to-text-transfer-transformer releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              text-to-text-transfer-transformer saves you 2608 person hours of effort in developing the same functionality from scratch.
              It has 5662 lines of code, 463 functions and 40 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed text-to-text-transfer-transformer and discovered the below as its top functions. This is intended to give you an instant insight into text-to-text-transfer-transformer implemented functionality, and help decide if they suit your requirements.
            • Compute rank classification formatter
            • Wrapper for sklearn metric_wrapper
            • Calculate the mean Multiclass F1 score
            • Perform rank classification
            • Run the evaluation
            • Return the vocabulary from the gin config
            • Write lines to a file
            • Add a task provider
            • Fills a pretrained model
            • Compute the average value for each metric
            • Calculate ROUGE
            • Return a preprocessor for a GLUE
            • Translate from source to target language
            • Convert noise_token to a random token
            • Deduplicated metric function
            • Creates a squad example from the input dataset
            • Compute F1 F1 score
            • Compute a squad similarity between targets and predictions
            • Convenience function for creating examples from a dataset
            • Fill in a dataset with fill
            • Generates the pre - filled preprocessed examples
            • Predicts next sentence prediction
            • Calculate span corruption
            • Provide information about questions
            • Return predictions for the model
            • Returns an iterator over the checkpoint files
            Get all kandi verified functions for this library.

            text-to-text-transfer-transformer Key Features

            No Key Features are available at this moment for text-to-text-transfer-transformer.

            text-to-text-transfer-transformer Examples and Code Snippets

            CPU Bound Task - Multiprocessing Approach Performance Way Worse Than Synchronous Approach -Why?
            Pythondot img1Lines of Code : 85dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from time import perf_counter as now
            import gzip
            from collections import Counter
            PATH = r".\googlebooks-eng-all-1gram-20120701-a.gz"
            def chunked_read(f, byte_limit=10**9):
                while True:
                    lines = f.readlines(byte_limit)
            how can I select a variable based on a number that the user inputs
            Pythondot img2Lines of Code : 53dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            class AllTeams:
              def __init__(self, TeamNum, TeamName, TeamScore):
                self.TeamNum = TeamNum
                self.TeamName = TeamName
                self.TeamScore = TeamScore
              def __repr__(self):
                return f'Team Number: {self.TeamNum} |-| Team Name: {self
            Including an image in a PyInstaller onefile executable
            Pythondot img3Lines of Code : 2dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pyinstaller --noconfirm --onefile --console --add-data "[workspace]/src/logo.png:."  "[workspace]/src/gui.py"
            Pandas Merge Dataframes
            Pythondot img4Lines of Code : 13dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            #rename the comlumns
            #merge all the dfs; outer merge
            from functools import reduce
            dfs = [df1, df2
            Printing colored string in class python and kivy
            Pythondot img5Lines of Code : 20dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def set_color(letter, color):
                COLORS_DICT  = {"W" : "ffffff", "Y" : "ffff00", "G" : "00ff00"}
                c = COLORS_DICT.get(color, "W")
                return f"[color={c}]{letter}[/color]"
                def ShowPreviousWord(self):
            how to write a conditional for loop in DAX?
            Pythondot img6Lines of Code : 10dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            how many halts = 
            var _index = LOOKUPVALUE('Table'[Index], 'Table'[Index], [Index])
            var _train = LOOKUPVALUE('Table'[Train nr],'Table'[Train nr], [Train nr])
            var _haltafter = FILTER('Table', [Halt]=1 && [Index] > value(_index) 
            Convert a tensorflow dataset to a python list with strings
            Pythondot img7Lines of Code : 24dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def print_dataset(ds):
                list_sets = []
                for input, targets in ds:
                    input = np.transpose(np.array(inputs)[0])
                    label = np.transpose(np.array(targets)[0])
                    for input_set, label_set in zip(input, label):
            Convert a tensorflow dataset to a python list with strings
            Pythondot img8Lines of Code : 11dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            features = np.concatenate(list(ds.map(lambda x, y: tf.transpose(tf.squeeze(x, axis=0)))))
            targets = np.concatenate(list(ds.map(lambda x, y: tf.transpose(tf.squeeze(y, axis=0)))))
            values = list(map(lambda x: x[0]+ "\t" + x[1], zip([" ".joi
            White-out output when trying to get the ploty treemap and plotly sunburst
            Pythondot img9Lines of Code : 6dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            my_data_parent = ['', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'D', 'D', 'D', 'D', 'E', 'E', 'E', 'F', 'F', 'F', 'F', 'G', 'G', 'G', 'H', 'AV', 'CF', 'CF', 'EL', 'EL', 'EV', 'EV', 'MT', 'DI'
            Why doesn't my function for finding card combinations work for more than 2 cards?
            Pythondot img10Lines of Code : 62dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from itertools import combinations
            kinds      = "dhsc"
            faces      = "A23456789TJQK"
            cardValues = {kind+face:min(10,value) for kind in kinds
                          for value,face in enumerate(faces,1)}
            def count15s(cards):
                points = 0

            Community Discussions


            Error Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_index_select
            Asked 2020-Aug-02 at 01:50

            I have the following code taken directly from here with some pretty little modifications:



            Answered 2020-Aug-02 at 01:50

            Try explicitly moving your model to the GPU.

            Source https://stackoverflow.com/questions/63211463

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install text-to-text-transfer-transformer

            To install the T5 package, simply run:.
            You will first need to launch a Virtual Machine (VM) on Google Cloud. Details about launching the VM can be found at the Google Cloud Documentation. In order to run training or eval on Cloud TPUs, you must set up the following variables based on your project, zone and GCS bucket appropriately. Please refer to the Cloud TPU Quickstart guide for more details. Please use the following command to create a TPU device in the Cloud VM.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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