transformer | PyTorch Implementation of `` Attention Is All | Machine Learning library

 by   hyunwoongko Python Version: Current License: No License

kandi X-RAY | transformer Summary

kandi X-RAY | transformer Summary

transformer is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Transformer applications. transformer has no bugs, it has no vulnerabilities and it has medium support. However transformer build file is not available. You can download it from GitHub.

My own implementation Transformer model (Attention is All You Need - Google Brain, 2017).
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            kandi-support Support

              transformer has a medium active ecosystem.
              It has 995 star(s) with 209 fork(s). There are 5 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 5 open issues and 6 have been closed. On average issues are closed in 136 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of transformer is current.

            kandi-Quality Quality

              transformer has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              transformer does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              transformer releases are not available. You will need to build from source code and install.
              transformer has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed transformer and discovered the below as its top functions. This is intended to give you an instant insight into transformer implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Evaluate the model
            • Split a tensor into multiple heads
            • Convert an index to a list of words
            • Calculate BLEU score
            • Calculate the elapsed time between two epochs
            • Forward the source
            • Make a no peak mask for q and k
            • Compute the mask for the given mask
            • Concatenate attention matrices
            • Concatenate tensor
            • Draw training results
            • Return a list of floats
            • Create dataset
            • Load a record from file
            • Create dataset iterator
            • Builds the vocabulary
            • Count the number of parameters in the model
            • Load weights from saved files
            Get all kandi verified functions for this library.

            transformer Key Features

            No Key Features are available at this moment for transformer.

            transformer Examples and Code Snippets

            Initialize the transformer .
            pythondot img1Lines of Code : 106dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def __init__(self,
                           input_shape,
                           dilation_rate,
                           padding,
                           build_op,
                           filter_shape=None,
                           spatial_dims=None,
                           data_format=None,
                           num_batc  
            Transformer L2 regularizer .
            pythondot img2Lines of Code : 58dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _contrib_layers_l2_regularizer_transformer(
                parent, node, full_name, name, logs):
              """Replace slim l2 regularizer with Keras one, with l=0.5*scale.
            
              Also drops the scope argument.
              """
              def _replace_scale_node(parent, old_value):
                """  
            Initialize the transformer .
            pythondot img3Lines of Code : 57dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def __init__(self,
                           init_args,
                           init_func,
                           next_func,
                           finalize_func,
                           output_signature,
                           name=None):
                """Constructs a `_GeneratorDataset`.
            
                Args:
                  init_  

            Community Discussions

            QUESTION

            Cannot read properties of undefined (reading 'transformFile') at Bundler.transformFile
            Asked 2022-Mar-29 at 12:36

            I have updated node today and I'm getting this error:

            ...

            ANSWER

            Answered 2021-Oct-27 at 17:19

            Ran into the same issue with Node.js 17.0.0. To solve it, I downgraded to version 14.18.1, deleted node_modules and reinstalled.

            Source https://stackoverflow.com/questions/69647332

            QUESTION

            What is this GHC feature called? `forall` in type definitions
            Asked 2022-Feb-01 at 19:28

            I learned that you can redefine ContT from transformers such that the r type parameter is made implicit (and may be specified explicitly using TypeApplications), viz.:

            ...

            ANSWER

            Answered 2022-Feb-01 at 19:28

            Nobody uses this (invisible dependent quantification) for this purpose (where the dependency is not used) but it is the same as giving a Type -> .. parameter, implicitly.

            Source https://stackoverflow.com/questions/70946284

            QUESTION

            Why Reader implemented based ReaderT?
            Asked 2022-Jan-11 at 17:11

            https://hackage.haskell.org/package/transformers-0.6.0.2/docs/src/Control.Monad.Trans.Reader.html#ReaderT

            I found that Reader is implemented based on ReaderT using Identity. Why don't make Reader first and then make ReaderT? Is there specific reason to implement that way?

            ...

            ANSWER

            Answered 2022-Jan-11 at 17:11

            They are the same data type to share as much code as possible between Reader and ReaderT. As it stands, only runReader, mapReader, and withReader have any special cases. And withReader doesn't have any unique code, it's just a type specialization, so only two functions actually do anything special for Reader as opposed to ReaderT.

            You might look at the module exports and think that isn't buying much, but it actually is. There are a lot of instances defined for ReaderT that Reader automatically has as well, because it's the same type. So it's actually a fair bit less code to have only one underlying type for the two.

            Given that, your question boils down to asking why Reader is implemented on top of ReaderT, and not the other way around. And for that, well, it's just the only way that works.

            Let's try to go the other direction and see what goes wrong.

            Source https://stackoverflow.com/questions/70630098

            QUESTION

            How to get all properties of type alias into an array?
            Asked 2022-Jan-08 at 08:25

            Given this type alias:

            ...

            ANSWER

            Answered 2022-Jan-08 at 08:22
            You cannot do this easily, because of type erasure

            Typescript types only exist at compile time. They do not exist in the compiled javascript. Thus you cannot populate an array (a runtime entity) with compile-time data (such as the RequestObject type alias), unless you do something complicated like the library you found.

            Workarounds
            1. code something yourself that works like the library you found.
            2. find a different library that works with type aliases such as RequestObject.
            3. create an interface equivalent to your type alias and pass that to the library you found, e.g.:

            Source https://stackoverflow.com/questions/70630558

            QUESTION

            Netlify says, "error Gatsby requires Node.js 14.15.0 or higher (you have v12.18.0)"—yet I have the newest Node version?
            Asked 2022-Jan-08 at 07:21

            After migrating from Remark to MDX, my builds on Netlify are failing.

            I get this error when trying to build:

            ...

            ANSWER

            Answered 2022-Jan-08 at 07:21

            The problem is that you have Node 17.2.0. locally but in Netlify's environment, you are running a lower version (by default it's not set as 17.2.0). So the local environment is OK, Netlify environment is KO because of this mismatch of Node versions.

            When Netlify deploys your site it installs and builds again your site so you should ensure that both environments work under the same conditions. Otherwise, both node_modules will differ so your application will have different behavior or eventually won't even build because of dependency errors.

            You can easily play with the Node version in multiple ways but I'd recommend using the .nvmrc file. Just run the following command in the root of your project:

            Source https://stackoverflow.com/questions/70362755

            QUESTION

            Determine whether the Columns of a Dataset are invariant under any given Scikit-Learn Transformer
            Asked 2021-Dec-19 at 08:42

            Given an sklearn tranformer t, is there a way to determine whether t changes columns/column order of any given input dataset X, without applying it to the data?

            For example with t = sklearn.preprocessing.StandardScaler there is a 1-to-1 mapping between the columns of X and t.transform(X), namely X[:, i] -> t.transform(X)[:, i], whereas this is obviously not the case for sklearn.decomposition.PCA.

            A corollary of that would be: Can we know, how the columns of the input will change by applying t, e.g. which columns an already fitted sklearn.feature_selection.SelectKBest chooses.

            I am not looking for solutions to specific transformers, but a solution applicable to all or at least a wide selection of transformers.

            Feel free to implement your own Pipeline class or wrapper if necessary.

            ...

            ANSWER

            Answered 2021-Nov-23 at 15:01

            I found a partial answer. Both StandardScaler and SelectKBest have .get_feature_names_out methods. I did not find the time to investigate further.

            Source https://stackoverflow.com/questions/70017034

            QUESTION

            ValueError after attempting to use OneHotEncoder and then normalize values with make_column_transformer
            Asked 2021-Dec-09 at 20:59

            So I was trying to convert my data's timestamps from Unix timestamps to a more readable date format. I created a simple Java program to do so and write to a .csv file, and that went smoothly. I tried using it for my model by one-hot encoding it into numbers and then turning everything into normalized data. However, after my attempt to one-hot encode (which I am not sure if it even worked), my normalization process using make_column_transformer failed.

            ...

            ANSWER

            Answered 2021-Dec-09 at 20:59

            using OneHotEncoder is not the way to go here, it's better to extract the features from the column time as separate features like year, month, day, hour, minutes etc... and give these columns as input to your model.

            Source https://stackoverflow.com/questions/70118623

            QUESTION

            What are differences between AutoModelForSequenceClassification vs AutoModel
            Asked 2021-Dec-05 at 09:07

            We can create a model from AutoModel(TFAutoModel) function:

            ...

            ANSWER

            Answered 2021-Dec-05 at 09:07

            The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model

            Source https://stackoverflow.com/questions/69907682

            QUESTION

            How can I check a confusion_matrix after fine-tuning with custom datasets?
            Asked 2021-Nov-24 at 13:26

            This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.

            Background

            I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.

            Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.

            After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?

            An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image

            ...

            ANSWER

            Answered 2021-Nov-24 at 13:26

            What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.

            Source https://stackoverflow.com/questions/68691450

            QUESTION

            How to get SHAP values for Huggingface Transformer Model Prediction [Zero-Shot Classification]?
            Asked 2021-Oct-25 at 13:25

            Given a Zero-Shot Classification Task via Huggingface as follows:

            ...

            ANSWER

            Answered 2021-Oct-22 at 21:51

            The ZeroShotClassificationPipeline is currently not supported by shap, but you can use a workaround. The workaround is required because:

            1. The shap Explainer forwards only one parameter to the model (a pipeline in this case), but the ZeroShotClassificationPipeline requires two parameters, namely text, and labels.
            2. The shap Explainer will access the config of your model and use its label2id and id2label properties. They do not match the labels returned from the ZeroShotClassificationPipeline and will result in an error.

            Below is a suggestion for one possible workaround. I recommend opening an issue at shap and requesting official support for huggingface's ZeroShotClassificationPipeline.

            Source https://stackoverflow.com/questions/69628487

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

            Vulnerabilities

            No vulnerabilities reported

            Install transformer

            You can download it from GitHub.
            You can use transformer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            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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