transformer | A TensorFlow Implementation of the Transformer : Attention | Machine Learning library

by Kyubyong Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets(6)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | transformer Summary

transformer is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow, Transformer applications. transformer has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. However transformer has 10 bugs. You can download it from GitHub.

When I opened this repository in 2017, there was no official code yet. I tried to implement the paper as I understood, but to no surprise it had several bugs. I realized them mostly thanks to people who issued here, so I'm very grateful to all of them. Though there is the official implementation as well as several other unofficial github repos, I decided to update my own one. This update focuses on:. I still stick to IWSLT 2016 de-en. I guess if you'd like to test on a big data such as WMT, you would rely on the official implementation. After all, it's pleasant to check quickly if your model works. The initial code for TF1.2 is moved to the tf1.2_lecacy folder for the record.

Support

Quality

Security

License

Reuse

Support

transformer has a highly active ecosystem.

It has 3954 star(s) with 1258 fork(s). There are 109 watchers for this library.

It had no major release in the last 6 months.

There are 125 open issues and 34 have been closed. On average issues are closed in 37 days. There are 10 open pull requests and 0 closed requests.

It has a positive sentiment in the developer community.

The latest version of transformer is current.

Quality

transformer has 10 bugs (10 blocker, 0 critical, 0 major, 0 minor) and 28 code smells.

Security

transformer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

transformer code analysis shows 0 unresolved vulnerabilities.

There are 5 security hotspots that need review.

License

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.

Reuse

transformer releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

transformer saves you 402 person hours of effort in developing the same functionality from scratch.

It has 955 lines of code, 49 functions and 14 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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.

Evaluate the inference graph
Position - wise feedforward layer
Implements decoder
Logarithm
Evaluate inference
Load English vocabulary
Load DE vocab file
Load test data
Generate a generator function from two sentences
Encode the given input
Load vocabulary
Train the model
Define noam scheme
Helper function for label smoothing
Feed forward layer
Normalize the input tensor
Load data from training data
Load training data
Save hparams to file
Create a vocabulary
Get a list of num_batches hypotheses
Run preproprocessing
Get a batch of input files
Calculate bleu score
Save global variables info
Load hparams

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

Vision Transformer (ViT)-How do I use this model on an image?

Python

Lines of Code : 33

License : Permissive (Apache-2.0)

Copy

import timm
model = timm.create_model('vit_base_patch16_224', pretrained=True)
model.eval()

import urllib
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

config = resolve_dat

Vision Transformer for Small Datasets

Python

Lines of Code : 30

License : Permissive (MIT)

Copy

import torch
from vit_pytorch.vit_for_small_dataset import ViT

v = ViT(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

Vision Transformer for Small Datasets

pypi

Lines of Code : 30

License : No License

Copy

import torch
from vit_pytorch.vit_for_small_dataset import ViT

v = ViT(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

pytorch_geometric - point transformer segmentation

Python

Lines of Code : 158

License : Permissive (MIT License)

Copy

import os.path as osp

import torch
import torch.nn.functional as F
from point_transformer_classification import TransformerBlock, TransitionDown
from torch_cluster import knn_graph
from torch_scatter import scatter
from torchmetrics.functional impor

pytorch_geometric - point transformer classification

Python

Lines of Code : 118

License : Permissive (MIT License)

Copy

import os.path as osp

import torch
import torch.nn.functional as F
from torch.nn import Linear as Lin
from torch_cluster import fps, knn_graph
from torch_scatter import scatter_max

import torch_geometric.transforms as T
from torch_geometric.dataset

Initialize Transformer .

python

Lines of Code : 110

License : Non-SPDX (Apache License 2.0)

Copy

def __init__(self,
               input_saved_model_dir=None,
               input_saved_model_tags=None,
               input_saved_model_signature_key=None,
               input_graph_def=None,
               nodes_denylist=None,
               max

Community Discussions

Trending Discussions on transformer

Extracting multiple Wikipedia pages using Pythons Wikipedia

Hugging Face: NameError: name 'sentences' is not defined

Reply Channel for Messaging Gateway using Java DSL

Remove XML node based on attribute value

is it possible to assign a value to a field added with EventListener symfony in $builder symfony?

unable to mmap 1024 bytes - Cannot allocate memory - even though there is more than enough ram

Creating a executable far jar with dependancies (gradle or maven)

Force BERT transformer to use CUDA

How and where can i freeze classifier layer?

sklearn "Pipeline instance is not fitted yet." error, even though it is

QUESTION

Extracting multiple Wikipedia pages using Pythons Wikipedia

Asked 2021-Jun-15 at 13:10

I am not sure how to extract multiple pages from a search result using Pythons Wikipedia plugin. Some advice would be appreciated.

My code so far:

...

ANSWER

Answered 2021-Jun-15 at 13:10

You have done the hard part, the results are already in the results variable.

But the results need parsing by the wiki.page() nethod, which only takes one argument.

The solution? Use a loop to parse all results one by one.

The easiest way will be using for loops, but the list comprehension method is the best.

Replace the last two lines with the following:

Source https://stackoverflow.com/questions/67986624

QUESTION

Hugging Face: NameError: name 'sentences' is not defined

Asked 2021-Jun-14 at 15:16

I am following this tutorial here: https://huggingface.co/transformers/training.html - though, I am coming across an error, and I think the tutorial is missing an import, but i do not know which.

These are my current imports:

...

ANSWER

Answered 2021-Jun-14 at 15:08

The error states that you do not have a variable called sentences in the scope. I believe the tutorial presumes you already have a list of sentences and are tokenizing it.

Have a look at the documentation The first argument can be either a string or list of string or list of list of strings.

Source https://stackoverflow.com/questions/67972661

QUESTION

Reply Channel for Messaging Gateway using Java DSL

Asked 2021-Jun-14 at 14:28

I have a REST API which receives a POST request from a client application.

...

ANSWER

Answered 2021-Jun-14 at 14:28

Your current flow does not return a value, you are simply logging the message.

A terminating .log() ends the flow.

Delete the .log() element so the result of the transform will automatically be routed back to the gateway.

Or add a .bridge() (a bridge to nowhere) after the log and it will bridge the output to the reply channel.

Source https://stackoverflow.com/questions/67960788

QUESTION

Remove XML node based on attribute value

Asked 2021-Jun-14 at 13:14

I have the following XML file from which I am trying to remove the whole AuditTrailEntry node if the EventType matched start or assign. I've seen a similar case here on stackoverflow but the solution just doesn't work for me, I always get an error - NOT_FOUND_ERR: Raised if oldChild is not a child of this node. Do you have an idea how to solve this?

...

ANSWER

Answered 2021-Jun-11 at 23:26

Using XPath, things becomes much easier:

Source https://stackoverflow.com/questions/67944138

QUESTION

is it possible to assign a value to a field added with EventListener symfony in $builder symfony?

Asked 2021-Jun-14 at 12:48

I would like to know if it is possible to automatically assign values to added fields of type:

datetime
entity

Thanks for your help

...

ANSWER

Answered 2021-Jun-14 at 12:48

From what i can see, you have some form and you want to plug 3 data to the form on submit.

Depending on your database configuration, you can do 3 different way:

The best one is to use the mapping

Your evaluation have those 3 fields:

date
user
player

Then just add them to the original builder as hidden field whith default value what you have:

Source https://stackoverflow.com/questions/67959752

QUESTION

unable to mmap 1024 bytes - Cannot allocate memory - even though there is more than enough ram

Asked 2021-Jun-14 at 11:16

I'm currently working on a seminar paper on nlp, summarization of sourcecode function documentation. I've therefore created my own dataset with ca. 64000 samples (37453 is the size of the training dataset) and I want to fine tune the BART model. I use for this the package simpletransformers which is based on the huggingface package. My dataset is a pandas dataframe. An example of my dataset:

My code:

...

ANSWER

Answered 2021-Jun-08 at 08:27

While I do not know how to deal with this problem directly, I had a somewhat similar issue(and solved). The difference is:

I use fairseq
I can run my code on google colab with 1 GPU
Got RuntimeError: unable to mmap 280 bytes from file : Cannot allocate memory (12) immediately when I tried to run it on multiple GPUs.

From the other people's code, I found that he uses python -m torch.distributed.launch -- ... to run fairseq-train, and I added it to my bash script and the RuntimeError is gone and training is going.

So I guess if you can run with 21000 samples, you may use torch.distributed to make whole data into small batches and distribute them to several workers.

Source https://stackoverflow.com/questions/67876741

QUESTION

Creating a executable far jar with dependancies (gradle or maven)

Asked 2021-Jun-13 at 18:26

I have a very simple program that just produces a JTable that is populated via a predetermined ResultSet, it works fine inside the ide, (intelliJ). It only has the one sqlite dependency.

I'm trying to get an standalone executable jar out of it that spits out the same table.

I did the project on gradle as that was the most common result when looking up fat jars.

The guides did not work at all but i did eventually end up on here.

Gradle fat jar does not contain libraries

running "gradle uberJar" on the terminal did produce a jar but it doesn't run when double clicked and running the jar on the cmd line produces:

no main manifest attribute, in dbtest-1.0-SNAPSHOT-uber.jar

here is the gradle build text:

...

ANSWER

Answered 2021-Jun-12 at 23:04

You can add a manifest to your task since it is type Jar. Specifying an entrypoint with the Main-Class attribute should make your Jar executable.

Source https://stackoverflow.com/questions/67952878

QUESTION

Force BERT transformer to use CUDA

Asked 2021-Jun-13 at 09:57

I want to force the Huggingface transformer (BERT) to make use of CUDA. nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0 or .to(cuda:0).

The code below is basically a customized part from german sentiment BERT working example

...

ANSWER

Answered 2021-Jun-12 at 16:19

You can make the entire class inherit torch.nn.Module like so:

Source https://stackoverflow.com/questions/67948945

QUESTION

How and where can i freeze classifier layer?

Asked 2021-Jun-12 at 20:29

If I need to freeze the output layer of this model which is doing the classification as I don't need it.

...

ANSWER

Answered 2021-Jun-11 at 15:33

You are confusing a few things here (I think)

Freezing layers

You freeze the layer if you don't want them to be trained (and don't want them to be part of the graph also).

Usually we freeze part of the network creating features, in your case it would be everything up to self.head.

After that, we usually only train bottleneck (self.head in this case) to fine-tune it for the task at hand.

In case of your model it would be:

Source https://stackoverflow.com/questions/67939448

QUESTION

sklearn "Pipeline instance is not fitted yet." error, even though it is

Asked 2021-Jun-11 at 23:28

A similar question is already asked, but the answer did not help me solve my problem: Sklearn components in pipeline is not fitted even if the whole pipeline is?

I'm trying to use multiple pipelines to preprocess my data with a One Hot Encoder for categorical and numerical data (as suggested in this blog).

Here is my code, and even though my classifier produces 78% accuracy, I can't figure out why I cannot plot the decision-tree I'm training and what can help me fix the problem. Here is the code snippet:

...

ANSWER

Answered 2021-Jun-11 at 22:09

You cannot use the export_text function on the whole pipeline as it only accepts Decision Tree objects, i.e. DecisionTreeClassifier or DecisionTreeRegressor. Only pass the fitted estimator of your pipeline and it will work:

Source https://stackoverflow.com/questions/67943229

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 .

Find more information at: