kandi background
Explore Kits

FakeBuster | Fake News Detection with Machine Learning | Machine Learning library

 by   FakeNewsDetection Python Version: Current License: No License

 by   FakeNewsDetection Python Version: Current License: No License

Download this library from

kandi X-RAY | FakeBuster Summary

FakeBuster is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. FakeBuster has no bugs, it has no vulnerabilities and it has low support. However FakeBuster build file is not available. You can download it from GitHub.
The topic of fake news detection on social media has recently attracted tremendous attention. The basic countermeasure of comparing websites against a list of labeled fake news sources is inflexible, and so a machine learning approach is desirable. Our project aims to use Natural Language Processing to detect fake news directly, based on the text content of news articles.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • FakeBuster has a low active ecosystem.
  • It has 60 star(s) with 38 fork(s). There are 8 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 0 have been closed. On average issues are closed in 511 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of FakeBuster is current.
FakeBuster Support
Best in #Machine Learning
Average in #Machine Learning
FakeBuster Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • FakeBuster has 0 bugs and 0 code smells.
FakeBuster Quality
Best in #Machine Learning
Average in #Machine Learning
FakeBuster Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • FakeBuster has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • FakeBuster code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
FakeBuster Security
Best in #Machine Learning
Average in #Machine Learning
FakeBuster Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • FakeBuster does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
FakeBuster License
Best in #Machine Learning
Average in #Machine Learning
FakeBuster License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • FakeBuster releases are not available. You will need to build from source code and install.
  • FakeBuster 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.
  • FakeBuster saves you 150 person hours of effort in developing the same functionality from scratch.
  • It has 374 lines of code, 14 functions and 6 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
FakeBuster Reuse
Best in #Machine Learning
Average in #Machine Learning
FakeBuster Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed FakeBuster and discovered the below as its top functions. This is intended to give you an instant insight into FakeBuster implemented functionality, and help decide if they suit your requirements.

  • Load embeddings from a text file
    • Clean text
    • Construct a list of LabeledSentences
    • Clean up text
  • Clean data
    • Create a baseline model
      • Plot confusion matrix

        Get all kandi verified functions for this library.

        Get all kandi verified functions for this library.

        FakeBuster Key Features

        Fake News Detection with Machine Learning

        Community Discussions

        Trending Discussions on Machine Learning
        • Using RNN Trained Model without pytorch installed
        • Flux.jl : Customizing optimizer
        • How can I check a confusion_matrix after fine-tuning with custom datasets?
        • CUDA OOM - But the numbers don't add upp?
        • How to compare baseline and GridSearchCV results fair?
        • Getting Error 524 while running jupyter lab in google cloud platform
        • TypeError: brain.NeuralNetwork is not a constructor
        • Ordinal Encoding or One-Hot-Encoding
        • How to increase dimension-vector size of BERT sentence-transformers embedding
        • How to identify what features affect predictions result?
        Trending Discussions on Machine Learning

        QUESTION

        Using RNN Trained Model without pytorch installed

        Asked 2022-Feb-28 at 20:17

        I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

        I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

        I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

        import torch
        import torch.nn as nn
        import torch.nn.functional as F
        import torch.optim as optim
        import random
        
        torch.manual_seed(1)
        random.seed(1)
        device = torch.device('cpu')
        
        class RNN(nn.Module):
          def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
            super(RNN, self).__init__()
            self.input_size = input_size
            self.hidden_size = hidden_size
            self.output_size = output_size
            self.num_layers = num_layers
            self.batch_size = batch_size
            self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
            self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
            self.hidden2out = nn.Linear(hidden_size, output_size)
            self.hidden = self.init_hidden()
          def forward(self, feature_list):
            feature_list=torch.tensor(feature_list)
            
            if self.matching_in_out:
              lstm_out, _ = self.lstm( feature_list.view(len( feature_list), 1, -1))
              output_space = self.hidden2out(lstm_out.view(len( feature_list), -1))
              output_scores = torch.sigmoid(output_space) #we'll need to check if we need this sigmoid
              return output_scores #output_scores
            else:
              for i in range(len(feature_list)):
                cur_ft_tensor=feature_list[i]#.view([1,1,self.input_size])
                cur_ft_tensor=cur_ft_tensor.view([1,1,self.input_size])
                lstm_out, self.hidden = self.lstm(cur_ft_tensor, self.hidden)
                outs=self.hidden2out(lstm_out)
              return outs
          def init_hidden(self):
            #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
            return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device),
                    torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device))
        

        I am aware of this question, but I'm willing to go as low level as possible. I can work with numpy array instead of tensors, and reshape instead of view, and I don't need a device setting.

        Based on the class definition above, what I can see here is that I only need the following components from torch to get an output from the forward function:

        • nn.LSTM
        • nn.Linear
        • torch.sigmoid

        I think I can easily implement the sigmoid function using numpy. However, can I have some implementation for the nn.LSTM and nn.Linear using something not involving pytorch? Also, how will I use the weights from the state dict into the new class?

        So, the question is, how can I "translate" this RNN definition into a class that doesn't need pytorch, and how to use the state dict weights for it? Alternatively, is there a "light" version of pytorch, that I can use just to run the model and yield a result?

        EDIT

        I think it might be useful to include the numpy/scipy equivalent for both nn.LSTM and nn.linear. It would help us compare the numpy output to torch output for the same code, and give us some modular code/functions to use. Specifically, a numpy equivalent for the following would be great:

        rnn = nn.LSTM(10, 20, 2)
        input = torch.randn(5, 3, 10)
        h0 = torch.randn(2, 3, 20)
        c0 = torch.randn(2, 3, 20)
        output, (hn, cn) = rnn(input, (h0, c0))
        

        and also for linear:

        m = nn.Linear(20, 30)
        input = torch.randn(128, 20)
        output = m(input)
        

        ANSWER

        Answered 2022-Feb-17 at 10:47

        You should try to export the model using torch.onnx. The page gives you an example that you can start with.

        An alternative is to use TorchScript, but that requires torch libraries.

        Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

        ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

        A running example

        Just modifying a little your example to go over the errors I found

        Notice that via tracing any if/elif/else, for, while will be unrolled

        import torch
        import torch.nn as nn
        import torch.nn.functional as F
        import torch.optim as optim
        import random
        
        torch.manual_seed(1)
        random.seed(1)
        device = torch.device('cpu')
        
        class RNN(nn.Module):
          def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
            super(RNN, self).__init__()
            self.input_size = input_size
            self.hidden_size = hidden_size
            self.output_size = output_size
            self.num_layers = num_layers
            self.batch_size = batch_size
            self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
            self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
            self.hidden2out = nn.Linear(hidden_size, output_size)
          def forward(self, x, h0, c0):
            lstm_out, (hidden_a, hidden_b) = self.lstm(x, (h0, c0))
            outs=self.hidden2out(lstm_out)
            return outs, (hidden_a, hidden_b)
          def init_hidden(self):
            #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
            return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach(),
                    torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach())
        
        # convert the arguments passed during onnx.export call
        class MWrapper(nn.Module):
            def __init__(self, model):
                super(MWrapper, self).__init__()
                self.model = model;
            def forward(self, kwargs):
                return self.model(**kwargs)
        

        Run an example

        rnn = RNN(10, 10, 10, 3)
        X = torch.randn(3,1,10)
        h0,c0  = rnn.init_hidden()
        print(rnn(X, h0, c0)[0])
        

        Use the same input to trace the model and export an onnx file

        
        torch.onnx.export(MWrapper(rnn), {'x':X,'h0':h0,'c0':c0}, 'rnn.onnx', 
                          dynamic_axes={'x':{1:'N'},
                                       'c0':{1: 'N'},
                                       'h0':{1: 'N'}
                                       },
                          input_names=['x', 'h0', 'c0'],
                          output_names=['y', 'hn', 'cn']
                         )
        

        Notice that you can use symbolic values for the dimensions of some axes of some inputs. Unspecified dimensions will be fixed with the values from the traced inputs. By default LSTM uses dimension 1 as batch.

        Next we load the ONNX model and pass the same inputs

        import onnxruntime
        ort_model = onnxruntime.InferenceSession('rnn.onnx')
        print(ort_model.run(['y'], {'x':X.numpy(), 'c0':c0.numpy(), 'h0':h0.numpy()}))
        

        Source https://stackoverflow.com/questions/71146140

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

        Vulnerabilities

        No vulnerabilities reported

        Install FakeBuster

        You can download it from GitHub.
        You can use FakeBuster 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 .

        DOWNLOAD this Library from

        Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
        over 430 million Knowledge Items
        Find more libraries
        Reuse Solution Kits and Libraries Curated by Popular Use Cases
        Explore Kits

        Save this library and start creating your kit

        Share this Page

        share link
        Reuse Pre-built Kits with FakeBuster
        Consider Popular Machine Learning Libraries
        Compare Machine Learning Libraries with Highest Support
        Compare Machine Learning Libraries with Highest Quality
        Compare Machine Learning Libraries with Highest Security
        Compare Machine Learning Libraries with Permissive License
        Compare Machine Learning Libraries with Highest Reuse
        Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
        over 430 million Knowledge Items
        Find more libraries
        Reuse Solution Kits and Libraries Curated by Popular Use Cases
        Explore Kits

        Save this library and start creating your kit

        • © 2022 Open Weaver Inc.