convmodel | convmodel provides features to support your Conversational | Natural Language Processing library

by colorfulscoop Python Version: 0.3.0 License: MIT

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

kandi X-RAY | convmodel Summary

convmodel is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. convmodel has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install convmodel' or download it from GitHub, PyPI.

convmodel provides a conversation model based on transformers GPT-2 model :wink:.

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Support

convmodel has a low active ecosystem.

It has 4 star(s) with 0 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

convmodel has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of convmodel is 0.3.0

Quality

convmodel has 0 bugs and 0 code smells.

Security

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

convmodel code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

convmodel is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Deployable package is available in PyPI.

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

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

It has 625 lines of code, 47 functions and 13 files.

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

Top functions reviewed by kandi - BETA

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

Fit the model .
Evaluate the model .
Convert texts into tokens .
Build a data loader .
Collate items into a tensor .
Run streamlit .
Configure the estimability .
Initialize tokenizer .
Display a conversation .
Check that the conversation is empty .

Get all kandi verified functions for this library.

convmodel Key Features

No Key Features are available at this moment for convmodel.

convmodel Examples and Code Snippets

No Code Snippets are available at this moment for convmodel.

Community Discussions

Trending Discussions on convmodel

ValueError: Output tensors of a Functional model must be the output of a TensorFlow `Layer` when using custom callback to plot conv layer feature maps

How to avoid move of possibly-uninitialized variable for MutexGuard interface?

Tensorflow deep learning model produces less accuracy for 10 classes, whereas performs very good as set of 3 classes

RuntimeError: Given groups=3, weight of size 12 64 3 768, expected input[32, 12, 30, 768] to have 192 channels, but got 12 channels instead

How do I put a Conv1D layer after an Embedding layer in Tensorflow 2?

Tensorflow 2: Getting "WARNING:tensorflow:9 out of the last 9 calls to triggered tf.function retracing. Tracing is expensive"

QUESTION

ValueError: Output tensors of a Functional model must be the output of a TensorFlow `Layer` when using custom callback to plot conv layer feature maps

Asked 2021-Oct-31 at 09:43

I'm trying to implement a custom callback to get the feature maps of each Conv2D layer in the network plotted in TensorBoard.

When I run the code in Example 1 I get the following error:

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ANSWER

Answered 2021-Oct-31 at 09:43

Just figured out the problem:

Source https://stackoverflow.com/questions/69775239

QUESTION

How to avoid move of possibly-uninitialized variable for MutexGuard interface?

Asked 2021-Oct-07 at 17:14

for following code:

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ANSWER

Answered 2021-Oct-07 at 07:48

You can extract &mut T from both and use that. Something like the following should work:

Source https://stackoverflow.com/questions/69477156

QUESTION

Tensorflow deep learning model produces less accuracy for 10 classes, whereas performs very good as set of 3 classes

Asked 2020-Aug-07 at 12:55

I am working on a computer vision project, word classification based on lip movement. There are 10 classes (words) to classify. each class in the dataset will have a sequence of images or frames. I chose a time distributed model and LSTM model for the task. Intially, the dataset will be converted into a numpy array which is first fed to the CNN layers to identify features in each images. The result is fed to the Time distributed layer and LSTM to treat the frames as time series. Finally some dense layers are used for classfication.

The problem i am facing is, when i train the model separatly for 3 to 4 classes or words I am getting high accuracy(~ around 80 to 90%) and the prediction is really good. But when I train the model for 10 classes or words all together the accuracy is very very low.

I don't know what is the reason behind this. Could some one help me with this ?

My code

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ANSWER

Answered 2020-Aug-07 at 12:55

I think it is because your training data-set of 79 is too small. You can:

increase training data-set by acquiring additional data such as this one - http://spandh.dcs.shef.ac.uk/gridcorpus/

use pre-trained weights for transfer-learning from some other relevant neural network such as LipNet - https://github.com/rizkiarm/LipNet . Their repository contains step-by-step instructions.

Source https://stackoverflow.com/questions/63281676

QUESTION

RuntimeError: Given groups=3, weight of size 12 64 3 768, expected input[32, 12, 30, 768] to have 192 channels, but got 12 channels instead

Asked 2020-Jun-08 at 14:17

I started working with Pytorch recently so my understanding of it isn't quite strong. I previously had a 1 layer CNN but wanted to extend it to 2 layers, but the input and output channels have been throwing errors I can seem to decipher. Why does it expect 192 channels? Can someone give me a pointer to help me understand this better? I have seen several related problems on here, but I don't understand those solutions either.

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ANSWER

Answered 2020-Jun-08 at 14:17

It seems that the original version of the code you had in this question behaved differently. The final version of the code you have here gives me a different error from what you posted, more specifically - this:

Source https://stackoverflow.com/questions/62099558

QUESTION

How do I put a Conv1D layer after an Embedding layer in Tensorflow 2?

Asked 2020-May-08 at 16:35

For an evaluation, I need to be able to apply a convolutional layer to text data. So I'm trying to perform sentiment analysis on Amazon reviews. After the Embedding layer, however, the Conv1D layer will not get the required shape.

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ANSWER

Answered 2020-May-06 at 04:58

The out_dim of word embedding layer should match with conv1D input filter size. Try changing the out_dim to 32. Proper way: https://machinelearningmastery.com/predict-sentiment-movie-reviews-using-deep-learning/

Source https://stackoverflow.com/questions/61627052

QUESTION

Tensorflow 2: Getting "WARNING:tensorflow:9 out of the last 9 calls to triggered tf.function retracing. Tracing is expensive"

Asked 2020-May-07 at 05:47

I think this error is coming from a problem with shapes, but I have no idea where. The complete error message suggests to do the following:

Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing.

When I enter this argument in the function decorator, it does work.

...

ANSWER

Answered 2020-May-07 at 05:47

TF/DR: Root-cause of this error is due to change in shape of train_data which varies from batch to batch. Fixing the size/shape of train_data resolves this tracing warning. I changed the following line, then everything works as expected. Full gist is here

Source https://stackoverflow.com/questions/61647404

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

Vulnerabilities

No vulnerabilities reported

Install convmodel

You can install using 'pip install convmodel' or download it from GitHub, PyPI.
You can use convmodel 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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