CodeGen | source model for program synthesis | Machine Learning library

 by   salesforce Python Version: Current License: BSD-3-Clause

kandi X-RAY | CodeGen Summary

kandi X-RAY | CodeGen Summary

CodeGen is a Python library typically used in Artificial Intelligence, Machine Learning applications. CodeGen has no bugs, it has a Permissive License and it has medium support. However CodeGen has 1 vulnerabilities and it build file is not available. You can download it from GitHub.

Official release for the CodeGen models (350M, 2B, 6B, 16B) for Program Synthesis as presented in:. Title: CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. Authors: Erik Nijkamp*, Bo Pang*, Hiroaki Hayashi*, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong (* indicates equal contribution).
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              CodeGen has a medium active ecosystem.
              It has 3741 star(s) with 268 fork(s). There are 69 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 21 open issues and 31 have been closed. On average issues are closed in 11 days. There are 5 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of CodeGen is current.

            kandi-Quality Quality

              CodeGen has no bugs reported.

            kandi-Security Security

              CodeGen has 1 vulnerability issues reported (1 critical, 0 high, 0 medium, 0 low).

            kandi-License License

              CodeGen is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

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

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            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of CodeGen
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            CodeGen Key Features

            No Key Features are available at this moment for CodeGen.

            CodeGen Examples and Code Snippets

            Register Your Codegen
            Pythondot img1Lines of Code : 0dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            runtime::Module CCompiler(const NodeRef& ref) {
              CSourceCodegen csource;
              return csource.CreateCSourceModule(ref);
            }
            TVM_REGISTER_GLOBAL("relay.ext.ccompiler").set_body_typed(CCompiler);
            if(USE_CODEGENC)
              file(GLOB CSOURCE_RELAY_CONTRIB_SRC sr  

            Community Discussions

            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.

            ...

            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

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

            QUESTION

            Flux.jl : Customizing optimizer
            Asked 2022-Jan-25 at 07:58

            I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.

            optimizer_pseudocode

            I'm using MNIST dataset.

            ...

            ANSWER

            Answered 2022-Jan-14 at 23:47

            Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.

            Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:

            Source https://stackoverflow.com/questions/70641453

            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

            CUDA OOM - But the numbers don't add upp?
            Asked 2021-Nov-23 at 06:13

            I am trying to train a model using PyTorch. When beginning model training I get the following error message:

            RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)

            I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi I see these processes running

            ...

            ANSWER

            Answered 2021-Nov-23 at 06:13

            This is more of a comment, but worth pointing out.

            The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):

            Let's run the following python commands interactively:

            Source https://stackoverflow.com/questions/70074789

            QUESTION

            How to compare baseline and GridSearchCV results fair?
            Asked 2021-Nov-04 at 21:17

            I am a bit confusing with comparing best GridSearchCV model and baseline.
            For example, we have classification problem.
            As a baseline, we'll fit a model with default settings (let it be logistic regression):

            ...

            ANSWER

            Answered 2021-Nov-04 at 21:17

            No, they aren't comparable.

            Your baseline model used X_train to fit the model. Then you're using the fitted model to score the X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.

            The grid searched model is at a disadvantage because:

            1. It's working with less data since you have split the X_train sample.
            2. Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of X_val per fold).

            So your score for the grid search is going to be worse than your baseline.

            Now you might ask, "so what's the point of best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.

            So how should one go about conducting a fair comparison?

            1. Split your training data for both models.

            Source https://stackoverflow.com/questions/69844028

            QUESTION

            Getting Error 524 while running jupyter lab in google cloud platform
            Asked 2021-Oct-15 at 02:14

            I am not able to access jupyter lab created on google cloud

            I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.

            I tried the diagnostic tool, which gave the following result:

            but didn't fix it.

            Thanks in advance.

            ...

            ANSWER

            Answered 2021-Aug-20 at 14:00

            QUESTION

            TypeError: brain.NeuralNetwork is not a constructor
            Asked 2021-Sep-29 at 22:47

            I am new to Machine Learning.

            Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:

            I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!

            Kindly point out what I am missing here!

            Find below my code:

            ...

            ANSWER

            Answered 2021-Sep-29 at 22:47

            Turns out its just documented incorrectly.

            In reality the export from brain.js is this:

            Source https://stackoverflow.com/questions/69348213

            QUESTION

            Ordinal Encoding or One-Hot-Encoding
            Asked 2021-Sep-04 at 06:43

            IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?

            I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:

            ...

            ANSWER

            Answered 2021-Sep-04 at 06:43

            You're right. Just one thing to consider for choosing OrdinalEncoder or OneHotEncoder is that does the order of data matter?

            Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:

            • quality = ["bad", "average", "good", "excellent"] or
            • shirt_size = ["large", "medium", "small"]

            but it is obviously not the case for the:

            • color = ["white","orange","black","green"]

            column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white category should be encoded as 0 and black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)

            Source https://stackoverflow.com/questions/69052776

            QUESTION

            How to increase dimension-vector size of BERT sentence-transformers embedding
            Asked 2021-Aug-15 at 13:35

            I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language

            by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.

            code:

            ...

            ANSWER

            Answered 2021-Aug-10 at 07:39

            Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.

            Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.

            If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.

            Source https://stackoverflow.com/questions/68686272

            QUESTION

            How to identify what features affect predictions result?
            Asked 2021-Aug-11 at 15:55

            I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.

            Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?

            Table example:

            ...

            ANSWER

            Answered 2021-Aug-11 at 15:55

            You could build a model like this.

            x = features you have. y = true_lable

            from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).

            Source https://stackoverflow.com/questions/68744565

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

            Vulnerabilities

            No vulnerabilities reported

            Install CodeGen

            You can download it from GitHub.
            You can use CodeGen 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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            https://github.com/salesforce/CodeGen.git

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            gh repo clone salesforce/CodeGen

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            git@github.com:salesforce/CodeGen.git

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