pytorch-sentiment-analysis | getting started with PyTorch and TorchText for sentiment | Machine Learning library

 by   bentrevett Jupyter Notebook Version: Current License: MIT

kandi X-RAY | pytorch-sentiment-analysis Summary

kandi X-RAY | pytorch-sentiment-analysis Summary

pytorch-sentiment-analysis is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Bert, Neural Network applications. pytorch-sentiment-analysis has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
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              pytorch-sentiment-analysis has a medium active ecosystem.
              It has 3924 star(s) with 1109 fork(s). There are 89 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 22 open issues and 86 have been closed. On average issues are closed in 28 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch-sentiment-analysis is current.

            kandi-Quality Quality

              pytorch-sentiment-analysis has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              pytorch-sentiment-analysis is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

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              pytorch-sentiment-analysis releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              It has 16 lines of code, 0 functions and 16 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            pytorch-sentiment-analysis Key Features

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            pytorch-sentiment-analysis Examples and Code Snippets

            No Code Snippets are available at this moment for pytorch-sentiment-analysis.

            Community Discussions

            QUESTION

            PyTorch ValueError: Target size (torch.Size([64])) must be the same as input size (torch.Size([15]))
            Asked 2021-Apr-07 at 13:16

            I'm currently using this repo to perform NLP and learn more about CNN's using my own dataset, and I keep running into an error regarding a shape mismatch:

            ...

            ANSWER

            Answered 2021-Apr-07 at 13:16

            QUESTION

            PyTorch torch.no_grad() versus requires_grad=False
            Asked 2020-Sep-08 at 16:54

            I'm following a PyTorch tutorial which uses the BERT NLP model (feature extractor) from the Huggingface Transformers library. There are two pieces of interrelated code for gradient updates that I don't understand.

            (1) torch.no_grad()

            The tutorial has a class where the forward() function creates a torch.no_grad() block around a call to the BERT feature extractor, like this:

            ...

            ANSWER

            Answered 2020-Sep-08 at 10:27

            This is an older discussion, which has changed slightly over the years (mainly due to the purpose of with torch.no_grad() as a pattern. An excellent answer that kind of answers your question as well can be found on Stackoverflow already.
            However, since the original question is vastly different, I'll refrain from marking as duplicate, especially due to the second part about the memory.

            An initial explanation of no_grad is given here:

            with torch.no_grad() is a context manager and is used to prevent calculating gradients [...].

            requires_grad on the other hand is used

            to freeze part of your model and train the rest [...].

            Source again the SO post.

            Essentially, with requires_grad you are just disabling parts of a network, whereas no_grad will not store any gradients at all, since you're likely using it for inference and not training.
            To analyze the behavior of your combinations of parameters, let us investigate what is happening:

            • a) and b) do not store any gradients at all, which means that you have vastly more memory available to you, no matter the number of parameters, since you're not retaining them for a potential backward pass.
            • c) has to store the forward pass for later backpropagation, however, only a limited number of parameter (3 million) are stored, which makes this still manageable.
            • d), however, needs to store the forward pass for all 112 million parameters, which causes you to run out of memory.

            Source https://stackoverflow.com/questions/63785319

            QUESTION

            PyTorch: Loading word vectors into Field vocabulary vs. Embedding layer
            Asked 2020-Jun-10 at 00:21

            I'm coming from Keras to PyTorch. I would like to create a PyTorch Embedding layer (a matrix of size V x D, where V is over vocabulary word indices and D is the embedding vector dimension) with GloVe vectors but am confused by the needed steps.

            In Keras, you can load the GloVe vectors by having the Embedding layer constructor take a weights argument:

            ...

            ANSWER

            Answered 2020-Jun-10 at 00:21

            When torchtext builds the vocabulary, it aligns the the token indices with the embedding. If your vocabulary doesn't have the same size and ordering as the pre-trained embeddings, the indices wouldn't be guaranteed to match, therefore you might look up incorrect embeddings. build_vocab() creates the vocabulary for your dataset with the corresponding embeddings and discards the rest of the embeddings, because those are unused.

            The GloVe-6B embeddings includes a vocabulary of size 400K. For example the IMDB dataset only uses about 120K of these, the other 280K are unused.

            Source https://stackoverflow.com/questions/62291303

            QUESTION

            Optimizer and scheduler for BERT fine-tuning
            Asked 2020-May-05 at 19:10

            I'm trying to fine-tune a model with BERT (using transformers library), and I'm a bit unsure about the optimizer and scheduler.

            First, I understand that I should use transformers.AdamW instead of Pytorch's version of it. Also, we should use a warmup scheduler as suggested in the paper, so the scheduler is created using get_linear_scheduler_with_warmup function from transformers package.

            The main questions I have are:

            1. get_linear_scheduler_with_warmup should be called with the warm up. Is it ok to use 2 for warmup out of 10 epochs?
            2. When should I call scheduler.step()? If I do after train, the learning rate is zero for the first epoch. Should I call it for each batch?

            Am I doing something wrong with this?

            ...

            ANSWER

            Answered 2020-Feb-20 at 08:58

            I think it is hardly possible to give a 100% perfect answer, but you can certainly get inspiration from the way other scripts are doing it. The best place to start is the examples/ directory of the huggingface repository itself, where you can for example find this excerpt:

            Source https://stackoverflow.com/questions/60120043

            QUESTION

            PyTorch LSTM for multiclass classification: TypeError: '<' not supported between instances of 'Example' and 'Example'
            Asked 2020-Apr-16 at 17:44

            I am trying to modify the code in this Tutorial to adapt it to a multiclass data (I have 55 distinct classes). An error is triggered and I am uncertain of the root cause. The changes I made to this tutorial have been annotated in same-line comments.

            One of two solutions would satisfy this questions:

            (A) Help identifying the root cause of the error, OR

            (B) A boilerplate script for multiclass classification using PyTorch LSTM

            ...

            ANSWER

            Answered 2020-Apr-16 at 17:44

            The BucketIterator sorts the data to make batches with examples of similar length to avoid having too much padding. For that it needs to know what the sorting criterion is, which should be the text length. Since it is not fixed to a specific data layout, you can freely choose which field it should use, but that also means you must provide that information to sort_key.

            In your case, there are two possible fields, text and wage_label, and you want to sort it based on the length of the text.

            Source https://stackoverflow.com/questions/61213493

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

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            No vulnerabilities reported

            Install pytorch-sentiment-analysis

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