bert | A client side , multi-style alerts system for Meteor

 by   themeteorchef CSS Version: Current License: No License

kandi X-RAY | bert Summary

kandi X-RAY | bert Summary

bert is a CSS library. bert has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Bert is a client side, multi-style alerts system for Meteor.
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              bert has a low active ecosystem.
              It has 187 star(s) with 25 fork(s). There are 9 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 26 open issues and 28 have been closed. On average issues are closed in 91 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of bert is current.

            kandi-Quality Quality

              bert has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              bert does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              bert releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.
              It has 348 lines of code, 0 functions and 9 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            bert Key Features

            No Key Features are available at this moment for bert.

            bert Examples and Code Snippets

            No Code Snippets are available at this moment for bert.

            Community Discussions

            QUESTION

            Convert pandas dataframe to datasetDict
            Asked 2022-Mar-25 at 15:47

            I cannot find anywhere how to convert a pandas dataframe to type datasets.dataset_dict.DatasetDict, for optimal use in a BERT workflow with a huggingface model. Take these simple dataframes, for example.

            ...

            ANSWER

            Answered 2022-Mar-25 at 15:47

            One possibility is to first create two Datasets and then join them:

            Source https://stackoverflow.com/questions/71618974

            QUESTION

            What is the loss function used in Trainer from the Transformers library of Hugging Face?
            Asked 2022-Mar-23 at 10:12

            What is the loss function used in Trainer from the Transformers library of Hugging Face?

            I am trying to fine tine a BERT model using the Trainer class from the Transformers library of Hugging Face.

            In their documentation, they mention that one can specify a customized loss function by overriding the compute_loss method in the class. However, if I do not do the method override and use the Trainer to fine tine a BERT model directly for sentiment classification, what is the default loss function being use? Is it the categorical crossentropy? Thanks!

            ...

            ANSWER

            Answered 2022-Mar-23 at 10:12

            It depends! Especially given your relatively vague setup description, it is not clear what loss will be used. But to start from the beginning, let's first check how the default compute_loss() function in the Trainer class looks like.

            You can find the corresponding function here, if you want to have a look for yourself (current version at time of writing is 4.17). The actual loss that will be returned with default parameters is taken from the model's output values:

            loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

            which means that the model itself is (by default) responsible for computing some sort of loss and returning it in outputs.

            Following this, we can then look into the actual model definitions for BERT (source: here, and in particular check out the model that will be used in your Sentiment Analysis task (I assume a BertForSequenceClassification model.

            The code relevant for defining a loss function looks like this:

            Source https://stackoverflow.com/questions/71581197

            QUESTION

            how to save and load custom siamese bert model
            Asked 2022-Mar-09 at 10:34

            I am following this tutorial on how to train a siamese bert network:

            https://keras.io/examples/nlp/semantic_similarity_with_bert/

            all good, but I am not sure what is the best way to save the model after train it and save it. any suggestion?

            I was trying with

            model.save('models/bert_siamese_v1')

            which creates a folder with save_model.bp keras_metadata.bp and two subfolders (variables and assets)

            then I try to load it with:

            ...

            ANSWER

            Answered 2022-Mar-08 at 16:13

            Try using tf.saved_model.save to save your model:

            Source https://stackoverflow.com/questions/71396540

            QUESTION

            How to change AllenNLP BERT based Semantic Role Labeling to RoBERTa in AllenNLP
            Asked 2022-Feb-24 at 12:34

            Currently i'm able to train a Semantic Role Labeling model using the config file below. This config file is based on the one provided by AllenNLP and works for the default bert-base-uncased model and also GroNLP/bert-base-dutch-cased.

            ...

            ANSWER

            Answered 2022-Feb-24 at 02:14

            The easiest way to resolve this is to patch SrlReader so that it uses PretrainedTransformerTokenizer (from AllenNLP) or AutoTokenizer (from Huggingface) instead of BertTokenizer. SrlReader is an old class, and was written against an old version of the Huggingface tokenizer API, so it's not so easy to upgrade.

            If you want to submit a pull request in the AllenNLP project, I'd be happy to help you get it merged into AllenNLP!

            Source https://stackoverflow.com/questions/71223907

            QUESTION

            Simple Transformers producing nothing?
            Asked 2022-Feb-22 at 11:54

            I have a simple transformers script looking like this.

            ...

            ANSWER

            Answered 2022-Feb-22 at 11:54

            Use this model instead.

            Source https://stackoverflow.com/questions/71200243

            QUESTION

            Organize data for transformer fine-tuning
            Asked 2022-Feb-02 at 14:58

            I have a corpus of synonyms and non-synonyms. These are stored in a list of python dictionaries like {"sentence1": , "sentence2": , "label": <1.0 or 0.0> }. Note that this words (or sentences) do not have to be a single token in the tokenizer.

            I want to fine-tune a BERT-based model to take both sentences like: [[CLS], ], ...,, [SEP], ], ..., , [SEP]] and predict the "label" (a measurement between 0.0 and 1.0).

            What is the best approach to organized this data to facilitate the fine-tuning of the huggingface transformer?

            ...

            ANSWER

            Answered 2022-Feb-02 at 14:58

            You can use the Tokenizer __call__ method to join both sentences when encoding them.

            In case you're using the PyTorch implementation, here is an example:

            Source https://stackoverflow.com/questions/70957390

            QUESTION

            attributeerror: 'dataframe' object has no attribute 'data_type'
            Asked 2022-Jan-10 at 08:41

            I am getting the following error : attributeerror: 'dataframe' object has no attribute 'data_type'" . I am trying to recreate the code from this link which is based on this article with my own dataset which is similar to the article

            ...

            ANSWER

            Answered 2022-Jan-10 at 08:41

            The error means you have no data_type column in your dataframe because you missed this step

            Source https://stackoverflow.com/questions/70649379

            QUESTION

            InternalError when using TPU for training Keras model
            Asked 2021-Dec-31 at 08:18

            I am attempting to fine-tune a BERT model on Google Colab from the Tensorflow Hub using this link.

            However, I run into the following error:

            ...

            ANSWER

            Answered 2021-Dec-31 at 08:18

            As I don't exactly know what changes you have made in the code... I don't have idea about your dataset. But I can see that you are trying to train the whole datset with one epoch and passing the steps per epoch directly. I would recommend to write it like this

            set some batch_size 2^n power (for example 16 or 32 or etc) if you don't want to batch the dataset just set batch_size to 1

            Source https://stackoverflow.com/questions/70479279

            QUESTION

            How to calculate perplexity of a sentence using huggingface masked language models?
            Asked 2021-Dec-25 at 21:51

            I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?

            From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.

            For example in this SO question they calculated it using the function

            ...

            ANSWER

            Answered 2021-Dec-25 at 21:51

            There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.

            As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:

            Source https://stackoverflow.com/questions/70464428

            QUESTION

            XPath 1.0, 1st node in subtree
            Asked 2021-Dec-23 at 19:40

            So what I want to do is identify the 1st node in some subtree of a xml tree.

            here's an example

            ...

            ANSWER

            Answered 2021-Dec-23 at 19:40

            This seems to be what you’re after, using the descendant axis:

            Source https://stackoverflow.com/questions/70466321

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

            Vulnerabilities

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            You can download it from GitHub.

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