bert | TensorFlow code and pre-trained models for BERT | Natural Language Processing library

 by   google-research Python Version: Current License: Apache-2.0

kandi X-RAY | bert Summary

kandi X-RAY | bert Summary

bert is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Natural Language Processing, Tensorflow, Bert, Neural Network, Transformer applications. bert has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install bert' or download it from GitHub, PyPI.

BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. Our academic paper which describes BERT in detail and provides full results on a number of tasks can be found here:

            kandi-support Support

              bert has a highly active ecosystem.
              It has 34473 star(s) with 9256 fork(s). There are 994 watchers for this library.
              It had no major release in the last 6 months.
              There are 774 open issues and 349 have been closed. On average issues are closed in 157 days. There are 97 open pull requests and 0 closed requests.
              It has a positive 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 is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              bert 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.
              bert saves you 1764 person hours of effort in developing the same functionality from scratch.
              It has 3902 lines of code, 187 functions and 13 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed bert and discovered the below as its top functions. This is intended to give you an instant insight into bert implemented functionality, and help decide if they suit your requirements.
            • Writes predictions
            • Compute softmax
            • Returns the n_best_size of the logits
            • Return the final prediction
            • Convert examples to features
            • Convert a single example
            • Return a string representation of text
            • Truncate a sequence pair
            • Validate flags
            • Validate a case insensitive case
            • Returns a list of input examples
            • Embed word embedding
            • Return a list of input examples
            • Builds the input function
            • Tokenize text
            • Validates that the case matches the given checkpoint
            • Build a file - based input function
            • Create TrainingInstances
            • Reads input_file
            • Creates an attention mask from from_tensor
            • Converts examples into features
            • Reads squad examples
            • Process a feature
            • Write examples to examples
            • Transformer transformer model
            • Embedding postprocessor
            • Build a function for TPUEstimator
            Get all kandi verified functions for this library.

            bert Key Features

            No Key Features are available at this moment for bert.

            bert Examples and Code Snippets

            copy iconCopy
                title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
                author = "Reimers, Nils and Gurevych, Iryna",
                booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Nat  
            copy iconCopy
            from sentence_transformers import SentenceTransformer
            model = SentenceTransformer('all-MiniLM-L6-v2')
            sentences = ['This framework generates embeddings for each input sentence',
                'Sentences are passed as a list of string.', 
                'The quick brown   
            copy iconCopy
            pip install -U sentence-transformers
            conda install -c conda-forge sentence-transformers
            pip install -e .
            How can I train an XGBoost with a generator?
            Pythondot img4Lines of Code : 21dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def generator(X_data,y_data,batch_size):
                while True:
                  for step in range(X_data.shape[0]//batch_size):
            Can't import bert.tokenization
            Pythondot img5Lines of Code : 2dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            !pip install bert-tensorflow
            Error importing BERT: module 'tensorflow._api.v2.train' has no attribute 'Optimizer'
            Pythondot img6Lines of Code : 4dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            class AdamWeightDecayOptimizer(tf.train.Optimizer):
            class AdamWeightDecayOptimizer(tf.compat.v1.train.Optimizer):
            Getting embedding lookup result from BERT
            Pythondot img7Lines of Code : 6dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            embeddings = bert_model.bert.get_input_embeddings()
            word_embeddings = embeddings.word_embeddings
            inputs_embeds = tf.gather(word_embeddings, input_ids)
            full_embeddings = embeddings(inputs=[None, None, token_type_ids
            HuggingFace BERT `inputs_embeds` giving unexpected result
            Pythondot img8Lines of Code : 5dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            inputs_embeds = result[-1][0]
            embeddings = bert_model.bert.get_input_embeddings().word_embeddings
            inputs_embeds = tf.gather(embeddings, input_ids)
            Saving a 'fine-tuned' bert model
            Pythondot img9Lines of Code : 10dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            python \
              --task_name=MRPC \
              --do_predict=true \
              --data_dir=$GLUE_DIR/MRPC \
              --vocab_file=$BERT_BASE_DIR/vocab.txt \
              --bert_config_file=$BERT_BASE_DIR/bert_config.json \
            copy iconCopy
            elif self.pooling == "mean": 
                result = self.bert(inputs=bert_inputs, signature="tokens", as_dict=True)["sequence_output" ] 
                pooled = result
            embedding_size = 768
            in_id = Input(shape=(max_seq_length,), name="

            Community Discussions


            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.



            Answered 2022-Mar-25 at 15:47

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



            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!



            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:



            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:


            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


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

            then I try to load it with:



            Answered 2022-Mar-08 at 16:13

            Try using to save your model:



            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.



            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!



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

            I have a simple transformers script looking like this.



            Answered 2022-Feb-22 at 11:54

            Use this model instead.



            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?



            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:



            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



            Answered 2022-Jan-10 at 08:41

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



            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:



            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



            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



            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:



            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



            Answered 2021-Dec-23 at 19:40

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


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


            No vulnerabilities reported

            Install bert

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


            For help or issues using BERT, please submit a GitHub issue. For personal communication related to BERT, please contact Jacob Devlin (, Ming-Wei Chang (, or Kenton Lee (
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
          • HTTPS


          • CLI

            gh repo clone google-research/bert

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link