transformers | 🤗 Transformers : State-of-the-art Machine Learning | Natural Language Processing library

 by   huggingface Python Version: 4.38.1 License: Apache-2.0

kandi X-RAY | transformers Summary

kandi X-RAY | transformers Summary

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

Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

            kandi-support Support

              transformers has a medium active ecosystem.
              It has 104111 star(s) with 20970 fork(s). There are 1020 watchers for this library.
              There were 10 major release(s) in the last 6 months.
              There are 587 open issues and 11328 have been closed. On average issues are closed in 27 days. There are 160 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of transformers is 4.38.1

            kandi-Quality Quality

              transformers has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              transformers 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

              transformers releases are available to install and integrate.
              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 429963 lines of code, 21977 functions and 1495 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed transformers and discovered the below as its top functions. This is intended to give you an instant insight into transformers implemented functionality, and help decide if they suit your requirements.
            • Generates a beam search output .
            • Perform beam search .
            • Performs the Bigbird block - sparse attention .
            • Instantiate a pipeline .
            • Fetches the given model .
            • Train a discriminator .
            • Perform beam search .
            • Convert bort checkpoint to pytorch .
            • Convert a Segformer checkpoint checkpoint .
            • Wrapper for selftrain .
            Get all kandi verified functions for this library.

            transformers Key Features

            No Key Features are available at this moment for transformers.

            transformers Examples and Code Snippets

            Quick tour
            Pythondot img1Lines of Code : 33dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            >>> from transformers import pipeline
            # Allocate a pipeline for sentiment-analysis
            >>> classifier = pipeline('sentiment-analysis')
            >>> classifier('We are very happy to introduce pipeline to the transformers repository.')
            Writing source documentation
            Pythondot img2Lines of Code : 29dot img2License : Permissive (Apache-2.0)
            copy iconCopy
                    n_layers (`int`): The number of layers of the model.
                    input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                        Indices of input sequence tokens in the vocabulary.
                        Indices ca  
            Testing documentation examples-Writing documentation examples
            Pythondot img3Lines of Code : 25dot img3License : Permissive (Apache-2.0)
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                >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
                >>> from datasets import load_dataset
                >>> import torch
                >>> dataset = load_dataset("hf-internal-testing/lib  
            sentence-transformers - train bi encoder margin mse
            Pythondot img4Lines of Code : 168dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            import sys
            import json
            from import DataLoader
            from sentence_transformers import SentenceTransformer, LoggingHandler, util, models, evaluation, losses, InputExample
            import logging
            from datetime import datetime
            import gzip
            import os
            sentence-transformers - make multilingual
            Pythondot img5Lines of Code : 140dot img5License : Non-SPDX (Apache License 2.0)
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            This script contains an example how to extend an existent sentence embedding model to new languages.
            Given a (monolingual) teacher model you would like to extend to new languages, which is specified in the teacher_model_name
            variable. We train a  
            sentence-transformers - bucc2018
            Pythondot img6Lines of Code : 135dot img6License : Non-SPDX (Apache License 2.0)
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            This script tests the approach on the BUCC 2018 shared task on finding parallel sentences:
            You can download the necessary files from there.
            We have used it in our paper (  
            copy iconCopy
            ner = pipeline("ner", aggregation_strategy="simple", model="dbmdz/bert-large-cased-finetuned-conll03-english")  # Named Entity Recognition (NER)
            How to use the DeBERTa model by He et al. (2022) on Spyder?
            Pythondot img8Lines of Code : 12dot img8License : Strong Copyleft (CC BY-SA 4.0)
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            from transformers import DebertaTokenizer, DebertaModel
            import torch
            # downloading the models
            tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
            model = DebertaModel.from_pretrained("microsoft/deberta-base")
            # tokenizin
            Is it possible to access hugging face transformer embedding layer?
            Pythondot img9Lines of Code : 2dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            Is it possible to access hugging face transformer embedding layer?
            Pythondot img10Lines of Code : 26dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from transformers import BertModel
            model = BertModel.from_pretrained("bert-base-uncased")
            # output is
              (word_embeddings): Embedding(30522, 768, padding_idx=0)
              (position_embeddings): Embedding(512

            Community Discussions


            Unpickle instance from Jupyter Notebook in Flask App
            Asked 2022-Feb-28 at 18:03

            I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:

            AttributeError: module '__main__' has no attribute 'GensimWord2VecVectorizer'

            I am creating the model on Google Colab.

            Code in Jupyter Notebook:



            Answered 2022-Feb-24 at 11:48

            Import GensimWord2VecVectorizer in your Flask Web app python file.



            ModuleNotFoundError: No module named 'milvus'
            Asked 2022-Feb-15 at 19:23

            Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.

            Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.



            Answered 2022-Feb-03 at 09:29

            I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic:



            Which model/technique to use for specific sentence extraction?
            Asked 2022-Feb-08 at 18:35

            I have a dataset of tens of thousands of dialogues / conversations between a customer and customer support. These dialogues, which could be forum posts, or long-winded email conversations, have been hand-annotated to highlight the sentence containing the customers problem. For example:

            Dear agent, I am writing to you because I have a very annoying problem with my washing machine. I bought it three weeks ago and was very happy with it. However, this morning the door does not lock properly. Please help

            Dear customer.... etc

            The highlighted sentence would be:

            However, this morning the door does not lock properly.

            1. What approaches can I take to model this, so that in future I can automatically extract the customers problem? The domain of the datasets are broad, but within the hardware space, so it could be appliances, gadgets, machinery etc.
            2. What is this type of problem called? I thought this might be called "intent recognition", but most guides seem to refer to multiclass classification. The sentence either is or isn't the customers problem. I considered analysing each sentence and performing binary classification, but I'd like to explore options that take into account the context of the rest of the conversation if possible.
            3. What resources are available to research how to implement this in Python (using tensorflow or pytorch)

            I found a model on HuggingFace which has been pre-trained with customer dialogues, and have read the research paper, so I was considering fine-tuning this as a starting point, but I only have experience with text (multiclass/multilabel) classification when it comes to transformers.



            Answered 2022-Feb-07 at 10:21

            This type of problem where you want to extract the customer problem from the original text is called Extractive Summarization and this type of task is solved by Sequence2Sequence models.

            The main reason for this type of model being called Sequence2Sequence is because the input and the output of this model would both be text.

            I recommend you to use a transformers model called Pegasus which has been pre-trained to predict a masked text, but its main application is to be fine-tuned for text summarization (extractive or abstractive).

            This Pegasus model is listed on Transformers library, which provides you with a simple but powerful way of fine-tuning transformers with custom datasets. I think this notebook will be extremely useful as guidance and for understanding how to fine-tune this Pegasus model.



            What is this GHC feature called? `forall` in type definitions
            Asked 2022-Feb-01 at 19:28

            I learned that you can redefine ContT from transformers such that the r type parameter is made implicit (and may be specified explicitly using TypeApplications), viz.:



            Answered 2022-Feb-01 at 19:28

            Nobody uses this (invisible dependent quantification) for this purpose (where the dependency is not used) but it is the same as giving a Type -> .. parameter, implicitly.



            Relation between Arrow suspend functions and monad comprehension
            Asked 2022-Jan-31 at 08:59

            I am new to Arrow and try to establish my mental model of how its effects system works; in particular, how it leverages Kotlin's suspend system. My very vague understanding is as follows; if would be great if someone could confirm, clarify, or correct it:

            Because Kotlin does not support higher-kinded types, implementing applicatives and monads as type classes is cumbersome. Instead, arrow derives its monad functionality (bind and return) for all of Arrow's monadic types from the continuation primitive offered by Kotlin's suspend mechanism. Ist this correct? In particular, short-circuiting behavior (e.g., for nullable or either) is somehow implemented as a delimited continuation. I did not quite get which particular feature of Kotlin's suspend machinery comes into play here.

            If the above is broadly correct, I have two follow-up questions: How should I contain the scope of non-IO monadic operations? Take a simple object construction and validation example:



            Answered 2022-Jan-31 at 08:52

            I don't think I can answer everything you asked, but I'll do my best for the parts that I do know how to answer.

            What is the recommended way to implement non-IO monad comprehensions in Arrow without making all functions into suspend functions? Or is this actually the way to go?

            you can use nullable.eager and either.eager respectively for pure code. Using nullable/either (without .eager) allows you to call suspend functions inside. Using eager means you can only call non-suspend functions. (not all effectual functions in kotlin are marked suspend)

            Second: If in addition to non-IO monads (nullable, reader, etc.), I want to have IO - say, reading in a file and parsing it - how would i combine these two effects? Is it correct to say that there would be multiple suspend scopes corresponding to the different monads involved, and I would need to somehow nest these scopes, like I would stack monad transformers in Haskell?

            You can use extension functions to emulate Reader. For example:



            Jest encountered an unexpected token - SyntaxError: Unexpected token 'export'
            Asked 2022-Jan-22 at 23:12

            I'm using jest to test a react TypeScript app.

            This is the test I'm running:



            Answered 2022-Jan-22 at 22:37

            react-markdown is shipped as js, add babel-jest as a transformer in your jest config



            Why Reader implemented based ReaderT?
            Asked 2022-Jan-11 at 17:11


            I found that Reader is implemented based on ReaderT using Identity. Why don't make Reader first and then make ReaderT? Is there specific reason to implement that way?



            Answered 2022-Jan-11 at 17:11

            They are the same data type to share as much code as possible between Reader and ReaderT. As it stands, only runReader, mapReader, and withReader have any special cases. And withReader doesn't have any unique code, it's just a type specialization, so only two functions actually do anything special for Reader as opposed to ReaderT.

            You might look at the module exports and think that isn't buying much, but it actually is. There are a lot of instances defined for ReaderT that Reader automatically has as well, because it's the same type. So it's actually a fair bit less code to have only one underlying type for the two.

            Given that, your question boils down to asking why Reader is implemented on top of ReaderT, and not the other way around. And for that, well, it's just the only way that works.

            Let's try to go the other direction and see what goes wrong.



            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



            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:



            Determine whether the Columns of a Dataset are invariant under any given Scikit-Learn Transformer
            Asked 2021-Dec-19 at 08:42

            Given an sklearn tranformer t, is there a way to determine whether t changes columns/column order of any given input dataset X, without applying it to the data?

            For example with t = sklearn.preprocessing.StandardScaler there is a 1-to-1 mapping between the columns of X and t.transform(X), namely X[:, i] -> t.transform(X)[:, i], whereas this is obviously not the case for sklearn.decomposition.PCA.

            A corollary of that would be: Can we know, how the columns of the input will change by applying t, e.g. which columns an already fitted sklearn.feature_selection.SelectKBest chooses.

            I am not looking for solutions to specific transformers, but a solution applicable to all or at least a wide selection of transformers.

            Feel free to implement your own Pipeline class or wrapper if necessary.



            Answered 2021-Nov-23 at 15:01

            I found a partial answer. Both StandardScaler and SelectKBest have .get_feature_names_out methods. I did not find the time to investigate further.


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


            No vulnerabilities reported

            Install transformers

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