fairseq | Facebook AI Research Sequence-to-Sequence Toolkit | Machine Learning library

 by   pytorch Python Version: 0.12.2 License: MIT

kandi X-RAY | fairseq Summary

kandi X-RAY | fairseq Summary

fairseq is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. fairseq 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 fairseq' or download it from GitHub, PyPI.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

            kandi-support Support

              fairseq has a highly active ecosystem.
              It has 16692 star(s) with 4399 fork(s). There are 336 watchers for this library.
              It had no major release in the last 12 months.
              There are 998 open issues and 2321 have been closed. On average issues are closed in 624 days. There are 46 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of fairseq is 0.12.2

            kandi-Quality Quality

              fairseq has no bugs reported.

            kandi-Security Security

              fairseq has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

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

            kandi-Reuse Reuse

              fairseq 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, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed fairseq and discovered the below as its top functions. This is intended to give you an instant insight into fairseq implemented functionality, and help decide if they suit your requirements.
            • Generate the model .
            • Generate Jitens .
            • Generate and re - process nbest results .
            • Load checkpoint .
            • Perform multi - head attention .
            • Wrapper for inference .
            • Run lm scoring .
            • Quantize a model .
            • Evaluate Lm .
            • A fixed pooling layer .
            Get all kandi verified functions for this library.

            fairseq Key Features

            No Key Features are available at this moment for fairseq.

            fairseq Examples and Code Snippets

            SDT Transformer on Fairseq,Runtime Environment
            Pythondot img1Lines of Code : 29dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8
            python3 -u train.py data-bin/$data_dir \
            --distributed-world-size 8 -s en -t de \
            --ddp-backend no_c10d \
            --arch sdt_transformer_t2t_wmt_en_de_6l \
            Creating or migrating components-Tasks and Models
            Pythondot img2Lines of Code : 27dot img2License : Permissive (MIT)
            copy iconCopy
            class LanguageModelingConfig(FairseqDataclass):
                data: Optional[str] = field(
                    default=None, metadata={"help": "path to data directory"}
            @register_task("language_modeling", dataclass=LanguageModelingConfig)
            class L  
            GPKD Method on Fairseq,Runtime Environment,Group-Permutation Training:
            Pythondot img3Lines of Code : 25dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8
            python3 -u train.py data-bin/$data_dir \
            --distributed-world-size 8 -s en -t de \
            --ddp-backend no_c10d \
            --arch group_transformer_t2t_wmt_en_de \

            Community Discussions


            torch.hub.load() raises HTTPError: HTTP Error 404: Not Found when loading model
            Asked 2021-Sep-27 at 15:45

            I had this simple piece of code found on the fairseq GitHub repository which basically loads the bart.base PyTorch model from torch.hub:



            Answered 2021-Sep-27 at 15:30

            Apparently, the fairseq folks decided to change the default branch of their GitHub repository from master to main exactly 7 days ago. (check this commit).

            So, adding the main branch to the repo info will fix the problem:

            Source https://stackoverflow.com/questions/69349308


            What does x[x!=x] mean?
            Asked 2021-Jun-02 at 16:12

            I don't understand this line:



            Answered 2021-Jun-02 at 16:12

            It's a combination of fancy indexing with a boolean mask, and a "trick" (although intended by design) to check for NaN: x != x holds iff x is NaN (for floats, that is).

            They could alternatively have written

            Source https://stackoverflow.com/questions/67499644


            Error when loading torch.hub.load('pytorch/fairseq', 'roberta.large.mnli') on AWS EC2
            Asked 2020-Dec-26 at 02:03

            I'm trying to run some code using Torch (and Roberta language model) on an EC2 instance on AWS. The compilation seems to fail, does anyone have a pointer to fix?

            Confirm that Torch is correctly installed



            Answered 2020-Dec-26 at 02:03

            Got it to work by loading the pretrained model locally instead of from the hub.

            Source https://stackoverflow.com/questions/65430580


            How to create symbolic links for custom installation without root permission?
            Asked 2020-Dec-25 at 19:44

            First of all, I must say I'm not very comfortable with UNIX commands, shell language etc. I'm willing to install Google library SentencePiece (> https://github.com/google/sentencepiece), which is a dependency of a fairseq script to preprocess some speech data. I followed installation procedure as stated in the README.md from the git repo.

            I do not have root privileges, hence I couldn't install it the precognized way with cmake (meaning in /etc), and I had to install it in a custom directory. For the same reasons, as I do not have root privileges nor access to /etc directory, I couldn't execute the last line : sudo ldconfig -v. Trying the execution without sudo obviously returned a permission error. And now, if I try to call a function from SentencePiece, I get an error :



            Answered 2020-Dec-25 at 19:44

            Running ldconfig -n path/to/shared/libs

            and export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/shared/libs seems to solve my problem !

            Source https://stackoverflow.com/questions/65408209


            generating segment labels for a Tensor given a value indicating segment boundaries
            Asked 2020-Dec-07 at 11:51

            Does anyone know of a way to generate a 'segment label' for a Tensor, given a unique value that represents segment boundaries within the Tensor?

            For example, given a 1D input tensor where the value 1 represents a segment boundary,

            x = torch.Tensor([5, 4, 1, 3, 6, 2])

            the resulting segment label Tensor should have the same shape with values representing the two segments:

            segment_label = torch.Tensor([1, 1, 1, 2, 2, 2])

            Likewise, for a batch of inputs, e.g. batch size = 3,



            Answered 2020-Dec-07 at 11:51

            You can use torch.cumsum to pull the trick:

            Source https://stackoverflow.com/questions/65175941


            Error Running "config = RobertaConfig.from_pretrained( "/Absolute-path-to/BERTweet_base_transformers/config.json""
            Asked 2020-Oct-24 at 17:16

            I'm trying to run the code 'transformers' version of this code to use the new pre-trained BERTweet model and I'm getting an error.

            The following lines of code ran successfully in my Google Colab notebook:



            Answered 2020-Jun-16 at 12:15

            First of all you have to download the proper package as described in the github readme:

            Source https://stackoverflow.com/questions/62405867


            Why does the output from VQ-Wav2Vec from FairSeq missing frames?
            Asked 2020-Jun-14 at 20:24

            I am using the fairseq library to run an example code for feature extraction with the VQ-Wav2Vec code as written below:



            Answered 2020-Jun-14 at 20:24

            From the article (arxiv.org/pdf/1904.05862.pdf): "The output of the encoder is a low frequency feature representation zi ∈Z which encodes about 30 ms of 16 kHz of audio and the striding results in representations zi every 10ms." => The windows are overlapping and this explains why you are getting 2 frames fewer. Indeed we are moving a 30 ms window by 10ms steps. In your example, the 30 ms window takes 60 different positions.

            Source https://stackoverflow.com/questions/62376687


            Understanding this Python ‘if statement’ with unfamiliar expression type
            Asked 2020-May-10 at 23:50

            Apologies if this is a straightforward thing, I’m just not having luck getting answers online (and if this isn’t a good place to post).

            I’ve been trying to improve my Python, and I’ve been trying to make sense of some code for a Neural Network/Natural Language Processing package. I came across this:



            Answered 2020-May-10 at 23:50

            Empty sequences (e.g., lists, tuples, strings) evaluate to False. Non-empty sequences evaluate to True.

            args.encoder_layers_to_keep seems to be a string variable. An empty string "" evaluates to False, and a non-empty string evaluates to True.

            You can prove this to yourself by using the builtin function bool to convert to a boolean. Try bool("") and bool("foobar").

            This is suggested in the Python style guide (PEP8):

            For sequences, (strings, lists, tuples), use the fact that empty sequences are false:

            Source https://stackoverflow.com/questions/61720155


            Return predictions wav2vec fairseq
            Asked 2020-Mar-11 at 15:33

            I'm trying to use wav2vec to train my own Automatic Speech Recognition System:




            Answered 2020-Mar-02 at 14:23

            You missed the latter part of the example code

            Source https://stackoverflow.com/questions/60377747


            How can I implement these bash commands in Google Colab
            Asked 2020-Mar-01 at 16:46

            I'm a beginner who is working on Neural Machine Translation, the transformer model. I want to implement fairseq Scaling Neural Machine Translation using Google Colab. I guess the commands shown in the README file is written in bash. I know that bash commands can be run in Google Colab by prefixing the command with !. Following commands are from the Github repository mentioned above.



            Answered 2020-Mar-01 at 16:46

            Individual bash commands marked by ! are executed in a sub-shell, so variables aren't preserved between lines. If you want to execute a multi-line bash script, use the %%bash cell magic:

            Source https://stackoverflow.com/questions/60477299

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


            No vulnerabilities reported

            Install fairseq

            PyTorch version >= 1.5.0
            Python version >= 3.6
            For training new models, you'll also need an NVIDIA GPU and NCCL
            To install fairseq and develop locally:
            For faster training install NVIDIA's apex library:
            For large datasets install PyArrow: pip install pyarrow
            If you use Docker make sure to increase the shared memory size either with --ipc=host or --shm-size as command line options to nvidia-docker run .
            The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks.


            Twitter: https://twitter.com/fairseqFacebook page: https://www.facebook.com/groups/fairseq.usersGoogle group: https://groups.google.com/forum/#!forum/fairseq-users
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