fairseq | Facebook AI Research Sequence-to-Sequence Toolkit | Machine Learning library
kandi X-RAY | fairseq Summary
kandi X-RAY | fairseq Summary
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.
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Top functions reviewed by kandi - BETA
- 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 .
fairseq Key Features
fairseq Examples and Code Snippets
Lines of Code : 29License : Non-SPDX (NOASSERTION)
Copyexport CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8
max_token=2048
data_dir=google
save_dir_1=
python3 -u train.py data-bin/$data_dir \
--distributed-world-size 8 -s en -t de \
--ddp-backend no_c10d \
--arch sdt_transformer_t2t_wmt_en_de_6l \
--optimizer @dataclass
class LanguageModelingConfig(FairseqDataclass):
data: Optional[str] = field(
default=None, metadata={"help": "path to data directory"}
)
...
@register_task("language_modeling", dataclass=LanguageModelingConfig)
class L export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7,8
max_token=2048
data_dir=google
save_dir_1=
python3 -u train.py data-bin/$data_dir \
--distributed-world-size 8 -s en -t de \
--ddp-backend no_c10d \
--arch group_transformer_t2t_wmt_en_de \
--optimizer Community Discussions
Trending Discussions on fairseq
QUESTION
I had this simple piece of code found on the fairseq GitHub repository which basically loads the bart.base PyTorch model from torch.hub:
ANSWER
Answered 2021-Sep-27 at 15:30Apparently, 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:
QUESTION
I don't understand this line:
...ANSWER
Answered 2021-Jun-02 at 16:12It'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
QUESTION
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
...ANSWER
Answered 2020-Dec-26 at 02:03Got it to work by loading the pretrained model locally instead of from the hub.
QUESTION
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 :
ANSWER
Answered 2020-Dec-25 at 19:44Running ldconfig -n path/to/shared/libs
and export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/shared/libs seems to solve my problem !
QUESTION
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,
...ANSWER
Answered 2020-Dec-07 at 11:51You can use torch.cumsum to pull the trick:
QUESTION
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:
...ANSWER
Answered 2020-Jun-16 at 12:15First of all you have to download the proper package as described in the github readme:
QUESTION
I am using the fairseq library to run an example code for feature extraction with the VQ-Wav2Vec code as written below:
...ANSWER
Answered 2020-Jun-14 at 20:24From 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.
QUESTION
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:
...ANSWER
Answered 2020-May-10 at 23:50Empty 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:
QUESTION
I'm trying to use wav2vec to train my own Automatic Speech Recognition System:
https://github.com/pytorch/fairseq/tree/master/examples/wav2vec
...ANSWER
Answered 2020-Mar-02 at 14:23You missed the latter part of the example code
QUESTION
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.
ANSWER
Answered 2020-Mar-01 at 16:46Individual 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:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install fairseq
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.
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