Transformers
  • 🌍GET STARTED
    • Transformers
    • Quick tour
    • Installation
  • 🌍TUTORIALS
    • Run inference with pipelines
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    • Share your model
    • Agents
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  • 🌍TASK GUIDES
    • 🌍NATURAL LANGUAGE PROCESSING
      • Text classification
      • Token classification
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      • Causal language modeling
      • Masked language modeling
      • Translation
      • Summarization
      • Multiple choice
    • 🌍AUDIO
      • Audio classification
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    • 🌍COMPUTER VISION
      • Image classification
      • Semantic segmentation
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      • Object detection
      • Zero-shot object detection
      • Zero-shot image classification
      • Depth estimation
    • 🌍MULTIMODAL
      • Image captioning
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    • 🌍GENERATION
      • Customize the generation strategy
    • 🌍PROMPTING
      • Image tasks with IDEFICS
  • 🌍DEVELOPER GUIDES
    • Use fast tokenizers from BOINC AI Tokenizers
    • Run inference with multilingual models
    • Use model-specific APIs
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    • Templates for chat models
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    • Benchmarks
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  • 🌍PERFORMANCE AND SCALABILITY
    • Overview
    • 🌍EFFICIENT TRAINING TECHNIQUES
      • Methods and tools for efficient training on a single GPU
      • Multiple GPUs and parallelism
      • Efficient training on CPU
      • Distributed CPU training
      • Training on TPUs
      • Training on TPU with TensorFlow
      • Training on Specialized Hardware
      • Custom hardware for training
      • Hyperparameter Search using Trainer API
    • 🌍OPTIMIZING INFERENCE
      • Inference on CPU
      • Inference on one GPU
      • Inference on many GPUs
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    • Instantiating a big model
    • Troubleshooting
    • XLA Integration for TensorFlow Models
    • Optimize inference using `torch.compile()`
  • 🌍CONTRIBUTE
    • How to contribute to transformers?
    • How to add a model to BOINC AI Transformers?
    • How to convert a BOINC AI Transformers model to TensorFlow?
    • How to add a pipeline to BOINC AI Transformers?
    • Testing
    • Checks on a Pull Request
  • 🌍CONCEPTUAL GUIDES
    • Philosophy
    • Glossary
    • What BOINC AI Transformers can do
    • How BOINC AI Transformers solve tasks
    • The Transformer model family
    • Summary of the tokenizers
    • Attention mechanisms
    • Padding and truncation
    • BERTology
    • Perplexity of fixed-length models
    • Pipelines for webserver inference
    • Model training anatomy
  • 🌍API
    • 🌍MAIN CLASSES
      • Agents and Tools
      • 🌍Auto Classes
        • Extending the Auto Classes
        • AutoConfig
        • AutoTokenizer
        • AutoFeatureExtractor
        • AutoImageProcessor
        • AutoProcessor
        • Generic model classes
          • AutoModel
          • TFAutoModel
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        • Natural Language Processing
          • AutoModelForCausalLM
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          • FlaxAutoModelForCausalLM
          • AutoModelForMaskedLM
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          • FlaxAutoModelForMaskedLM
          • AutoModelForMaskGenerationge
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          • FlaxAutoModelForSequenceClassification
          • AutoModelForMultipleChoice
          • TFAutoModelForMultipleChoice
          • FlaxAutoModelForMultipleChoice
          • AutoModelForNextSentencePrediction
          • TFAutoModelForNextSentencePrediction
          • FlaxAutoModelForNextSentencePrediction
          • AutoModelForTokenClassification
          • TFAutoModelForTokenClassification
          • FlaxAutoModelForTokenClassification
          • AutoModelForQuestionAnswering
          • TFAutoModelForQuestionAnswering
          • FlaxAutoModelForQuestionAnswering
          • AutoModelForTextEncoding
          • TFAutoModelForTextEncoding
        • Computer vision
          • AutoModelForDepthEstimation
          • AutoModelForImageClassification
          • TFAutoModelForImageClassification
          • FlaxAutoModelForImageClassification
          • AutoModelForVideoClassification
          • AutoModelForMaskedImageModeling
          • TFAutoModelForMaskedImageModeling
          • AutoModelForObjectDetection
          • AutoModelForImageSegmentation
          • AutoModelForImageToImage
          • AutoModelForSemanticSegmentation
          • TFAutoModelForSemanticSegmentation
          • AutoModelForInstanceSegmentation
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          • AutoModelForZeroShotImageClassification
          • TFAutoModelForZeroShotImageClassification
          • AutoModelForZeroShotObjectDetection
        • Audio
          • AutoModelForAudioClassification
          • AutoModelForAudioFrameClassification
          • TFAutoModelForAudioFrameClassification
          • AutoModelForCTC
          • AutoModelForSpeechSeq2Seq
          • TFAutoModelForSpeechSeq2Seq
          • FlaxAutoModelForSpeechSeq2Seq
          • AutoModelForAudioXVector
          • AutoModelForTextToSpectrogram
          • AutoModelForTextToWaveform
        • Multimodal
          • AutoModelForTableQuestionAnswering
          • TFAutoModelForTableQuestionAnswering
          • AutoModelForDocumentQuestionAnswering
          • TFAutoModelForDocumentQuestionAnswering
          • AutoModelForVisualQuestionAnswering
          • AutoModelForVision2Seq
          • TFAutoModelForVision2Seq
          • FlaxAutoModelForVision2Seq
      • Callbacks
      • Configuration
      • Data Collator
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      • Logging
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    • 🌍MODELS
      • 🌍TEXT MODELS
        • ALBERT
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        • BertJapanese
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        • Encoder Decoder Models
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        • GPT
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        • GPT NeoX Japanese
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        • Longformer
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      • 🌍VISION MODELS
        • BEiT
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        • Vision Transformer (ViT)
        • ViT Hybrid
        • ViTDet
        • ViTMAE
        • ViTMatte
        • ViTMSN
        • ViViT
        • YOLOS
      • 🌍AUDIO MODELS
        • Audio Spectrogram Transformer
        • Bark
        • CLAP
        • EnCodec
        • Hubert
        • MCTCT
        • MMS
        • MusicGen
        • Pop2Piano
        • SEW
        • SEW-D
        • Speech2Text
        • Speech2Text2
        • SpeechT5
        • UniSpeech
        • UniSpeech-SAT
        • VITS
        • Wav2Vec2
        • Wav2Vec2-Conformer
        • Wav2Vec2Phoneme
        • WavLM
        • Whisper
        • XLS-R
        • XLSR-Wav2Vec2
      • 🌍MULTIMODAL MODELS
        • ALIGN
        • AltCLIP
        • BLIP
        • BLIP-2
        • BridgeTower
        • BROS
        • Chinese-CLIP
        • CLIP
        • CLIPSeg
        • Data2Vec
        • DePlot
        • Donut
        • FLAVA
        • GIT
        • GroupViT
        • IDEFICS
        • InstructBLIP
        • LayoutLM
        • LayoutLMV2
        • LayoutLMV3
        • LayoutXLM
        • LiLT
        • LXMERT
        • MatCha
        • MGP-STR
        • Nougat
        • OneFormer
        • OWL-ViT
        • Perceiver
        • Pix2Struct
        • Segment Anything
        • Speech Encoder Decoder Models
        • TAPAS
        • TrOCR
        • TVLT
        • ViLT
        • Vision Encoder Decoder Models
        • Vision Text Dual Encoder
        • VisualBERT
        • X-CLIP
      • 🌍REINFORCEMENT LEARNING MODELS
        • Decision Transformer
        • Trajectory Transformer
      • 🌍TIME SERIES MODELS
        • Autoformer
        • Informer
        • Time Series Transformer
      • 🌍GRAPH MODELS
        • Graphormer
  • 🌍INTERNAL HELPERS
    • Custom Layers and Utilities
    • Utilities for pipelines
    • Utilities for Tokenizers
    • Utilities for Trainer
    • Utilities for Generation
    • Utilities for Image Processors
    • Utilities for Audio processing
    • General Utilities
    • Utilities for Time Series
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On this page
  • Logging
  • Base setters
  • Other functions
  1. API
  2. MAIN CLASSES

Logging

Logging

🌍 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.

Currently the default verbosity of the library is WARNING.

To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity to the INFO level.

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import transformers

transformers.logging.set_verbosity_info()

You can also use the environment variable TRANSFORMERS_VERBOSITY to override the default verbosity. You can set it to one of the following: debug, info, warning, error, critical. For example:

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TRANSFORMERS_VERBOSITY=error ./myprogram.py

Additionally, some warnings can be disabled by setting the environment variable TRANSFORMERS_NO_ADVISORY_WARNINGS to a true value, like 1. This will disable any warning that is logged using logger.warning_advice. For example:

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TRANSFORMERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py

Here is an example of how to use the same logger as the library in your own module or script:

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from transformers.utils import logging

logging.set_verbosity_info()
logger = logging.get_logger("transformers")
logger.info("INFO")
logger.warning("WARN")
  • transformers.logging.CRITICAL or transformers.logging.FATAL (int value, 50): only report the most critical errors.

  • transformers.logging.ERROR (int value, 40): only report errors.

  • transformers.logging.WARNING or transformers.logging.WARN (int value, 30): only reports error and warnings. This the default level used by the library.

  • transformers.logging.INFO (int value, 20): reports error, warnings and basic information.

  • transformers.logging.DEBUG (int value, 10): report all information.

Base setters

transformers.utils.logging.set_verbosity_error

( )

Set the verbosity to the ERROR level.

transformers.utils.logging.set_verbosity_warning

( )

Set the verbosity to the WARNING level.

transformers.utils.logging.set_verbosity_info

( )

Set the verbosity to the INFO level.

transformers.utils.logging.set_verbosity_debug

( )

Set the verbosity to the DEBUG level.

Other functions

transformers.utils.logging.get_verbosity

( ) → int

Returns

int

The logging level.

Return the current level for the 🌍 Transformers’s root logger as an int.

🌍 Transformers has following logging levels:

  • 50: transformers.logging.CRITICAL or transformers.logging.FATAL

  • 40: transformers.logging.ERROR

  • 30: transformers.logging.WARNING or transformers.logging.WARN

  • 20: transformers.logging.INFO

  • 10: transformers.logging.DEBUG

transformers.utils.logging.set_verbosity

( verbosity: int )

Parameters

  • verbosity (int) — Logging level, e.g., one of:

    • transformers.logging.CRITICAL or transformers.logging.FATAL

    • transformers.logging.ERROR

    • transformers.logging.WARNING or transformers.logging.WARN

    • transformers.logging.INFO

    • transformers.logging.DEBUG

Set the verbosity level for the 🌍 Transformers’s root logger.

transformers.utils.logging.get_logger

( name: typing.Optional[str] = None )

Return a logger with the specified name.

This function is not supposed to be directly accessed unless you are writing a custom transformers module.

transformers.utils.logging.enable_default_handler

( )

Enable the default handler of the BOINC AI Transformers’s root logger.

transformers.utils.logging.disable_default_handler

( )

Disable the default handler of the BOINC AI Transformers’s root logger.

transformers.utils.logging.enable_explicit_format

( )

Enable explicit formatting for every BOINC AI Transformers’s logger. The explicit formatter is as follows:

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    [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE

All handlers currently bound to the root logger are affected by this method.

transformers.utils.logging.reset_format

( )

Resets the formatting for BOINC AI Transformers’s loggers.

All handlers currently bound to the root logger are affected by this method.

transformers.utils.logging.enable_progress_bar

( )

Enable tqdm progress bar.

transformers.utils.logging.disable_progress_bar

( )

Disable tqdm progress bar.

PreviousKeras callbacksNextModels

Last updated 1 year ago

All the methods of this logging module are documented below, the main ones are to get the current level of verbosity in the logger and to set the verbosity to the level of your choice. In order (from the least verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:

By default, tqdm progress bars will be displayed during model download. and can be used to suppress or unsuppress this behavior.

🌍
🌍
logging.get_verbosity()
logging.set_verbosity()
logging.disable_progress_bar()
logging.enable_progress_bar()
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