transformers/main/en/main_classes/processors.md

Processors

Processors can mean two different things in the Transformers library:

• the objects that pre-process inputs for multi-modal models such as Wav2Vec2 (speech and text) or CLIP (text and vision)
• deprecated objects that were used in older versions of the library to preprocess data for GLUE or SQUAD.

Multi-modal processors[[transformers.ProcessorMixin]]

Any multi-modal model will require an object to encode or decode the data that groups several modalities (among text, vision and audio). This is handled by objects called processors, which group together two or more processing objects such as tokenizers (for the text modality), image processors (for vision) and feature extractors (for audio).

Those processors inherit from the following base class that implements the saving and loading functionality:

transformers.ProcessorMixin[[transformers.ProcessorMixin]]

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This is a mixin used to provide saving/loading functionality for all processor classes.

__call__transformers.ProcessorMixin.__call__https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L642[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None"}, {"name": "text", "val": ": str | list[str] | list[list[str]] | None = None"}, {"name": "videos", "val": ": typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = None"}, {"name": "audio", "val": ": typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ProcessingKwargs]"}]- images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]], optional) -- Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.

• text (Union[str, list[str], list[list[str]]], optional) -- The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set is_split_into_words=True to avoid ambiguity with batched inputs.

• videos (Union[list[PIL.Image.Image], numpy.ndarray, torch.Tensor, list[numpy.ndarray], list[torch.Tensor], list[list[PIL.Image.Image]], list[list[numpy.ndarray]], list[list[torch.Tensor]], ~video_utils.URL, list[~video_utils.URL], list[list[~video_utils.URL]], ~video_utils.Path, list[~video_utils.Path], list[list[~video_utils.Path]]], optional) -- Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos with pixel values between 0 and 1, set do_rescale=False.

• audio (Union[numpy.ndarray, torch.Tensor, collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence[torch.Tensor]], optional) -- The audio or batch of audios to be prepared. Each audio can be a NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T is the sample length of the audio.

• return_tensors (str or TensorType, optional) -- If set, will return tensors of a particular framework. Acceptable values are:

  • 'pt': Return PyTorch torch.Tensor objects.
  • 'np': Return NumPy np.ndarray objects.

• **kwargs (ProcessingKwargs, optional) -- Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.0

Parameters:

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]], optional) : Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.

text (Union[str, list[str], list[list[str]]], optional) : The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set is_split_into_words=True to avoid ambiguity with batched inputs.

videos (Union[list[PIL.Image.Image], numpy.ndarray, torch.Tensor, list[numpy.ndarray], list[torch.Tensor], list[list[PIL.Image.Image]], list[list[numpy.ndarray]], list[list[torch.Tensor]], ~video_utils.URL, list[~video_utils.URL], list[list[~video_utils.URL]], ~video_utils.Path, list[~video_utils.Path], list[list[~video_utils.Path]]], optional) : Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos with pixel values between 0 and 1, set do_rescale=False.

audio (Union[numpy.ndarray, torch.Tensor, collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence[torch.Tensor]], optional) : The audio or batch of audios to be prepared. Each audio can be a NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T is the sample length of the audio.

return_tensors (str or TensorType, optional) : If set, will return tensors of a particular framework. Acceptable values are: - 'pt': Return PyTorch torch.Tensor objects. - 'np': Return NumPy np.ndarray objects.

• **kwargs (ProcessingKwargs, optional) : Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.

prepare_inputs_layout[[transformers.ProcessorMixin.prepare_inputs_layout]]

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Normalize and prefetch inputs before processing. Wraps text in a list for multimodal processors, fetches remote images and audio if URLs are provided, and ensures audio is properly batched. Returns the normalized (images, text, videos, audio) tuple.

validate_inputs[[transformers.ProcessorMixin.validate_inputs]]

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Validate that at least one input is provided and that no deprecated keyword arguments are used. Raises ValueError otherwise.

Override when the processor needs additional validation on the input args.

get_text_with_replacements[[transformers.ProcessorMixin.get_text_with_replacements]]

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Replace multimodal placeholder tokens in a batch of text strings with their expanded representations, and return the modified texts alongside offset metadata.

This method is the core text-side preprocessing step for multimodal inputs. It scans each text in the batch for special tokens (image, video, audio) and replaces them in-order with the pre-computed replacement strings produced by self.replace_image_token / self.replace_video_token / self.replace_audio_token. Replacements are consumed from each modality's list sequentially, so the i-th occurrence of e.g. self.image_token is replaced by images_replacements[i].

To add a new multimodal processor with placeholder tokens, you need to define a correct self.image_token which is the same token that is embedded in input text and also used as placeholder and repeated many times. Then you need to override self.replace_image_token to return the correct replacement string for a given image at index i. Same goes for all other supported modalities.

Parameters:

text (list[str]) : Batch of raw text strings, each potentially containing multimodal placeholder tokens. Note that it will be modified in-place and returned.

images_replacements (list[str], optional, defaults to []) : Expanded replacement strings for each image, in the order they appear across the batch. Produced by self._process_images.

videos_replacements (list[str], optional, defaults to []) : Expanded replacement strings for each video. Produced by self._process_videos.

audio_replacements (list[str], optional, defaults to []) : Expanded replacement strings for each audio input. Produced by self._process_audio.

Returns:

*tuple[list[str], list[dict[str, Any]]]*

A tuple of:

• The modified text batch with all placeholder tokens expanded.
• batch_replacement_offsets: one entry per batch item, each being a list of dicts with keys:
• "type" (str): modality name — "image", "video", or "audio"
• "span" (tuple[int, int]): original (start, end) char offsets of the placeholder token
• "new_span" (tuple[int, int]): (start, end) offsets of placeholder in the expanded string
• "text" (str): the original placeholder token string that was matched
• "replacement" (str): the string it was replaced with

create_mm_token_type_ids[[transformers.ProcessorMixin.create_mm_token_type_ids]]

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Build per-token modality type IDs for a batch of token_id sequences.

Each position is assigned an integer indicating which modality it belongs to: 0 for regular text, 1 for image tokens, 2 for video tokens, and 3 for audio tokens. Membership is determined by comparing against self.image_token_ids, self.video_token_ids, and self.audio_token_ids.

Parameters:

input_ids (list[list[int]]) : Batch of token ID sequences. May be unpadded (variable length), so a plain Python list of lists is expected rather than a tensor or uniformly-shaped array.

Returns:

*list[list[int]]*

A list of the same structure as input_ids, where each integer is the modality type ID for the corresponding token.

apply_chat_template[[transformers.ProcessorMixin.apply_chat_template]]

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Similar to the apply_chat_template method on tokenizers, this method applies a Jinja template to input conversations to turn them into a single tokenizable string.

The input is expected to be in the following format, where each message content is a list consisting of text and optionally image or video inputs. One can also provide an image, video, URL or local path which will be used to form pixel_values when return_dict=True. If not provided, one will get only the formatted text, optionally tokenized text.

conversation = [ { "role": "user", "content": [ {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, {"type": "text", "text": "Please describe this image in detail."}, ], }, ]

Parameters:

conversation (Union[list[Dict, [str, str]], list[list[dict[str, str]]]]) : The conversation to format.

chat_template (Optional[str], optional) : The Jinja template to use for formatting the conversation. If not provided, the tokenizer's chat template is used.

Processing kwargs[[transformers.ProcessingKwargs]]

Processor __call__ methods accept keyword arguments organized by modality. The following TypedDict classes define the available keyword arguments for each modality. Model-specific processors may subclass these to add or override fields.

transformers.ProcessingKwargs[[transformers.ProcessingKwargs]]

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Base class for kwargs passing to processors. In case a model has specific kwargs that are not present in the base class or default values for existing keys, it should have its own ModelProcessorKwargs class that inherits from ProcessingKwargs to provide:

1. Additional typed keys and that this model requires to process inputs.
2. Default values for existing keys under a _defaults attribute. New keys have to be defined as follows to ensure type hinting is done correctly.

# adding a new image kwarg for this model
class ModelImagesKwargs(ImagesKwargs, total=False):
    new_image_kwarg: Optional[bool]

class ModelProcessorKwargs(ProcessingKwargs, total=False):
    images_kwargs: ModelImagesKwargs
    _defaults = {
        "images_kwargs: {
            "new_image_kwarg": False,
        }
        "text_kwargs": {
            "padding": "max_length",
        },
    }

For Python 3.8 compatibility, when inheriting from this class and overriding one of the kwargs,

you need to manually update the annotations dictionary. This can be done as follows:

class CustomProcessorKwargs(ProcessingKwargs, total=False):
    images_kwargs: CustomImagesKwargs

CustomProcessorKwargs.__annotations__["images_kwargs"] = CustomImagesKwargs  # python 3.8 compatibility

transformers.TextKwargs[[transformers.TextKwargs]]

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Keyword arguments for text processing. For extended documentation, check out tokenization_utils_base methods and docstrings associated.

Parameters:

add_special_tokens (bool, optional) : Whether or not to add special tokens when encoding the sequences.

padding (bool, str or PaddingStrategy, optional) : Activates and controls padding.

truncation (bool, str or TruncationStrategy, optional) : Activates and controls truncation.

max_length (int, optional) : Controls the maximum length to use by one of the truncation/padding parameters.

stride (int, optional) : If set, the overflowing tokens will contain some tokens from the end of the truncated sequence.

is_split_into_words (bool, optional) : Whether or not the input is already pre-tokenized.

pad_to_multiple_of (int, optional) : If set, will pad the sequence to a multiple of the provided value.

return_token_type_ids (bool, optional) : Whether to return token type IDs.

return_attention_mask (bool, optional) : Whether to return the attention mask.

return_overflowing_tokens (bool, optional) : Whether or not to return overflowing token sequences.

return_special_tokens_mask (bool, optional) : Whether or not to return special tokens mask information.

return_offsets_mapping (bool, optional) : Whether or not to return (char_start, char_end) for each token.

return_length (bool, optional) : Whether or not to return the lengths of the encoded inputs.

verbose (bool, optional) : Whether or not to print more information and warnings.

padding_side (str, optional) : The side on which padding will be applied.

return_mm_token_type_ids (bool, optional) : Whether to return multimodal token type ids indicating mm placeholder token positions.

return_tensors (str or TensorType, optional) : If set, will return tensors of a particular framework. Acceptable values are: - 'pt': Return PyTorch torch.Tensor objects. - 'np': Return NumPy np.ndarray objects.

transformers.ImagesKwargs[[transformers.ImagesKwargs]]

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Keyword arguments for image processing. For extended documentation, check the appropriate ImageProcessor class methods and docstrings.

Parameters:

do_convert_rgb (bool) : Whether to convert the image to RGB format.

do_resize (bool, optional) : Whether to resize the image.

size (dict[str, int], optional) : Resize the shorter side of the input to size["shortest_edge"].

default_to_square (bool, optional, defaults to self.default_to_square) : Whether to default to a square when resizing, if size is an int.

crop_size (dict[str, int], optional) : Desired output size when applying center-cropping.

resample (PILImageResampling, optional) : Resampling filter to use if resizing the image.

do_rescale (bool, optional) : Whether to rescale the image by the specified scale rescale_factor.

rescale_factor (int or float, optional) : Scale factor to use if rescaling the image.

do_normalize (bool, optional) : Whether to normalize the image.

image_mean (float or list[float] or tuple[float, float, float], optional) : Mean to use if normalizing the image.

image_std (float or list[float] or tuple[float, float, float], optional) : Standard deviation to use if normalizing the image.

do_pad (bool, optional) : Whether to pad the images in the batch.

pad_size (dict[str, int], optional) : The size {"height": int, "width" int} to pad the images to.

do_center_crop (bool, optional) : Whether to center crop the image.

data_format (ChannelDimension or str, optional) : The channel dimension format for the output image.

input_data_format (ChannelDimension or str, optional) : The channel dimension format for the input image.

device (Union[str, torch.Tensor], optional) : The device to use for processing (e.g. "cpu", "cuda"), only relevant for torchvision backend.

return_tensors (str or TensorType, optional) : If set, will return tensors of a particular framework. Acceptable values are: - 'pt': Return PyTorch torch.Tensor objects. - 'np': Return NumPy np.ndarray objects.

disable_grouping (bool, optional) : Whether to group images by shapes when processing or not, only relevant for torchvision backend.

image_seq_length (int, optional) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.

transformers.VideosKwargs[[transformers.VideosKwargs]]

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Keyword arguments for video processing.

Parameters:

do_convert_rgb (bool) : Whether to convert the video to RGB format.

do_resize (bool) : Whether to resize the video.

size (dict[str, int], optional) : Resize the shorter side of the input to size["shortest_edge"].

default_to_square (bool, optional, defaults to self.default_to_square) : Whether to default to a square when resizing, if size is an int.

resample (PILImageResampling, optional) : Resampling filter to use if resizing the video.

do_rescale (bool, optional) : Whether to rescale the video by the specified scale rescale_factor.

rescale_factor (int or float, optional) : Scale factor to use if rescaling the video.

do_normalize (bool, optional) : Whether to normalize the video.

image_mean (float or list[float] or tuple[float, float, float], optional) : Mean to use if normalizing the video.

image_std (float or list[float] or tuple[float, float, float], optional) : Standard deviation to use if normalizing the video.

do_center_crop (bool, optional) : Whether to center crop the video.

do_pad (bool, optional) : Whether to pad the images in the batch.

do_sample_frames (bool, optional) : Whether to sample frames from the video before processing or to process the whole video.

video_metadata (Union[VideoMetadata, dict], optional) : Metadata of the video containing information about total duration, fps and total number of frames.

num_frames (int, optional) : Maximum number of frames to sample when do_sample_frames=True.

fps (int or float, optional) : Target frames to sample per second when do_sample_frames=True.

crop_size (dict[str, int], optional) : Desired output size when applying center-cropping.

data_format (ChannelDimension or str, optional) : The channel dimension format for the output video.

input_data_format (ChannelDimension or str, optional) : The channel dimension format for the input video.

device (Union[str, torch.Tensor], optional) : The device to use for processing (e.g. "cpu", "cuda"), only relevant for fast image processing.

return_metadata (bool, optional) : Whether to return video metadata or not.

return_tensors (str or TensorType, optional) : If set, will return tensors of a particular framework. Acceptable values are: - 'pt': Return PyTorch torch.Tensor objects. - 'np': Return NumPy np.ndarray objects.

transformers.AudioKwargs[[transformers.AudioKwargs]]

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Keyword arguments for audio processing.

Parameters:

sampling_rate (int, optional) : The sampling rate at which the raw_speech input was sampled.

raw_speech (np.ndarray, list[float], list[np.ndarray], list[list[float]]) : The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not stereo, i.e. single float per timestep.

padding (bool, str or PaddingStrategy, optional) : Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. - False or 'do_not_pad'

max_length (int, optional) : Maximum length of the returned list and optionally padding length (see above).

truncation (bool, optional) : Activates truncation to cut input sequences longer than max_length to max_length.

pad_to_multiple_of (int, optional) : If set, will pad the sequence to a multiple of the provided value.

return_attention_mask (bool, optional) : Whether or not call() should return attention_mask.

return_tensors (str or TensorType, optional) : If set, will return tensors of a particular framework. Acceptable values are: - 'pt': Return PyTorch torch.Tensor objects. - 'np': Return NumPy np.ndarray objects.

Deprecated processors[[transformers.DataProcessor]]

All processors follow the same architecture which is that of the DataProcessor. The processor returns a list of InputExample. These InputExample can be converted to InputFeatures in order to be fed to the model.

transformers.DataProcessor[[transformers.DataProcessor]]

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Base class for data converters for sequence classification data sets.

get_dev_examplestransformers.DataProcessor.get_dev_exampleshttps://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L95[{"name": "data_dir", "val": ""}] Gets a collection of InputExample for the dev set.

get_example_from_tensor_dict[[transformers.DataProcessor.get_example_from_tensor_dict]]

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Gets an example from a dict.

Parameters:

tensor_dict : Keys and values should match the corresponding Glue tensorflow_dataset examples.

get_labels[[transformers.DataProcessor.get_labels]]

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Gets the list of labels for this data set.

get_test_examples[[transformers.DataProcessor.get_test_examples]]

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Gets a collection of InputExample for the test set.

get_train_examples[[transformers.DataProcessor.get_train_examples]]

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Gets a collection of InputExample for the train set.

tfds_map[[transformers.DataProcessor.tfds_map]]

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Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts examples to the correct format.

transformers.InputExample[[transformers.InputExample]]

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A single training/test example for simple sequence classification.

to_json_stringtransformers.InputExample.to_json_stringhttps://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L47[] Serializes this instance to a JSON string.

Parameters:

guid : Unique id for the example.

text_a : string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified.

text_b : (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks.

label : (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples.

transformers.InputFeatures[[transformers.InputFeatures]]

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A single set of features of data. Property names are the same names as the corresponding inputs to a model.

to_json_stringtransformers.InputFeatures.to_json_stringhttps://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L73[] Serializes this instance to a JSON string.

Parameters:

input_ids : Indices of input sequence tokens in the vocabulary.

attention_mask : Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]: Usually 1 for tokens that are NOT MASKED, 0 for MASKED (padded) tokens.

token_type_ids : (Optional) Segment token indices to indicate first and second portions of the inputs. Only some models use them.

label : (Optional) Label corresponding to the input. Int for classification problems, float for regression problems.

GLUE[[transformers.glue_convert_examples_to_features]]

General Language Understanding Evaluation (GLUE) is a benchmark that evaluates the performance of models across a diverse set of existing NLU tasks. It was released together with the paper GLUE: A multi-task benchmark and analysis platform for natural language understanding

This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.

Those processors are:

• ~data.processors.utils.MrpcProcessor
• ~data.processors.utils.MnliProcessor
• ~data.processors.utils.MnliMismatchedProcessor
• ~data.processors.utils.Sst2Processor
• ~data.processors.utils.StsbProcessor
• ~data.processors.utils.QqpProcessor
• ~data.processors.utils.QnliProcessor
• ~data.processors.utils.RteProcessor
• ~data.processors.utils.WnliProcessor

Additionally, the following method can be used to load values from a data file and convert them to a list of InputExample.

transformers.glue_convert_examples_to_features[[transformers.glue_convert_examples_to_features]]

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Loads a data file into a list of InputFeatures

Parameters:

examples : List of InputExamples containing the examples.

tokenizer : Instance of a tokenizer that will tokenize the examples

max_length : Maximum example length. Defaults to the tokenizer's max_len

task : GLUE task

label_list : List of labels. Can be obtained from the processor using the processor.get_labels() method

output_mode : String indicating the output mode. Either regression or classification

Returns:

Will return a list of task-specific InputFeatures which can be fed to the model.

XNLI

The Cross-Lingual NLI Corpus (XNLI) is a benchmark that evaluates the quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on MultiNLI: pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).

It was released together with the paper XNLI: Evaluating Cross-lingual Sentence Representations

This library hosts the processor to load the XNLI data:

• ~data.processors.utils.XnliProcessor

Please note that since the gold labels are available on the test set, evaluation is performed on the test set.

An example using these processors is given in the run_xnli.py script.

SQuAD

The Stanford Question Answering Dataset (SQuAD) is a benchmark that evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper SQuAD: 100,000+ Questions for Machine Comprehension of Text. The second version (v2.0) was released alongside the paper Know What You Don't Know: Unanswerable Questions for SQuAD.

This library hosts a processor for each of the two versions:

Processors[[transformers.data.processors.squad.SquadProcessor]]

Those processors are:

• ~data.processors.utils.SquadV1Processor
• ~data.processors.utils.SquadV2Processor

They both inherit from the abstract class ~data.processors.utils.SquadProcessor

transformers.data.processors.squad.SquadProcessor[[transformers.data.processors.squad.SquadProcessor]]

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Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and version 2.0 of SQuAD, respectively.

get_dev_examplestransformers.data.processors.squad.SquadProcessor.get_dev_exampleshttps://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/squad.py#L521[{"name": "data_dir", "val": ""}, {"name": "filename", "val": " = None"}]- data_dir -- Directory containing the data files used for training and evaluating.

• filename -- None by default, specify this if the evaluation file has a different name than the original one which is dev-v1.1.json and dev-v2.0.json for squad versions 1.1 and 2.0 respectively.0

Returns the evaluation example from the data directory.

Parameters:

data_dir : Directory containing the data files used for training and evaluating.

filename : None by default, specify this if the evaluation file has a different name than the original one which is dev-v1.1.json and dev-v2.0.json for squad versions 1.1 and 2.0 respectively.

get_examples_from_dataset[[transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset]]

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Creates a list of SquadExample using a TFDS dataset.

Examples:

>>> import tensorflow_datasets as tfds

>>> dataset = tfds.load("squad")

>>> training_examples = get_examples_from_dataset(dataset, evaluate=False)
>>> evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)

Parameters:

dataset : The tfds dataset loaded from tensorflow_datasets.load("squad")

evaluate : Boolean specifying if in evaluation mode or in training mode

Returns:

List of SquadExample

get_train_examples[[transformers.data.processors.squad.SquadProcessor.get_train_examples]]

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Returns the training examples from the data directory.

Parameters:

data_dir : Directory containing the data files used for training and evaluating.

filename : None by default, specify this if the training file has a different name than the original one which is train-v1.1.json and train-v2.0.json for squad versions 1.1 and 2.0 respectively.

Additionally, the following method can be used to convert SQuAD examples into ~data.processors.utils.SquadFeatures that can be used as model inputs.

transformers.squad_convert_examples_to_features[[transformers.squad_convert_examples_to_features]]

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Converts a list of examples into a list of features that can be directly given as input to a model. It is model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.

Example:

processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)

features = squad_convert_examples_to_features(
    examples=examples,
    tokenizer=tokenizer,
    max_seq_length=args.max_seq_length,
    doc_stride=args.doc_stride,
    max_query_length=args.max_query_length,
    is_training=not evaluate,
)

Parameters:

examples : list of SquadExample

tokenizer : an instance of a child of PreTrainedTokenizer

max_seq_length : The maximum sequence length of the inputs.

doc_stride : The stride used when the context is too large and is split across several features.

max_query_length : The maximum length of the query.

is_training : whether to create features for model evaluation or model training.

padding_strategy : Default to "max_length". Which padding strategy to use

return_dataset : Default False. Can also be 'pt'. if 'pt': returns a torch.data.TensorDataset.

threads : multiple processing threads.

Returns:

list of SquadFeatures

These processors as well as the aforementioned method can be used with files containing the data as well as with the tensorflow_datasets package. Examples are given below.

Example usage

Here is an example using the processors as well as the conversion method using data files:

# Loading a V2 processor
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)

# Loading a V1 processor
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)

features = squad_convert_examples_to_features(
    examples=examples,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    doc_stride=args.doc_stride,
    max_query_length=max_query_length,
    is_training=not evaluate,
)

Using tensorflow_datasets is as easy as using a data file:

# tensorflow_datasets only handle Squad V1.
tfds_examples = tfds.load("squad")
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)

features = squad_convert_examples_to_features(
    examples=examples,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    doc_stride=args.doc_stride,
    max_query_length=max_query_length,
    is_training=not evaluate,
)

Another example using these processors is given in the run_squad.py script.