class transformers.CLIPConfigtransformers.CLIPConfighttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/configuration_clip.py#L227[{"name": "text_config", "val": " = None"}, {"name": "vision_config", "val": " = None"}, {"name": "projection_dim", "val": " = 512"}, {"name": "logit_scale_init_value", "val": " = 2.6592"}, {"name": "**kwargs", "val": ""}]- **text_config** (`dict`, *optional*) --
Dictionary of configuration options used to initialize [CLIPTextConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextConfig).
- **vision_config** (`dict`, *optional*) --
Dictionary of configuration options used to initialize [CLIPVisionConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionConfig).
- **projection_dim** (`int`, *optional*, defaults to 512) --
Dimensionality of text and vision projection layers.
- **logit_scale_init_value** (`float`, *optional*, defaults to 2.6592) --
The initial value of the *logit_scale* parameter. Default is used as per the original CLIP implementation.
- **kwargs** (*optional*) --
Dictionary of keyword arguments.0
[CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig) is the configuration class to store the configuration of a [CLIPModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPModel). It is used to instantiate
a CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating
a configuration with the defaults will yield a similar configuration to that of the CLIP
[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.
Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) for more information.
Example:
```python
>>> from transformers import CLIPConfig, CLIPModel
>>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPConfig()
>>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig
>>> from transformers import CLIPTextConfig, CLIPVisionConfig
>>> # Initializing a CLIPText and CLIPVision configuration
>>> config_text = CLIPTextConfig()
>>> config_vision = CLIPVisionConfig()
>>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision)
```
from_text_vision_configstransformers.CLIPConfig.from_text_vision_configshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/configuration_utils.py#L1271[{"name": "text_config", "val": ""}, {"name": "vision_config", "val": ""}, {"name": "**kwargs", "val": ""}]`PreTrainedConfig`An instance of a configuration object
Instantiate a model config (or a derived class) from text model configuration and vision model
configuration.
## CLIPTextConfig[[transformers.CLIPTextConfig]]
class transformers.CLIPTextConfigtransformers.CLIPTextConfighttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/configuration_clip.py#L34[{"name": "vocab_size", "val": " = 49408"}, {"name": "hidden_size", "val": " = 512"}, {"name": "intermediate_size", "val": " = 2048"}, {"name": "projection_dim", "val": " = 512"}, {"name": "num_hidden_layers", "val": " = 12"}, {"name": "num_attention_heads", "val": " = 8"}, {"name": "max_position_embeddings", "val": " = 77"}, {"name": "hidden_act", "val": " = 'quick_gelu'"}, {"name": "layer_norm_eps", "val": " = 1e-05"}, {"name": "attention_dropout", "val": " = 0.0"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "initializer_factor", "val": " = 1.0"}, {"name": "pad_token_id", "val": " = 1"}, {"name": "bos_token_id", "val": " = 49406"}, {"name": "eos_token_id", "val": " = 49407"}, {"name": "**kwargs", "val": ""}]- **vocab_size** (`int`, *optional*, defaults to 49408) --
Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [CLIPModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPModel).
- **hidden_size** (`int`, *optional*, defaults to 512) --
Dimensionality of the encoder layers and the pooler layer.
- **intermediate_size** (`int`, *optional*, defaults to 2048) --
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
- **projection_dim** (`int`, *optional*, defaults to 512) --
Dimensionality of text and vision projection layers.
- **num_hidden_layers** (`int`, *optional*, defaults to 12) --
Number of hidden layers in the Transformer encoder.
- **num_attention_heads** (`int`, *optional*, defaults to 8) --
Number of attention heads for each attention layer in the Transformer encoder.
- **max_position_embeddings** (`int`, *optional*, defaults to 77) --
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
- **hidden_act** (`str` or `function`, *optional*, defaults to `"quick_gelu"`) --
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
- **layer_norm_eps** (`float`, *optional*, defaults to 1e-05) --
The epsilon used by the layer normalization layers.
- **attention_dropout** (`float`, *optional*, defaults to 0.0) --
The dropout ratio for the attention probabilities.
- **initializer_range** (`float`, *optional*, defaults to 0.02) --
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- **initializer_factor** (`float`, *optional*, defaults to 1.0) --
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
- **pad_token_id** (`int`, *optional*, defaults to 1) --
Padding token id.
- **bos_token_id** (`int`, *optional*, defaults to 49406) --
Beginning of stream token id.
- **eos_token_id** (`int`, *optional*, defaults to 49407) --
End of stream token id.0
This is the configuration class to store the configuration of a [CLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextModel). It is used to instantiate a CLIP
text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the text encoder of the CLIP
[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.
Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) for more information.
Example:
```python
>>> from transformers import CLIPTextConfig, CLIPTextModel
>>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPTextConfig()
>>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
class transformers.CLIPVisionConfigtransformers.CLIPVisionConfighttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/configuration_clip.py#L135[{"name": "hidden_size", "val": " = 768"}, {"name": "intermediate_size", "val": " = 3072"}, {"name": "projection_dim", "val": " = 512"}, {"name": "num_hidden_layers", "val": " = 12"}, {"name": "num_attention_heads", "val": " = 12"}, {"name": "num_channels", "val": " = 3"}, {"name": "image_size", "val": " = 224"}, {"name": "patch_size", "val": " = 32"}, {"name": "hidden_act", "val": " = 'quick_gelu'"}, {"name": "layer_norm_eps", "val": " = 1e-05"}, {"name": "attention_dropout", "val": " = 0.0"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "initializer_factor", "val": " = 1.0"}, {"name": "**kwargs", "val": ""}]- **hidden_size** (`int`, *optional*, defaults to 768) --
Dimensionality of the encoder layers and the pooler layer.
- **intermediate_size** (`int`, *optional*, defaults to 3072) --
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
- **projection_dim** (`int`, *optional*, defaults to 512) --
Dimensionality of text and vision projection layers.
- **num_hidden_layers** (`int`, *optional*, defaults to 12) --
Number of hidden layers in the Transformer encoder.
- **num_attention_heads** (`int`, *optional*, defaults to 12) --
Number of attention heads for each attention layer in the Transformer encoder.
- **num_channels** (`int`, *optional*, defaults to 3) --
The number of input channels.
- **image_size** (`int`, *optional*, defaults to 224) --
The size (resolution) of each image.
- **patch_size** (`int`, *optional*, defaults to 32) --
The size (resolution) of each patch.
- **hidden_act** (`str` or `function`, *optional*, defaults to `"quick_gelu"`) --
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
- **layer_norm_eps** (`float`, *optional*, defaults to 1e-05) --
The epsilon used by the layer normalization layers.
- **attention_dropout** (`float`, *optional*, defaults to 0.0) --
The dropout ratio for the attention probabilities.
- **initializer_range** (`float`, *optional*, defaults to 0.02) --
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- **initializer_factor** (`float`, *optional*, defaults to 1.0) --
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).0
This is the configuration class to store the configuration of a [CLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionModel). It is used to instantiate a
CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIP
[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture.
Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) for more information.
Example:
```python
>>> from transformers import CLIPVisionConfig, CLIPVisionModel
>>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPVisionConfig()
>>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
## CLIPTokenizer[[transformers.CLIPTokenizer]]
class transformers.CLIPTokenizertransformers.CLIPTokenizerhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/tokenization_clip.py#L254[{"name": "vocab_file", "val": ""}, {"name": "merges_file", "val": ""}, {"name": "errors", "val": " = 'replace'"}, {"name": "unk_token", "val": " = '<|endoftext|>'"}, {"name": "bos_token", "val": " = '<|startoftext|>'"}, {"name": "eos_token", "val": " = '<|endoftext|>'"}, {"name": "pad_token", "val": " = '<|endoftext|>'"}, {"name": "**kwargs", "val": ""}]- **vocab_file** (`str`) --
Path to the vocabulary file.
- **merges_file** (`str`) --
Path to the merges file.
- **errors** (`str`, *optional*, defaults to `"replace"`) --
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
- **unk_token** (`str`, *optional*, defaults to `"<|endoftext|>"`) --
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
- **bos_token** (`str`, *optional*, defaults to `"<|startoftext|>"`) --
The beginning of sequence token.
- **eos_token** (`str`, *optional*, defaults to `"<|endoftext|>"`) --
The end of sequence token.
- **pad_token** (`str`, *optional*, defaults to `"<|endoftext|>"`) --
The token used for padding, for example when batching sequences of different lengths.0
Construct a CLIP tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v4.57.0/ja/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
build_inputs_with_special_tokenstransformers.CLIPTokenizer.build_inputs_with_special_tokenshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/tokenization_clip.py#L339[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- **token_ids_0** (`list[int]`) --
List of IDs to which the special tokens will be added.
- **token_ids_1** (`list[int]`, *optional*) --
Optional second list of IDs for sequence pairs.0`list[int]`List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CLIP sequence has the following format:
- single sequence: `<|startoftext|> X <|endoftext|>`
Pairs of sequences are not the expected use case, but they will be handled without a separator.
get_special_tokens_masktransformers.CLIPTokenizer.get_special_tokens_maskhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/tokenization_clip.py#L366[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]- **token_ids_0** (`list[int]`) --
List of IDs.
- **token_ids_1** (`list[int]`, *optional*) --
Optional second list of IDs for sequence pairs.
- **already_has_special_tokens** (`bool`, *optional*, defaults to `False`) --
Whether or not the token list is already formatted with special tokens for the model.0`list[int]`A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
create_token_type_ids_from_sequencestransformers.CLIPTokenizer.create_token_type_ids_from_sequenceshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/tokenization_clip.py#L394[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- **token_ids_0** (`list[int]`) --
List of IDs.
- **token_ids_1** (`list[int]`, *optional*) --
Optional second list of IDs for sequence pairs.0`list[int]`List of zeros.
Create a mask from the two sequences passed. CLIP does not make use of token type ids, therefore a list of
zeros is returned.
class transformers.CLIPTokenizerFasttransformers.CLIPTokenizerFasthttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/tokenization_clip_fast.py#L31[{"name": "vocab_file", "val": " = None"}, {"name": "merges_file", "val": " = None"}, {"name": "tokenizer_file", "val": " = None"}, {"name": "unk_token", "val": " = '<|endoftext|>'"}, {"name": "bos_token", "val": " = '<|startoftext|>'"}, {"name": "eos_token", "val": " = '<|endoftext|>'"}, {"name": "pad_token", "val": " = '<|endoftext|>'"}, {"name": "**kwargs", "val": ""}]- **vocab_file** (`str`, *optional*) --
Path to the vocabulary file.
- **merges_file** (`str`, *optional*) --
Path to the merges file.
- **tokenizer_file** (`str`, *optional*) --
The path to a tokenizer file to use instead of the vocab file.
- **unk_token** (`str`, *optional*, defaults to `"<|endoftext|>"`) --
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
- **bos_token** (`str`, *optional*, defaults to `"<|startoftext|>"`) --
The beginning of sequence token.
- **eos_token** (`str`, *optional*, defaults to `"<|endoftext|>"`) --
The end of sequence token.
- **pad_token** (`str`, *optional*, defaults to `"<|endoftext|>"`) --
The token used for padding, for example when batching sequences of different lengths.0
Construct a "fast" CLIP tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer inherits from [PreTrainedTokenizerFast](/docs/transformers/v4.57.0/ja/main_classes/tokenizer#transformers.PreTrainedTokenizerFast) which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
build_inputs_with_special_tokenstransformers.CLIPTokenizerFast.build_inputs_with_special_tokenshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/tokenization_clip_fast.py#L109[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- **token_ids_0** (`list[int]`) --
List of IDs to which the special tokens will be added.
- **token_ids_1** (`list[int]`, *optional*) --
Optional second list of IDs for sequence pairs.0`list[int]`List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CLIP sequence has the following format:
- single sequence: `<|startoftext|> X <|endoftext|>`
Pairs of sequences are not the expected use case, but they will be handled without a separator.
create_token_type_ids_from_sequencestransformers.CLIPTokenizerFast.create_token_type_ids_from_sequenceshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/tokenization_clip_fast.py#L136[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- **token_ids_0** (`list[int]`) --
List of IDs.
- **token_ids_1** (`list[int]`, *optional*) --
Optional second list of IDs for sequence pairs.0`list[int]`List of zeros.
Create a mask from the two sequences passed. CLIP does not make use of token type ids, therefore a list of
zeros is returned.
class transformers.CLIPImageProcessortransformers.CLIPImageProcessorhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/image_processing_clip.py#L54[{"name": "do_resize", "val": ": bool = True"}, {"name": "size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "resample", "val": ": Resampling = "}, {"name": "do_center_crop", "val": ": bool = True"}, {"name": "crop_size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "do_rescale", "val": ": bool = True"}, {"name": "rescale_factor", "val": ": typing.Union[int, float] = 0.00392156862745098"}, {"name": "do_normalize", "val": ": bool = True"}, {"name": "image_mean", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "image_std", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "do_convert_rgb", "val": ": bool = True"}, {"name": "**kwargs", "val": ""}]- **do_resize** (`bool`, *optional*, defaults to `True`) --
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
- **size** (`dict[str, int]` *optional*, defaults to `{"shortest_edge" -- 224}`):
Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
method.
- **resample** (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`) --
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
- **do_center_crop** (`bool`, *optional*, defaults to `True`) --
Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the
`preprocess` method.
- **crop_size** (`dict[str, int]` *optional*, defaults to 224) --
Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
method.
- **do_rescale** (`bool`, *optional*, defaults to `True`) --
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
the `preprocess` method.
- **rescale_factor** (`int` or `float`, *optional*, defaults to `1/255`) --
Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
method.
- **do_normalize** (`bool`, *optional*, defaults to `True`) --
Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
- **image_mean** (`float` or `list[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`) --
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
- **image_std** (`float` or `list[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`) --
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
- **do_convert_rgb** (`bool`, *optional*, defaults to `True`) --
Whether to convert the image to RGB.0
Constructs a CLIP image processor.
preprocesstransformers.CLIPImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/image_processing_clip.py#L202[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "do_resize", "val": ": typing.Optional[bool] = None"}, {"name": "size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "resample", "val": ": typing.Optional[PIL.Image.Resampling] = None"}, {"name": "do_center_crop", "val": ": typing.Optional[bool] = None"}, {"name": "crop_size", "val": ": typing.Optional[int] = None"}, {"name": "do_rescale", "val": ": typing.Optional[bool] = None"}, {"name": "rescale_factor", "val": ": typing.Optional[float] = None"}, {"name": "do_normalize", "val": ": typing.Optional[bool] = None"}, {"name": "image_mean", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "image_std", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "do_convert_rgb", "val": ": typing.Optional[bool] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}, {"name": "data_format", "val": ": typing.Optional[transformers.image_utils.ChannelDimension] = "}, {"name": "input_data_format", "val": ": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"}, {"name": "**kwargs", "val": ""}]- **images** (`ImageInput`) --
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`.
- **do_resize** (`bool`, *optional*, defaults to `self.do_resize`) --
Whether to resize the image.
- **size** (`dict[str, int]`, *optional*, defaults to `self.size`) --
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
- **resample** (`int`, *optional*, defaults to `self.resample`) --
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
- **do_center_crop** (`bool`, *optional*, defaults to `self.do_center_crop`) --
Whether to center crop the image.
- **crop_size** (`dict[str, int]`, *optional*, defaults to `self.crop_size`) --
Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
- **do_rescale** (`bool`, *optional*, defaults to `self.do_rescale`) --
Whether to rescale the image.
- **rescale_factor** (`float`, *optional*, defaults to `self.rescale_factor`) --
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- **do_normalize** (`bool`, *optional*, defaults to `self.do_normalize`) --
Whether to normalize the image.
- **image_mean** (`float` or `list[float]`, *optional*, defaults to `self.image_mean`) --
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- **image_std** (`float` or `list[float]`, *optional*, defaults to `self.image_std`) --
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
- **do_convert_rgb** (`bool`, *optional*, defaults to `self.do_convert_rgb`) --
Whether to convert the image to RGB.
- **return_tensors** (`str` or `TensorType`, *optional*) --
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
- **data_format** (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) --
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
- **input_data_format** (`ChannelDimension` or `str`, *optional*) --
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.0
Preprocess an image or batch of images.
class transformers.CLIPImageProcessorFasttransformers.CLIPImageProcessorFasthttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/image_processing_clip_fast.py#L23[{"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.image_processing_utils_fast.DefaultFastImageProcessorKwargs]"}]
Constructs a fast Clip image processor.
preprocesstransformers.CLIPImageProcessorFast.preprocesshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_utils_fast.py#L734[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "*args", "val": ""}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.image_processing_utils_fast.DefaultFastImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]`) --
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`.
- **do_resize** (`bool`, *optional*) --
Whether to resize the image.
- **size** (`dict[str, int]`, *optional*) --
Describes the maximum input dimensions to the model.
- **default_to_square** (`bool`, *optional*) --
Whether to default to a square image when resizing, if size is an int.
- **resample** (`Union[PILImageResampling, F.InterpolationMode, NoneType]`) --
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
- **do_center_crop** (`bool`, *optional*) --
Whether to center crop the image.
- **crop_size** (`dict[str, int]`, *optional*) --
Size of the output image after applying `center_crop`.
- **do_rescale** (`bool`, *optional*) --
Whether to rescale the image.
- **rescale_factor** (`Union[int, float, NoneType]`) --
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- **do_normalize** (`bool`, *optional*) --
Whether to normalize the image.
- **image_mean** (`Union[float, list[float], NoneType]`) --
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- **image_std** (`Union[float, list[float], NoneType]`) --
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
- **do_pad** (`bool`, *optional*) --
Whether to pad the image. Padding is done either to the largest size in the batch
or to a fixed square size per image. The exact padding strategy depends on the model.
- **pad_size** (`dict[str, int]`, *optional*) --
The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
height and width in the batch. Applied only when `do_pad=True.`
- **do_convert_rgb** (`bool`, *optional*) --
Whether to convert the image to RGB.
- **return_tensors** (`Union[str, ~utils.generic.TensorType, NoneType]`) --
Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
- **data_format** (`~image_utils.ChannelDimension`, *optional*) --
Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors.
- **input_data_format** (`Union[str, ~image_utils.ChannelDimension, NoneType]`) --
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
- **device** (`torch.device`, *optional*) --
The device to process the images on. If unset, the device is inferred from the input images.
- **disable_grouping** (`bool`, *optional*) --
Whether to disable grouping of images by size to process them individually and not in batches.
If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on
empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/381570``- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.
class transformers.CLIPFeatureExtractortransformers.CLIPFeatureExtractorhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/feature_extraction_clip.py#L28[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]
## CLIPProcessor[[transformers.CLIPProcessor]]
class transformers.CLIPProcessortransformers.CLIPProcessorhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/processing_clip.py#L24[{"name": "image_processor", "val": " = None"}, {"name": "tokenizer", "val": " = None"}, {"name": "**kwargs", "val": ""}]- **image_processor** ([CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor), *optional*) --
The image processor is a required input.
- **tokenizer** ([AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer), *optional*) --
The tokenizer is a required input.0
Constructs a CLIP processor which wraps a CLIP image processor and a CLIP tokenizer into a single processor.
[CLIPProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPProcessor) offers all the functionalities of [CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor) and [CLIPTokenizerFast](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTokenizerFast). See the
[__call__()](/docs/transformers/v4.57.0/ja/model_doc/bridgetower#transformers.BridgeTowerProcessor.__call__) and [decode()](/docs/transformers/v4.57.0/ja/main_classes/processors#transformers.ProcessorMixin.decode) for more information.
## CLIPModel[[transformers.CLIPModel]]
class transformers.CLIPModeltransformers.CLIPModelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L821[{"name": "config", "val": ": CLIPConfig"}]- **config** ([CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig)) --
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
The bare Clip Model outputting raw hidden-states without any specific head on top.
This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forwardtransformers.CLIPModel.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L937[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "return_loss", "val": ": typing.Optional[bool] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
The tensors corresponding to the input images. Pixel values can be obtained using
[CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details ([CLIPProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPProcessor) uses
[CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor) for processing images).
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
- **return_loss** (`bool`, *optional*) --
Whether or not to return the contrastive loss.
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
- **interpolate_pos_encoding** (`bool`, defaults to `False`) --
Whether to interpolate the pre-trained position encodings.0`transformers.models.clip.modeling_clip.CLIPOutput` or `tuple(torch.FloatTensor)`A `transformers.models.clip.modeling_clip.CLIPOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig)) and inputs.
- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`) -- Contrastive loss for image-text similarity.
- **logits_per_image** (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`) -- The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
- **logits_per_text** (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`) -- The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
- **text_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The text embeddings obtained by applying the projection layer to the pooled output of [CLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextModel).
- **image_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The image embeddings obtained by applying the projection layer to the pooled output of [CLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionModel).
- **text_model_output** (`.text_model_output`, defaults to `None`) -- The output of the [CLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextModel).
- **vision_model_output** (`.vision_model_output`, defaults to `None`) -- The output of the [CLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionModel).
The [CLIPModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> import torch
>>> from transformers import AutoProcessor, CLIPModel
>>> from transformers.image_utils import load_image
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = load_image(url)
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
... )
>>> with torch.inference_mode():
... outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```
get_text_featurestransformers.CLIPModel.get_text_featureshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L861[{"name": "input_ids", "val": ": Tensor"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}]- **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)0text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)The text embeddings obtained by
applying the projection layer to the pooled output of [CLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextModel).
Examples:
```python
>>> import torch
>>> from transformers import AutoTokenizer, CLIPModel
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> with torch.inference_mode():
... text_features = model.get_text_features(**inputs)
```
get_image_featurestransformers.CLIPModel.get_image_featureshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L898[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
The tensors corresponding to the input images. Pixel values can be obtained using
[CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details ([CLIPProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPProcessor) uses
[CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor) for processing images).
- **interpolate_pos_encoding** (`bool`, defaults to `False`) --
Whether to interpolate the pre-trained position encodings.0image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)The image embeddings obtained by
applying the projection layer to the pooled output of [CLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionModel).
Examples:
```python
>>> import torch
>>> from transformers import AutoProcessor, CLIPModel
>>> from transformers.image_utils import load_image
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = load_image(url)
>>> inputs = processor(images=image, return_tensors="pt")
>>> with torch.inference_mode():
... image_features = model.get_image_features(**inputs)
```
## CLIPTextModel[[transformers.CLIPTextModel]]
class transformers.CLIPTextModeltransformers.CLIPTextModelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L662[{"name": "config", "val": ": CLIPTextConfig"}]- **config** ([CLIPTextConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextConfig)) --
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
The text model from CLIP without any head or projection on top.
This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forwardtransformers.CLIPTextModel.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L680[{"name": "input_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}]- **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.0[transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig)) and inputs.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The [CLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> from transformers import AutoTokenizer, CLIPTextModel
>>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```
class transformers.CLIPTextModelWithProjectiontransformers.CLIPTextModelWithProjectionhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L1029[{"name": "config", "val": ": CLIPTextConfig"}]- **config** ([CLIPTextConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextConfig)) --
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
The Clip Model with a projection layer on top (a linear layer on top of the pooled output).
This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forwardtransformers.CLIPTextModelWithProjection.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L1052[{"name": "input_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}]- **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.0`transformers.models.clip.modeling_clip.CLIPTextModelOutput` or `tuple(torch.FloatTensor)`A `transformers.models.clip.modeling_clip.CLIPTextModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig)) and inputs.
- **text_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`) -- The text embeddings obtained by applying the projection layer to the pooler_output.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The [CLIPTextModelWithProjection](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPTextModelWithProjection) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> import torch
>>> from transformers import AutoTokenizer, CLIPTextModelWithProjection
>>> model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> with torch.inference_mode():
... outputs = model(**inputs)
>>> text_embeds = outputs.text_embeds
```
class transformers.CLIPVisionModelWithProjectiontransformers.CLIPVisionModelWithProjectionhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L1098[{"name": "config", "val": ": CLIPVisionConfig"}]- **config** ([CLIPVisionConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionConfig)) --
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
The Clip Model with a projection layer on top (a linear layer on top of the pooled output).
This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forwardtransformers.CLIPVisionModelWithProjection.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L1116[{"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
The tensors corresponding to the input images. Pixel values can be obtained using
[CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details ([CLIPProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPProcessor) uses
[CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor) for processing images).
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
- **interpolate_pos_encoding** (`bool`, defaults to `False`) --
Whether to interpolate the pre-trained position encodings.0`transformers.models.clip.modeling_clip.CLIPVisionModelOutput` or `tuple(torch.FloatTensor)`A `transformers.models.clip.modeling_clip.CLIPVisionModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig)) and inputs.
- **image_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`) -- The image embeddings obtained by applying the projection layer to the pooler_output.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The [CLIPVisionModelWithProjection](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionModelWithProjection) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> import torch
>>> from transformers import AutoProcessor, CLIPVisionModelWithProjection
>>> from transformers.image_utils import load_image
>>> model = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = load_image(url)
>>> inputs = processor(images=image, return_tensors="pt")
>>> with torch.inference_mode():
... outputs = model(**inputs)
>>> image_embeds = outputs.image_embeds
```
class transformers.CLIPVisionModeltransformers.CLIPVisionModelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L768[{"name": "config", "val": ": CLIPVisionConfig"}]- **config** ([CLIPVisionConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionConfig)) --
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
The vision model from CLIP without any head or projection on top.
This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forwardtransformers.CLIPVisionModel.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_clip.py#L782[{"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
The tensors corresponding to the input images. Pixel values can be obtained using
[CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details ([CLIPProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPProcessor) uses
[CLIPImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPImageProcessor) for processing images).
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
- **interpolate_pos_encoding** (`bool`, defaults to `False`) --
Whether to interpolate the pre-trained position encodings.0[transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig)) and inputs.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The [CLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPVisionModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, CLIPVisionModel
>>> model = CLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states
```
## TFCLIPModel[[transformers.TFCLIPModel]]
class transformers.TFCLIPModeltransformers.TFCLIPModelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_tf_clip.py#L1301[{"name": "config", "val": ": CLIPConfig"}, {"name": "*inputs", "val": ""}, {"name": "**kwargs", "val": ""}]- **config** ([CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig)) -- Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.0
This model inherits from [TFPreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
calltransformers.TFCLIPModel.callhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_tf_clip.py#L1391[{"name": "input_ids", "val": ": TFModelInputType | None = None"}, {"name": "pixel_values", "val": ": TFModelInputType | None = None"}, {"name": "attention_mask", "val": ": np.ndarray | tf.Tensor | None = None"}, {"name": "position_ids", "val": ": np.ndarray | tf.Tensor | None = None"}, {"name": "return_loss", "val": ": bool | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "training", "val": ": bool = False"}]- **input_ids** (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]` ``dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, sequence_length)`) --
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
[PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.
[What are input IDs?](../glossary#input-ids)
- **pixel_values** (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]` `dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`) --
Pixel values. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoImageProcessor). See
[CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details.
- **attention_mask** (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
- **return_loss** (`bool`, *optional*) --
Whether or not to return the contrastive loss.
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False``) --
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).0`transformers.models.clip.modeling_tf_clip.TFCLIPOutput` or `tuple(tf.Tensor)`A `transformers.models.clip.modeling_tf_clip.TFCLIPOutput` or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration (``) and inputs.
- **loss** (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`) -- Contrastive loss for image-text similarity.
- **logits_per_image:(`tf.Tensor`** of shape `(image_batch_size, text_batch_size)`) -- The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
- **logits_per_text:(`tf.Tensor`** of shape `(text_batch_size, image_batch_size)`) -- The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
- **text_embeds(`tf.Tensor`** of shape `(batch_size, output_dim`) -- The text embeddings obtained by applying the projection layer to the pooled output of [TFCLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPTextModel).
- **image_embeds(`tf.Tensor`** of shape `(batch_size, output_dim`) -- The image embeddings obtained by applying the projection layer to the pooled output of
[TFCLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPVisionModel).
- **text_model_output(`~modeling_tf_utils.TFBaseModelOutputWithPooling`):**
The output of the [TFCLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPTextModel).
- **vision_model_output(`~modeling_tf_utils.TFBaseModelOutputWithPooling`):**
The output of the [TFCLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPVisionModel).
The [TFCLIPModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> import tensorflow as tf
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFCLIPModel
>>> model = TFCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
... )
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = tf.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities
```
get_text_featurestransformers.TFCLIPModel.get_text_featureshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_tf_clip.py#L1309[{"name": "input_ids", "val": ": TFModelInputType | None = None"}, {"name": "attention_mask", "val": ": np.ndarray | tf.Tensor | None = None"}, {"name": "position_ids", "val": ": np.ndarray | tf.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "training", "val": ": bool = False"}]- **input_ids** (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]` ``dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, sequence_length)`) --
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
[PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False``) --
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).0text_features (`tf.Tensor` of shape `(batch_size, output_dim`)The text embeddings obtained by applying
the projection layer to the pooled output of [TFCLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPTextModel).
The [TFCLIPModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> from transformers import AutoTokenizer, TFCLIPModel
>>> model = TFCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
>>> text_features = model.get_text_features(**inputs)
```
get_image_featurestransformers.TFCLIPModel.get_image_featureshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_tf_clip.py#L1349[{"name": "pixel_values", "val": ": TFModelInputType | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "training", "val": ": bool = False"}]- **pixel_values** (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]` ``dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`) --
Pixel values. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoImageProcessor). See
[CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details. output_attentions (`bool`, *optional*): Whether or not to
return the attentions tensors of all attention layers. See `attentions` under returned tensors for more
detail. This argument can be used only in eager mode, in graph mode the value in the config will be used
instead.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False``) --
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).0image_features (`tf.Tensor` of shape `(batch_size, output_dim`)The image embeddings obtained by applying
the projection layer to the pooled output of [TFCLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPVisionModel).
The [TFCLIPModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFCLIPModel
>>> model = TFCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="tf")
>>> image_features = model.get_image_features(**inputs)
```
calltransformers.TFCLIPTextModel.callhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_tf_clip.py#L1186[{"name": "input_ids", "val": ": TFModelInputType | None = None"}, {"name": "attention_mask", "val": ": np.ndarray | tf.Tensor | None = None"}, {"name": "position_ids", "val": ": np.ndarray | tf.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "training", "val": ": bool | None = False"}]- **input_ids** (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]` ``dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, sequence_length)`) --
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
[PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False``) --
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).0[transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling) or `tuple(tf.Tensor)`A [transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration (``) and inputs.
- **last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`tf.Tensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
This output is usually *not* a good summary of the semantic content of the input, you're often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
- **hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The [TFCLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPTextModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> from transformers import AutoTokenizer, TFCLIPTextModel
>>> model = TFCLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```
calltransformers.TFCLIPVisionModel.callhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_tf_clip.py#L1247[{"name": "pixel_values", "val": ": TFModelInputType | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "training", "val": ": bool | None = False"}]- **pixel_values** (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]` ``dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`) --
Pixel values. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoImageProcessor). See
[CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details. output_attentions (`bool`, *optional*): Whether or not to
return the attentions tensors of all attention layers. See `attentions` under returned tensors for more
detail. This argument can be used only in eager mode, in graph mode the value in the config will be used
instead.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False``) --
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).0[transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling) or `tuple(tf.Tensor)`A [transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration (``) and inputs.
- **last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`tf.Tensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
This output is usually *not* a good summary of the semantic content of the input, you're often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
- **hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The [TFCLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.TFCLIPVisionModel) forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFCLIPVisionModel
>>> model = TFCLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states
```
## FlaxCLIPModel[[transformers.FlaxCLIPModel]]
class transformers.FlaxCLIPModeltransformers.FlaxCLIPModelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_flax_clip.py#L1263[{"name": "config", "val": ": CLIPConfig"}, {"name": "input_shape", "val": ": typing.Optional[tuple] = None"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = "}, {"name": "_do_init", "val": ": bool = True"}, {"name": "**kwargs", "val": ""}]- **config** ([CLIPConfig](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.CLIPConfig)) -- Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
[to_bf16()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).0
This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a
[flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
__call__transformers.FlaxCLIPModel.__call__https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_flax_clip.py#L820[{"name": "input_ids", "val": ""}, {"name": "pixel_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": typing.Optional[dict] = None"}, {"name": "dropout_rng", "val": ": = None"}, {"name": "train", "val": ": bool = False"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}]- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
- **pixel_values** (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`) --
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[AutoImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details.
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`transformers.models.clip.modeling_flax_clip.FlaxCLIPOutput` or `tuple(torch.FloatTensor)`A `transformers.models.clip.modeling_flax_clip.FlaxCLIPOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (``) and inputs.
- **logits_per_image:(`jnp.ndarray`** of shape `(image_batch_size, text_batch_size)`) -- The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
- **logits_per_text:(`jnp.ndarray`** of shape `(text_batch_size, image_batch_size)`) -- The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
- **text_embeds(`jnp.ndarray`** of shape `(batch_size, output_dim`) -- The text embeddings obtained by applying the projection layer to the pooled output of
[FlaxCLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.FlaxCLIPTextModel).
- **image_embeds(`jnp.ndarray`** of shape `(batch_size, output_dim`) -- The image embeddings obtained by applying the projection layer to the pooled output of
[FlaxCLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.FlaxCLIPVisionModel).
- **text_model_output(`FlaxBaseModelOutputWithPooling`):**
The output of the [FlaxCLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.FlaxCLIPTextModel).
- **vision_model_output(`FlaxBaseModelOutputWithPooling`):**
The output of the [FlaxCLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.FlaxCLIPVisionModel).
The `FlaxCLIPPreTrainedModel` forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
```python
>>> import jax
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, FlaxCLIPModel
>>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="np", padding=True
... )
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities
```
get_text_featurestransformers.FlaxCLIPModel.get_text_featureshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_flax_clip.py#L865[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": typing.Optional[dict] = None"}, {"name": "dropout_rng", "val": ": = None"}, {"name": "train", "val": " = False"}]- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)0text_features (`jnp.ndarray` of shape `(batch_size, output_dim`)The text embeddings obtained by applying
the projection layer to the pooled output of [FlaxCLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.FlaxCLIPTextModel).
Examples:
```python
>>> from transformers import AutoTokenizer, FlaxCLIPModel
>>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="np")
>>> text_features = model.get_text_features(**inputs)
```
get_image_featurestransformers.FlaxCLIPModel.get_image_featureshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_flax_clip.py#L932[{"name": "pixel_values", "val": ""}, {"name": "params", "val": ": typing.Optional[dict] = None"}, {"name": "dropout_rng", "val": ": = None"}, {"name": "train", "val": " = False"}]- **pixel_values** (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`) --
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained
using [AutoImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details.0image_features (`jnp.ndarray` of shape `(batch_size, output_dim`)The image embeddings obtained by
applying the projection layer to the pooled output of [FlaxCLIPVisionModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.FlaxCLIPVisionModel)
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, FlaxCLIPModel
>>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="np")
>>> image_features = model.get_image_features(**inputs)
```
__call__transformers.FlaxCLIPTextModel.__call__https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_flax_clip.py#L665[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": typing.Optional[dict] = None"}, {"name": "dropout_rng", "val": ": = None"}, {"name": "train", "val": ": bool = False"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}]- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (``) and inputs.
- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`jnp.ndarray` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The `FlaxCLIPTextPreTrainedModel` forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
```python
>>> from transformers import AutoTokenizer, FlaxCLIPTextModel
>>> model = FlaxCLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="np")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooler_output = outputs.pooler_output # pooled (EOS token) states
```
__call__transformers.FlaxCLIPTextModelWithProjection.__call__https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_flax_clip.py#L665[{"name": "input_ids", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "params", "val": ": typing.Optional[dict] = None"}, {"name": "dropout_rng", "val": ": = None"}, {"name": "train", "val": ": bool = False"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}]- **input_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`) --
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
[PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.
[What are input IDs?](../glossary#input-ids)
- **attention_mask** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
- **position_ids** (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`transformers.models.clip.modeling_flax_clip.FlaxCLIPTextModelOutput` or `tuple(torch.FloatTensor)`A `transformers.models.clip.modeling_flax_clip.FlaxCLIPTextModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (``) and inputs.
- **text_embeds** (`jnp.ndarray` of shape `(batch_size, output_dim`) -- The text embeddings obtained by applying the projection layer to the pooled output of
[FlaxCLIPTextModel](/docs/transformers/v4.57.0/ja/model_doc/clip#transformers.FlaxCLIPTextModel).
- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The `FlaxCLIPTextPreTrainedModel` forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
```python
>>> from transformers import AutoTokenizer, FlaxCLIPTextModelWithProjection
>>> model = FlaxCLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="np")
>>> outputs = model(**inputs)
>>> text_embeds = outputs.text_embeds
```
__call__transformers.FlaxCLIPVisionModel.__call__https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/clip/modeling_flax_clip.py#L745[{"name": "pixel_values", "val": ""}, {"name": "params", "val": ": typing.Optional[dict] = None"}, {"name": "dropout_rng", "val": ": = None"}, {"name": "train", "val": ": bool = False"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}]- **pixel_values** (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`) --
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[AutoImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details.
- **output_attentions** (`bool`, *optional*) --
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (``) and inputs.
- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`jnp.ndarray` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The `FlaxCLIPVisionPreTrainedModel` forward method, overrides the `__call__` special method.
Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, FlaxCLIPVisionModel
>>> model = FlaxCLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooler_output = outputs.pooler_output # pooled CLS states
```