# TrOCR [TrOCR](https://huggingface.co/papers/2109.10282) is a text recognition model for both image understanding and text generation. It doesn't require separate models for image processing or character generation. TrOCR is a simple single end-to-end system that uses a transformer to handle visual understanding and text generation. You can find all the original TrOCR checkpoints under the [Microsoft](https://huggingface.co/microsoft/models?search=trocr) organization. drawing TrOCR architecture. Taken from the original paper. > [!TIP] > This model was contributed by [nielsr](https://huggingface.co/nielsr). > > Click on the TrOCR models in the right sidebar for more examples of how to apply TrOCR to different image and text tasks. The example below demonstrates how to perform optical character recognition (OCR) with the [AutoModel](/docs/transformers/v5.8.0/en/model_doc/auto#transformers.AutoModel) class. ```python import requests from PIL import Image from transformers import TrOCRProcessor, VisionEncoderDecoderModel processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten", device_map="auto") # load image from the IAM dataset url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") pixel_values = processor(image, return_tensors="pt").to(model.device).pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_text) ``` ## Quantization Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends. The example below uses [bitsandbytes](../quantization/bitsandbytes) to quantize the weights to 8-bits. ```python # pip install bitsandbytes accelerate from transformers import TrOCRProcessor, VisionEncoderDecoderModel, BitsandBytesConfig import requests from PIL import Image # Set up the quantization configuration quantization_config = BitsandBytesConfig(load_in_8bit=True) # Use a large checkpoint for a more noticeable impact processor = TrOCRProcessor.from_pretrained("microsoft/trocr-large-handwritten") model = VisionEncoderDecoderModel.from_pretrained( "microsoft/trocr-large-handwritten", quantization_config=quantization_config device_map="auto") # load image from the IAM dataset url = "[https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg](https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg)" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") pixel_values = processor(image, return_tensors="pt").to(model.device).pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_text) ``` ## Notes - TrOCR wraps [ViTImageProcessor](/docs/transformers/v5.8.0/en/model_doc/vit#transformers.ViTImageProcessor)/[DeiTImageProcessor](/docs/transformers/v5.8.0/en/model_doc/deit#transformers.DeiTImageProcessor) and [RobertaTokenizer](/docs/transformers/v5.8.0/en/model_doc/mvp#transformers.RobertaTokenizer)/[XLMRobertaTokenizer](/docs/transformers/v5.8.0/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizer) into a single instance of [TrOCRProcessor](/docs/transformers/v5.8.0/en/model_doc/trocr#transformers.TrOCRProcessor) to handle images and text. - TrOCR is always used within the [VisionEncoderDecoder](vision-encoder-decoder) framework. ## Resources - A blog post on [Accelerating Document AI](https://huggingface.co/blog/document-ai) with TrOCR. - A blog post on how to [Document AI](https://github.com/philschmid/document-ai-transformers) with TrOCR. - A notebook on how to [finetune TrOCR on IAM Handwriting Database using Seq2SeqTrainer](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Fine_tune_TrOCR_on_IAM_Handwriting_Database_using_Seq2SeqTrainer.ipynb). - An interactive-demo on [TrOCR handwritten character recognition](https://huggingface.co/spaces/nielsr/TrOCR-handwritten). - A notebook on [inference with TrOCR](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Inference_with_TrOCR_%2B_Gradio_demo.ipynb) and Gradio demo. - A notebook on [evaluating TrOCR on the IAM test set](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Evaluating_TrOCR_base_handwritten_on_the_IAM_test_set.ipynb). ## TrOCRConfig[[transformers.TrOCRConfig]] #### transformers.TrOCRConfig[[transformers.TrOCRConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/trocr/configuration_trocr.py#L24) This is the configuration class to store the configuration of a TrocrModel. It is used to instantiate a Trocr model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.8.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.8.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> from transformers import TrOCRConfig, TrOCRForCausalLM >>> # Initializing a TrOCR-base style configuration >>> configuration = TrOCRConfig() >>> # Initializing a model (with random weights) from the TrOCR-base style configuration >>> model = TrOCRForCausalLM(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` **Parameters:** vocab_size (`int`, *optional*, defaults to `50265`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`. d_model (`int`, *optional*, defaults to `1024`) : Size of the encoder layers and the pooler layer. decoder_layers (`int`, *optional*, defaults to `12`) : Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. decoder_attention_heads (`int`, *optional*, defaults to `16`) : Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to `4096`) : Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc. max_position_embeddings (`int`, *optional*, defaults to `512`) : The maximum sequence length that this model might ever be used with. dropout (`Union[float, int]`, *optional*, defaults to `0.1`) : The ratio for all dropout layers. attention_dropout (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities. activation_dropout (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for activations inside the fully connected layer. decoder_start_token_id (`int`, *optional*, defaults to `2`) : If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token. init_std (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices. decoder_layerdrop (`Union[float, int]`, *optional*, defaults to `0.0`) : The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`) : Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True` or when the model is a decoder-only generative model. scale_embedding (`bool`, *optional*, defaults to `False`) : Whether to scale embeddings by dividing by sqrt(d_model). use_learned_position_embeddings (`bool`, *optional*, defaults to `True`) : Whether or not to use learned position embeddings. If not, sinusoidal position embeddings will be used. layernorm_embedding (`bool`, *optional*, defaults to `True`) : Whether or not to use a layernorm after the word + position embeddings. pad_token_id (`int`, *optional*, defaults to `1`) : Token id used for padding in the vocabulary. bos_token_id (`int`, *optional*, defaults to `0`) : Token id used for beginning-of-stream in the vocabulary. eos_token_id (`Union[int, list[int]]`, *optional*, defaults to `2`) : Token id used for end-of-stream in the vocabulary. cross_attention_hidden_size (`int`, *optional*) : Hidden size of the encoder outputs projected into the cross-attention key/value space of the decoder. Used when the encoder and decoder have different hidden sizes. is_decoder (`bool`, *optional*, defaults to `False`) : Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. tie_word_embeddings (`bool`, *optional*, defaults to `True`) : Whether to tie weight embeddings according to model's `tied_weights_keys` mapping. ## TrOCRProcessor[[transformers.TrOCRProcessor]] #### transformers.TrOCRProcessor[[transformers.TrOCRProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/trocr/processing_trocr.py#L30) Constructs a TrOCRProcessor which wraps a image processor and a tokenizer into a single processor. [TrOCRProcessor](/docs/transformers/v5.8.0/en/model_doc/trocr#transformers.TrOCRProcessor) offers all the functionalities of [ViTImageProcessor](/docs/transformers/v5.8.0/en/model_doc/vit#transformers.ViTImageProcessor) and [XLMRobertaTokenizer](/docs/transformers/v5.8.0/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizer). See the [~ViTImageProcessor](/docs/transformers/v5.8.0/en/model_doc/vit#transformers.ViTImageProcessor) and [~XLMRobertaTokenizer](/docs/transformers/v5.8.0/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizer) for more information. __call__transformers.TrOCRProcessor.__call__https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/trocr/processing_trocr.py#L34[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None"}, {"name": "text", "val": ": str | list[str] | list[list[str]] | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.trocr.processing_trocr.TrOCRProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`, *optional*) -- Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. - **text** (`Union[str, list[str], list[list[str]]]`, *optional*) -- The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set `is_split_into_words=True` to avoid ambiguity with batched inputs. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.8.0/en/internal/file_utils#transformers.TensorType), *optional*) -- If set, will return tensors of a particular framework. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - ****kwargs** ([ProcessingKwargs](/docs/transformers/v5.8.0/en/main_classes/processors#transformers.ProcessingKwargs), *optional*) -- Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **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/Numpy Tensors at initialization. **Parameters:** image_processor (`ViTImageProcessor`) : The image processor is a required input. tokenizer (`XLMRobertaTokenizer`) : The tokenizer is a required input. **Returns:** ``~image_processing_base.BatchFeature`` - **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/Numpy Tensors at initialization. #### from_pretrained[[transformers.TrOCRProcessor.from_pretrained]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/processing_utils.py#L1380) Instantiate a processor associated with a pretrained model. This class method is simply calling the feature extractor [from_pretrained()](/docs/transformers/v5.8.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained), image processor [ImageProcessingMixin](/docs/transformers/v5.8.0/en/main_classes/image_processor#transformers.ImageProcessingMixin) and the tokenizer `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained` methods. Please refer to the docstrings of the methods above for more information. **Parameters:** pretrained_model_name_or_path (`str` or `os.PathLike`) : This can be either: - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on huggingface.co. - a path to a *directory* containing a feature extractor file saved using the [save_pretrained()](/docs/transformers/v5.8.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) method, e.g., `./my_model_directory/`. - a path to a saved feature extractor JSON *file*, e.g., `./my_model_directory/preprocessor_config.json`. - ****kwargs** : Additional keyword arguments passed along to both [from_pretrained()](/docs/transformers/v5.8.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained) and `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`. #### save_pretrained[[transformers.TrOCRProcessor.save_pretrained]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/processing_utils.py#L807) Saves the attributes of this processor (feature extractor, tokenizer...) in the specified directory so that it can be reloaded using the [from_pretrained()](/docs/transformers/v5.8.0/en/main_classes/processors#transformers.ProcessorMixin.from_pretrained) method. This class method is simply calling [save_pretrained()](/docs/transformers/v5.8.0/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) and [save_pretrained()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained). Please refer to the docstrings of the methods above for more information. **Parameters:** save_directory (`str` or `os.PathLike`) : Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will be created if it does not exist). push_to_hub (`bool`, *optional*, defaults to `False`) : Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). kwargs (`dict[str, Any]`, *optional*) : Additional key word arguments passed along to the [push_to_hub()](/docs/transformers/v5.8.0/en/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub) method. #### batch_decode[[transformers.TrOCRProcessor.batch_decode]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/processing_utils.py#L1617) This method forwards all its arguments to PreTrainedTokenizer's [batch_decode()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.batch_decode). Please refer to the docstring of this method for more information. #### decode[[transformers.TrOCRProcessor.decode]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/processing_utils.py#L1626) This method forwards all its arguments to PreTrainedTokenizer's [decode()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.decode). Please refer to the docstring of this method for more information. ## TrOCRForCausalLM[[transformers.TrOCRForCausalLM]] #### transformers.TrOCRForCausalLM[[transformers.TrOCRForCausalLM]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/trocr/modeling_trocr.py#L638) The TrOCR Decoder with a language modeling head. Can be used as the decoder part of [EncoderDecoderModel](/docs/transformers/v5.8.0/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel) and This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/en/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.TrOCRForCausalLM.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/trocr/modeling_trocr.py#L664[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "encoder_hidden_states", "val": ": torch.FloatTensor | None = None"}, {"name": "encoder_attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "use_cache", "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": "**kwargs", "val": ""}]- **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/v5.8.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/en/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) - **encoder_hidden_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. - **encoder_attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - **use_cache** (`bool`, *optional*) -- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - **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/v5.8.0/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[CausalLMOutputWithCrossAttentions](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithCrossAttentions](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) 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 ([TrOCRConfig](/docs/transformers/v5.8.0/en/model_doc/trocr#transformers.TrOCRConfig)) and inputs. The [TrOCRForCausalLM](/docs/transformers/v5.8.0/en/model_doc/trocr#transformers.TrOCRForCausalLM) 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. - **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction). - **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). - **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. - **cross_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)`. Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads. - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. Example: ```python >>> from transformers import ( ... TrOCRConfig, ... TrOCRProcessor, ... TrOCRForCausalLM, ... ViTConfig, ... ViTModel, ... VisionEncoderDecoderModel, ... ) >>> import httpx >>> from io import BytesIO >>> from PIL import Image >>> # TrOCR is a decoder model and should be used within a VisionEncoderDecoderModel >>> # init vision2text model with random weights >>> encoder = ViTModel(ViTConfig()) >>> decoder = TrOCRForCausalLM(TrOCRConfig()) >>> model = VisionEncoderDecoderModel(encoder=encoder, decoder=decoder) >>> # If you want to start from the pretrained model, load the checkpoint with `VisionEncoderDecoderModel` >>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") >>> # load image from the IAM dataset >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())).convert("RGB") >>> pixel_values = processor(image, return_tensors="pt").pixel_values >>> text = "industry, ' Mr. Brown commented icily. ' Let us have a" >>> # training >>> model.config.decoder_start_token_id = processor.tokenizer.eos_token_id >>> model.config.pad_token_id = processor.tokenizer.pad_token_id >>> model.config.vocab_size = model.config.decoder.vocab_size >>> labels = processor.tokenizer(text, return_tensors="pt").input_ids >>> outputs = model(pixel_values, labels=labels) >>> loss = outputs.loss >>> round(loss.item(), 2) 5.30 >>> # inference >>> generated_ids = model.generate(pixel_values) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] >>> generated_text 'industry, " Mr. Brown commented icily. " Let us have a' ``` **Parameters:** config ([TrOCRForCausalLM](/docs/transformers/v5.8.0/en/model_doc/trocr#transformers.TrOCRForCausalLM)) : 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/v5.8.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[CausalLMOutputWithCrossAttentions](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`` A [CausalLMOutputWithCrossAttentions](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) 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 ([TrOCRConfig](/docs/transformers/v5.8.0/en/model_doc/trocr#transformers.TrOCRConfig)) and inputs.