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A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLMv3. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
<Tip>
LayoutLMv3 is nearly identical to LayoutLMv2, so we've also included LayoutLMv2 resources you can adapt for LayoutLMv3 tasks. For these notebooks, take care to use [`LayoutLMv2Processor`] instead when preparing data for the model!
</Tip>
- Demo notebooks for LayoutLMv3 can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3).
- Demo scripts can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3).
<PipelineTag pipeline="text-classification"/>
- [`LayoutLMv2ForSequenceClassification`] is supported by this [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/RVL-CDIP/Fine_tuning_LayoutLMv2ForSequenceClassification_on_RVL_CDIP.ipynb).
- [Text classification task guide](../tasks/sequence_classification)
<PipelineTag pipeline="token-classification"/>
- [`LayoutLMv3ForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3) and [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv3/Fine_tune_LayoutLMv3_on_FUNSD_(HuggingFace_Trainer).ipynb).
- A [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/Inference_with_LayoutLMv2ForTokenClassification.ipynb) for how to perform inference with [`LayoutLMv2ForTokenClassification`] and a [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/True_inference_with_LayoutLMv2ForTokenClassification_%2B_Gradio_demo.ipynb) for how to perform inference when no labels are available with [`LayoutLMv2ForTokenClassification`].
- A [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/Fine_tuning_LayoutLMv2ForTokenClassification_on_FUNSD_using_HuggingFace_Trainer.ipynb) for how to finetune [`LayoutLMv2ForTokenClassification`] with the 🤗 Trainer.
- [Token classification task guide](../tasks/token_classification)
<PipelineTag pipeline="question-answering"/>
- [`LayoutLMv2ForQuestionAnswering`] is supported by this [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/DocVQA/Fine_tuning_LayoutLMv2ForQuestionAnswering_on_DocVQA.ipynb).
- [Question answering task guide](../tasks/question_answering)
**Document question answering**
- [Document question answering task guide](../tasks/document_question_answering) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#resources | #resources | .md | 148_3 |
This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an
LayoutLMv3 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 LayoutLMv3
[microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the LayoutLMv3 model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`LayoutLMv3Model`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
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.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
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).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv3Model`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the 2D position embedding might ever be used with. Typically set this to something
large just in case (e.g., 1024).
coordinate_size (`int`, *optional*, defaults to `128`):
Dimension of the coordinate embeddings.
shape_size (`int`, *optional*, defaults to `128`):
Dimension of the width and height embeddings.
has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use a relative attention bias in the self-attention mechanism.
rel_pos_bins (`int`, *optional*, defaults to 32):
The number of relative position bins to be used in the self-attention mechanism.
max_rel_pos (`int`, *optional*, defaults to 128):
The maximum number of relative positions to be used in the self-attention mechanism.
max_rel_2d_pos (`int`, *optional*, defaults to 256):
The maximum number of relative 2D positions in the self-attention mechanism.
rel_2d_pos_bins (`int`, *optional*, defaults to 64):
The number of 2D relative position bins in the self-attention mechanism.
has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use a spatial attention bias in the self-attention mechanism.
visual_embed (`bool`, *optional*, defaults to `True`):
Whether or not to add patch embeddings.
input_size (`int`, *optional*, defaults to `224`):
The size (resolution) of the images.
num_channels (`int`, *optional*, defaults to `3`):
The number of channels of the images.
patch_size (`int`, *optional*, defaults to `16`)
The size (resolution) of the patches.
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
Example:
```python
>>> from transformers import LayoutLMv3Config, LayoutLMv3Model
>>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
>>> configuration = LayoutLMv3Config()
>>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration
>>> model = LayoutLMv3Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3config | #layoutlmv3config | .md | 148_4 |
No docstring available for LayoutLMv3FeatureExtractor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3featureextractor | #layoutlmv3featureextractor | .md | 148_5 |
Constructs a LayoutLMv3 image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to `(size["height"], size["width"])`. Can be
overridden by `do_resize` in `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
Size of the image after resizing. Can be overridden by `size` in `preprocess`.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image's pixel values by the specified `rescale_value`. Can be overridden by
`do_rescale` in `preprocess`.
rescale_factor (`float`, *optional*, defaults to 1 / 255):
Value by which the image's pixel values are rescaled. Can be overridden by `rescale_factor` in
`preprocess`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`Iterable[float]` or `float`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
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 (`Iterable[float]` or `float`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
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.
apply_ocr (`bool`, *optional*, defaults to `True`):
Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
the `apply_ocr` parameter in the `preprocess` method.
ocr_lang (`str`, *optional*):
The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
used. Can be overridden by the `ocr_lang` parameter in the `preprocess` method.
tesseract_config (`str`, *optional*):
Any additional custom configuration flags that are forwarded to the `config` parameter when calling
Tesseract. For example: '--psm 6'. Can be overridden by the `tesseract_config` parameter in the
`preprocess` method.
Methods: preprocess | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3imageprocessor | #layoutlmv3imageprocessor | .md | 148_6 |
Construct a LayoutLMv3 tokenizer. Based on [`RoBERTatokenizer`] (Byte Pair Encoding or BPE).
[`LayoutLMv3Tokenizer`] can be used to turn words, word-level bounding boxes and optional word labels to
token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and optional `labels` (for token
classification).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
[`LayoutLMv3Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
word-level bounding boxes into token-level bounding boxes.
Args:
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.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
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.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `True`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
The bounding box to use for the special [CLS] token.
sep_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
The bounding box to use for the special [SEP] token.
pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
The bounding box to use for the special [PAD] token.
pad_token_label (`int`, *optional*, defaults to -100):
The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
CrossEntropyLoss.
only_label_first_subword (`bool`, *optional*, defaults to `True`):
Whether or not to only label the first subword, in case word labels are provided.
Methods: __call__
- save_vocabulary | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3tokenizer | #layoutlmv3tokenizer | .md | 148_7 |
Construct a "fast" LayoutLMv3 tokenizer (backed by HuggingFace's *tokenizers* library). Based on BPE.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
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.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
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.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
trim_offsets (`bool`, *optional*, defaults to `True`):
Whether the post processing step should trim offsets to avoid including whitespaces.
cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
The bounding box to use for the special [CLS] token.
sep_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
The bounding box to use for the special [SEP] token.
pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
The bounding box to use for the special [PAD] token.
pad_token_label (`int`, *optional*, defaults to -100):
The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
CrossEntropyLoss.
only_label_first_subword (`bool`, *optional*, defaults to `True`):
Whether or not to only label the first subword, in case word labels are provided.
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3tokenizerfast | #layoutlmv3tokenizerfast | .md | 148_8 |
Constructs a LayoutLMv3 processor which combines a LayoutLMv3 image processor and a LayoutLMv3 tokenizer into a
single processor.
[`LayoutLMv3Processor`] offers all the functionalities you need to prepare data for the model.
It first uses [`LayoutLMv3ImageProcessor`] to resize and normalize document images, and optionally applies OCR to
get words and normalized bounding boxes. These are then provided to [`LayoutLMv3Tokenizer`] or
[`LayoutLMv3TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
`attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
into token-level `labels` for token classification tasks (such as FUNSD, CORD).
Args:
image_processor (`LayoutLMv3ImageProcessor`, *optional*):
An instance of [`LayoutLMv3ImageProcessor`]. The image processor is a required input.
tokenizer (`LayoutLMv3Tokenizer` or `LayoutLMv3TokenizerFast`, *optional*):
An instance of [`LayoutLMv3Tokenizer`] or [`LayoutLMv3TokenizerFast`]. The tokenizer is a required input.
Methods: __call__
<frameworkcontent>
<pt> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3processor | #layoutlmv3processor | .md | 148_9 |
The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`LayoutLMv3Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3model | #layoutlmv3model | .md | 148_10 |
LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the
[CLS] token) e.g. for document image classification tasks such as the
[RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`LayoutLMv3Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3forsequenceclassification | #layoutlmv3forsequenceclassification | .md | 148_11 |
LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g.
for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/),
[SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and
[Kleister-NDA](https://github.com/applicaai/kleister-nda).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`LayoutLMv3Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3fortokenclassification | #layoutlmv3fortokenclassification | .md | 148_12 |
LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as
[DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the text part of the hidden-states output to
compute `span start logits` and `span end logits`).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`LayoutLMv3Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#layoutlmv3forquestionanswering | #layoutlmv3forquestionanswering | .md | 148_13 |
No docstring available for TFLayoutLMv3Model
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#tflayoutlmv3model | #tflayoutlmv3model | .md | 148_14 |
No docstring available for TFLayoutLMv3ForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#tflayoutlmv3forsequenceclassification | #tflayoutlmv3forsequenceclassification | .md | 148_15 |
No docstring available for TFLayoutLMv3ForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#tflayoutlmv3fortokenclassification | #tflayoutlmv3fortokenclassification | .md | 148_16 |
No docstring available for TFLayoutLMv3ForQuestionAnswering
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv3.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv3/#tflayoutlmv3forquestionanswering | #tflayoutlmv3forquestionanswering | .md | 148_17 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/ | .md | 149_0 |
|
The Deformable DETR model was proposed in [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
Deformable DETR mitigates the slow convergence issues and limited feature spatial resolution of the original [DETR](detr) by leveraging a new deformable attention module which only attends to a small set of key sampling points around a reference.
The abstract from the paper is the following:
*DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png"
alt="drawing" width="600"/>
<small> Deformable DETR architecture. Taken from the <a href="https://arxiv.org/abs/2010.04159">original paper</a>.</small>
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/fundamentalvision/Deformable-DETR). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#overview | #overview | .md | 149_1 |
- Training Deformable DETR is equivalent to training the original [DETR](detr) model. See the [resources](#resources) section below for demo notebooks. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#usage-tips | #usage-tips | .md | 149_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Deformable DETR.
<PipelineTag pipeline="object-detection"/>
- Demo notebooks regarding inference + fine-tuning on a custom dataset for [`DeformableDetrForObjectDetection`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Deformable-DETR).
- Scripts for finetuning [`DeformableDetrForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
- See also: [Object detection task guide](../tasks/object_detection).
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#resources | #resources | .md | 149_3 |
Constructs a Deformable DETR image processor.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be
overridden by the `do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter
in the `preprocess` method. Available options are:
- `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`.
Do NOT keep the aspect ratio.
- `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
less or equal to `longest_edge`.
- `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to
`max_width`.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use if resizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
`do_rescale` parameter 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 the `rescale_factor` parameter in the
`preprocess` method.
do_normalize:
Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
`preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
do_convert_annotations (`bool`, *optional*, defaults to `True`):
Controls whether to convert the annotations to the format expected by the DETR model. Converts the
bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`.
Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method.
do_pad (`bool`, *optional*, defaults to `True`):
Controls whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess`
method. If `True`, padding will be applied to the bottom and right of the image with zeros.
If `pad_size` is provided, the image will be padded to the specified dimensions.
Otherwise, the image will be padded to the maximum height and width of the batch.
pad_size (`Dict[str, int]`, *optional*):
The size `{"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.
Methods: preprocess
- post_process_object_detection | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#deformabledetrimageprocessor | #deformabledetrimageprocessor | .md | 149_4 |
Constructs a fast DeformableDetr image processor.
Args:
format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's `(height, width)` dimensions to the specified `size`. Can be
overridden by the `do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter
in the `preprocess` method. Available options are:
- `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`.
Do NOT keep the aspect ratio.
- `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
less or equal to `longest_edge`.
- `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to
`max_width`.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use if resizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
`do_rescale` parameter 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 the `rescale_factor` parameter in the
`preprocess` method.
do_normalize (`bool`, *optional*, defaults to `True`):
Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
`preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
do_convert_annotations (`bool`, *optional*, defaults to `True`):
Controls whether to convert the annotations to the format expected by the DEFORMABLE_DETR model. Converts the
bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`.
Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method.
do_pad (`bool`, *optional*, defaults to `True`):
Controls whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess`
method. If `True`, padding will be applied to the bottom and right of the image with zeros.
If `pad_size` is provided, the image will be padded to the specified dimensions.
Otherwise, the image will be padded to the maximum height and width of the batch.
pad_size (`Dict[str, int]`, *optional*):
The size `{"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.
Methods: preprocess
- post_process_object_detection | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#deformabledetrimageprocessorfast | #deformabledetrimageprocessorfast | .md | 149_5 |
No docstring available for DeformableDetrFeatureExtractor
Methods: __call__
- post_process_object_detection | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#deformabledetrfeatureextractor | #deformabledetrfeatureextractor | .md | 149_6 |
This is the configuration class to store the configuration of a [`DeformableDetrModel`]. It is used to instantiate
a Deformable DETR 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 Deformable DETR
[SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_timm_backbone (`bool`, *optional*, defaults to `True`):
Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
API.
backbone_config (`PretrainedConfig` or `dict`, *optional*):
The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which
case it will default to `ResNetConfig()`.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_queries (`int`, *optional*, defaults to 300):
Number of object queries, i.e. detection slots. This is the maximal number of objects
[`DeformableDetrModel`] can detect in a single image. In case `two_stage` is set to `True`, we use
`two_stage_num_proposals` instead.
d_model (`int`, *optional*, defaults to 256):
Dimension of the layers.
encoder_layers (`int`, *optional*, defaults to 6):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 6):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 1024):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 1024):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
init_xavier_std (`float`, *optional*, defaults to 1):
The scaling factor used for the Xavier initialization gain in the HM Attention map module.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
auxiliary_loss (`bool`, *optional*, defaults to `False`):
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
position_embedding_type (`str`, *optional*, defaults to `"sine"`):
Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
backbone (`str`, *optional*, defaults to `"resnet50"`):
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
use_pretrained_backbone (`bool`, *optional*, defaults to `True`):
Whether to use pretrained weights for the backbone.
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
dilation (`bool`, *optional*, defaults to `False`):
Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
`use_timm_backbone` = `True`.
class_cost (`float`, *optional*, defaults to 1):
Relative weight of the classification error in the Hungarian matching cost.
bbox_cost (`float`, *optional*, defaults to 5):
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
giou_cost (`float`, *optional*, defaults to 2):
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
mask_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the Focal loss in the panoptic segmentation loss.
dice_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
bbox_loss_coefficient (`float`, *optional*, defaults to 5):
Relative weight of the L1 bounding box loss in the object detection loss.
giou_loss_coefficient (`float`, *optional*, defaults to 2):
Relative weight of the generalized IoU loss in the object detection loss.
eos_coefficient (`float`, *optional*, defaults to 0.1):
Relative classification weight of the 'no-object' class in the object detection loss.
num_feature_levels (`int`, *optional*, defaults to 4):
The number of input feature levels.
encoder_n_points (`int`, *optional*, defaults to 4):
The number of sampled keys in each feature level for each attention head in the encoder.
decoder_n_points (`int`, *optional*, defaults to 4):
The number of sampled keys in each feature level for each attention head in the decoder.
two_stage (`bool`, *optional*, defaults to `False`):
Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
Deformable DETR, which are further fed into the decoder for iterative bounding box refinement.
two_stage_num_proposals (`int`, *optional*, defaults to 300):
The number of region proposals to be generated, in case `two_stage` is set to `True`.
with_box_refine (`bool`, *optional*, defaults to `False`):
Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes
based on the predictions from the previous layer.
focal_alpha (`float`, *optional*, defaults to 0.25):
Alpha parameter in the focal loss.
disable_custom_kernels (`bool`, *optional*, defaults to `False`):
Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
kernels are not supported by PyTorch ONNX export.
Examples:
```python
>>> from transformers import DeformableDetrConfig, DeformableDetrModel
>>> # Initializing a Deformable DETR SenseTime/deformable-detr style configuration
>>> configuration = DeformableDetrConfig()
>>> # Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration
>>> model = DeformableDetrModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#deformabledetrconfig | #deformabledetrconfig | .md | 149_7 |
The bare Deformable DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
hidden-states without any specific head on top.
This model inherits from [`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.
Parameters:
config ([`DeformableDetrConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#deformabledetrmodel | #deformabledetrmodel | .md | 149_8 |
Deformable DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on
top, for tasks such as COCO detection.
This model inherits from [`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.
Parameters:
config ([`DeformableDetrConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deformable_detr.md | https://huggingface.co/docs/transformers/en/model_doc/deformable_detr/#deformabledetrforobjectdetection | #deformabledetrforobjectdetection | .md | 149_9 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/ | .md | 150_0 |
|
The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text \+ images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image.
**Model Architecture:** Llama 3.2-Vision is built on top of Llama 3.1 text-only model, which is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. To support image recognition tasks, the Llama 3.2-Vision model uses a separately trained vision adapter that integrates with the pre-trained Llama 3.1 language model. The adapter consists of a series of cross-attention layers that feed image encoder representations into the core LLM. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#overview | #overview | .md | 150_1 |
- For image+text and text inputs use `MllamaForConditionalGeneration`.
- For text-only inputs use `MllamaForCausalLM` for generation to avoid loading vision tower.
- Each sample can contain multiple images, and the number of images can vary between samples. The processor will pad the inputs to the maximum number of images across samples and to a maximum number of tiles within each image.
- The text passed to the processor should have the `"<|image|>"` tokens where the images should be inserted.
- The processor has its own `apply_chat_template` method to convert chat messages to text that can then be passed as text to the processor.
<Tip warning={true}>
Mllama has an extra token used as a placeholder for image positions in the text. It means that input ids and an input embedding layer will have an extra token. But since the weights for input and output embeddings are not tied, the `lm_head` layer has one less token and will fail if you want to calculate loss on image tokens or apply some logit processors. In case you are training, make sure to mask out special `"<|image|>"` tokens in the `labels` as the model should not be trained on predicting them.
Otherwise if you see CUDA-side index erros when generating, use the below code to expand the `lm_head` by one more token.
```python
old_embeddings = model.get_output_embeddings()
num_tokens = model.vocab_size + 1
resized_embeddings = model._get_resized_lm_head(old_embeddings, new_num_tokens=num_tokens, mean_resizing=True)
resized_embeddings.requires_grad_(old_embeddings.weight.requires_grad)
model.set_output_embeddings(resized_embeddings)
```
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#usage-tips | #usage-tips | .md | 150_2 |
```python
import requests
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor
model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
model = MllamaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained(model_id)
messages = [
[
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What does the image show?"}
]
}
],
]
text = processor.apply_chat_template(messages, add_generation_prompt=True)
url = "https://llava-vl.github.io/static/images/view.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=text, images=image, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=25)
print(processor.decode(output[0]))
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#instruct-model | #instruct-model | .md | 150_3 |
```python
import requests
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor
model_id = "meta-llama/Llama-3.2-11B-Vision"
model = MllamaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained(model_id)
prompt = "<|image|>If I had to write a haiku for this one"
url = "https://llava-vl.github.io/static/images/view.jpg"
raw_image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=prompt, images=raw_image, return_tensors="pt").to(model.device)
output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
print(processor.decode(output[0], skip_special_tokens=True))
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#base-model | #base-model | .md | 150_4 |
This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an
Mllama 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 Mllama-9B.
e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaVisionConfig`):
The config object or dictionary of the vision backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaTextConfig`):
The config object or dictionary of the text backbone.
image_token_index (`int`, *optional*, defaults to 128256):
The image token index to encode the image prompt.
Example:
```python
>>> from transformers import MllamaForConditionalGeneration, MllamaConfig, MllamaVisionConfig, MllamaTextConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = MllamaVisionConfig()
>>> # Initializing a Llama config
>>> text_config = MllamaTextConfig()
>>> # Initializing a mllama-11b style configuration
>>> configuration = MllamaConfig(vision_config, text_config)
>>> # Initializing a model from the mllama-11b style configuration
>>> model = MllamaForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#mllamaconfig | #mllamaconfig | .md | 150_5 |
Constructs a Mllama processor which wraps [`MllamaImageProcessor`] and
[`PretrainedTokenizerFast`] into a single processor that inherits both the image processor and
tokenizer functionalities. See the [`~MllamaProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more
information.
The preferred way of passing kwargs is as a dictionary per modality, see usage example below.
```python
from transformers import MllamaProcessor
from PIL import Image
processor = MllamaProcessor.from_pretrained("meta-llama/Llama-3.2-11B-Vision")
processor(
images=your_pil_image,
text=["<|image|>If I had to write a haiku for this one"],
images_kwargs = {"size": {"height": 448, "width": 448}},
text_kwargs = {"padding": "right"},
common_kwargs = {"return_tensors": "pt"},
)
```
Args:
image_processor ([`MllamaImageProcessor`]):
The image processor is a required input.
tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`]):
The tokenizer is a required input. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#mllamaprocessor | #mllamaprocessor | .md | 150_6 |
Constructs a Mllama image processor.
Args:
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB. This is useful if the input image is of a different format e.g. RGBA.
Only has an effect if the input image is in the PIL format.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image tile. Should be a dictionary containing 'height' and 'width' keys, both with integer values.
The height and width values should be equal.
resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
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_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to 0.0):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
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_pad (`bool`, *optional*, defaults to `True`):
Whether or not to pad the images to the largest height and width in the batch.
max_image_tiles (`int`, *optional*, defaults to 4):
The maximum number of tiles to split the image into. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#mllamaimageprocessor | #mllamaimageprocessor | .md | 150_7 |
The Mllama model which consists of a vision encoder and a language model.
This model inherits from [`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.
Parameters:
config ([`MllamaConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#mllamaforconditionalgeneration | #mllamaforconditionalgeneration | .md | 150_8 |
The Mllama Text Model with a language modeling head on top.
This model inherits from [`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.
Parameters:
config ([`MllamaConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#mllamaforcausallm | #mllamaforcausallm | .md | 150_9 |
The Mllama Text Model which consists of transformer with self and cross attention layers.
This model inherits from [`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.
Parameters:
config ([`MllamaConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#mllamatextmodel | #mllamatextmodel | .md | 150_10 |
The Mllama Text Model with a language modeling head on top.
This model inherits from [`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.
Parameters:
config ([`MllamaConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#mllamaforcausallm | #mllamaforcausallm | .md | 150_11 |
The Mllama Vision Model which consists of two vision encoders.
This model inherits from [`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.
Parameters:
config ([`MllamaConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mllama.md | https://huggingface.co/docs/transformers/en/model_doc/mllama/#mllamavisionmodel | #mllamavisionmodel | .md | 150_12 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/ | .md | 151_0 |
|
The BLIP model was proposed in [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
BLIP is a model that is able to perform various multi-modal tasks including:
- Visual Question Answering
- Image-Text retrieval (Image-text matching)
- Image Captioning
The abstract from the paper is the following:
*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks.
However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
This model was contributed by [ybelkada](https://huggingface.co/ybelkada).
The original code can be found [here](https://github.com/salesforce/BLIP). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#overview | #overview | .md | 151_1 |
- [Jupyter notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) on how to fine-tune BLIP for image captioning on a custom dataset | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#resources | #resources | .md | 151_2 |
[`BlipConfig`] is the configuration class to store the configuration of a [`BlipModel`]. It is used to instantiate
a BLIP 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 BLIP-base
[Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`BlipTextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`BlipVisionConfig`].
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 BLIP implementation.
image_text_hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the hidden state of the image-text fusion layer.
label_smoothing (float, optional, *optional*, defaults to 0.0):
A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets
become a mixture of the original ground truth and a uniform distribution as described in
`Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import BlipConfig, BlipModel
>>> # Initializing a BlipConfig with Salesforce/blip-vqa-base style configuration
>>> configuration = BlipConfig()
>>> # Initializing a BlipPModel (with random weights) from the Salesforce/blip-vqa-base style configuration
>>> model = BlipModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a BlipConfig from a BlipTextConfig and a BlipVisionConfig
>>> # Initializing a BLIPText and BLIPVision configuration
>>> config_text = BlipTextConfig()
>>> config_vision = BlipVisionConfig()
>>> config = BlipConfig.from_text_vision_configs(config_text, config_vision)
```
Methods: from_text_vision_configs | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipconfig | #blipconfig | .md | 151_3 |
This is the configuration class to store the configuration of a [`BlipTextModel`]. It is used to instantiate a BLIP
text 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 `BlipText` used by the [base
architectures](https://huggingface.co/Salesforce/blip-vqa-base).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30524):
Vocabulary size of the `Blip` text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`BlipModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
encoder_hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers from the vision model.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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 512):
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 `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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.
bos_token_id (`int`, *optional*, defaults to 30522):
The id of the `beginning-of-sequence` token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the `end-of-sequence` token.
pad_token_id (`int`, *optional*, defaults to 0):
The id of the `padding` token.
sep_token_id (`int`, *optional*, defaults to 102):
The id of the `separator` token.
is_decoder (`bool`, *optional*, defaults to `True`):
Whether the model is used as a decoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
label_smoothing (float, *optional*):
A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets
become a mixture of the original ground truth and a uniform distribution as described in
`Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.
Example:
```python
>>> from transformers import BlipTextConfig, BlipTextModel
>>> # Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration
>>> configuration = BlipTextConfig()
>>> # Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration
>>> model = BlipTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#bliptextconfig | #bliptextconfig | .md | 151_4 |
This is the configuration class to store the configuration of a [`BlipVisionModel`]. It is used to instantiate a
BLIP vision model according to the specified arguments, defining the model architecture. Instantiating a
configuration defaults will yield a similar configuration to that of the Blip-base
[Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
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.
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.
image_size (`int`, *optional*, defaults to 384):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
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 1e-10):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import BlipVisionConfig, BlipVisionModel
>>> # Initializing a BlipVisionConfig with Salesforce/blip-vqa-base style configuration
>>> configuration = BlipVisionConfig()
>>> # Initializing a BlipVisionModel (with random weights) from the Salesforce/blip-vqa-base style configuration
>>> model = BlipVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipvisionconfig | #blipvisionconfig | .md | 151_5 |
Constructs a BLIP processor which wraps a BERT tokenizer and BLIP image processor into a single processor.
[`BlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`BertTokenizerFast`]. See the
docstring of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information.
Args:
image_processor (`BlipImageProcessor`):
An instance of [`BlipImageProcessor`]. The image processor is a required input.
tokenizer (`BertTokenizerFast`):
An instance of ['BertTokenizerFast`]. The tokenizer is a required input. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipprocessor | #blipprocessor | .md | 151_6 |
Constructs a BLIP image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
overridden by the `resample` parameter 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 the
`do_rescale` parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
overridden by the `rescale_factor` parameter in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
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. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
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.
Methods: preprocess
<frameworkcontent>
<pt> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipimageprocessor | #blipimageprocessor | .md | 151_7 |
`BlipModel` is going to be deprecated in future versions, please use `BlipForConditionalGeneration`, `BlipForImageTextRetrieval` or `BlipForQuestionAnswering` depending on your usecase.
This model is going to be deprecated in future versions. Please use `BlipForConditionalGeneration`, `BlipForQuestionAnswering` or `BlipForImageTextRetrieval` depending on your usecase.
This model inherits from [`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.
Parameters:
config ([`BlipConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
- get_text_features
- get_image_features | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipmodel | #blipmodel | .md | 151_8 |
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and `is_decoder` set to `True`; an
`encoder_hidden_states` is then expected as an input to the forward pass.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#bliptextmodel | #bliptextmodel | .md | 151_9 |
No docstring available for BlipVisionModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipvisionmodel | #blipvisionmodel | .md | 151_10 |
BLIP Model for image captioning. The model consists of a vision encoder and a text decoder. One can optionally pass
`input_ids` to the model, which serve as a text prompt, to make the text decoder continue the prompt. Otherwise,
the decoder starts generating text from the [BOS] (beginning-of-sequence) token. will start generating the caption
from the text input. If no text input is provided, the decoder will start with the [BOS] token only.
This model inherits from [`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.
Parameters:
config ([`BlipConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipforconditionalgeneration | #blipforconditionalgeneration | .md | 151_11 |
BLIP Model with a vision and text projector, and a classification head on top. The model is used in the context of
image-text retrieval. Given an image and a text, the model returns the probability of the text being relevant to
the image.
This model inherits from [`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.
Parameters:
config ([`BlipConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipforimagetextretrieval | #blipforimagetextretrieval | .md | 151_12 |
BLIP Model for visual question answering. The model consists of a vision encoder, a text encoder as well as a text
decoder. The vision encoder will encode the input image, the text encoder will encode the input question together
with the encoding of the image, and the text decoder will output the answer to the question.
This model inherits from [`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.
Parameters:
config ([`BlipConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#blipforquestionanswering | #blipforquestionanswering | .md | 151_13 |
No docstring available for TFBlipModel
Methods: call
- get_text_features
- get_image_features | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#tfblipmodel | #tfblipmodel | .md | 151_14 |
No docstring available for TFBlipTextModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#tfbliptextmodel | #tfbliptextmodel | .md | 151_15 |
No docstring available for TFBlipVisionModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#tfblipvisionmodel | #tfblipvisionmodel | .md | 151_16 |
No docstring available for TFBlipForConditionalGeneration
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#tfblipforconditionalgeneration | #tfblipforconditionalgeneration | .md | 151_17 |
No docstring available for TFBlipForImageTextRetrieval
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#tfblipforimagetextretrieval | #tfblipforimagetextretrieval | .md | 151_18 |
No docstring available for TFBlipForQuestionAnswering
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blip.md | https://huggingface.co/docs/transformers/en/model_doc/blip/#tfblipforquestionanswering | #tfblipforquestionanswering | .md | 151_19 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/persimmon.md | https://huggingface.co/docs/transformers/en/model_doc/persimmon/ | .md | 152_0 |
|
The Persimmon model was created by [ADEPT](https://www.adept.ai/blog/persimmon-8b), and authored by Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani.
The authors introduced Persimmon-8B, a decoder model based on the classic transformers architecture, with query and key normalization. Persimmon-8B is a fully permissively-licensed model with approximately 8 billion parameters, released under the Apache license. Some of the key attributes of Persimmon-8B are long context size (16K), performance, and capabilities for multimodal extensions.
The authors showcase their approach to model evaluation, focusing on practical text generation, mirroring how users interact with language models. The work also includes a comparative analysis, pitting Persimmon-8B against other prominent models (MPT 7B Instruct and Llama 2 Base 7B 1-Shot), across various evaluation tasks. The results demonstrate Persimmon-8B's competitive performance, even with limited training data.
In terms of model details, the work outlines the architecture and training methodology of Persimmon-8B, providing insights into its design choices, sequence length, and dataset composition. The authors present a fast inference code that outperforms traditional implementations through operator fusion and CUDA graph utilization while maintaining code coherence. They express their anticipation of how the community will leverage this contribution to drive innovation, hinting at further upcoming releases as part of an ongoing series of developments.
This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ).
The original code can be found [here](https://github.com/persimmon-ai-labs/adept-inference). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/persimmon.md | https://huggingface.co/docs/transformers/en/model_doc/persimmon/#overview | #overview | .md | 152_1 |
<Tip warning={true}>
The `Persimmon` models were trained using `bfloat16`, but the original inference uses `float16` The checkpoints uploaded on the hub use `torch_dtype = 'float16'` which will be
used by the `AutoModel` API to cast the checkpoints from `torch.float32` to `torch.float16`.
The `dtype` of the online weights is mostly irrelevant, unless you are using `torch_dtype="auto"` when initializing a model using `model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto")`. The reason is that the model will first be downloaded ( using the `dtype` of the checkpoints online) then it will be cast to the default `dtype` of `torch` (becomes `torch.float32`). Users should specify the `torch_dtype` they want, and if they don't it will be `torch.float32`.
Finetuning the model in `float16` is not recommended and known to produce `nan`, as such the model should be fine-tuned in `bfloat16`.
</Tip>
Tips:
- To convert the model, you need to clone the original repository using `git clone https://github.com/persimmon-ai-labs/adept-inference`, then get the checkpoints:
```bash
git clone https://github.com/persimmon-ai-labs/adept-inference
wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_base_model_release.tar
tar -xvf 8b_base_model_release.tar
python src/transformers/models/persimmon/convert_persimmon_weights_to_hf.py --input_dir /path/to/downloaded/persimmon/weights/ --output_dir /output/path \
--pt_model_path /path/to/8b_chat_model_release/iter_0001251/mp_rank_00/model_optim_rng.pt
--ada_lib_path /path/to/adept-inference
```
For the chat model:
```bash
wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar
tar -xvf 8b_base_model_release.tar
```
Thereafter, models can be loaded via:
```py
from transformers import PersimmonForCausalLM, PersimmonTokenizer
model = PersimmonForCausalLM.from_pretrained("/output/path")
tokenizer = PersimmonTokenizer.from_pretrained("/output/path")
```
- Perismmon uses a `sentencepiece` based tokenizer, with a `Unigram` model. It supports bytefallback, which is only available in `tokenizers==0.14.0` for the fast tokenizer.
The `LlamaTokenizer` is used as it is a standard wrapper around sentencepiece. The `chat` template will be updated with the templating functions in a follow up PR!
- The authors suggest to use the following prompt format for the chat mode: `f"human: {prompt}\n\nadept:"` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/persimmon.md | https://huggingface.co/docs/transformers/en/model_doc/persimmon/#usage-tips | #usage-tips | .md | 152_2 |
This is the configuration class to store the configuration of a [`PersimmonModel`]. It is used to instantiate an
Persimmon 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
[adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 262144):
Vocabulary size of the Persimmon model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`PersimmonModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 16384):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 36):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 16384):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the rms normalization layers.
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`.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 25000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
qk_layernorm (`bool`, *optional*, default to `True`):
Whether or not to normalize the Queries and Keys after projecting the hidden states
hidden_dropout (`float`, *optional*, default to 0.0):
The dropout ratio after applying the MLP to the hidden states.
attention_dropout (`float`, *optional*, default to 0.0):
The dropout ratio after computing the attention scores.
partial_rotary_factor (`float`, *optional*, default to 0.5):
Percentage of the query and keys which will have rotary embedding.
Example:
```python
>>> from transformers import PersimmonModel, PersimmonConfig
>>> # Initializing a Persimmon persimmon-7b style configuration
>>> configuration = PersimmonConfig()
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/persimmon.md | https://huggingface.co/docs/transformers/en/model_doc/persimmon/#persimmonconfig | #persimmonconfig | .md | 152_3 |
The bare Persimmon Model outputting raw hidden-states without any specific head on top.
This model inherits from [`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.
Parameters:
config ([`PersimmonConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PersimmonDecoderLayer`]
Args:
config: PersimmonConfig
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/persimmon.md | https://huggingface.co/docs/transformers/en/model_doc/persimmon/#persimmonmodel | #persimmonmodel | .md | 152_4 |
No docstring available for PersimmonForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/persimmon.md | https://huggingface.co/docs/transformers/en/model_doc/persimmon/#persimmonforcausallm | #persimmonforcausallm | .md | 152_5 |
The Persimmon transformer with a sequence classification head on top (linear layer).
[`PersimmonForSequenceClassification`] uses the last token in order to do the classification, as other causal
models (e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
This model inherits from [`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.
Parameters:
config ([`PersimmonConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/persimmon.md | https://huggingface.co/docs/transformers/en/model_doc/persimmon/#persimmonforsequenceclassification | #persimmonforsequenceclassification | .md | 152_6 |
The Persimmon Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`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.
Parameters:
config ([`PersimmonConfig`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/persimmon.md | https://huggingface.co/docs/transformers/en/model_doc/persimmon/#persimmonfortokenclassification | #persimmonfortokenclassification | .md | 152_7 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deplot.md | https://huggingface.co/docs/transformers/en/model_doc/deplot/ | .md | 153_0 |
|
DePlot was proposed in the paper [DePlot: One-shot visual language reasoning by plot-to-table translation](https://arxiv.org/abs/2212.10505) from Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
The abstract of the paper states the following:
*Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models require at least tens of thousands of training examples and their reasoning capabilities are still much limited, especially on complex human-written queries. This paper presents the first one-shot solution to visual language reasoning. We decompose the challenge of visual language reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over the translated text. The key in this method is a modality conversion module, named as DePlot, which translates the image of a plot or chart to a linearized table. The output of DePlot can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs. To obtain DePlot, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DePlot end-to-end on this task. DePlot can then be used off-the-shelf together with LLMs in a plug-and-play fashion. Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24.0% improvement over finetuned SOTA on human-written queries from the task of chart QA.*
DePlot is a model that is trained using `Pix2Struct` architecture. You can find more information about `Pix2Struct` in the [Pix2Struct documentation](https://huggingface.co/docs/transformers/main/en/model_doc/pix2struct).
DePlot is a Visual Question Answering subset of `Pix2Struct` architecture. It renders the input question on the image and predicts the answer. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deplot.md | https://huggingface.co/docs/transformers/en/model_doc/deplot/#overview | #overview | .md | 153_1 |
Currently one checkpoint is available for DePlot:
- `google/deplot`: DePlot fine-tuned on ChartQA dataset
```python
from transformers import AutoProcessor, Pix2StructForConditionalGeneration
import requests
from PIL import Image
model = Pix2StructForConditionalGeneration.from_pretrained("google/deplot")
processor = AutoProcessor.from_pretrained("google/deplot")
url = "https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/5090.png"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, text="Generate underlying data table of the figure below:", return_tensors="pt")
predictions = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(predictions[0], skip_special_tokens=True))
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deplot.md | https://huggingface.co/docs/transformers/en/model_doc/deplot/#usage-example | #usage-example | .md | 153_2 |
To fine-tune DePlot, refer to the pix2struct [fine-tuning notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_pix2struct.ipynb). For `Pix2Struct` models, we have found out that fine-tuning the model with Adafactor and cosine learning rate scheduler leads to faster convergence:
```python
from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup
optimizer = Adafactor(self.parameters(), scale_parameter=False, relative_step=False, lr=0.01, weight_decay=1e-05)
scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=1000, num_training_steps=40000)
```
<Tip>
DePlot is a model trained using `Pix2Struct` architecture. For API reference, see [`Pix2Struct` documentation](pix2struct).
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deplot.md | https://huggingface.co/docs/transformers/en/model_doc/deplot/#fine-tuning | #fine-tuning | .md | 153_3 |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gemma2.md | https://huggingface.co/docs/transformers/en/model_doc/gemma2/ | .md | 154_0 |
|
The Gemma2 model was proposed in [Gemma2: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/google-gemma-2/) by Gemma2 Team, Google.
Two Gemma2 models are released, with parameters sizes of 9 billion (9B) and 27 billion (27B).
The abstract from the blog post is the following:
*Now we’re officially releasing Gemma 2 to researchers and developers globally. Available in both 9 billion (9B) and 27 billion (27B) parameter sizes, Gemma 2 is higher-performing and more efficient at inference than the first generation, with significant safety advancements built in. In fact, at 27B, it offers competitive alternatives to models more than twice its size, delivering the kind of performance that was only possible with proprietary models as recently as December.*
Tips:
- The original checkpoints can be converted using the conversion script `src/transformers/models/Gemma2/convert_Gemma2_weights_to_hf.py`
<Tip warning={true}>
- Gemma2 uses sliding window attention every second layer, which makes it unsuitable for typical kv caching with [`~DynamicCache`] or tuples of tensors. To enable caching in Gemma2 forward call, you must initialize a [`~HybridCache`] instance and pass it as `past_key_values` to the forward call. Note, that you also have to prepare `cache_position` if the `past_key_values` already contains previous keys and values.
</Tip>
This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ), [Pedro Cuenca](https://huggingface.co/pcuenq) and [Tom Arsen](). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gemma2.md | https://huggingface.co/docs/transformers/en/model_doc/gemma2/#overview | #overview | .md | 154_1 |
This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2
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 Gemma2-7B.
e.g. [google/gemma2-7b](https://huggingface.co/google/gemma2-7b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 256000):
Vocabulary size of the Gemma2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Gemma2Model`]
hidden_size (`int`, *optional*, defaults to 2304):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 9216):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 26):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 4):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
head_dim (`int`, *optional*, defaults to 256):
The attention head dimension.
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
max_position_embeddings (`int`, *optional*, defaults to 8192):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
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`.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
bos_token_id (`int`, *optional*, defaults to 2):
Beginning of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores
sliding_window (`int`, *optional*, defaults to 4096): in Gemma2, every other layer uses sliding window attention. This is the
size of the sliding window.
final_logit_softcapping (`float`, *optional*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits.
attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores.
cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.
```python
>>> from transformers import Gemma2Model, Gemma2Config
>>> # Initializing a Gemma2 gemma2-7b style configuration
>>> configuration = Gemma2Config()
>>> # Initializing a model from the gemma2-7b style configuration
>>> model = Gemma2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gemma2.md | https://huggingface.co/docs/transformers/en/model_doc/gemma2/#gemma2config | #gemma2config | .md | 154_2 |
The bare Gemma2 Model outputting raw hidden-states without any specific head on top.
This model inherits from [`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.
Parameters:
config ([`Gemma2Config`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Gemma2DecoderLayer`]
Args:
config: Gemma2Config
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gemma2.md | https://huggingface.co/docs/transformers/en/model_doc/gemma2/#gemma2model | #gemma2model | .md | 154_3 |
No docstring available for Gemma2ForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gemma2.md | https://huggingface.co/docs/transformers/en/model_doc/gemma2/#gemma2forcausallm | #gemma2forcausallm | .md | 154_4 |
The Gemma2 Model transformer with a sequence classification head on top (linear layer).
[`Gemma2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
This model inherits from [`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.
Parameters:
config ([`Gemma2Config`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gemma2.md | https://huggingface.co/docs/transformers/en/model_doc/gemma2/#gemma2forsequenceclassification | #gemma2forsequenceclassification | .md | 154_5 |
The Gemma2 Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`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.
Parameters:
config ([`Gemma2Config`]):
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
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gemma2.md | https://huggingface.co/docs/transformers/en/model_doc/gemma2/#gemma2fortokenclassification | #gemma2fortokenclassification | .md | 154_6 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/ | .md | 155_0 |
|
The Moshi model was proposed in [Moshi: a speech-text foundation model for real-time dialogue](https://kyutai.org/Moshi.pdf) by Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave and Neil Zeghidour.
Moshi is a speech-text foundation model that casts spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. Moshi also predicts time-aligned text tokens as a prefix to audio tokens. This “Inner Monologue” method significantly improves the linguistic quality of generated speech and provides streaming speech recognition and text-to-speech. As a result, Moshi is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ylacombe/benchmark-comparison/resolve/main/moshi_architecture.png">
</div>
The abstract from the paper is the following:
*We introduce Moshi, a speech-text foundation model and full-duplex spoken dialogue framework. Current systems for spoken dialogue rely on pipelines of independent components, namely voice activity detection, speech recognition, textual dialogue and text-to-speech. Such frameworks cannot emulate the experience of real conversations. First, their complexity induces a latency of several seconds between interactions. Second, text being the intermediate modality for dialogue, non-linguistic information that modifies meaning— such as emotion or non-speech sounds— is lost in the interaction. Finally, they rely on a segmentation into speaker turns, which does not take into account overlapping speech, interruptions and interjections. Moshi solves these independent issues altogether by casting spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. We moreover extend the hierarchical semantic-to-acoustic token generation of previous work to first predict time-aligned text tokens as a prefix to audio tokens. Not only this “Inner Monologue” method significantly improves the linguistic quality of generated speech, but we also illustrate how it can provide streaming speech recognition and text-to-speech. Our resulting model is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice, and is available at github.com/kyutai-labs/moshi.*
Moshi deals with 3 streams of information:
1. The user's audio
2. Moshi's audio
3. Moshi's textual output
Similarly to [`~MusicgenModel`], audio is represented with audio codebooks, which can be interpreted like tokens. The main difference between text tokens and audio codebooks is that audio codebooks introduce an additional dimension of information.
Text tokens are typically of dim `(batch_size, sequence_length)` but audio tokens are of dim `(batch_size, num_codebooks, sequence_length)`.
Moshi's made of 3 components:
**1. The main decoder (Helium in the paper)**
It corresponds to [`MoshiForCausalLM`]. It is strictly a classic text LLM, that uses an architecture similar to [` ~GemmaForCausalLM`]. In other words, it takes text tokens, embeds them, pass them through the decoder and a language head, to get text logits.
**2. The depth decoder**
On its own, it's also a classic LLM, but this time, instead of generating over the time dimension, it generates over the codebook dimension.
It also means that its context length is `num_codebooks`, thus it can't generate more than `num_codebooks`.
Note that each timestamp - i.e each codebook - gets its own set of Linear Layers and Embeddings.
**3. [`MimiModel`]**
It's the audio encoder from Kyutai, that has recently been integrated to transformers, which is used to "tokenize" audio. It has the same use that [`~EncodecModel`] has in [`~MusicgenModel`]. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#overview | #overview | .md | 155_1 |
The original checkpoints can be converted using the conversion script `src/transformers/models/moshi/convert_moshi_transformers.py` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#tips | #tips | .md | 155_2 |
This implementation has two main aims:
1. quickly test model generation by simplifying the original API
2. simplify training. A training guide will come soon, but user contributions are welcomed!
<Tip>
It is designed for intermediate use. We strongly recommend using the original [implementation](https://github.com/kyutai-labs/moshi) to infer the model in real-time streaming.
</Tip>
**1. Model generation**
Moshi is a streaming auto-regressive model with two streams of audio. To put it differently, one audio stream corresponds to what the model said/will say and the other audio stream corresponds to what the user said/will say.
[`MoshiForConditionalGeneration.generate`] thus needs 3 inputs:
1. `input_ids` - corresponding to the text token history
2. `moshi_input_values` or `moshi_audio_codes`- corresponding to the model audio history
3. `user_input_values` or `user_audio_codes` - corresponding to the user audio history
These three inputs must be synchronized. Meaning that their lengths must correspond to the same number of tokens.
You can dynamically use the 3 inputs depending on what you want to test:
1. Simply check the model response to an user prompt - in that case, `input_ids` can be filled with pad tokens and `user_input_values` can be a zero tensor of the same shape than the user prompt.
2. Test more complex behaviour - in that case, you must be careful about how the input tokens are synchronized with the audios.
<Tip>
The original model is synchronized text with audio by padding the text in between each token enunciation.
To follow the example of the following image, `"Hello, I'm Moshi"` could be transformed to `"Hello,<pad><unk>I'm Moshi"`.
</Tip>
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ylacombe/benchmark-comparison/resolve/main/moshi_text_sync.png">
</div>
[`MoshiForConditionalGeneration.generate`] then auto-regressively feeds to itself its own audio stream, but since it doesn't have access to the user input stream while using `transformers`, it will thus **assume that the user is producing blank audio**.
```python
>>> from datasets import load_dataset, Audio
>>> import torch, math
>>> from transformers import MoshiForConditionalGeneration, AutoFeatureExtractor, AutoTokenizer
>>> librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> # prepare user input audio
>>> librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
>>> audio_sample = librispeech_dummy[-1]["audio"]["array"]
>>> user_input_values = feature_extractor(raw_audio=audio_sample, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt").to(device=device, dtype=dtype)
>>> # prepare moshi input values - we suppose moshi didn't say anything while the user spoke
>>> moshi_input_values = torch.zeros_like(user_input_values.input_values)
>>> # prepare moshi input ids - we suppose moshi didn't say anything while the user spoke
>>> num_tokens = math.ceil(moshi_input_values.shape[-1] * waveform_to_token_ratio)
>>> input_ids = torch.ones((1, num_tokens), device=device, dtype=torch.int64) * tokenizer.encode("<pad>")[0]
>>> # generate 25 new tokens (around 2s of audio)
>>> output = model.generate(input_ids=input_ids, user_input_values=user_input_values.input_values, moshi_input_values=moshi_input_values, max_new_tokens=25)
>>> text_tokens = output.sequences
>>> audio_waveforms = output.audio_sequences
```
**2. Model training**
Most of the work has to be done during data creation/pre-processing, because of the need to align/synchronize streams.
Once it's done, you can simply forward `text_labels` and `audio_labels` to [`MoshiForConditionalGeneration.forward`], alongside the usual inputs, to get the model loss.
A training guide will come soon, but user contributions are welcomed! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#how-to-use-the-model | #how-to-use-the-model | .md | 155_3 |
1. The input streams are embedded and combined into `inputs_embeds`.
2. `inputs_embeds` is passed through the main decoder, which processes it like a normal LLM would.
3. The main decoder outputs `text logits` but also its `last hidden state` which is called `temporal context` in the paper.
3. The depth decoder switches the dimension on which we forward / generate (codebooks instead of time). It uses the token generated from `text logits` and the `temporal context` to auto-regressively generate audio codebooks.
This model was contributed by [Yoach Lacombe (ylacombe)](https://huggingface.co/ylacombe).
The original code can be found [here](https://github.com/kyutai-labs/moshi). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#how-does-the-model-forward-the-inputs--generate | #how-does-the-model-forward-the-inputs--generate | .md | 155_4 |
This is the configuration class to store the configuration of a [`MoshiModel`]. It is used to instantiate a
Moshi model according to the specified arguments, defining the audio encoder, Moshi depth decoder and Moshi decoder
configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the Moshiko model,
e.g. [kmhf/hf-moshiko](https://huggingface.co/kmhf/hf-moshiko)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the MoshiDecoder model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`MoshiDecoder`].
hidden_size (`int`, *optional*, defaults to 4096):
Dimensionality of the layers and the pooler layer of the main decoder.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of decoder layers.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the main decoder block.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`.
audio_vocab_size (`int`, *optional*):
Vocabulary size of the audio part of model. Defines the number of different tokens that can be
represented by the `audio_codes` passed when calling the Moshi models.
max_position_embeddings (`int`, *optional*, defaults to 3000):
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).
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
The attention head dimension.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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`.
sliding_window (`int`, *optional*, defaults to 3000):
Sliding window attention window size. If not specified, will default to `3000`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
ffn_dim (`int`, *optional*, defaults to 22528):
Dimensionality of the "intermediate" (often named feed-forward) layer in the main decoder block. Must be even.
rms_norm_eps (`float`, *optional*, defaults to 1e-08):
The epsilon used by the rms normalization layers.
num_codebooks (`int`, *optional*, defaults to 8):
The number of audio codebooks for each audio channels.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
kwargs (*optional*):
Dictionary of keyword arguments. Notably:
- **audio_encoder_config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the audio encoder config.
- **depth__config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the depth decoder config.
Example:
```python
>>> from transformers import (
... MoshiConfig,
... MoshiForConditionalGeneration,
... )
>>> configuration = MoshiConfig()
>>> # Initializing a MoshiForConditionalGeneration (with random weights) from the kmhf/hf-moshiko style configuration
>>> model = MoshiForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # Saving the model, including its configuration
>>> model.save_pretrained("kmhf/hf-moshiko")
>>> # loading model and config from pretrained folder
>>> moshi_config = MoshiConfig.from_pretrained("kmhf/hf-moshiko")
>>> model = MoshiForConditionalGeneration.from_pretrained("kmhf/hf-moshiko", config=moshi_config)
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#moshiconfig | #moshiconfig | .md | 155_5 |
This is the configuration class to store the configuration of a [`MoshiDepthDecoder`]. It is used to instantiate a
Moshi depth decoder model according to the specified arguments, defining the Moshi depth decoder config.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the MoshiDepthDecoder model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`MoshiDepthDecoder`].
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer of the depth decoder.
input_size (`int`, *optional*, defaults to 4096):
Dimensionality of the input hidden states. Used to connect the main decoder to the depth decoder.
num_hidden_layers (`int`, *optional*, defaults to 6):
Number of depth decoder layers.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the depth decoder block.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`.
audio_vocab_size (`int`, *optional*, defaults to 2048):
Vocabulary size of the audio part of model. Defines the number of different tokens that can be
represented by the `audio_codes` passed when calling the Moshi models.
max_position_embeddings (`int`, *optional*, defaults to 9):
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 `"silu"`):
The non-linear activation function (function or string) in the depth decoder.
head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
The attention head dimension.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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`.
sliding_window (`int`, *optional*, defaults to 8):
Sliding window attention window size. If not specified, will default to `8`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
ffn_dim (`int`, *optional*, defaults to 5632):
Dimensionality of the "intermediate" (often named feed-forward) layer in the depth decoder block. Must be even.
rms_norm_eps (`float`, *optional*, defaults to 1e-08):
The epsilon used by the rms normalization layers.
num_codebooks (`int`, *optional*, defaults to 8):
The number of audio codebooks for each audio channels.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
kwargs (*optional*):
Dictionary of keyword arguments. Notably:
- **audio_encoder_config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the audio encoder config.
Example:
```python
>>> from transformers import (
... MoshiDepthConfig,
... MoshiDepthDecoder,
... )
>>> configuration = MoshiDepthConfig()
>>> # Initializing a MoshiDepthDecoder (with random weights) from the kmhf/hf-moshiko style configuration
>>> model = MoshiDepthDecoder(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#moshidepthconfig | #moshidepthconfig | .md | 155_6 |
The bare Moshi Model outputting raw hidden-states without any specific head on top.
This model inherits from [`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.
Parameters:
config ([`MoshiConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MoshiDecoderLayer`]
Args:
config: MoshiConfig
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#moshimodel | #moshimodel | .md | 155_7 |
The Moshi decoder model with a text language modelling head on top. Only usable for text.
This model inherits from [`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.
Parameters:
config ([`MoshiConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#moshiforcausallm | #moshiforcausallm | .md | 155_8 |
The original Moshi model with an audio encoder, a Moshi depth decoder and a Moshi decoder, for speech-to-speech.
This model inherits from [`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.
Parameters:
config ([`MoshiConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
- generate
- get_unconditional_inputs | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moshi.md | https://huggingface.co/docs/transformers/en/model_doc/moshi/#moshiforconditionalgeneration | #moshiforconditionalgeneration | .md | 155_9 |
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|
<Tip>
This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title).
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#maskformer | #maskformer | .md | 156_1 |
The MaskFormer model was proposed in [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov. MaskFormer addresses semantic segmentation with a mask classification paradigm instead of performing classic pixel-level classification.
The abstract from the paper is the following:
*Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction. Overall, the proposed mask classification-based method simplifies the landscape of effective approaches to semantic and panoptic segmentation tasks and shows excellent empirical results. In particular, we observe that MaskFormer outperforms per-pixel classification baselines when the number of classes is large. Our mask classification-based method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.*
The figure below illustrates the architecture of MaskFormer. Taken from the [original paper](https://arxiv.org/abs/2107.06278).
<img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/maskformer_architecture.png"/>
This model was contributed by [francesco](https://huggingface.co/francesco). The original code can be found [here](https://github.com/facebookresearch/MaskFormer). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#overview | #overview | .md | 156_2 |
- MaskFormer's Transformer decoder is identical to the decoder of [DETR](detr). During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help the model output the correct number of objects of each class. If you set the parameter `use_auxiliary_loss` of [`MaskFormerConfig`] to `True`, then prediction feedforward neural networks and Hungarian losses are added after each decoder layer (with the FFNs sharing parameters).
- If you want to train the model in a distributed environment across multiple nodes, then one should update the
`get_num_masks` function inside in the `MaskFormerLoss` class of `modeling_maskformer.py`. When training on multiple nodes, this should be
set to the average number of target masks across all nodes, as can be seen in the original implementation [here](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169).
- One can use [`MaskFormerImageProcessor`] to prepare images for the model and optional targets for the model.
- To get the final segmentation, depending on the task, you can call [`~MaskFormerImageProcessor.post_process_semantic_segmentation`] or [`~MaskFormerImageProcessor.post_process_panoptic_segmentation`]. Both tasks can be solved using [`MaskFormerForInstanceSegmentation`] output, panoptic segmentation accepts an optional `label_ids_to_fuse` argument to fuse instances of the target object/s (e.g. sky) together. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#usage-tips | #usage-tips | .md | 156_3 |
<PipelineTag pipeline="image-segmentation"/>
- All notebooks that illustrate inference as well as fine-tuning on custom data with MaskFormer can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MaskFormer).
- Scripts for finetuning [`MaskFormer`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/instance-segmentation). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#resources | #resources | .md | 156_4 |
models.maskformer.modeling_maskformer.MaskFormerModelOutput
Class for outputs of [`MaskFormerModel`]. This class returns all the needed hidden states to compute the logits.
Args:
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Last hidden states (final feature map) of the last stage of the encoder model (backbone).
pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN).
transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Last hidden states (final feature map) of the last stage of the transformer decoder model.
encoder_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 + one for the output of each stage) of
shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
model at the output of each stage.
pixel_decoder_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 + one for the output of each stage) of
shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
decoder model at the output of each stage.
transformer_decoder_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 + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
transformer decoder at the output of each stage.
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` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and
`decoder_hidden_states`
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 from Detr's decoder after the attention softmax, used to compute the
weighted average in the self-attention heads.
models.maskformer.modeling_maskformer.MaskFormerForInstanceSegmentationOutput
Class for outputs of [`MaskFormerForInstanceSegmentation`].
This output can be directly passed to [`~MaskFormerImageProcessor.post_process_semantic_segmentation`] or or
[`~MaskFormerImageProcessor.post_process_instance_segmentation`] or
[`~MaskFormerImageProcessor.post_process_panoptic_segmentation`] depending on the task. Please, see
[`~MaskFormerImageProcessor] for details regarding usage.
Args:
loss (`torch.Tensor`, *optional*):
The computed loss, returned when labels are present.
class_queries_logits (`torch.FloatTensor`):
A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each
query. Note the `+ 1` is needed because we incorporate the null class.
masks_queries_logits (`torch.FloatTensor`):
A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each
query.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Last hidden states (final feature map) of the last stage of the encoder model (backbone).
pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN).
transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Last hidden states (final feature map) of the last stage of the transformer decoder model.
encoder_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 + one for the output of each stage) of
shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
model at the output of each stage.
pixel_decoder_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 + one for the output of each stage) of
shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
decoder model at the output of each stage.
transformer_decoder_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 + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the transformer decoder at the output
of each stage.
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` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and
`decoder_hidden_states`.
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 from Detr's decoder after the attention softmax, used to compute the
weighted average in the self-attention heads. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#maskformer-specific-outputs | #maskformer-specific-outputs | .md | 156_5 |
This is the configuration class to store the configuration of a [`MaskFormerModel`]. It is used to instantiate a
MaskFormer 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 MaskFormer
[facebook/maskformer-swin-base-ade](https://huggingface.co/facebook/maskformer-swin-base-ade) architecture trained
on [ADE20k-150](https://huggingface.co/datasets/scene_parse_150).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Currently, MaskFormer only supports the [Swin Transformer](swin) as backbone.
Args:
mask_feature_size (`int`, *optional*, defaults to 256):
The masks' features size, this value will also be used to specify the Feature Pyramid Network features'
size.
no_object_weight (`float`, *optional*, defaults to 0.1):
Weight to apply to the null (no object) class.
use_auxiliary_loss(`bool`, *optional*, defaults to `False`):
If `True` [`MaskFormerForInstanceSegmentationOutput`] will contain the auxiliary losses computed using the
logits from each decoder's stage.
backbone_config (`Dict`, *optional*):
The configuration passed to the backbone, if unset, the configuration corresponding to
`swin-base-patch4-window12-384` will be used.
backbone (`str`, *optional*):
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
use_pretrained_backbone (`bool`, *optional*, `False`):
Whether to use pretrained weights for the backbone.
use_timm_backbone (`bool`, *optional*, `False`):
Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
library.
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
decoder_config (`Dict`, *optional*):
The configuration passed to the transformer decoder model, if unset the base config for `detr-resnet-50`
will be used.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
init_xavier_std (`float`, *optional*, defaults to 1):
The scaling factor used for the Xavier initialization gain in the HM Attention map module.
dice_weight (`float`, *optional*, defaults to 1.0):
The weight for the dice loss.
cross_entropy_weight (`float`, *optional*, defaults to 1.0):
The weight for the cross entropy loss.
mask_weight (`float`, *optional*, defaults to 20.0):
The weight for the mask loss.
output_auxiliary_logits (`bool`, *optional*):
Should the model output its `auxiliary_logits` or not.
Raises:
`ValueError`:
Raised if the backbone model type selected is not in `["swin"]` or the decoder model type selected is not
in `["detr"]`
Examples:
```python
>>> from transformers import MaskFormerConfig, MaskFormerModel
>>> # Initializing a MaskFormer facebook/maskformer-swin-base-ade configuration
>>> configuration = MaskFormerConfig()
>>> # Initializing a model (with random weights) from the facebook/maskformer-swin-base-ade style configuration
>>> model = MaskFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#maskformerconfig | #maskformerconfig | .md | 156_6 |
Constructs a MaskFormer image processor. The image processor can be used to prepare image(s) and optional targets
for the model.
This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the input to a certain `size`.
size (`int`, *optional*, defaults to 800):
Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
height / width, size)`.
size_divisor (`int`, *optional*, defaults to 32):
Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
Swin Transformer.
resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
`PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
`PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
to `True`.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the input to a certain `scale`.
rescale_factor (`float`, *optional*, defaults to `1/ 255`):
Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the input with mean and standard deviation.
image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
ImageNet std.
ignore_index (`int`, *optional*):
Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
denoted with 0 (background) will be replaced with `ignore_index`.
do_reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
The background label will be replaced by `ignore_index`.
num_labels (`int`, *optional*):
The number of labels in the segmentation map.
Methods: preprocess
- encode_inputs
- post_process_semantic_segmentation
- post_process_instance_segmentation
- post_process_panoptic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#maskformerimageprocessor | #maskformerimageprocessor | .md | 156_7 |
No docstring available for MaskFormerFeatureExtractor
Methods: __call__
- encode_inputs
- post_process_semantic_segmentation
- post_process_instance_segmentation
- post_process_panoptic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#maskformerfeatureextractor | #maskformerfeatureextractor | .md | 156_8 |
The bare MaskFormer Model outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MaskFormerConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#maskformermodel | #maskformermodel | .md | 156_9 |
No docstring available for MaskFormerForInstanceSegmentation
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/#maskformerforinstancesegmentation | #maskformerforinstancesegmentation | .md | 156_10 |
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|
The KOSMOS-2 model was proposed in [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://arxiv.org/abs/2306.14824) by Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, Furu Wei.
KOSMOS-2 is a Transformer-based causal language model and is trained using the next-word prediction task on a web-scale
dataset of grounded image-text pairs [GRIT](https://huggingface.co/datasets/zzliang/GRIT). The spatial coordinates of
the bounding boxes in the dataset are converted to a sequence of location tokens, which are appended to their respective
entity text spans (for example, `a snowman` followed by `<patch_index_0044><patch_index_0863>`). The data format is
similar to “hyperlinks” that connect the object regions in an image to their text span in the corresponding caption.
The abstract from the paper is the following:
*We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new capabilities of perceiving object descriptions (e.g., bounding boxes) and grounding text to the visual world. Specifically, we represent refer expressions as links in Markdown, i.e., ``[text span](bounding boxes)'', where object descriptions are sequences of location tokens. Together with multimodal corpora, we construct large-scale data of grounded image-text pairs (called GrIT) to train the model. In addition to the existing capabilities of MLLMs (e.g., perceiving general modalities, following instructions, and performing in-context learning), Kosmos-2 integrates the grounding capability into downstream applications. We evaluate Kosmos-2 on a wide range of tasks, including (i) multimodal grounding, such as referring expression comprehension, and phrase grounding, (ii) multimodal referring, such as referring expression generation, (iii) perception-language tasks, and (iv) language understanding and generation. This work lays out the foundation for the development of Embodiment AI and sheds light on the big convergence of language, multimodal perception, action, and world modeling, which is a key step toward artificial general intelligence. Code and pretrained models are available at https://aka.ms/kosmos-2.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/kosmos_2_overview.jpg"
alt="drawing" width="600"/>
<small> Overview of tasks that KOSMOS-2 can handle. Taken from the <a href="https://arxiv.org/abs/2306.14824">original paper</a>. </small> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/kosmos-2.md | https://huggingface.co/docs/transformers/en/model_doc/kosmos-2/#overview | #overview | .md | 157_1 |
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