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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 ... | /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
[microsof... | /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": 2... | /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).
T... | /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 t... | /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 opt... | /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 u... | /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/st... | /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-ND... | /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://p... | /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 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /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 th... | /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/Ni... | /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 speci... | /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)` di... | /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 D... | /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 downloadin... | /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 ... | /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 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /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, capti... | /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 ima... | /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 = AutoProce... | /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... | /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... | /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... | /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`):... | /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 a... | /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 ... | /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.) ... | /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 ... | /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 PyTor... | /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 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /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... | /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-b... | /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 [... | /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-vq... | /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:
... | /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}... | /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 `B... | /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, Jak... | /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... | /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 documentatio... | /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... | /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 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /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,... | /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 ... | /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-ba... | /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.)
T... | /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 toke... | /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 (... | /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 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /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... | /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 ... | /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:
```pyth... | /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 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /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 followi... | /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/gemm... | /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... | /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 toke... | /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 (suc... | /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 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /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 dialogu... | /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/... | /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 s... | /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 t... | /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 outpu... | /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 ... | /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.) ... | /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... | /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 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/maskformer.md | https://huggingface.co/docs/transformers/en/model_doc/maskformer/ | .md | 156_0 | |
<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). ... | /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-lev... | /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 [`MaskFormer... | /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:... | /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 m... | /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/... | /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... | /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
beh... | /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 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/kosmos-2.md | https://huggingface.co/docs/transformers/en/model_doc/kosmos-2/ | .md | 157_0 | |
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 predi... | /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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