text stringlengths 5 58.6k | source stringclasses 470
values | url stringlengths 49 167 | source_section stringlengths 0 90 | file_type stringclasses 1
value | id stringlengths 3 6 |
|---|---|---|---|---|---|
The MobileNet model was proposed in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
The abstract from the paper is the foll... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v1.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v1/#overview | #overview | .md | 122_1 |
- The checkpoints are named **mobilenet\_v1\_*depth*\_*size***, for example **mobilenet\_v1\_1.0\_224**, where **1.0** is the depth multiplier (sometimes also referred to as "alpha" or the width multiplier) and **224** is the resolution of the input images the model was trained on.
- Even though the checkpoint is tra... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v1.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v1/#usage-tips | #usage-tips | .md | 122_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileNetV1.
<PipelineTag pipeline="image-classification"/>
- [`MobileNetV1ForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/imag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v1.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v1/#resources | #resources | .md | 122_3 |
This is the configuration class to store the configuration of a [`MobileNetV1Model`]. It is used to instantiate a
MobileNetV1 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 MobileNetV1
[google... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v1.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v1/#mobilenetv1config | #mobilenetv1config | .md | 122_4 |
No docstring available for MobileNetV1FeatureExtractor
Methods: preprocess | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v1.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v1/#mobilenetv1featureextractor | #mobilenetv1featureextractor | .md | 122_5 |
Constructs a MobileNetV1 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
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`):
S... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v1.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v1/#mobilenetv1imageprocessor | #mobilenetv1imageprocessor | .md | 122_6 |
The bare MobileNetV1 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) subclass. 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/mobilenet_v1.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v1/#mobilenetv1model | #mobilenetv1model | .md | 122_7 |
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
This model is 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 m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v1.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v1/#mobilenetv1forimageclassification | #mobilenetv1forimageclassification | .md | 122_8 |
<!--Copyright 2024 JetMoe team and 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 appli... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jetmoe.md | https://huggingface.co/docs/transformers/en/model_doc/jetmoe/ | .md | 123_0 | |
**JetMoe-8B** is an 8B Mixture-of-Experts (MoE) language model developed by [Yikang Shen](https://scholar.google.com.hk/citations?user=qff5rRYAAAAJ) and [MyShell](https://myshell.ai/).
JetMoe project aims to provide a LLaMA2-level performance and efficient language model with a limited budget.
To achieve this goal, Jet... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jetmoe.md | https://huggingface.co/docs/transformers/en/model_doc/jetmoe/#overview | #overview | .md | 123_1 |
This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a configuration of the JetMoe-4B.
[jetmoe/jetmoe-8b](https://huggi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jetmoe.md | https://huggingface.co/docs/transformers/en/model_doc/jetmoe/#jetmoeconfig | #jetmoeconfig | .md | 123_2 |
The bare JetMoe 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
behavio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jetmoe.md | https://huggingface.co/docs/transformers/en/model_doc/jetmoe/#jetmoemodel | #jetmoemodel | .md | 123_3 |
No docstring available for JetMoeForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jetmoe.md | https://huggingface.co/docs/transformers/en/model_doc/jetmoe/#jetmoeforcausallm | #jetmoeforcausallm | .md | 123_4 |
The JetMoe Model transformer with a sequence classification head on top (linear layer).
[`JetMoeForSequenceClassification`] 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/jetmoe.md | https://huggingface.co/docs/transformers/en/model_doc/jetmoe/#jetmoeforsequenceclassification | #jetmoeforsequenceclassification | .md | 123_5 |
<!--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/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/ | .md | 124_0 | |
The XLM-RoBERTa-XL model was proposed in [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
The abstract from the paper is the following:
*Recent work has demonstrated the effectiveness of c... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#overview | #overview | .md | 124_1 |
XLM-RoBERTa-XL is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
not require `lang` tensors to understand which language is used, and should be able to determine the correct
language from the input ids. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#usage-tips | #usage-tips | .md | 124_2 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/mask... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#resources | #resources | .md | 124_3 |
This is the configuration class to store the configuration of a [`XLMRobertaXLModel`] or a [`TFXLMRobertaXLModel`].
It is used to instantiate a XLM_ROBERTA_XL model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#xlmrobertaxlconfig | #xlmrobertaxlconfig | .md | 124_4 |
The bare XLM-RoBERTa-XL Model 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, prunin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#xlmrobertaxlmodel | #xlmrobertaxlmodel | .md | 124_5 |
XLM-RoBERTa-XL Model with a `language modeling` head on top for CLM fine-tuning.
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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#xlmrobertaxlforcausallm | #xlmrobertaxlforcausallm | .md | 124_6 |
XLM-RoBERTa-XL 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/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#xlmrobertaxlformaskedlm | #xlmrobertaxlformaskedlm | .md | 124_7 |
XLM-RoBERTa-XL Model transformer with a sequence classification/regression head on top (a linear layer on top
of the pooled output) e.g. for GLUE tasks.
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/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#xlmrobertaxlforsequenceclassification | #xlmrobertaxlforsequenceclassification | .md | 124_8 |
XLM-RoBERTa-XL Model with a multiple choice classification head on top (a linear layer on top of the pooled
output and a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#xlmrobertaxlformultiplechoice | #xlmrobertaxlformultiplechoice | .md | 124_9 |
XLM-RoBERTa-XL Model 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 dow... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#xlmrobertaxlfortokenclassification | #xlmrobertaxlfortokenclassification | .md | 124_10 |
XLM-RoBERTa-XL Model with a span classification head on top for extractive question-answering tasks like SQuAD
(a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic met... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta-xl.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta-xl/#xlmrobertaxlforquestionanswering | #xlmrobertaxlforquestionanswering | .md | 124_11 |
<!--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/bit.md | https://huggingface.co/docs/transformers/en/model_doc/bit/ | .md | 125_0 | |
The BiT model was proposed in [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
BiT is a simple recipe for scaling up pre-training of [ResNet](resnet)-like archite... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bit.md | https://huggingface.co/docs/transformers/en/model_doc/bit/#overview | #overview | .md | 125_1 |
- BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by [group normalization](https://arxiv.org/abs/1803.08494),
2) [weight standardization](https://arxiv.org/abs/1903.10520) is used for convolutional layers. The authors show that the combination ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bit.md | https://huggingface.co/docs/transformers/en/model_doc/bit/#usage-tips | #usage-tips | .md | 125_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BiT.
<PipelineTag pipeline="image-classification"/>
- [`BitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bit.md | https://huggingface.co/docs/transformers/en/model_doc/bit/#resources | #resources | .md | 125_3 |
This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT
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 BiT
[google/bit-50](https://huggin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bit.md | https://huggingface.co/docs/transformers/en/model_doc/bit/#bitconfig | #bitconfig | .md | 125_4 |
Constructs a BiT 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
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
Size of t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bit.md | https://huggingface.co/docs/transformers/en/model_doc/bit/#bitimageprocessor | #bitimageprocessor | .md | 125_5 |
The bare BiT model outputting raw features without any specific head on top.
This model is 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.
Para... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bit.md | https://huggingface.co/docs/transformers/en/model_doc/bit/#bitmodel | #bitmodel | .md | 125_6 |
BiT Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
This model is 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 re... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bit.md | https://huggingface.co/docs/transformers/en/model_doc/bit/#bitforimageclassification | #bitforimageclassification | .md | 125_7 |
<!--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/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/ | .md | 126_0 | |
The IDEFICS model was proposed in [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
](https://huggingface.co/papers/2306.16527
) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/#overview | #overview | .md | 126_1 |
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics 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 Idefics-9B.
e.g. [Huggin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/#ideficsconfig | #ideficsconfig | .md | 126_2 |
The bare LLaMA 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/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/#ideficsmodel | #ideficsmodel | .md | 126_3 |
No docstring available for IdeficsForVisionText2Text
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/#ideficsforvisiontext2text | #ideficsforvisiontext2text | .md | 126_4 |
No docstring available for TFIdeficsModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/#tfideficsmodel | #tfideficsmodel | .md | 126_5 |
No docstring available for TFIdeficsForVisionText2Text
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/#tfideficsforvisiontext2text | #tfideficsforvisiontext2text | .md | 126_6 |
Constructs a Idefics image processor.
Args:
image_size (`int`, *optional*, defaults to 224):
Resize to image size
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/#ideficsimageprocessor | #ideficsimageprocessor | .md | 126_7 |
Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics.md | https://huggingface.co/docs/transformers/en/model_doc/idefics/#ideficsprocessor | #ideficsprocessor | .md | 126_8 |
<!--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/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/ | .md | 127_0 | |
The ViLT model was proposed in [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334)
by Wonjae Kim, Bokyung Son, Ildoo Kim. ViLT incorporates text embeddings into a Vision Transformer (ViT), allowing it to have a minimal design
for Vision-and-Language Pre-tr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#overview | #overview | .md | 127_1 |
- The quickest way to get started with ViLT is by checking the [example notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ViLT)
(which showcase both inference and fine-tuning on custom data).
- ViLT is a model that takes both `pixel_values` and `input_ids` as input. One can use [`ViltProcessor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#usage-tips | #usage-tips | .md | 127_2 |
This is the configuration class to store the configuration of a [`ViLTModel`]. It is used to instantiate an ViLT
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 ViLT
[dandelin/vilt-b32-mlm](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltconfig | #viltconfig | .md | 127_3 |
No docstring available for ViltFeatureExtractor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltfeatureextractor | #viltfeatureextractor | .md | 127_4 |
Constructs a ViLT 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[str, int]` *optional*, defaults to `{"shortest_edge": 38... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltimageprocessor | #viltimageprocessor | .md | 127_5 |
Constructs a ViLT processor which wraps a BERT tokenizer and ViLT image processor into a single processor.
[`ViltProcessor`] offers all the functionalities of [`ViltImageProcessor`] and [`BertTokenizerFast`]. See the
docstring of [`~ViltProcessor.__call__`] and [`~ViltProcessor.decode`] for more information.
Args:
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltprocessor | #viltprocessor | .md | 127_6 |
The bare ViLT 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>`_ subclass. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltmodel | #viltmodel | .md | 127_7 |
ViLT Model with a language modeling head on top as done during pretraining.
This model is 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.
P... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltformaskedlm | #viltformaskedlm | .md | 127_8 |
Vilt Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the [CLS]
token) for visual question answering, e.g. for VQAv2.
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ subclass. Use
it as a regular PyTorch Module and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltforquestionanswering | #viltforquestionanswering | .md | 127_9 |
Vilt Model transformer with a classifier head on top for natural language visual reasoning, e.g. NLVR2.
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokeniz... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltforimagesandtextclassification | #viltforimagesandtextclassification | .md | 127_10 |
Vilt Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the [CLS]
token) for image-to-text or text-to-image retrieval, e.g. MSCOCO and F30K.
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ subclass. Use
it as a regul... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltforimageandtextretrieval | #viltforimageandtextretrieval | .md | 127_11 |
ViLT Model with a token classification head on top (a linear layer on top of the final hidden-states of the text
tokens) e.g. for Named-Entity-Recognition (NER) tasks.
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ subclass. Use
it as a regular PyTorch Module and ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vilt.md | https://huggingface.co/docs/transformers/en/model_doc/vilt/#viltfortokenclassification | #viltfortokenclassification | .md | 127_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/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/ | .md | 128_0 | |
The MPT model was proposed by the [MosaicML](https://www.mosaicml.com/) team and released with multiple sizes and finetuned variants. The MPT models are a series of open source and commercially usable LLMs pre-trained on 1T tokens.
MPT models are GPT-style decoder-only transformers with several improvements: performa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#overview | #overview | .md | 128_1 |
- Learn more about some techniques behind training of the model [in this section of llm-foundry repository](https://github.com/mosaicml/llm-foundry/blob/main/TUTORIAL.md#faqs)
- If you want to use the advanced version of the model (triton kernels, direct flash attention integration), you can still use the original mode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#usage-tips | #usage-tips | .md | 128_2 |
- [Fine-tuning Notebook](https://colab.research.google.com/drive/1HCpQkLL7UXW8xJUJJ29X7QAeNJKO0frZ?usp=sharing) on how to fine-tune MPT-7B on a free Google Colab instance to turn the model into a Chatbot. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#resources | #resources | .md | 128_3 |
This is the configuration class to store the configuration of a [`MptModel`]. It is used to instantiate a Mpt model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to the Mpt-7b architecture
[mosaicml/mpt-7b](https... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#mptconfig | #mptconfig | .md | 128_4 |
The bare Mpt Model 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 etc.)
This mod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#mptmodel | #mptmodel | .md | 128_5 |
The MPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#mptforcausallm | #mptforcausallm | .md | 128_6 |
The MPT Model transformer with a sequence classification head on top (linear layer).
[`MptForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last token. If ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#mptforsequenceclassification | #mptforsequenceclassification | .md | 128_7 |
MPT Model 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#mptfortokenclassification | #mptfortokenclassification | .md | 128_8 |
The MPT Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD
(a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpt.md | https://huggingface.co/docs/transformers/en/model_doc/mpt/#mptforquestionanswering | #mptforquestionanswering | .md | 128_9 |
<!--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/code_llama.md | https://huggingface.co/docs/transformers/en/model_doc/code_llama/ | .md | 129_0 | |
The Code Llama model was proposed in [Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/code_llama.md | https://huggingface.co/docs/transformers/en/model_doc/code_llama/#overview | #overview | .md | 129_1 |
<Tip warning={true}>
The `Llama2` family models, on which Code Llama is based, were trained using `bfloat16`, but the original inference uses `float16`. Let's look at the different precisions:
* `float32`: PyTorch convention on model initialization is to load models in `float32`, no matter with which `dtype` the mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/code_llama.md | https://huggingface.co/docs/transformers/en/model_doc/code_llama/#usage-tips-and-examples | #usage-tips-and-examples | .md | 129_2 |
Construct a CodeLlama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as
there is no padding token in the original model.
The default configuration match that of
[codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf/blob/main/tokenizer_co... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/code_llama.md | https://huggingface.co/docs/transformers/en/model_doc/code_llama/#codellamatokenizer | #codellamatokenizer | .md | 129_3 |
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and no normalization.
```python
>>> from transformers import CodeLlamaTokenizerFast
>>> tokenizer = CodeLlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
>>> tokenizer.encode("Hello this is a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/code_llama.md | https://huggingface.co/docs/transformers/en/model_doc/code_llama/#codellamatokenizerfast | #codellamatokenizerfast | .md | 129_4 |
<!--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/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/ | .md | 130_0 | |
The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Improved reasoning, OCR, and world knowledge](https://llava-vl.github.io/blog/2024-01-30-llava-next/) by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon [LLaVa](llava) by increasing th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#overview | #overview | .md | 130_1 |
- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to call `processor.tokenizer.padding_side = "left"` before generating.
<Tip warning={true}>
- Llava-Next uses different number of patches for images and thus has to pad the inputs ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#usage-tips | #usage-tips | .md | 130_2 |
Here's how to load the model and perform inference in half-precision (`torch.float16`):
```python
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
import torch
from PIL import Image
import requests
processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
mode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#single-image-inference | #single-image-inference | .md | 130_3 |
LLaVa-Next can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). Here is how you can do it:
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
# Load... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#multi-image-inference | #multi-image-inference | .md | 130_4 |
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes`, and to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is being refacto... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#quantization-using-bitsandbytes | #quantization-using-bitsandbytes | .md | 130_5 |
First make sure to install flash-attn. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
```python
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#use-flash-attention-2-to-further-speed-up-generation | #use-flash-attention-2-to-further-speed-up-generation | .md | 130_6 |
This is the configuration class to store the configuration of a [`LlavaNextForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT 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 [l... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#llavanextconfig | #llavanextconfig | .md | 130_7 |
Constructs a LLaVa-NeXT image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques
for processing high resolution images as explained in the [LLaVa paper](https://arxiv.org/abs/2310.03744).
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (heigh... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#llavanextimageprocessor | #llavanextimageprocessor | .md | 130_8 |
Constructs a LLaVa-NeXT processor which wraps a LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
[`LlavaNextProcessor`] offers all the functionalities of [`LlavaNextImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~LlavaNextProcessor.__call__`] and [`~LlavaNextProcessor.decode`] for mor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#llavanextprocessor | #llavanextprocessor | .md | 130_9 |
The LLAVA-NeXT model which consists of a vision backbone 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_next.md | https://huggingface.co/docs/transformers/en/model_doc/llava_next/#llavanextforconditionalgeneration | #llavanextforconditionalgeneration | .md | 130_10 |
<!--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/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/ | .md | 131_0 | |
The XGLM model was proposed in [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668)
by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal,
Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#overview | #overview | .md | 131_1 |
- [Causal language modeling task guide](../tasks/language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#resources | #resources | .md | 131_2 |
This is the configuration class to store the configuration of a [`XGLMModel`]. It is used to instantiate an XGLM
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 XGLM
[facebook/xglm-564M](https:... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#xglmconfig | #xglmconfig | .md | 131_3 |
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#xglmtokenizer | #xglmtokenizer | .md | 131_4 |
Construct a "fast" XGLM tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from [`RobertaTokenizer`]
and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#xglmtokenizerfast | #xglmtokenizerfast | .md | 131_5 |
The bare XGLM Model 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
et... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#xglmmodel | #xglmmodel | .md | 131_6 |
The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#xglmforcausallm | #xglmforcausallm | .md | 131_7 |
No docstring available for TFXGLMModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#tfxglmmodel | #tfxglmmodel | .md | 131_8 |
No docstring available for TFXGLMForCausalLM
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#tfxglmforcausallm | #tfxglmforcausallm | .md | 131_9 |
No docstring available for FlaxXGLMModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#flaxxglmmodel | #flaxxglmmodel | .md | 131_10 |
No docstring available for FlaxXGLMForCausalLM
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xglm.md | https://huggingface.co/docs/transformers/en/model_doc/xglm/#flaxxglmforcausallm | #flaxxglmforcausallm | .md | 131_11 |
<!--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/layoutlmv2.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv2/ | .md | 132_0 | |
The LayoutLMV2 model was proposed in [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu,
Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves [Lay... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv2.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv2/#overview | #overview | .md | 132_1 |
- The main difference between LayoutLMv1 and LayoutLMv2 is that the latter incorporates visual embeddings during
pre-training (while LayoutLMv1 only adds visual embeddings during fine-tuning).
- LayoutLMv2 adds both a relative 1D attention bias as well as a spatial 2D attention bias to the attention scores in
the self-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv2.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv2/#usage-tips | #usage-tips | .md | 132_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLMv2. 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/layoutlmv2.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv2/#resources | #resources | .md | 132_3 |
The easiest way to prepare data for the model is to use [`LayoutLMv2Processor`], which internally
combines a image processor ([`LayoutLMv2ImageProcessor`]) and a tokenizer
([`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]). The image processor
handles the image modality, while the tokenizer handles the text modal... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv2.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv2/#usage-layoutlmv2processor | #usage-layoutlmv2processor | .md | 132_4 |
This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an
LayoutLMv2 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 LayoutLMv2
[microsof... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlmv2.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlmv2/#layoutlmv2config | #layoutlmv2config | .md | 132_5 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.