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 |
|---|---|---|---|---|---|
No docstring available for TFElectraModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#tfelectramodel | #tfelectramodel | .md | 353_17 |
No docstring available for TFElectraForPreTraining
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#tfelectraforpretraining | #tfelectraforpretraining | .md | 353_18 |
No docstring available for TFElectraForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#tfelectraformaskedlm | #tfelectraformaskedlm | .md | 353_19 |
No docstring available for TFElectraForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#tfelectraforsequenceclassification | #tfelectraforsequenceclassification | .md | 353_20 |
No docstring available for TFElectraForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#tfelectraformultiplechoice | #tfelectraformultiplechoice | .md | 353_21 |
No docstring available for TFElectraForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#tfelectrafortokenclassification | #tfelectrafortokenclassification | .md | 353_22 |
No docstring available for TFElectraForQuestionAnswering
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#tfelectraforquestionanswering | #tfelectraforquestionanswering | .md | 353_23 |
No docstring available for FlaxElectraModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#flaxelectramodel | #flaxelectramodel | .md | 353_24 |
No docstring available for FlaxElectraForPreTraining
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#flaxelectraforpretraining | #flaxelectraforpretraining | .md | 353_25 |
No docstring available for FlaxElectraForCausalLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#flaxelectraforcausallm | #flaxelectraforcausallm | .md | 353_26 |
No docstring available for FlaxElectraForMaskedLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#flaxelectraformaskedlm | #flaxelectraformaskedlm | .md | 353_27 |
No docstring available for FlaxElectraForSequenceClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#flaxelectraforsequenceclassification | #flaxelectraforsequenceclassification | .md | 353_28 |
No docstring available for FlaxElectraForMultipleChoice
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#flaxelectraformultiplechoice | #flaxelectraformultiplechoice | .md | 353_29 |
No docstring available for FlaxElectraForTokenClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#flaxelectrafortokenclassification | #flaxelectrafortokenclassification | .md | 353_30 |
No docstring available for FlaxElectraForQuestionAnswering
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#flaxelectraforquestionanswering | #flaxelectraforquestionanswering | .md | 353_31 |
<!--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/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/ | .md | 354_0 | |
The RoBERTa-PreLayerNorm model was proposed in [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://arxiv.org/abs/1904.01038) by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli.
It is identical to using the `--encoder-normalize-before` flag in [fair... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#overview | #overview | .md | 354_1 |
- The implementation is the same as [Roberta](roberta) except instead of using _Add and Norm_ it does _Norm and Add_. _Add_ and _Norm_ refers to the Addition and LayerNormalization as described in [Attention Is All You Need](https://arxiv.org/abs/1706.03762).
- This is identical to using the `--encoder-normalize-before... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#usage-tips | #usage-tips | .md | 354_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/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#resources | #resources | .md | 354_3 |
This is the configuration class to store the configuration of a [`RobertaPreLayerNormModel`] or a [`TFRobertaPreLayerNormModel`]. It is
used to instantiate a RoBERTa-PreLayerNorm model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a sim... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#robertaprelayernormconfig | #robertaprelayernormconfig | .md | 354_4 |
The bare RoBERTa-PreLayerNorm 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#robertaprelayernormmodel | #robertaprelayernormmodel | .md | 354_5 |
RoBERTa-PreLayerNorm 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.)
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#robertaprelayernormforcausallm | #robertaprelayernormforcausallm | .md | 354_6 |
RoBERTa-PreLayerNorm 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#robertaprelayernormformaskedlm | #robertaprelayernormformaskedlm | .md | 354_7 |
RoBERTa-PreLayerNorm 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 down... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#robertaprelayernormforsequenceclassification | #robertaprelayernormforsequenceclassification | .md | 354_8 |
RobertaPreLayerNorm 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#robertaprelayernormformultiplechoice | #robertaprelayernormformultiplechoice | .md | 354_9 |
RobertaPreLayerNorm 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#robertaprelayernormfortokenclassification | #robertaprelayernormfortokenclassification | .md | 354_10 |
RobertaPreLayerNorm 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 generi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#robertaprelayernormforquestionanswering | #robertaprelayernormforquestionanswering | .md | 354_11 |
No docstring available for TFRobertaPreLayerNormModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#tfrobertaprelayernormmodel | #tfrobertaprelayernormmodel | .md | 354_12 |
No docstring available for TFRobertaPreLayerNormForCausalLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#tfrobertaprelayernormforcausallm | #tfrobertaprelayernormforcausallm | .md | 354_13 |
No docstring available for TFRobertaPreLayerNormForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#tfrobertaprelayernormformaskedlm | #tfrobertaprelayernormformaskedlm | .md | 354_14 |
No docstring available for TFRobertaPreLayerNormForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#tfrobertaprelayernormforsequenceclassification | #tfrobertaprelayernormforsequenceclassification | .md | 354_15 |
No docstring available for TFRobertaPreLayerNormForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#tfrobertaprelayernormformultiplechoice | #tfrobertaprelayernormformultiplechoice | .md | 354_16 |
No docstring available for TFRobertaPreLayerNormForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#tfrobertaprelayernormfortokenclassification | #tfrobertaprelayernormfortokenclassification | .md | 354_17 |
No docstring available for TFRobertaPreLayerNormForQuestionAnswering
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#tfrobertaprelayernormforquestionanswering | #tfrobertaprelayernormforquestionanswering | .md | 354_18 |
No docstring available for FlaxRobertaPreLayerNormModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#flaxrobertaprelayernormmodel | #flaxrobertaprelayernormmodel | .md | 354_19 |
No docstring available for FlaxRobertaPreLayerNormForCausalLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#flaxrobertaprelayernormforcausallm | #flaxrobertaprelayernormforcausallm | .md | 354_20 |
No docstring available for FlaxRobertaPreLayerNormForMaskedLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#flaxrobertaprelayernormformaskedlm | #flaxrobertaprelayernormformaskedlm | .md | 354_21 |
No docstring available for FlaxRobertaPreLayerNormForSequenceClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#flaxrobertaprelayernormforsequenceclassification | #flaxrobertaprelayernormforsequenceclassification | .md | 354_22 |
No docstring available for FlaxRobertaPreLayerNormForMultipleChoice
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#flaxrobertaprelayernormformultiplechoice | #flaxrobertaprelayernormformultiplechoice | .md | 354_23 |
No docstring available for FlaxRobertaPreLayerNormForTokenClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#flaxrobertaprelayernormfortokenclassification | #flaxrobertaprelayernormfortokenclassification | .md | 354_24 |
No docstring available for FlaxRobertaPreLayerNormForQuestionAnswering
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roberta-prelayernorm.md | https://huggingface.co/docs/transformers/en/model_doc/roberta-prelayernorm/#flaxrobertaprelayernormforquestionanswering | #flaxrobertaprelayernormforquestionanswering | .md | 354_25 |
<!--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/mobilevitv2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilevitv2/ | .md | 355_0 | |
The MobileViTV2 model was proposed in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
MobileViTV2 is the second version of MobileViT, constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilevitv2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilevitv2/#overview | #overview | .md | 355_1 |
- MobileViTV2 is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map.
- One can use [`MobileViTImageProcessor`] to prepare images for the model. Note that if you do your own preprocessing, the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilevitv2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilevitv2/#usage-tips | #usage-tips | .md | 355_2 |
This is the configuration class to store the configuration of a [`MobileViTV2Model`]. It is used to instantiate a
MobileViTV2 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 MobileViTV2
[apple/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilevitv2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilevitv2/#mobilevitv2config | #mobilevitv2config | .md | 355_3 |
The bare MobileViTV2 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/mobilevitv2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilevitv2/#mobilevitv2model | #mobilevitv2model | .md | 355_4 |
MobileViTV2 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/mobilevitv2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilevitv2/#mobilevitv2forimageclassification | #mobilevitv2forimageclassification | .md | 355_5 |
MobileViTV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.
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. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilevitv2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilevitv2/#mobilevitv2forsemanticsegmentation | #mobilevitv2forsemanticsegmentation | .md | 355_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/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/ | .md | 356_0 | |
The Grounding DINO model was proposed in [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/abs/2303.05499) by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a cl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#overview | #overview | .md | 356_1 |
- One can use [`GroundingDinoProcessor`] to prepare image-text pairs for the model.
- To separate classes in the text use a period e.g. "a cat. a dog."
- When using multiple classes (e.g. `"a cat. a dog."`), use `post_process_grounded_object_detection` from [`GroundingDinoProcessor`] to post process outputs. Since, the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#usage-tips | #usage-tips | .md | 356_2 |
One can combine Grounding DINO with the [Segment Anything](sam) model for text-based mask generation as introduced in [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://arxiv.org/abs/2401.14159). You can refer to this [demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#grounded-sam | #grounded-sam | .md | 356_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Grounding DINO. 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 duplicat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#resources | #resources | .md | 356_4 |
Constructs a Grounding DINO 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) dimension... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#groundingdinoimageprocessor | #groundingdinoimageprocessor | .md | 356_5 |
Constructs a Grounding DINO processor which wraps a Deformable DETR image processor and a BERT tokenizer into a
single processor.
[`GroundingDinoProcessor`] offers all the functionalities of [`GroundingDinoImageProcessor`] and
[`AutoTokenizer`]. See the docstring of [`~GroundingDinoProcessor.__call__`] and [`~Groundi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#groundingdinoprocessor | #groundingdinoprocessor | .md | 356_6 |
This is the configuration class to store the configuration of a [`GroundingDinoModel`]. It is used to instantiate a
Grounding DINO 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 Grounding DINO... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#groundingdinoconfig | #groundingdinoconfig | .md | 356_7 |
The bare Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#groundingdinomodel | #groundingdinomodel | .md | 356_8 |
Grounding DINO 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 o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/grounding-dino.md | https://huggingface.co/docs/transformers/en/model_doc/grounding-dino/#groundingdinoforobjectdetection | #groundingdinoforobjectdetection | .md | 356_9 |
<!--Copyright 2020 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/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/ | .md | 357_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=marian">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/opus-mt-zh-en">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#marianmt | #marianmt | .md | 357_1 |
A framework for translation models, using the same models as BART. Translations should be similar, but not identical to output in the test set linked to in each model card.
This model was contributed by [sshleifer](https://huggingface.co/sshleifer). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#overview | #overview | .md | 357_2 |
- Each model is about 298 MB on disk, there are more than 1,000 models.
- The list of supported language pairs can be found [here](https://huggingface.co/Helsinki-NLP).
- Models were originally trained by [Jörg Tiedemann](https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann) using the [Marian](https://mari... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#implementation-notes | #implementation-notes | .md | 357_3 |
- All model names use the following format: `Helsinki-NLP/opus-mt-{src}-{tgt}`
- The language codes used to name models are inconsistent. Two digit codes can usually be found [here](https://developers.google.com/admin-sdk/directory/v1/languages), three digit codes require googling "language
code {code}".
- Codes format... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#naming | #naming | .md | 357_4 |
- Since Marian models are smaller than many other translation models available in the library, they can be useful for
fine-tuning experiments and integration tests.
- [Fine-tune on GPU](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/train_distil_marian_enro.sh) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#examples | #examples | .md | 357_5 |
- All model names use the following format: `Helsinki-NLP/opus-mt-{src}-{tgt}`:
- If a model can output multiple languages, and you should specify a language code by prepending the desired output
language to the `src_text`.
- You can see a models's supported language codes in its model card, under target constituents, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#multilingual-models | #multilingual-models | .md | 357_6 |
These are the old style multi-lingual models ported from the OPUS-MT-Train repo: and the members of each language
group:
```python no-style
['Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU',
'Helsinki-NLP/opus-mt-ROMANCE-en',
'Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA',
'Helsinki-NLP/opus-mt-de-ZH',
'Helsinki-NLP/opus-mt-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#old-style-multi-lingual-models | #old-style-multi-lingual-models | .md | 357_7 |
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
- [Causal language modeling task guide](../tasks/language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#resources | #resources | .md | 357_8 |
This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an
Marian 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 Marian
[Helsinki-NLP/opus-mt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#marianconfig | #marianconfig | .md | 357_9 |
Construct a Marian tokenizer. 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:
source_spm (`str`):
[SentencePiece]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#mariantokenizer | #mariantokenizer | .md | 357_10 |
The bare Marian 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/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#marianmodel | #marianmodel | .md | 357_11 |
The Marian Model with a language modeling head. Can be used for summarization.
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/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#marianmtmodel | #marianmtmodel | .md | 357_12 |
No docstring available for MarianForCausalLM
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#marianforcausallm | #marianforcausallm | .md | 357_13 |
No docstring available for TFMarianModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#tfmarianmodel | #tfmarianmodel | .md | 357_14 |
No docstring available for TFMarianMTModel
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#tfmarianmtmodel | #tfmarianmtmodel | .md | 357_15 |
No docstring available for FlaxMarianModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#flaxmarianmodel | #flaxmarianmodel | .md | 357_16 |
No docstring available for FlaxMarianMTModel
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/marian.md | https://huggingface.co/docs/transformers/en/model_doc/marian/#flaxmarianmtmodel | #flaxmarianmtmodel | .md | 357_17 |
<!--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/vision-text-dual-encoder.md | https://huggingface.co/docs/transformers/en/model_doc/vision-text-dual-encoder/ | .md | 358_0 | |
The [`VisionTextDualEncoderModel`] can be used to initialize a vision-text dual encoder model with
any pretrained vision autoencoding model as the vision encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit)) and any pretrained text autoencoding model as the text encoder (*e.g.* [RoBERTa](roberta), [BERT](bert)). Two ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vision-text-dual-encoder.md | https://huggingface.co/docs/transformers/en/model_doc/vision-text-dual-encoder/#overview | #overview | .md | 358_1 |
[`VisionTextDualEncoderConfig`] is the configuration class to store the configuration of a
[`VisionTextDualEncoderModel`]. It is used to instantiate [`VisionTextDualEncoderModel`] model according to the
specified arguments, defining the text model and vision model configs.
Configuration objects inherit from [`Pretrai... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vision-text-dual-encoder.md | https://huggingface.co/docs/transformers/en/model_doc/vision-text-dual-encoder/#visiontextdualencoderconfig | #visiontextdualencoderconfig | .md | 358_2 |
Constructs a VisionTextDualEncoder processor which wraps an image processor and a tokenizer into a single
processor.
[`VisionTextDualEncoderProcessor`] offers all the functionalities of [`AutoImageProcessor`] and [`AutoTokenizer`].
See the [`~VisionTextDualEncoderProcessor.__call__`] and [`~VisionTextDualEncoderProce... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vision-text-dual-encoder.md | https://huggingface.co/docs/transformers/en/model_doc/vision-text-dual-encoder/#visiontextdualencoderprocessor | #visiontextdualencoderprocessor | .md | 358_3 |
This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model
as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded
via the [`~AutoModel.from_pretrained`] method. The projection layers are automatically adde... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vision-text-dual-encoder.md | https://huggingface.co/docs/transformers/en/model_doc/vision-text-dual-encoder/#visiontextdualencodermodel | #visiontextdualencodermodel | .md | 358_4 |
No docstring available for FlaxVisionTextDualEncoderModel
Methods: __call__
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vision-text-dual-encoder.md | https://huggingface.co/docs/transformers/en/model_doc/vision-text-dual-encoder/#flaxvisiontextdualencodermodel | #flaxvisiontextdualencodermodel | .md | 358_5 |
No docstring available for TFVisionTextDualEncoderModel
Methods: call
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vision-text-dual-encoder.md | https://huggingface.co/docs/transformers/en/model_doc/vision-text-dual-encoder/#tfvisiontextdualencodermodel | #tfvisiontextdualencodermodel | .md | 358_6 |
<!--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/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/ | .md | 359_0 | |
The NLLB model was presented in [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by Marta R. Costa-jussà, James Cross, Onur Çelebi,
Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guil... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#overview | #overview | .md | 359_1 |
- M2M100ForConditionalGeneration is the base model for both NLLB and NLLB MoE
- The NLLB-MoE is very similar to the NLLB model, but it's feed forward layer is based on the implementation of SwitchTransformers.
- The tokenizer is the same as the NLLB models. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#usage-tips | #usage-tips | .md | 359_2 |
The biggest difference is the way the tokens are routed. NLLB-MoE uses a `top-2-gate` which means that for each input, only the top two experts are selected based on the
highest predicted probabilities from the gating network, and the remaining experts are ignored. In `SwitchTransformers`, only the top-1 probabilities ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#implementation-differences-with-switchtransformers | #implementation-differences-with-switchtransformers | .md | 359_3 |
The available checkpoints require around 350GB of storage. Make sure to use `accelerate` if you do not have enough RAM on your machine.
While generating the target text set the `forced_bos_token_id` to the target language id. The following
example shows how to translate English to French using the *facebook/nllb-200-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#generating-with-nllb-moe | #generating-with-nllb-moe | .md | 359_4 |
English (`eng_Latn`) is set as the default language from which to translate. In order to specify that you'd like to translate from a different language,
you should specify the BCP-47 code in the `src_lang` keyword argument of the tokenizer initialization.
See example below for a translation from romanian to german: ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#generating-from-any-other-language-than-english | #generating-from-any-other-language-than-english | .md | 359_5 |
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#resources | #resources | .md | 359_6 |
This is the configuration class to store the configuration of a [`NllbMoeModel`]. It is used to instantiate an
NLLB-MoE 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 NLLB-MoE
[facebook/nllb-m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#nllbmoeconfig | #nllbmoeconfig | .md | 359_7 |
Router using tokens choose top-2 experts assignment.
This router uses the same mechanism as in NLLB-MoE from the fairseq repository. Items are sorted by router_probs
and then routed to their choice of expert until the expert's expert_capacity is reached. **There is no guarantee
that each token is processed by an expe... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#nllbmoetop2router | #nllbmoetop2router | .md | 359_8 |
Implementation of the NLLB-MoE sparse MLP module.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#nllbmoesparsemlp | #nllbmoesparsemlp | .md | 359_9 |
The bare NllbMoe 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.)
Thi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#nllbmoemodel | #nllbmoemodel | .md | 359_10 |
The NllbMoe Model with a language modeling head. Can be used for summarization.
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 mode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb-moe.md | https://huggingface.co/docs/transformers/en/model_doc/nllb-moe/#nllbmoeforconditionalgeneration | #nllbmoeforconditionalgeneration | .md | 359_11 |
<!--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/gptsan-japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gptsan-japanese/ | .md | 360_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Ti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptsan-japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gptsan-japanese/#gptsan-japanese | #gptsan-japanese | .md | 360_1 |
The GPTSAN-japanese model was released in the repository by Toshiyuki Sakamoto (tanreinama).
GPTSAN is a Japanese language model using Switch Transformer. It has the same structure as the model introduced as Prefix LM
in the T5 paper, and support both Text Generation and Masked Language Modeling tasks. These basic ta... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptsan-japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gptsan-japanese/#overview | #overview | .md | 360_2 |
The `generate()` method can be used to generate text using GPTSAN-Japanese model.
```python
>>> from transformers import AutoModel, AutoTokenizer
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").cuda()
>>> x_tok =... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptsan-japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gptsan-japanese/#usage-example | #usage-example | .md | 360_3 |
GPTSAN has some unique features. It has a model structure of Prefix-LM. It works as a shifted Masked Language Model for Prefix Input tokens. Un-prefixed inputs behave like normal generative models.
The Spout vector is a GPTSAN specific input. Spout is pre-trained with random inputs, but you can specify a class of text ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptsan-japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gptsan-japanese/#gptsan-features | #gptsan-features | .md | 360_4 |
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