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The current implementation supports the sliding window attention mechanism and memory efficient cache management.
To enable sliding window attention, just make sure to have a `flash-attn` version that is compatible with sliding window attention (`>=2.3.0`).
The Flash Attention-2 model uses also a more memory efficien... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#sliding-window-attention | #sliding-window-attention | .md | 274_7 |
As the Mistral model has 7 billion parameters, that would require about 14GB of GPU RAM in half precision (float16), since each parameter is stored in 2 bytes. However, one can shrink down the size of the model using [quantization](../quantization.md). If the model is quantized to 4 bits (or half a byte per parameter),... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#shrinking-down-mistral-using-quantization | #shrinking-down-mistral-using-quantization | .md | 274_8 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mistral. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#resources | #resources | .md | 274_9 |
This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
Mistral 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 Mistral-7B-v0.1 or Mistral... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | #mistralconfig | .md | 274_10 |
The bare Mistral 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/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralmodel | #mistralmodel | .md | 274_11 |
No docstring available for MistralForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforcausallm | #mistralforcausallm | .md | 274_12 |
The Mistral Model transformer with a sequence classification head on top (linear layer).
[`MistralForSequenceClassification`] 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 to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforsequenceclassification | #mistralforsequenceclassification | .md | 274_13 |
The Mistral Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (su... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralfortokenclassification | #mistralfortokenclassification | .md | 274_14 |
The Mistral Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer 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 gen... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforquestionanswering | #mistralforquestionanswering | .md | 274_15 |
No docstring available for FlaxMistralModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#flaxmistralmodel | #flaxmistralmodel | .md | 274_16 |
No docstring available for FlaxMistralForCausalLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#flaxmistralforcausallm | #flaxmistralforcausallm | .md | 274_17 |
No docstring available for TFMistralModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralmodel | #tfmistralmodel | .md | 274_18 |
No docstring available for TFMistralForCausalLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralforcausallm | #tfmistralforcausallm | .md | 274_19 |
No docstring available for TFMistralForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralforsequenceclassification | #tfmistralforsequenceclassification | .md | 274_20 |
<!--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/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/ | .md | 275_0 | |
The Swin2SR model was proposed in [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
Swin2SR improves the [SwinIR](https://github.com/JingyunLiang/SwinIR/) model by incorporating [Swin Transf... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | #overview | .md | 275_1 |
Demo notebooks for Swin2SR can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Swin2SR).
A demo Space for image super-resolution with SwinSR can be found [here](https://huggingface.co/spaces/jjourney1125/swin2sr). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#resources | #resources | .md | 275_2 |
Constructs a Swin2SR image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srimageprocessor | #swin2srimageprocessor | .md | 275_3 |
This is the configuration class to store the configuration of a [`Swin2SRModel`]. It is used to instantiate a Swin
Transformer v2 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 Swin Transforme... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | #swin2srconfig | .md | 275_4 |
The bare Swin2SR Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srmodel | #swin2srmodel | .md | 275_5 |
Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration.
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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srforimagesuperresolution | #swin2srforimagesuperresolution | .md | 275_6 |
<!--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/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/ | .md | 276_0 | |
The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
The paper aims at creating a single unifie... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#overview | #overview | .md | 276_1 |
[`FlavaConfig`] is the configuration class to store the configuration of a [`FlavaModel`]. It is used to
instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
and multimodal model configs. Instantiating a configuration with the defaults will yield a similar c... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaconfig | #flavaconfig | .md | 276_2 |
This is the configuration class to store the configuration of a [`FlavaTextModel`]. It is used to instantiate an
FLAVA 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 FLAVA
[facebook/flava-fu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavatextconfig | #flavatextconfig | .md | 276_3 |
This is the configuration class to store the configuration of a [`FlavaImageModel`]. It is used to instantiate an
FLAVA 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 FLAVA
[facebook/flava-f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimageconfig | #flavaimageconfig | .md | 276_4 |
This is the configuration class to store the configuration of a [`FlavaMultimodalModel`]. It is used to instantiate
an FLAVA 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 FLAVA
[facebook/fl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavamultimodalconfig | #flavamultimodalconfig | .md | 276_5 |
No docstring available for FlavaImageCodebookConfig | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimagecodebookconfig | #flavaimagecodebookconfig | .md | 276_6 |
Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.
[`FlavaProcessor`] offers all the functionalities of [`FlavaImageProcessor`] and [`BertTokenizerFast`]. See the
[`~FlavaProcessor.__call__`] and [`~FlavaProcessor.decode`] for more information.
Args:
imag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaprocessor | #flavaprocessor | .md | 276_7 |
No docstring available for FlavaFeatureExtractor | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavafeatureextractor | #flavafeatureextractor | .md | 276_8 |
Constructs a Flava 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 `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimageprocessor | #flavaimageprocessor | .md | 276_9 |
The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs.
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 usa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaforpretraining | #flavaforpretraining | .md | 276_10 |
The bare FLAVA 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavamodel | #flavamodel | .md | 276_11 |
The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used
to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use
`get_codebook_indices` to get image tokens for an image.
This model is a PyTorch [torch.nn.Module](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimagecodebook | #flavaimagecodebook | .md | 276_12 |
The bare FLAVA Text 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 us... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavatextmodel | #flavatextmodel | .md | 276_13 |
The bare FLAVA Image 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 u... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaimagemodel | #flavaimagemodel | .md | 276_14 |
The bare FLAVA Multimodal 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 gene... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavamultimodalmodel | #flavamultimodalmodel | .md | 276_15 |
<!--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/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/ | .md | 277_0 | |
DiNAT was proposed in [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001)
by Ali Hassani and Humphrey Shi.
It extends [NAT](nat) by adding a Dilated Neighborhood Attention pattern to capture global context,
and shows significant performance improvements over it.
The abstract from the pape... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#overview | #overview | .md | 277_1 |
DiNAT can be used as a *backbone*. When `output_hidden_states = True`,
it will output both `hidden_states` and `reshaped_hidden_states`. The `reshaped_hidden_states` have a shape of `(batch, num_channels, height, width)` rather than `(batch_size, height, width, num_channels)`.
Notes:
- DiNAT depends on [NATTEN](https... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#usage-tips | #usage-tips | .md | 277_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DiNAT.
<PipelineTag pipeline="image-classification"/>
- [`DinatForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classifica... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#resources | #resources | .md | 277_3 |
This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat
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 Dinat
[shi-labs/dinat-mini-in1k... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#dinatconfig | #dinatconfig | .md | 277_4 |
The bare Dinat Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#dinatmodel | #dinatmodel | .md | 277_5 |
Dinat Model transformer with an image classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.
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 PyT... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinat.md | https://huggingface.co/docs/transformers/en/model_doc/dinat/#dinatforimageclassification | #dinatforimageclassification | .md | 277_6 |
<!--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/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/ | .md | 278_0 | |
The Wav2Vec2-Conformer was added to an updated version of [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
The official results of the model can be found in Table 3 and Table 4 of the pap... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#overview | #overview | .md | 278_1 |
- Wav2Vec2-Conformer follows the same architecture as Wav2Vec2, but replaces the *Attention*-block with a *Conformer*-block
as introduced in [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100).
- For the same number of layers, Wav2Vec2-Conformer requires more paramete... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#usage-tips | #usage-tips | .md | 278_2 |
- [Audio classification task guide](../tasks/audio_classification)
- [Automatic speech recognition task guide](../tasks/asr) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#resources | #resources | .md | 278_3 |
This is the configuration class to store the configuration of a [`Wav2Vec2ConformerModel`]. It is used to
instantiate an Wav2Vec2Conformer 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 Wav2Ve... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerconfig | #wav2vec2conformerconfig | .md | 278_4 |
models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput
Output type of [`Wav2Vec2ConformerForPreTraining`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
Total loss as... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformer-specific-outputs | #wav2vec2conformer-specific-outputs | .md | 278_5 |
The bare Wav2Vec2Conformer Model transformer outputting raw hidden-states without any specific head on top.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Au... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformermodel | #wav2vec2conformermodel | .md | 278_6 |
Wav2Vec2Conformer Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michae... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforctc | #wav2vec2conformerforctc | .md | 278_7 |
Wav2Vec2Conformer Model with a sequence classification head on top (a linear layer over the pooled output) for
tasks like SUPERB Keyword Spotting.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, He... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforsequenceclassification | #wav2vec2conformerforsequenceclassification | .md | 278_8 |
Wav2Vec2Conformer Model with a frame classification head on top for tasks like Speaker Diarization.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforaudioframeclassification | #wav2vec2conformerforaudioframeclassification | .md | 278_9 |
Wav2Vec2Conformer Model with an XVector feature extraction head on top for tasks like Speaker Verification.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforxvector | #wav2vec2conformerforxvector | .md | 278_10 |
Wav2Vec2Conformer Model with a quantizer and `VQ` head on top.
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
This model inherits from [`PreTrainedMo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-conformer.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-conformer/#wav2vec2conformerforpretraining | #wav2vec2conformerforpretraining | .md | 278_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/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/ | .md | 279_0 | |
The MarkupLM model was proposed in [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document
Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei. MarkupLM is BERT, but
applied to HTML pages instead of raw text documents. The model incorporates additional emb... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#overview | #overview | .md | 279_1 |
- In addition to `input_ids`, [`~MarkupLMModel.forward`] expects 2 additional inputs, namely `xpath_tags_seq` and `xpath_subs_seq`.
These are the XPATH tags and subscripts respectively for each token in the input sequence.
- One can use [`MarkupLMProcessor`] to prepare all data for the model. Refer to the [usage guide]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#usage-tips | #usage-tips | .md | 279_2 |
The easiest way to prepare data for the model is to use [`MarkupLMProcessor`], which internally combines a feature extractor
([`MarkupLMFeatureExtractor`]) and a tokenizer ([`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]). The feature extractor is
used to extract all nodes and xpaths from the HTML strings, which are... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#usage-markuplmprocessor | #usage-markuplmprocessor | .md | 279_3 |
- [Demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM)
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#resources | #resources | .md | 279_4 |
This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
MarkupLM 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 MarkupLM
[microsoft/marku... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmconfig | #markuplmconfig | .md | 279_5 |
Constructs a MarkupLM feature extractor. This can be used to get a list of nodes and corresponding xpaths from HTML
strings.
This feature extractor inherits from [`~feature_extraction_utils.PreTrainedFeatureExtractor`] which contains most
of the main methods. Users should refer to this superclass for more information... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmfeatureextractor | #markuplmfeatureextractor | .md | 279_6 |
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to
turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and
`xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmtokenizer | #markuplmtokenizer | .md | 279_7 |
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
[`MarkupLMTokenizerFast`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`,
`token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which
contains most ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmtokenizerfast | #markuplmtokenizerfast | .md | 279_8 |
Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single
processor.
[`MarkupLMProcessor`] offers all the functionalities you need to prepare data for the model.
It first uses [`MarkupLMFeatureExtractor`] to extract nodes and corresponding xpaths from one or ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmprocessor | #markuplmprocessor | .md | 279_9 |
The bare MarkupLM Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmmodel | #markuplmmodel | .md | 279_10 |
MarkupLM 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 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 d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmforsequenceclassification | #markuplmforsequenceclassification | .md | 279_11 |
MarkupLM Model with a `token_classification` head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmfortokenclassification | #markuplmfortokenclassification | .md | 279_12 |
MarkupLM 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-cla... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/markuplm.md | https://huggingface.co/docs/transformers/en/model_doc/markuplm/#markuplmforquestionanswering | #markuplmforquestionanswering | .md | 279_13 |
<!--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/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/ | .md | 280_0 | |
The BEiT model was proposed in [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by
Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of
Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-traini... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#overview | #overview | .md | 280_1 |
- BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They
outperform both the [original model (ViT)](vit) as well as [Data-efficient Image Transformers (DeiT)](deit) when fine-tuned on ImageNet-1K and CIFAR-100. You can check out demo notebooks regarding infere... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#usage-tips | #usage-tips | .md | 280_2 |
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 280_3 |
| num_training_steps | batch_size | image_size | is_cuda | Time per batch (eager - s) | Time per batch (sdpa - s) | Speedup (%) | Eager peak mem (MB) | SDPA peak mem (MB) | Mem saving (%) |
|--------------------|------------|--------------|---------|----------------------------|---------------------------|-----------... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#training | #training | .md | 280_4 |
| Image batch size | Eager (s/iter) | Eager CI, % | Eager memory (MB) | SDPA (s/iter) | SDPA CI, % | SDPA memory (MB) | SDPA speedup | SDPA memory saved (%) |
|-------------------:|-----------------:|:--------------|--------------------:|----------------:|:-------------|-------------------:|------------... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#inference | #inference | .md | 280_5 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BEiT.
<PipelineTag pipeline="image-classification"/>
- [`BeitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classificati... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#resources | #resources | .md | 280_6 |
models.beit.modeling_beit.BeitModelOutputWithPooling
Class for outputs of [`BeitModel`].
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_si... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beit-specific-outputs | #beit-specific-outputs | .md | 280_7 |
This is the configuration class to store the configuration of a [`BeitModel`]. It is used to instantiate an BEiT
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 BEiT
[microsoft/beit-base-patch1... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitconfig | #beitconfig | .md | 280_8 |
No docstring available for BeitFeatureExtractor
Methods: __call__
- post_process_semantic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitfeatureextractor | #beitfeatureextractor | .md | 280_9 |
Constructs a BEiT 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 `{"height": 256, "wid... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitimageprocessor | #beitimageprocessor | .md | 280_10 |
The bare Beit 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 an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitmodel | #beitmodel | .md | 280_11 |
Beit Model transformer with a 'language' modeling head on top. BEiT does masked image modeling by predicting
visual tokens of a Vector-Quantize Variational Autoencoder (VQ-VAE), whereas other vision models like ViT and DeiT
predict RGB pixel values. As a result, this class is incompatible with [`AutoModelForMaskedImage... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitformaskedimagemodeling | #beitformaskedimagemodeling | .md | 280_12 |
Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
hidden states of the patch tokens) 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 r... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitforimageclassification | #beitforimageclassification | .md | 280_13 |
Beit Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
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 an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#beitforsemanticsegmentation | #beitforsemanticsegmentation | .md | 280_14 |
No docstring available for FlaxBeitModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#flaxbeitmodel | #flaxbeitmodel | .md | 280_15 |
No docstring available for FlaxBeitForMaskedImageModeling
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#flaxbeitformaskedimagemodeling | #flaxbeitformaskedimagemodeling | .md | 280_16 |
No docstring available for FlaxBeitForImageClassification
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/beit.md | https://huggingface.co/docs/transformers/en/model_doc/beit/#flaxbeitforimageclassification | #flaxbeitforimageclassification | .md | 280_17 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/ | .md | 281_0 | |
The RoCBert model was proposed in [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
It's a pretrained Chinese language model that is robust under various forms of adversarial attac... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#overview | #overview | .md | 281_1 |
- [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/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#resources | #resources | .md | 281_2 |
This is the configuration class to store the configuration of a [`RoCBertModel`]. It is used to instantiate a
RoCBert 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 RoCBert
[weiweishi/roc-bert... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertconfig | #rocbertconfig | .md | 281_3 |
Args:
Construct a RoCBert tokenizer. Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizer`] which
contains most of the main methods. Users should refer to this superclass for more information regarding those
methods.
vocab_file (`str`):
File containing the vocabulary.
word_shape_file (`str`):
File con... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocberttokenizer | #rocberttokenizer | .md | 281_4 |
The bare RoCBert Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertmodel | #rocbertmodel | .md | 281_5 |
RoCBert Model with contrastive loss and masked_lm_loss during the pretraining.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertforpretraining | #rocbertforpretraining | .md | 281_6 |
RoCBert Model with a `language modeling` head on top for CLM fine-tuning.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parame... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertforcausallm | #rocbertforcausallm | .md | 281_7 |
RoCBert Model with a `language modeling` head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoCB... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertformaskedlm | #rocbertformaskedlm | .md | 281_8 |
RoCBert 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 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 docu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertforsequenceclassification | #rocbertforsequenceclassification | .md | 281_9 |
RoCBert 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 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 PyT... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertformultiplechoice | #rocbertformultiplechoice | .md | 281_10 |
RoCBert 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 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 PyTor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertfortokenclassification | #rocbertfortokenclassification | .md | 281_11 |
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