text stringlengths 5 58.6k | source stringclasses 470
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|---|---|---|---|---|---|
Cvt 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) subclass. Use it
as a regular PyTorch Module and refer to the PyTorc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/#cvtforimageclassification | #cvtforimageclassification | .md | 401_6 |
No docstring available for TFCvtModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/#tfcvtmodel | #tfcvtmodel | .md | 401_7 |
No docstring available for TFCvtForImageClassification
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/#tfcvtforimageclassification | #tfcvtforimageclassification | .md | 401_8 |
<!--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/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/ | .md | 402_0 | |
The DINOv2 model was proposed in [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Balla... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/#overview | #overview | .md | 402_1 |
The model can be traced using `torch.jit.trace` which leverages JIT compilation to optimize the model making it faster to run. Note this still produces some mis-matched elements and the difference between the original model and the traced model is of the order of 1e-4.
```python
import torch
from transformers import ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/#usage-tips | #usage-tips | .md | 402_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DINOv2.
- Demo notebooks for DINOv2 can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DINOv2). 🌎
<PipelineTag pipeline="image-classification"/>
- [`Dinov2ForImageClassificatio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/#resources | #resources | .md | 402_3 |
This is the configuration class to store the configuration of a [`Dinov2Model`]. It is used to instantiate an
Dinov2 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 Dinov2
[google/dinov2-base-p... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/#dinov2config | #dinov2config | .md | 402_4 |
The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/#dinov2model | #dinov2model | .md | 402_5 |
Dinov2 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) subclass. Use it
as a regular PyTorch Module and refer to the PyT... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/#dinov2forimageclassification | #dinov2forimageclassification | .md | 402_6 |
No docstring available for FlaxDinov2Model
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/#flaxdinov2model | #flaxdinov2model | .md | 402_7 |
No docstring available for FlaxDinov2ForImageClassification
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dinov2.md | https://huggingface.co/docs/transformers/en/model_doc/dinov2/#flaxdinov2forimageclassification | #flaxdinov2forimageclassification | .md | 402_8 |
<!--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/univnet.md | https://huggingface.co/docs/transformers/en/model_doc/univnet/ | .md | 403_0 | |
The UnivNet model was proposed in [UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation](https://arxiv.org/abs/2106.07889) by Won Jang, Dan Lim, Jaesam Yoon, Bongwan Kin, and Juntae Kim.
The UnivNet model is a generative adversarial network (GAN) trained to sy... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/univnet.md | https://huggingface.co/docs/transformers/en/model_doc/univnet/#overview | #overview | .md | 403_1 |
This is the configuration class to store the configuration of a [`UnivNetModel`]. It is used to instantiate a
UnivNet vocoder 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 UnivNet
[dg845/univ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/univnet.md | https://huggingface.co/docs/transformers/en/model_doc/univnet/#univnetconfig | #univnetconfig | .md | 403_2 |
Constructs a UnivNet feature extractor.
This class extracts log-mel-filter bank features from raw speech using the short time Fourier Transform (STFT). The
STFT implementation follows that of TacoTron 2 and Hifi-GAN.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/univnet.md | https://huggingface.co/docs/transformers/en/model_doc/univnet/#univnetfeatureextractor | #univnetfeatureextractor | .md | 403_3 |
UnivNet GAN vocoder.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/univnet.md | https://huggingface.co/docs/transformers/en/model_doc/univnet/#univnetmodel | #univnetmodel | .md | 403_4 |
<!--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/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/ | .md | 404_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/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#jukebox | #jukebox | .md | 404_1 |
The Jukebox model was proposed in [Jukebox: A generative model for music](https://arxiv.org/pdf/2005.00341.pdf)
by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford,
Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditioned on
an artis... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#overview | #overview | .md | 404_2 |
- This model only supports inference. This is for a few reasons, mostly because it requires a crazy amount of memory to train. Feel free to open a PR and add what's missing to have a full integration with the hugging face trainer!
- This model is very slow, and takes 8h to generate a minute long audio using the 5b top ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#usage-tips | #usage-tips | .md | 404_3 |
This is the configuration class to store the configuration of a [`JukeboxModel`].
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. Instantiating a configuration with the defaults will
yield a si... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#jukeboxconfig | #jukeboxconfig | .md | 404_4 |
This is the configuration class to store the configuration of a [`JukeboxPrior`]. It is used to instantiate a
`JukeboxPrior` according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the top level prior from the
[... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#jukeboxpriorconfig | #jukeboxpriorconfig | .md | 404_5 |
This is the configuration class to store the configuration of a [`JukeboxVQVAE`]. It is used to instantiate a
`JukeboxVQVAE` according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the VQVAE from
[openai/jukebox... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#jukeboxvqvaeconfig | #jukeboxvqvaeconfig | .md | 404_6 |
Constructs a Jukebox tokenizer. Jukebox can be conditioned on 3 different inputs :
- Artists, unique ids are associated to each artist from the provided dictionary.
- Genres, unique ids are associated to each genre from the provided dictionary.
- Lyrics, character based tokenization. Must be initialized with the list o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#jukeboxtokenizer | #jukeboxtokenizer | .md | 404_7 |
The bare JUKEBOX Model used for music generation. 4 sampling techniques are supported : `primed_sample`, `upsample`,
`continue_sample` and `ancestral_sample`. It does not have a `forward` method as the training is not end to end. If
you want to fine-tune the model, it is recommended to use the `JukeboxPrior` class and ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#jukeboxmodel | #jukeboxmodel | .md | 404_8 |
The JukeboxPrior class, which is a wrapper around the various conditioning and the transformer. JukeboxPrior can be
seen as language models trained on music. They model the next `music token` prediction task. If a (lyric) `encoderù
is defined, it also models the `next character` prediction on the lyrics. Can be conditi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#jukeboxprior | #jukeboxprior | .md | 404_9 |
The Hierarchical VQ-VAE model used in Jukebox. This model follows the Hierarchical VQVAE paper from [Will Williams, Sam
Ringer, Tom Ash, John Hughes, David MacLeod, Jamie Dougherty](https://arxiv.org/abs/2002.08111).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic meth... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/jukebox.md | https://huggingface.co/docs/transformers/en/model_doc/jukebox/#jukeboxvqvae | #jukeboxvqvae | .md | 404_10 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/ | .md | 405_0 | |
The MusicGen model was proposed in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284)
by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
MusicGen is a single stage auto-regressive Transformer model capable of generating ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#overview | #overview | .md | 405_1 |
- After downloading the original checkpoints from [here](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md#importing--exporting-models) , you can convert them using the **conversion script** available at
`src/transformers/models/musicgen/convert_musicgen_transformers.py` with the following comma... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#usage-tips | #usage-tips | .md | 405_2 |
MusicGen is compatible with two generation modes: greedy and sampling. In practice, sampling leads to significantly
better results than greedy, thus we encourage sampling mode to be used where possible. Sampling is enabled by default,
and can be explicitly specified by setting `do_sample=True` in the call to [`Musicgen... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#generation | #generation | .md | 405_3 |
The inputs for unconditional (or 'null') generation can be obtained through the method
[`MusicgenForConditionalGeneration.get_unconditional_inputs`]:
```python
>>> from transformers import MusicgenForConditionalGeneration
>>> model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
>>> unc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#unconditional-generation | #unconditional-generation | .md | 405_4 |
The model can generate an audio sample conditioned on a text prompt through use of the [`MusicgenProcessor`] to pre-process
the inputs:
```python
>>> from transformers import AutoProcessor, MusicgenForConditionalGeneration
>>> processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
>>> model = MusicgenF... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#text-conditional-generation | #text-conditional-generation | .md | 405_5 |
The same [`MusicgenProcessor`] can be used to pre-process an audio prompt that is used for audio continuation. In the
following example, we load an audio file using the 🤗 Datasets library, which can be pip installed through the command
below:
```bash
pip install --upgrade pip
pip install datasets[audio]
```
```pyt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#audio-prompted-generation | #audio-prompted-generation | .md | 405_6 |
The default parameters that control the generation process, such as sampling, guidance scale and number of generated
tokens, can be found in the model's generation config, and updated as desired:
```python
>>> from transformers import MusicgenForConditionalGeneration
>>> model = MusicgenForConditionalGeneration.from... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#generation-configuration | #generation-configuration | .md | 405_7 |
The MusicGen model can be de-composed into three distinct stages:
1. Text encoder: maps the text inputs to a sequence of hidden-state representations. The pre-trained MusicGen models use a frozen text encoder from either T5 or Flan-T5
2. MusicGen decoder: a language model (LM) that auto-regressively generates audio tok... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#model-structure | #model-structure | .md | 405_8 |
This is the configuration class to store the configuration of an [`MusicgenDecoder`]. It is used to instantiate a
MusicGen decoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the MusicGen
[facebook/m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#musicgendecoderconfig | #musicgendecoderconfig | .md | 405_9 |
This is the configuration class to store the configuration of a [`MusicgenModel`]. It is used to instantiate a
MusicGen model according to the specified arguments, defining the text encoder, audio encoder and MusicGen decoder
configs.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#musicgenconfig | #musicgenconfig | .md | 405_10 |
Constructs a MusicGen processor which wraps an EnCodec feature extractor and a T5 tokenizer into a single processor
class.
[`MusicgenProcessor`] offers all the functionalities of [`EncodecFeatureExtractor`] and [`TTokenizer`]. See
[`~MusicgenProcessor.__call__`] and [`~MusicgenProcessor.decode`] for more information.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#musicgenprocessor | #musicgenprocessor | .md | 405_11 |
The bare Musicgen decoder model outputting raw hidden-states without any specific head on top.
The Musicgen model was proposed in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by
Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. I... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#musicgenmodel | #musicgenmodel | .md | 405_12 |
The MusicGen decoder model with a language modelling head on top.
The Musicgen model was proposed in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by
Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. It is an
encoder decoder trans... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#musicgenforcausallm | #musicgenforcausallm | .md | 405_13 |
The composite MusicGen model with a text encoder, audio encoder and Musicgen decoder, for music generation tasks with one or both of text and audio prompts.
The Musicgen model was proposed in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by
Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen/#musicgenforconditionalgeneration | #musicgenforconditionalgeneration | .md | 405_14 |
<!--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/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/ | .md | 406_0 | |
The Swin Transformer was proposed in [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
The abstract from the paper is the following:
*This paper presents a new vision ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#overview | #overview | .md | 406_1 |
- Swin pads the inputs supporting any input height and width (if divisible by `32`).
- Swin 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 `... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#usage-tips | #usage-tips | .md | 406_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Swin Transformer.
<PipelineTag pipeline="image-classification"/>
- [`SwinForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#resources | #resources | .md | 406_3 |
This is the configuration class to store the configuration of a [`SwinModel`]. It is used to instantiate a Swin
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
[microsoft/swin-tiny-patch4-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#swinconfig | #swinconfig | .md | 406_4 |
The bare Swin 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#swinmodel | #swinmodel | .md | 406_5 |
Swin Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://arxiv.org/abs/2111.09886).
<Tip>
Note that we provide a script to pre-train this model on custom data in our [examples
directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
<... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#swinformaskedimagemodeling | #swinformaskedimagemodeling | .md | 406_6 |
Swin 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.
<Tip>
Note that it's possible to fine-tune Swin on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#swinforimageclassification | #swinforimageclassification | .md | 406_7 |
No docstring available for TFSwinModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#tfswinmodel | #tfswinmodel | .md | 406_8 |
No docstring available for TFSwinForMaskedImageModeling
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#tfswinformaskedimagemodeling | #tfswinformaskedimagemodeling | .md | 406_9 |
No docstring available for TFSwinForImageClassification
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin.md | https://huggingface.co/docs/transformers/en/model_doc/swin/#tfswinforimageclassification | #tfswinforimageclassification | .md | 406_10 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/ | .md | 407_0 | |
The Perceiver IO model was proposed in [Perceiver IO: A General Architecture for Structured Inputs &
Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch,
Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Héna... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#overview | #overview | .md | 407_1 |
- The quickest way to get started with the Perceiver is by checking the [tutorial
notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Perceiver).
- Refer to the [blog post](https://huggingface.co/blog/perceiver) if you want to fully understand how the model works and
is implemented in the librar... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#resources | #resources | .md | 407_2 |
models.perceiver.modeling_perceiver.PerceiverModelOutput
Base class for Perceiver base model's outputs, with potential hidden states, attentions and cross-attentions.
Args:
logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiver-specific-outputs | #perceiver-specific-outputs | .md | 407_3 |
This is the configuration class to store the configuration of a [`PerceiverModel`]. It is used to instantiate an
Perceiver 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 Perceiver
[deepmind/la... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverconfig | #perceiverconfig | .md | 407_4 |
Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding.
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:
pad_token (`str`, *optional*, defaults to `"[PA... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceivertokenizer | #perceivertokenizer | .md | 407_5 |
No docstring available for PerceiverFeatureExtractor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverfeatureextractor | #perceiverfeatureextractor | .md | 407_6 |
Constructs a Perceiver image processor.
Args:
do_center_crop (`bool`, `optional`, defaults to `True`):
Whether or not to center crop the image. If the input size if smaller than `crop_size` along any edge, the
image will be padded with zeros and then center cropped. Can be overridden by the `do_center_crop`
parameter... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverimageprocessor | #perceiverimageprocessor | .md | 407_7 |
models.perceiver.modeling_perceiver.PerceiverTextPreprocessor
Text preprocessing for Perceiver Encoder. Can be used to embed `inputs` and add positional encodings.
The dimensionality of the embeddings is determined by the `d_model` attribute of the configuration.
Args:
config ([`PerceiverConfig`]):
Model configur... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceivertextpreprocessor | #perceivertextpreprocessor | .md | 407_8 |
models.perceiver.modeling_perceiver.PerceiverImagePreprocessor
Image preprocessing for Perceiver Encoder.
Note: the *out_channels* argument refers to the output channels of a convolutional layer, if *prep_type* is set to
"conv1x1" or "conv". If one adds absolute position embeddings, one must make sure the *num_chan... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverimagepreprocessor | #perceiverimagepreprocessor | .md | 407_9 |
models.perceiver.modeling_perceiver.PerceiverOneHotPreprocessor
One-hot preprocessor for Perceiver Encoder. Can be used to add a dummy index dimension to the input.
Args:
config ([`PerceiverConfig`]):
Model configuration. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiveronehotpreprocessor | #perceiveronehotpreprocessor | .md | 407_10 |
models.perceiver.modeling_perceiver.PerceiverAudioPreprocessor
Audio preprocessing for Perceiver Encoder.
Args:
config ([*PerceiverConfig*]):
Model configuration.
prep_type (`str`, *optional*, defaults to `"patches"`):
Preprocessor type to use. Only "patches" is supported.
samples_per_patch (`int`, *optional*, defa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiveraudiopreprocessor | #perceiveraudiopreprocessor | .md | 407_11 |
models.perceiver.modeling_perceiver.PerceiverMultimodalPreprocessor
Multimodal preprocessing for Perceiver Encoder.
Inputs for each modality are preprocessed, then padded with trainable position embeddings to have the same number
of channels.
Args:
modalities (`Mapping[str, PreprocessorType]`):
Dict mapping modal... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceivermultimodalpreprocessor | #perceivermultimodalpreprocessor | .md | 407_12 |
models.perceiver.modeling_perceiver.PerceiverProjectionDecoder
Baseline projection decoder (no cross-attention).
Args:
config ([`PerceiverConfig`]):
Model configuration. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverprojectiondecoder | #perceiverprojectiondecoder | .md | 407_13 |
models.perceiver.modeling_perceiver.PerceiverBasicDecoder
Cross-attention-based decoder. This class can be used to decode the final hidden states of the latents using a
cross-attention operation, in which the latents produce keys and values.
The shape of the output of this class depends on how one defines the outpu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverbasicdecoder | #perceiverbasicdecoder | .md | 407_14 |
models.perceiver.modeling_perceiver.PerceiverClassificationDecoder
Cross-attention based classification decoder. Light-weight wrapper of [`PerceiverBasicDecoder`] for logit output.
Will turn the output of the Perceiver encoder which is of shape (batch_size, num_latents, d_latents) to a tensor of
shape (batch_size, nu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverclassificationdecoder | #perceiverclassificationdecoder | .md | 407_15 |
models.perceiver.modeling_perceiver.PerceiverOpticalFlowDecoder
Cross-attention based optical flow decoder. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiveropticalflowdecoder | #perceiveropticalflowdecoder | .md | 407_16 |
models.perceiver.modeling_perceiver.PerceiverBasicVideoAutoencodingDecoder
Cross-attention based video-autoencoding decoder. Light-weight wrapper of [*PerceiverBasicDecoder*] with video
reshaping logic.
Args:
config ([*PerceiverConfig*]):
Model configuration.
output_shape (`List[int]`):
Shape of the output as (batc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverbasicvideoautoencodingdecoder | #perceiverbasicvideoautoencodingdecoder | .md | 407_17 |
models.perceiver.modeling_perceiver.PerceiverMultimodalDecoder
Multimodal decoding by composing uni-modal decoders. The *modalities* argument of the constructor is a dictionary
mapping modality name to the decoder of that modality. That decoder will be used to construct queries for that
modality. Modality-specific qu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceivermultimodaldecoder | #perceivermultimodaldecoder | .md | 407_18 |
models.perceiver.modeling_perceiver.PerceiverProjectionPostprocessor
Projection postprocessing for Perceiver. Can be used to project the channels of the decoder output to a lower
dimension.
Args:
in_channels (`int`):
Number of channels in the input.
out_channels (`int`):
Number of channels in the output. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverprojectionpostprocessor | #perceiverprojectionpostprocessor | .md | 407_19 |
models.perceiver.modeling_perceiver.PerceiverAudioPostprocessor
Audio postprocessing for Perceiver. Can be used to convert the decoder output to audio features.
Args:
config ([*PerceiverConfig*]):
Model configuration.
in_channels (`int`):
Number of channels in the input.
postproc_type (`str`, *optional*, defaults t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiveraudiopostprocessor | #perceiveraudiopostprocessor | .md | 407_20 |
models.perceiver.modeling_perceiver.PerceiverClassificationPostprocessor
Classification postprocessing for Perceiver. Can be used to convert the decoder output to classification logits.
Args:
config ([*PerceiverConfig*]):
Model configuration.
in_channels (`int`):
Number of channels in the input. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverclassificationpostprocessor | #perceiverclassificationpostprocessor | .md | 407_21 |
models.perceiver.modeling_perceiver.PerceiverMultimodalPostprocessor
Multimodal postprocessing for Perceiver. Can be used to combine modality-specific postprocessors into a single
postprocessor.
Args:
modalities (`Mapping[str, PostprocessorType]`):
Dictionary mapping modality name to postprocessor class for that mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceivermultimodalpostprocessor | #perceivermultimodalpostprocessor | .md | 407_22 |
The Perceiver: a scalable, fully attentional architecture.
<Tip>
Note that it's possible to fine-tune Perceiver on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
position embeddings to ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceivermodel | #perceivermodel | .md | 407_23 |
Example use of Perceiver for masked language modeling.
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 ([`Per... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverformaskedlm | #perceiverformaskedlm | .md | 407_24 |
Example use of Perceiver for text classification.
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 ([`Perceive... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverforsequenceclassification | #perceiverforsequenceclassification | .md | 407_25 |
Example use of Perceiver for image classification, for tasks such as ImageNet.
This model uses learned position embeddings. In other words, this model is not given any privileged information about
the structure of images. As shown in the paper, this model can achieve a top-1 accuracy of 72.7 on ImageNet.
[`Perceive... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverforimageclassificationlearned | #perceiverforimageclassificationlearned | .md | 407_26 |
Example use of Perceiver for image classification, for tasks such as ImageNet.
This model uses fixed 2D Fourier position embeddings. As shown in the paper, this model can achieve a top-1 accuracy of
79.0 on ImageNet, and 84.5 when pre-trained on a large-scale dataset (i.e. JFT).
[`PerceiverForImageClassificationLea... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverforimageclassificationfourier | #perceiverforimageclassificationfourier | .md | 407_27 |
Example use of Perceiver for image classification, for tasks such as ImageNet.
This model uses a 2D conv+maxpool preprocessing network. As shown in the paper, this model can achieve a top-1 accuracy
of 82.1 on ImageNet.
[`PerceiverForImageClassificationLearned`] uses [`~models.perceiver.modeling_perceiver.Perceiver... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverforimageclassificationconvprocessing | #perceiverforimageclassificationconvprocessing | .md | 407_28 |
Example use of Perceiver for optical flow, for tasks such as Sintel and KITTI. [`PerceiverForOpticalFlow`] uses
[`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`] (with *prep_type="patches"*) to preprocess the
input images, and [`~models.perceiver.modeling_perceiver.PerceiverOpticalFlowDecoder`] to dec... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverforopticalflow | #perceiverforopticalflow | .md | 407_29 |
Example use of Perceiver for multimodal (video) autoencoding, for tasks such as Kinetics-700.
[`PerceiverForMultimodalAutoencoding`] uses [`~models.perceiver.modeling_perceiver.PerceiverMultimodalPreprocessor`] to
preprocess the 3 modalities: images, audio and class labels. This preprocessor uses modality-specific pr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/perceiver.md | https://huggingface.co/docs/transformers/en/model_doc/perceiver/#perceiverformultimodalautoencoding | #perceiverformultimodalautoencoding | .md | 407_30 |
<!--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/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/ | .md | 408_0 | |
The X-MOD model was proposed in [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, and Mikel Artetxe.
X-MOD extends multilingual masked language models like [XLM-R]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#overview | #overview | .md | 408_1 |
Tips:
- X-MOD is similar to [XLM-R](xlm-roberta), but a difference is that the input language needs to be specified so that the correct language adapter can be activated.
- The main models – base and large – have adapters for 81 languages. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#usage-tips | #usage-tips | .md | 408_2 |
There are two ways to specify the input language:
1. By setting a default language before using the model:
```python
from transformers import XmodModel
model = XmodModel.from_pretrained("facebook/xmod-base")
model.set_default_language("en_XX")
```
2. By explicitly passing the index of the language adapter for each... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#input-language | #input-language | .md | 408_3 |
The paper recommends that the embedding layer and the language adapters are frozen during fine-tuning. A method for doing this is provided:
```python
model.freeze_embeddings_and_language_adapters()
# Fine-tune the model ...
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#fine-tuning | #fine-tuning | .md | 408_4 |
After fine-tuning, zero-shot cross-lingual transfer can be tested by activating the language adapter of the target language:
```python
model.set_default_language("de_DE")
# Evaluate the model on German examples ...
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#cross-lingual-transfer | #cross-lingual-transfer | .md | 408_5 |
- [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/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#resources | #resources | .md | 408_6 |
This is the configuration class to store the configuration of a [`XmodModel`]. It is used to instantiate an X-MOD
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
[facebook/xmod-base](https://hu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#xmodconfig | #xmodconfig | .md | 408_7 |
The bare X-MOD Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#xmodmodel | #xmodmodel | .md | 408_8 |
X-MOD Model with a `language modeling` head on top for CLM fine-tuning.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#xmodforcausallm | #xmodforcausallm | .md | 408_9 |
X-MOD Model with a `language modeling` head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#xmodformaskedlm | #xmodformaskedlm | .md | 408_10 |
X-MOD 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 downloading or savi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#xmodforsequenceclassification | #xmodforsequenceclassification | .md | 408_11 |
X-MOD Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloadin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#xmodformultiplechoice | #xmodformultiplechoice | .md | 408_12 |
X-MOD Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#xmodfortokenclassification | #xmodfortokenclassification | .md | 408_13 |
X-MOD Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xmod.md | https://huggingface.co/docs/transformers/en/model_doc/xmod/#xmodforquestionanswering | #xmodforquestionanswering | .md | 408_14 |
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