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Splinter Model for the recurring span selection task as done during the pretraining. The difference to the QA task
is that we do not have a question, but multiple question tokens that replace the occurrences of recurring spans
instead.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/splinter.md | https://huggingface.co/docs/transformers/en/model_doc/splinter/#splinterforpretraining | #splinterforpretraining | .md | 305_9 |
<!--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/sew-d.md | https://huggingface.co/docs/transformers/en/model_doc/sew-d/ | .md | 306_0 | |
SEW-D (Squeezed and Efficient Wav2Vec with Disentangled attention) was proposed in [Performance-Efficiency Trade-offs
in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim,
Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
The abstract from the paper is t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew-d.md | https://huggingface.co/docs/transformers/en/model_doc/sew-d/#overview | #overview | .md | 306_1 |
- SEW-D is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- SEWDForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
using [`Wav2Vec2CTCTokenizer`]. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew-d.md | https://huggingface.co/docs/transformers/en/model_doc/sew-d/#usage-tips | #usage-tips | .md | 306_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/sew-d.md | https://huggingface.co/docs/transformers/en/model_doc/sew-d/#resources | #resources | .md | 306_3 |
This is the configuration class to store the configuration of a [`SEWDModel`]. It is used to instantiate a SEW-D
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 SEW-D
[asapp/sew-d-tiny-100k](ht... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew-d.md | https://huggingface.co/docs/transformers/en/model_doc/sew-d/#sewdconfig | #sewdconfig | .md | 306_4 |
The bare SEW-D Model transformer outputting raw hidden-states without any specific head on top.
SEW-D was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech
Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger,
Yoav Artzi.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew-d.md | https://huggingface.co/docs/transformers/en/model_doc/sew-d/#sewdmodel | #sewdmodel | .md | 306_5 |
SEW-D Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
SEW-D was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech
Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger,
Yoav Ar... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew-d.md | https://huggingface.co/docs/transformers/en/model_doc/sew-d/#sewdforctc | #sewdforctc | .md | 306_6 |
SEWD Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB
Keyword Spotting.
SEW-D was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech
Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Ky... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew-d.md | https://huggingface.co/docs/transformers/en/model_doc/sew-d/#sewdforsequenceclassification | #sewdforsequenceclassification | .md | 306_7 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapas.md | https://huggingface.co/docs/transformers/en/model_doc/tapas/ | .md | 307_0 | |
The TAPAS model was proposed in [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://www.aclweb.org/anthology/2020.acl-main.398)
by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically
designed (and pre-trained) for an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapas.md | https://huggingface.co/docs/transformers/en/model_doc/tapas/#overview | #overview | .md | 307_1 |
- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell of the table). Note that this is something that was added after the publication of the original TAPAS paper. According to the authors, this usually results in a slightly better performance, and allows ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapas.md | https://huggingface.co/docs/transformers/en/model_doc/tapas/#usage-tips | #usage-tips | .md | 307_2 |
Here we explain how you can fine-tune [`TapasForQuestionAnswering`] on your own dataset.
**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
Basically, there are 3 different ways in which one can fine-tune [`TapasForQuestionAnswering`], corresponding to the different datasets on which Ta... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapas.md | https://huggingface.co/docs/transformers/en/model_doc/tapas/#usage-fine-tuning | #usage-fine-tuning | .md | 307_3 |
<frameworkcontent>
<pt>
Here we explain how you can use [`TapasForQuestionAnswering`] or [`TFTapasForQuestionAnswering`] for inference (i.e. making predictions on new data). For inference, only `input_ids`, `attention_mask` and `token_type_ids` (which you can obtain using [`TapasTokenizer`]) have to be provided to the ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapas.md | https://huggingface.co/docs/transformers/en/model_doc/tapas/#usage-inference | #usage-inference | .md | 307_4 |
- [Text classification task guide](../tasks/sequence_classification)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
models.tapas.modeling_tapas.TableQuestionAnsweringOutput
Output type of [`TapasForQuestionAnswering`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, return... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapas.md | https://huggingface.co/docs/transformers/en/model_doc/tapas/#resources | #resources | .md | 307_5 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/ | .md | 308_0 | |
The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/abs/1908.07490) by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders
(one for the vision modality, one for the language modality, and then one to fuse both modalities... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#overview | #overview | .md | 308_1 |
- Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features
will work.
- Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the
cross-modality layer, so they contain information from both modalities. To access a mod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#usage-tips | #usage-tips | .md | 308_2 |
- [Question answering task guide](../tasks/question_answering) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#resources | #resources | .md | 308_3 |
This is the configuration class to store the configuration of a [`LxmertModel`] or a [`TFLxmertModel`]. It is used
to instantiate a LXMERT 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 Lxmert... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#lxmertconfig | #lxmertconfig | .md | 308_4 |
Construct a Lxmert 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.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *option... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#lxmerttokenizer | #lxmerttokenizer | .md | 308_5 |
Construct a "fast" Lxmert tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#lxmerttokenizerfast | #lxmerttokenizerfast | .md | 308_6 |
models.lxmert.modeling_lxmert.LxmertModelOutput
Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"
encoder")
Args:
language_output (`torch.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#lxmert-specific-outputs | #lxmert-specific-outputs | .md | 308_7 |
The bare Lxmert Model transformer outputting raw hidden-states without any specific head on top.
The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from
Transformers](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal. It's a vision and language transformer
model, pre... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#lxmertmodel | #lxmertmodel | .md | 308_8 |
Lxmert Model with a specified pretraining head on top.
The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from
Transformers](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal. It's a vision and language transformer
model, pretrained on a variety of multi-modal datase... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#lxmertforpretraining | #lxmertforpretraining | .md | 308_9 |
Lxmert Model with a visual-answering head on top for downstream QA tasks
The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from
Transformers](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal. It's a vision and language transformer
model, pretrained on a variety of ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#lxmertforquestionanswering | #lxmertforquestionanswering | .md | 308_10 |
No docstring available for TFLxmertModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#tflxmertmodel | #tflxmertmodel | .md | 308_11 |
No docstring available for TFLxmertForPreTraining
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lxmert.md | https://huggingface.co/docs/transformers/en/model_doc/lxmert/#tflxmertforpretraining | #tflxmertforpretraining | .md | 308_12 |
<!--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/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/ | .md | 309_0 | |
The DPT model was proposed in [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
DPT is a model that leverages the [Vision Transformer (ViT)](vit) as backbone for dense prediction tasks like semantic segmentation and depth estimation.
The a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#overview | #overview | .md | 309_1 |
DPT is compatible with the [`AutoBackbone`] class. This allows to use the DPT framework with various computer vision backbones available in the library, such as [`VitDetBackbone`] or [`Dinov2Backbone`]. One can create it as follows:
```python
from transformers import Dinov2Config, DPTConfig, DPTForDepthEstimation
# ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#usage-tips | #usage-tips | .md | 309_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DPT.
- Demo notebooks for [`DPTForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DPT).
- [Semantic segmentation task guide](../tasks/semantic_segmentation)
-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#resources | #resources | .md | 309_3 |
This is the configuration class to store the configuration of a [`DPTModel`]. It is used to instantiate an DPT
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 DPT
[Intel/dpt-large](https://hugg... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#dptconfig | #dptconfig | .md | 309_4 |
No docstring available for DPTFeatureExtractor
Methods: __call__
- post_process_semantic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#dptfeatureextractor | #dptfeatureextractor | .md | 309_5 |
Constructs a DPT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions. Can be overidden by `do_resize` in `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 384, "width": 384}`):
Size of the image after resizing. Can ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#dptimageprocessor | #dptimageprocessor | .md | 309_6 |
The bare DPT 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 and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#dptmodel | #dptmodel | .md | 309_7 |
DPT Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2.
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 g... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#dptfordepthestimation | #dptfordepthestimation | .md | 309_8 |
DPT Model 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 and
behavior. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dpt.md | https://huggingface.co/docs/transformers/en/model_doc/dpt/#dptforsemanticsegmentation | #dptforsemanticsegmentation | .md | 309_9 |
<!--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/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/ | .md | 310_0 | |
The CANINE model was proposed in [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language
Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. It's
among the first papers that trains a Transformer without using an explicit tokenization step (su... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#overview | #overview | .md | 310_1 |
- CANINE uses no less than 3 Transformer encoders internally: 2 "shallow" encoders (which only consist of a single
layer) and 1 "deep" encoder (which is a regular BERT encoder). First, a "shallow" encoder is used to contextualize
the character embeddings, using local attention. Next, after downsampling, a "deep" encode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#usage-tips | #usage-tips | .md | 310_2 |
CANINE works on raw characters, so it can be used **without a tokenizer**:
```python
>>> from transformers import CanineModel
>>> import torch
>>> model = CanineModel.from_pretrained("google/canine-c") # model pre-trained with autoregressive character loss
>>> text = "hello world"
>>> # use Python's built-in ord()... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#usage-example | #usage-example | .md | 310_3 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Multiple choice task guide](../tasks/multiple_choice) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#resources | #resources | .md | 310_4 |
This is the configuration class to store the configuration of a [`CanineModel`]. It is used to instantiate an
CANINE 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 CANINE
[google/canine-s](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#canineconfig | #canineconfig | .md | 310_5 |
Construct a CANINE tokenizer (i.e. a character splitter). It turns text into a sequence of characters, and then
converts each character into its Unicode code point.
[`CanineTokenizer`] inherits from [`PreTrainedTokenizer`].
Refer to superclass [`PreTrainedTokenizer`] for usage examples and documentation concerning ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#caninetokenizer | #caninetokenizer | .md | 310_6 |
models.canine.modeling_canine.CanineModelOutputWithPooling
Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly
different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow
Transformer encoders.
Args:
last_h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#canine-specific-outputs | #canine-specific-outputs | .md | 310_7 |
The bare CANINE 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/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#caninemodel | #caninemodel | .md | 310_8 |
CANINE 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 doc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#canineforsequenceclassification | #canineforsequenceclassification | .md | 310_9 |
CANINE 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 Py... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#canineformultiplechoice | #canineformultiplechoice | .md | 310_10 |
CANINE 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 PyTo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#caninefortokenclassification | #caninefortokenclassification | .md | 310_11 |
CANINE 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-class... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/canine.md | https://huggingface.co/docs/transformers/en/model_doc/canine/#canineforquestionanswering | #canineforquestionanswering | .md | 310_12 |
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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/vipllava.md | https://huggingface.co/docs/transformers/en/model_doc/vipllava/ | .md | 311_0 | |
The VipLlava model was proposed in [Making Large Multimodal Models Understand Arbitrary Visual Prompts](https://arxiv.org/abs/2312.00784) by Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee.
VipLlava enhances the training protocol of Llava by marking images and i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vipllava.md | https://huggingface.co/docs/transformers/en/model_doc/vipllava/#overview | #overview | .md | 311_1 |
- The architecture is similar than llava architecture except that the multi-modal projector takes a set of concatenated vision hidden states and has an additional layernorm layer on that module.
- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simp... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vipllava.md | https://huggingface.co/docs/transformers/en/model_doc/vipllava/#usage-tips | #usage-tips | .md | 311_2 |
This is the configuration class to store the configuration of a [`VipLlavaForConditionalGeneration`]. It is used to instantiate an
VipLlava 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 VipLl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vipllava.md | https://huggingface.co/docs/transformers/en/model_doc/vipllava/#vipllavaconfig | #vipllavaconfig | .md | 311_3 |
The VIPLLAVA model which consists of a vision backbone and a language model.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vipllava.md | https://huggingface.co/docs/transformers/en/model_doc/vipllava/#vipllavaforconditionalgeneration | #vipllavaforconditionalgeneration | .md | 311_4 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/ | .md | 312_0 | |
**DISCLAIMER:** The default behaviour for the tokenizer was fixed and thus changed in April 2023.
The previous version adds `[self.eos_token_id, self.cur_lang_code]` at the end of the token sequence for both target and source tokenization. This is wrong as the NLLB paper mentions (page 48, 6.1.1. Model Architecture) : ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#updated-tokenizer-behavior | #updated-tokenizer-behavior | .md | 312_1 |
The NLLB model was presented in [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by Marta R. Costa-jussà, James Cross, Onur Çelebi,
Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guil... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#overview | #overview | .md | 312_2 |
While generating the target text set the `forced_bos_token_id` to the target language id. The following
example shows how to translate English to French using the *facebook/nllb-200-distilled-600M* model.
Note that we're using the BCP-47 code for French `fra_Latn`. See [here](https://github.com/facebookresearch/flore... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#generating-with-nllb | #generating-with-nllb | .md | 312_3 |
English (`eng_Latn`) is set as the default language from which to translate. In order to specify that you'd like to translate from a different language,
you should specify the BCP-47 code in the `src_lang` keyword argument of the tokenizer initialization.
See example below for a translation from romanian to german: ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#generating-from-any-other-language-than-english | #generating-from-any-other-language-than-english | .md | 312_4 |
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#resources | #resources | .md | 312_5 |
Construct an NLLB tokenizer.
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
<tokens> <eos>` for target language documents.
E... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#nllbtokenizer | #nllbtokenizer | .md | 312_6 |
Construct a "fast" NLLB tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this supe... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#nllbtokenizerfast | #nllbtokenizerfast | .md | 312_7 |
Flash Attention 2 is a faster, optimized version of the attention scores computation which relies on `cuda` kernels. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#using-flash-attention-2 | #using-flash-attention-2 | .md | 312_8 |
First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the [official documentation](https://github.com/Dao-AILab/flash-attention#installation-and-features).
Next, [install](https://github.com/Dao-AILab/flash-attention#installation-and-features) ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#installation | #installation | .md | 312_9 |
To load a model using Flash Attention 2, we can pass the argument `attn_implementation="flash_attention_2"` to [`.from_pretrained`](https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained). You can use either `torch.float16` or `torch.bfloat16` precision.
```py... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#usage | #usage | .md | 312_10 |
Below is an expected speedup diagram that compares pure inference time between the native implementation and the Flash Attention 2.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/visheratin/documentation-images/resolve/main/nllb-speedup.webp">
</div> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#expected-speedups | #expected-speedups | .md | 312_11 |
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/nllb.md | https://huggingface.co/docs/transformers/en/model_doc/nllb/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 312_12 |
<!--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/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/ | .md | 313_0 | |
The DeiT model was proposed in [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre
Sablayrolles, Hervé Jégou. The [Vision Transformer (ViT)](vit) introduced in [Dosovitskiy et al., 2020... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#overview | #overview | .md | 313_1 |
- Compared to ViT, DeiT models use a so-called distillation token to effectively learn from a teacher (which, in the
DeiT paper, is a ResNet like-model). The distillation token is learned through backpropagation, by interacting with
the class ([CLS]) and patch tokens through the self-attention layers.
- There are 2 way... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#usage-tips | #usage-tips | .md | 313_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/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 313_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DeiT.
<PipelineTag pipeline="image-classification"/>
- [`DeiTForImageClassification`] 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/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#resources | #resources | .md | 313_4 |
This is the configuration class to store the configuration of a [`DeiTModel`]. It is used to instantiate an DeiT
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 DeiT
[facebook/deit-base-distill... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#deitconfig | #deitconfig | .md | 313_5 |
No docstring available for DeiTFeatureExtractor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#deitfeatureextractor | #deitfeatureextractor | .md | 313_6 |
Constructs a DeiT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
Size of the ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#deitimageprocessor | #deitimageprocessor | .md | 313_7 |
The bare DeiT 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/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#deitmodel | #deitmodel | .md | 313_8 |
DeiT 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/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#deitformaskedimagemodeling | #deitformaskedimagemodeling | .md | 313_9 |
DeiT 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 PyTor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#deitforimageclassification | #deitforimageclassification | .md | 313_10 |
DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.
.. warning::
This model supports inference-only. Fine-tuning with distillation (i.e. wit... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#deitforimageclassificationwithteacher | #deitforimageclassificationwithteacher | .md | 313_11 |
No docstring available for TFDeiTModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#tfdeitmodel | #tfdeitmodel | .md | 313_12 |
No docstring available for TFDeiTForMaskedImageModeling
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#tfdeitformaskedimagemodeling | #tfdeitformaskedimagemodeling | .md | 313_13 |
No docstring available for TFDeiTForImageClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#tfdeitforimageclassification | #tfdeitforimageclassification | .md | 313_14 |
No docstring available for TFDeiTForImageClassificationWithTeacher
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deit.md | https://huggingface.co/docs/transformers/en/model_doc/deit/#tfdeitforimageclassificationwithteacher | #tfdeitforimageclassificationwithteacher | .md | 313_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/pegasus_x.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus_x/ | .md | 314_0 | |
The PEGASUS-X model was proposed in [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao and Peter J. Liu.
PEGASUS-X (PEGASUS eXtended) extends the PEGASUS models for long input summarization through additional long input pretraini... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus_x.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus_x/#overview | #overview | .md | 314_1 |
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)
<Tip>
PEGASUS-X uses the same tokenizer as [PEGASUS](pegasus).
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus_x.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus_x/#documentation-resources | #documentation-resources | .md | 314_2 |
This is the configuration class to store the configuration of a [`PegasusXModel`]. It is used to instantiate a
PEGASUS-X 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 PEGASUS-X
[google/pegasu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus_x.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus_x/#pegasusxconfig | #pegasusxconfig | .md | 314_3 |
The bare PEGASUS-X Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
T... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus_x.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus_x/#pegasusxmodel | #pegasusxmodel | .md | 314_4 |
The PEGASUS-X for conditional generation (e.g. summarization).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus_x.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus_x/#pegasusxforconditionalgeneration | #pegasusxforconditionalgeneration | .md | 314_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/ | .md | 315_0 | |
The Wav2Vec2-BERT model was proposed in [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team from Meta AI.
This model was pre-trained on 4.5M hours of unlabe... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#overview | #overview | .md | 315_1 |
- Wav2Vec2-BERT follows the same architecture as Wav2Vec2-Conformer, but employs a causal depthwise convolutional layer and uses as input a mel-spectrogram representation of the audio instead of the raw waveform.
- Wav2Vec2-BERT can use either no relative position embeddings, Shaw-like position embeddings, Transformer-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#usage-tips | #usage-tips | .md | 315_2 |
<PipelineTag pipeline="automatic-speech-recognition"/>
- [`Wav2Vec2BertForCTC`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
- You can also adapt these notebooks on [how to finetune a speech recognition model in English](https://cola... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#resources | #resources | .md | 315_3 |
This is the configuration class to store the configuration of a [`Wav2Vec2BertModel`]. It is used to
instantiate an Wav2Vec2Bert 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 Wav2Vec2Bert
[fa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#wav2vec2bertconfig | #wav2vec2bertconfig | .md | 315_4 |
Constructs a Wav2Vec2-BERT processor which wraps a Wav2Vec2-BERT feature extractor and a Wav2Vec2 CTC tokenizer into a single
processor.
[`Wav2Vec2Processor`] offers all the functionalities of [`SeamlessM4TFeatureExtractor`] and [`PreTrainedTokenizer`].
See the docstring of [`~Wav2Vec2Processor.__call__`] and [`~Wav2... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#wav2vec2bertprocessor | #wav2vec2bertprocessor | .md | 315_5 |
The bare Wav2Vec2Bert Model transformer outputting raw hidden-states without any specific head on top.
Wav2Vec2Bert 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#wav2vec2bertmodel | #wav2vec2bertmodel | .md | 315_6 |
Wav2Vec2Bert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
Wav2Vec2Bert 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-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#wav2vec2bertforctc | #wav2vec2bertforctc | .md | 315_7 |
Wav2Vec2Bert Model with a sequence classification head on top (a linear layer over the pooled output) for
tasks like SUPERB Keyword Spotting.
Wav2Vec2Bert 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, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#wav2vec2bertforsequenceclassification | #wav2vec2bertforsequenceclassification | .md | 315_8 |
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