text stringlengths 5 58.6k | source stringclasses 470
values | url stringlengths 49 167 | source_section stringlengths 0 90 | file_type stringclasses 1
value | id stringlengths 3 6 |
|---|---|---|---|---|---|
Wav2Vec2Bert Model with a frame classification head on top for tasks like Speaker Diarization.
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 model... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#wav2vec2bertforaudioframeclassification | #wav2vec2bertforaudioframeclassification | .md | 315_9 |
Wav2Vec2Bert Model with an XVector feature extraction head on top for tasks like Speaker Verification.
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.
Th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2-bert.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert/#wav2vec2bertforxvector | #wav2vec2bertforxvector | .md | 315_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/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/ | .md | 316_0 | |
The RoFormer model was proposed in [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
The abstract from the paper is the following:
*Position encoding in transformer architecture provides super... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#overview | #overview | .md | 316_1 |
RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown
improved performance on classification tasks with long texts. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#usage-tips | #usage-tips | .md | 316_2 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/mask... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#resources | #resources | .md | 316_3 |
This is the configuration class to store the configuration of a [`RoFormerModel`]. It is used to instantiate an
RoFormer 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 RoFormer
[junnyu/roforme... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformerconfig | #roformerconfig | .md | 316_4 |
Construct a RoFormer tokenizer. Based on [Rust Jieba](https://pypi.org/project/rjieba/).
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 voc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformertokenizer | #roformertokenizer | .md | 316_5 |
Construct a "fast" RoFormer tokenizer (backed by HuggingFace's *tokenizers* library).
[`RoFormerTokenizerFast`] is almost identical to [`BertTokenizerFast`] and runs end-to-end tokenization:
punctuation splitting and wordpiece. There are some difference between them when tokenizing Chinese.
This tokenizer inherits ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformertokenizerfast | #roformertokenizerfast | .md | 316_6 |
The bare RoFormer Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformermodel | #roformermodel | .md | 316_7 |
RoFormer Model with a `language modeling` head on top for CLM fine-tuning.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Param... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformerforcausallm | #roformerforcausallm | .md | 316_8 |
RoFormer Model with a `language modeling` head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RoF... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformerformaskedlm | #roformerformaskedlm | .md | 316_9 |
RoFormer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformerforsequenceclassification | #roformerforsequenceclassification | .md | 316_10 |
RoFormer 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformerformultiplechoice | #roformerformultiplechoice | .md | 316_11 |
RoFormer 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 Py... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformerfortokenclassification | #roformerfortokenclassification | .md | 316_12 |
RoFormer Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-cla... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#roformerforquestionanswering | #roformerforquestionanswering | .md | 316_13 |
No docstring available for TFRoFormerModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#tfroformermodel | #tfroformermodel | .md | 316_14 |
No docstring available for TFRoFormerForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#tfroformerformaskedlm | #tfroformerformaskedlm | .md | 316_15 |
No docstring available for TFRoFormerForCausalLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#tfroformerforcausallm | #tfroformerforcausallm | .md | 316_16 |
No docstring available for TFRoFormerForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#tfroformerforsequenceclassification | #tfroformerforsequenceclassification | .md | 316_17 |
No docstring available for TFRoFormerForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#tfroformerformultiplechoice | #tfroformerformultiplechoice | .md | 316_18 |
No docstring available for TFRoFormerForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#tfroformerfortokenclassification | #tfroformerfortokenclassification | .md | 316_19 |
No docstring available for TFRoFormerForQuestionAnswering
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#tfroformerforquestionanswering | #tfroformerforquestionanswering | .md | 316_20 |
No docstring available for FlaxRoFormerModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#flaxroformermodel | #flaxroformermodel | .md | 316_21 |
No docstring available for FlaxRoFormerForMaskedLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#flaxroformerformaskedlm | #flaxroformerformaskedlm | .md | 316_22 |
No docstring available for FlaxRoFormerForSequenceClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#flaxroformerforsequenceclassification | #flaxroformerforsequenceclassification | .md | 316_23 |
No docstring available for FlaxRoFormerForMultipleChoice
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#flaxroformerformultiplechoice | #flaxroformerformultiplechoice | .md | 316_24 |
No docstring available for FlaxRoFormerForTokenClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#flaxroformerfortokenclassification | #flaxroformerfortokenclassification | .md | 316_25 |
No docstring available for FlaxRoFormerForQuestionAnswering
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roformer.md | https://huggingface.co/docs/transformers/en/model_doc/roformer/#flaxroformerforquestionanswering | #flaxroformerforquestionanswering | .md | 316_26 |
<!--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/omdet-turbo.md | https://huggingface.co/docs/transformers/en/model_doc/omdet-turbo/ | .md | 317_0 | |
The OmDet-Turbo model was proposed in [Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head](https://arxiv.org/abs/2403.06892) by Tiancheng Zhao, Peng Liu, Xuan He, Lu Zhang, Kyusong Lee. OmDet-Turbo incorporates components from RT-DETR and introduces a swift multimodal fusion module to achi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/omdet-turbo.md | https://huggingface.co/docs/transformers/en/model_doc/omdet-turbo/#overview | #overview | .md | 317_1 |
One unique property of OmDet-Turbo compared to other zero-shot object detection models, such as [Grounding DINO](grounding-dino), is the decoupled classes and prompt embedding structure that allows caching of text embeddings. This means that the model needs both classes and task as inputs, where classes is a list of ob... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/omdet-turbo.md | https://huggingface.co/docs/transformers/en/model_doc/omdet-turbo/#usage-tips | #usage-tips | .md | 317_2 |
Here's how to load the model and prepare the inputs to perform zero-shot object detection on a single image:
```python
>>> import torch
>>> import requests
>>> from PIL import Image
>>> from transformers import AutoProcessor, OmDetTurboForObjectDetection
>>> processor = AutoProcessor.from_pretrained("omlab/omdet-tu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/omdet-turbo.md | https://huggingface.co/docs/transformers/en/model_doc/omdet-turbo/#single-image-inference | #single-image-inference | .md | 317_3 |
OmDet-Turbo can perform batched multi-image inference, with support for different text prompts and classes in the same batch:
```python
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> from transformers import AutoProcessor, OmDetTurboForObjectDetection
>>> processor = Au... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/omdet-turbo.md | https://huggingface.co/docs/transformers/en/model_doc/omdet-turbo/#multi-image-inference | #multi-image-inference | .md | 317_4 |
This is the configuration class to store the configuration of a [`OmDetTurboForObjectDetection`].
It is used to instantiate a OmDet-Turbo 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 OmDet-Tu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/omdet-turbo.md | https://huggingface.co/docs/transformers/en/model_doc/omdet-turbo/#omdetturboconfig | #omdetturboconfig | .md | 317_5 |
Constructs a OmDet-Turbo processor which wraps a Deformable DETR image processor and an AutoTokenizer into a
single processor.
[`OmDetTurboProcessor`] offers all the functionalities of [`DetrImageProcessor`] and
[`AutoTokenizer`]. See the docstring of [`~OmDetTurboProcessor.__call__`] and [`~OmDetTurboProcessor.decod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/omdet-turbo.md | https://huggingface.co/docs/transformers/en/model_doc/omdet-turbo/#omdetturboprocessor | #omdetturboprocessor | .md | 317_6 |
OmDetTurbo Model (consisting of a vision and a text backbone, and encoder-decoder architecture) outputting
bounding boxes and classes scores for tasks such as COCO detection.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its mod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/omdet-turbo.md | https://huggingface.co/docs/transformers/en/model_doc/omdet-turbo/#omdetturboforobjectdetection | #omdetturboforobjectdetection | .md | 317_7 |
<!--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/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/ | .md | 318_0 | |
The OWL-ViT (short for Vision Transformer for Open-World Localization) was proposed in [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anura... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#overview | #overview | .md | 318_1 |
OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#usage-tips | #usage-tips | .md | 318_2 |
A demo notebook on using OWL-ViT for zero- and one-shot (image-guided) object detection can be found [here](https://github.com/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#resources | #resources | .md | 318_3 |
[`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to
instantiate an OWL-ViT model according to the specified arguments, defining the text model and vision model
configs. Instantiating a configuration with the defaults will yield a similar configuration to that of t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvitconfig | #owlvitconfig | .md | 318_4 |
This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an
OwlViT text encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the OwlViT
[google/o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvittextconfig | #owlvittextconfig | .md | 318_5 |
This is the configuration class to store the configuration of an [`OwlViTVisionModel`]. It is used to instantiate
an OWL-ViT image encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the OWL-ViT
[goo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvitvisionconfig | #owlvitvisionconfig | .md | 318_6 |
Constructs an OWL-ViT image processor.
This image processor inherits from [`ImageProcessingMixin`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the shorter edge... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvitimageprocessor | #owlvitimageprocessor | .md | 318_7 |
OwlViTProcessor
Constructs an OWL-ViT processor which wraps [`OwlViTImageProcessor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`]
into a single processor that interits both the image processor and tokenizer functionalities. See the
[`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information.
A... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvitprocessor | #owlvitprocessor | .md | 318_8 |
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/docs/stable/nn.html#to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvitmodel | #owlvitmodel | .md | 318_9 |
No docstring available for OwlViTTextModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvittextmodel | #owlvittextmodel | .md | 318_10 |
No docstring available for OwlViTVisionModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvitvisionmodel | #owlvitvisionmodel | .md | 318_11 |
No docstring available for OwlViTForObjectDetection
Methods: forward
- image_guided_detection | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlvit.md | https://huggingface.co/docs/transformers/en/model_doc/owlvit/#owlvitforobjectdetection | #owlvitforobjectdetection | .md | 318_12 |
<!--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/granitemoe.md | https://huggingface.co/docs/transformers/en/model_doc/granitemoe/ | .md | 319_0 | |
The GraniteMoe model was proposed in [Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler](https://arxiv.org/abs/2408.13359) by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox and Rameswar Panda.
PowerMoE-3B is a 3B sp... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/granitemoe.md | https://huggingface.co/docs/transformers/en/model_doc/granitemoe/#overview | #overview | .md | 319_1 |
This is the configuration class to store the configuration of a [`GraniteMoeModel`]. It is used to instantiate an GraniteMoe
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 GraniteMoe-3B.
Con... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/granitemoe.md | https://huggingface.co/docs/transformers/en/model_doc/granitemoe/#granitemoeconfig | #granitemoeconfig | .md | 319_2 |
The bare GraniteMoe 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.)
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/granitemoe.md | https://huggingface.co/docs/transformers/en/model_doc/granitemoe/#granitemoemodel | #granitemoemodel | .md | 319_3 |
No docstring available for GraniteMoeForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/granitemoe.md | https://huggingface.co/docs/transformers/en/model_doc/granitemoe/#granitemoeforcausallm | #granitemoeforcausallm | .md | 319_4 |
<!--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/rwkv.md | https://huggingface.co/docs/transformers/en/model_doc/rwkv/ | .md | 320_0 | |
The RWKV model was proposed in [this repo](https://github.com/BlinkDL/RWKV-LM)
It suggests a tweak in the traditional Transformer attention to make it linear. This way, the model can be used as recurrent network: passing inputs for timestamp 0 and timestamp 1 together is the same as passing inputs at timestamp 0, the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rwkv.md | https://huggingface.co/docs/transformers/en/model_doc/rwkv/#overview | #overview | .md | 320_1 |
```py
import torch
from transformers import AutoTokenizer, RwkvConfig, RwkvModel
model = RwkvModel.from_pretrained("sgugger/rwkv-430M-pile")
tokenizer = AutoTokenizer.from_pretrained("sgugger/rwkv-430M-pile")
inputs = tokenizer("This is an example.", return_tensors="pt")
# Feed everything to the model
outputs = model... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rwkv.md | https://huggingface.co/docs/transformers/en/model_doc/rwkv/#usage-example | #usage-example | .md | 320_2 |
This is the configuration class to store the configuration of a [`RwkvModel`]. It is used to instantiate a RWKV
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 RWVK-4
[RWKV/rwkv-4-169m-pile](ht... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rwkv.md | https://huggingface.co/docs/transformers/en/model_doc/rwkv/#rwkvconfig | #rwkvconfig | .md | 320_3 |
The bare RWKV 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/rwkv.md | https://huggingface.co/docs/transformers/en/model_doc/rwkv/#rwkvmodel | #rwkvmodel | .md | 320_4 |
The RWKV Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rwkv.md | https://huggingface.co/docs/transformers/en/model_doc/rwkv/#rwkvlmheadmodel | #rwkvlmheadmodel | .md | 320_5 |
In a traditional auto-regressive Transformer, attention is written as
$$O = \hbox{softmax}(QK^{T} / \sqrt{d}) V$$
with \\(Q\\), \\(K\\) and \\(V\\) are matrices of shape `seq_len x hidden_size` named query, key and value (they are actually bigger matrices with a batch dimension and an attention head dimension but w... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rwkv.md | https://huggingface.co/docs/transformers/en/model_doc/rwkv/#rwkv-attention-and-the-recurrent-formulas | #rwkv-attention-and-the-recurrent-formulas | .md | 320_6 |
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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/dbrx.md | https://huggingface.co/docs/transformers/en/model_doc/dbrx/ | .md | 321_0 | |
DBRX is a [transformer-based](https://www.isattentionallyouneed.com/) decoder-only large language model (LLM) that was trained using next-token prediction.
It uses a *fine-grained* mixture-of-experts (MoE) architecture with 132B total parameters of which 36B parameters are active on any input.
It was pre-trained on 12T... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dbrx.md | https://huggingface.co/docs/transformers/en/model_doc/dbrx/#overview | #overview | .md | 321_1 |
The `generate()` method can be used to generate text using DBRX. You can generate using the standard attention implementation, flash-attention, and the PyTorch scaled dot product attention. The last two attention implementations give speed ups.
```python
from transformers import DbrxForCausalLM, AutoTokenizer
import ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dbrx.md | https://huggingface.co/docs/transformers/en/model_doc/dbrx/#usage-examples | #usage-examples | .md | 321_2 |
This is the configuration class to store the configuration of a [`DbrxModel`]. It is used to instantiate a Dbrx model according to the
specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a different configuration to that of the [databricks/dbrx-instruct](http... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dbrx.md | https://huggingface.co/docs/transformers/en/model_doc/dbrx/#dbrxconfig | #dbrxconfig | .md | 321_3 |
The bare DBRX Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dbrx.md | https://huggingface.co/docs/transformers/en/model_doc/dbrx/#dbrxmodel | #dbrxmodel | .md | 321_4 |
The DBRX Model transformer for causal language modeling.
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 [to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dbrx.md | https://huggingface.co/docs/transformers/en/model_doc/dbrx/#dbrxforcausallm | #dbrxforcausallm | .md | 321_5 |
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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/byt5.md | https://huggingface.co/docs/transformers/en/model_doc/byt5/ | .md | 322_0 | |
The ByT5 model was presented in [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir
Kale, Adam Roberts, Colin Raffel.
The abstract from the paper is the following:
*Most widely-used... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/byt5.md | https://huggingface.co/docs/transformers/en/model_doc/byt5/#overview | #overview | .md | 322_1 |
ByT5 works on raw UTF-8 bytes, so it can be used without a tokenizer:
```python
>>> from transformers import T5ForConditionalGeneration
>>> import torch
>>> model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
>>> num_special_tokens = 3
>>> # Model has 3 special tokens which take up the input ids... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/byt5.md | https://huggingface.co/docs/transformers/en/model_doc/byt5/#usage-example | #usage-example | .md | 322_2 |
Construct a ByT5 tokenizer. ByT5 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:
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/byt5.md | https://huggingface.co/docs/transformers/en/model_doc/byt5/#byt5tokenizer | #byt5tokenizer | .md | 322_3 |
<!--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/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/ | .md | 323_0 | |
The [Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/) model is a major update to [Qwen-VL](https://arxiv.org/pdf/2308.12966) from the Qwen team at Alibaba Research.
The abstract from the blog is the following:
*This blog introduces Qwen2-VL, an advanced version of the Qwen-VL model that has undergone significant ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#overview | #overview | .md | 323_1 |
The model can accept both images and videos as input. Here's an example code for inference.
```python
from PIL import Image
import requests
import torch
from torchvision import io
from typing import Dict
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
# Load the model in half-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#single-media-inference | #single-media-inference | .md | 323_2 |
The model can batch inputs composed of mixed samples of various types such as images, videos, and text. Here is an example.
```python
image1 = Image.open("/path/to/image1.jpg")
image2 = Image.open("/path/to/image2.jpg")
image3 = Image.open("/path/to/image3.jpg")
image4 = Image.open("/path/to/image4.jpg")
image5 = Ima... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#batch-mixed-media-inference | #batch-mixed-media-inference | .md | 323_3 |
The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs.
```python
min_pixels = ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#image-resolution-trade-off | #image-resolution-trade-off | .md | 323_4 |
By default, images and video content are directly included in the conversation. When handling multiple images, it's helpful to add labels to the images and videos for better reference. Users can control this behavior with the following settings:
```python
conversation = [
{
"role": "user",
"content": [
{"type": "imag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#multiple-image-inputs | #multiple-image-inputs | .md | 323_5 |
First, make sure to install the latest version of Flash Attention 2:
```bash
pip install -U flash-attn --no-build-isolation
```
Also, you should have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#flash-attention-2-to-speed-up-generation | #flash-attention-2-to-speed-up-generation | .md | 323_6 |
This is the configuration class to store the configuration of a [`Qwen2VLModel`]. It is used to instantiate a
Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen2-VL-7B-Instruct [Qwen/Qwe... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#qwen2vlconfig | #qwen2vlconfig | .md | 323_7 |
Constructs a Qwen2-VL image processor that dynamically resizes images based on the original images.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to us... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#qwen2vlimageprocessor | #qwen2vlimageprocessor | .md | 323_8 |
Constructs a Qwen2-VL processor which wraps a Qwen2-VL image processor and a Qwen2 tokenizer into a single processor.
[`Qwen2VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
[`~Qwen2VLProcessor.__call__`] and [`~Qwen2VLProcessor.decode`] for more information.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#qwen2vlprocessor | #qwen2vlprocessor | .md | 323_9 |
The bare Qwen2VL Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
Thi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#qwen2vlmodel | #qwen2vlmodel | .md | 323_10 |
No docstring available for Qwen2VLForConditionalGeneration
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_vl.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_vl/#qwen2vlforconditionalgeneration | #qwen2vlforconditionalgeneration | .md | 323_11 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the MIT License; you may not use this file except in compliance with
the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR C... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/superpoint.md | https://huggingface.co/docs/transformers/en/model_doc/superpoint/ | .md | 324_0 | |
The SuperPoint model was proposed
in [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel
DeTone, Tomasz Malisiewicz and Andrew Rabinovich.
This model is the result of a self-supervised training of a fully-convolutional network for interest point detection... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/superpoint.md | https://huggingface.co/docs/transformers/en/model_doc/superpoint/#overview | #overview | .md | 324_1 |
Here is a quick example of using the model to detect interest points in an image:
```python
from transformers import AutoImageProcessor, SuperPointForKeypointDetection
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/superpoint.md | https://huggingface.co/docs/transformers/en/model_doc/superpoint/#usage-tips | #usage-tips | .md | 324_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SuperPoint. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/superpoint.md | https://huggingface.co/docs/transformers/en/model_doc/superpoint/#resources | #resources | .md | 324_3 |
This is the configuration class to store the configuration of a [`SuperPointForKeypointDetection`]. It is used to instantiate a
SuperPoint 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 SuperP... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/superpoint.md | https://huggingface.co/docs/transformers/en/model_doc/superpoint/#superpointconfig | #superpointconfig | .md | 324_4 |
Constructs a SuperPoint image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden
by `do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"height": 480, "wid... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/superpoint.md | https://huggingface.co/docs/transformers/en/model_doc/superpoint/#superpointimageprocessor | #superpointimageprocessor | .md | 324_5 |
SuperPoint model outputting keypoints and descriptors.
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.
Parameters:
config ([`Supe... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/superpoint.md | https://huggingface.co/docs/transformers/en/model_doc/superpoint/#superpointforkeypointdetection | #superpointforkeypointdetection | .md | 324_6 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flan-t5.md | https://huggingface.co/docs/transformers/en/model_doc/flan-t5/ | .md | 325_0 | |
FLAN-T5 was released in the paper [Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf) - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.
One can directly use FLAN-T5 weights without finetuning the model:
```python
>>> from transformers import AutoModel... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flan-t5.md | https://huggingface.co/docs/transformers/en/model_doc/flan-t5/#overview | #overview | .md | 325_1 |
<!--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/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/ | .md | 326_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=ctrl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/tiny-ctrl">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#ctrl | #ctrl | .md | 326_1 |
CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#overview | #overview | .md | 326_2 |
- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
or links to generate coherent text. Refer to the [original implementation](https://github.com/salesforce/ctrl) for
more information.
- CTRL is a model with absolute position embeddings so it's usually a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#usage-tips | #usage-tips | .md | 326_3 |
- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#resources | #resources | .md | 326_4 |
This is the configuration class to store the configuration of a [`CTRLModel`] or a [`TFCTRLModel`]. It is used to
instantiate a CTRL 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
[Salesforce/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#ctrlconfig | #ctrlconfig | .md | 326_5 |
Construct a CTRL tokenizer. Based on Byte-Pair-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:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#ctrltokenizer | #ctrltokenizer | .md | 326_6 |
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