text stringlengths 5 58.6k | source stringclasses 470
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No docstring available for StableLmForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#stablelmforcausallm | #stablelmforcausallm | .md | 390_7 |
The StableLm transformer with a sequence classification head on top (linear layer).
[`StableLmForSequenceClassification`] uses the last token in order to do the classification, as other causal
models (e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#stablelmforsequenceclassification | #stablelmforsequenceclassification | .md | 390_8 |
The StableLm Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#stablelmfortokenclassification | #stablelmfortokenclassification | .md | 390_9 |
<!--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/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/ | .md | 391_0 | |
The DiffLlama model was proposed in [Differential Transformer](https://arxiv.org/abs/2410.05258) by Kazuma Matsumoto and .
This model is combine Llama model and Differential Transformer's Attention.
The abstract from the paper is the following:
*Transformer tends to overallocate attention to irrelevant context. In ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/#overview | #overview | .md | 391_1 |
The hyperparameters of this model is the same as Llama model. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/#usage-tips | #usage-tips | .md | 391_2 |
This is the configuration class to store the configuration of a [`DiffLlamaModel`]. It is used to instantiate an DiffLlama
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 [kajuma/DiffLlama-0.3B... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/#diffllamaconfig | #diffllamaconfig | .md | 391_3 |
The bare DiffLlama 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/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/#diffllamamodel | #diffllamamodel | .md | 391_4 |
No docstring available for DiffLlamaForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/#diffllamaforcausallm | #diffllamaforcausallm | .md | 391_5 |
The DiffLlama Model transformer with a sequence classification head on top (linear layer).
[`DiffLlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the las... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/#diffllamaforsequenceclassification | #diffllamaforsequenceclassification | .md | 391_6 |
The DiffLlama Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the g... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/#diffllamaforquestionanswering | #diffllamaforquestionanswering | .md | 391_7 |
The DiffLlama Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/diffllama.md | https://huggingface.co/docs/transformers/en/model_doc/diffllama/#diffllamafortokenclassification | #diffllamafortokenclassification | .md | 391_8 |
<!--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/bertweet.md | https://huggingface.co/docs/transformers/en/model_doc/bertweet/ | .md | 392_0 | |
The BERTweet model was proposed in [BERTweet: A pre-trained language model for English Tweets](https://www.aclweb.org/anthology/2020.emnlp-demos.2.pdf) by Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen.
The abstract from the paper is the following:
*We present BERTweet, the first public large-scale pre-trained language... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bertweet.md | https://huggingface.co/docs/transformers/en/model_doc/bertweet/#overview | #overview | .md | 392_1 |
```python
>>> import torch
>>> from transformers import AutoModel, AutoTokenizer
>>> bertweet = AutoModel.from_pretrained("vinai/bertweet-base")
>>> # For transformers v4.x+:
>>> tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)
>>> # For transformers v3.x:
>>> # tokenizer = AutoTokeni... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bertweet.md | https://huggingface.co/docs/transformers/en/model_doc/bertweet/#usage-example | #usage-example | .md | 392_2 |
Constructs a BERTweet tokenizer, using 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`):
Pat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bertweet.md | https://huggingface.co/docs/transformers/en/model_doc/bertweet/#bertweettokenizer | #bertweettokenizer | .md | 392_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/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/ | .md | 393_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=modernbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-modernbert-blueviolet">
</a>
<a href="https://arxiv.org/abs/2412.13663">
<img alt="Paper page" src="https://img.shields.io/badge/Paper%20page-2412.13663-g... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/#modernbert | #modernbert | .md | 393_1 |
The ModernBERT model was proposed in [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://arxiv.org/abs/2412.13663) by Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gal... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/#overview | #overview | .md | 393_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ModernBert.
<PipelineTag pipeline="text-classification"/>
- A notebook on how to [finetune for General Language Understanding Evaluation (GLUE) with Transformers](https://github.com/AnswerDotAI/ModernBERT/blob/mai... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/#resources | #resources | .md | 393_3 |
This is the configuration class to store the configuration of a [`ModernBertModel`]. It is used to instantiate an ModernBert
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 ModernBERT-base.
e.g... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/#modernbertconfig | #modernbertconfig | .md | 393_4 |
The bare ModernBert 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/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/#modernbertmodel | #modernbertmodel | .md | 393_5 |
The ModernBert Model with a decoder head on top that is used for masked 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.) ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/#modernbertformaskedlm | #modernbertformaskedlm | .md | 393_6 |
The ModernBert Model with a sequence classification head on top that performs pooling.
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.)
Th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/#modernbertforsequenceclassification | #modernbertforsequenceclassification | .md | 393_7 |
The ModernBert Model with a token classification head on top, e.g. for Named Entity Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/modernbert.md | https://huggingface.co/docs/transformers/en/model_doc/modernbert/#modernbertfortokenclassification | #modernbertfortokenclassification | .md | 393_8 |
<!--Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. 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.apac... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/ | .md | 394_0 | |
The BridgeTower model was proposed in [BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The goal of this model is to build a
bridge between each uni-modal encoder an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#overview | #overview | .md | 394_1 |
BridgeTower consists of a visual encoder, a textual encoder and cross-modal encoder with multiple lightweight bridge layers.
The goal of this approach was to build a bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#usage-tips-and-examples | #usage-tips-and-examples | .md | 394_2 |
This is the configuration class to store the configuration of a [`BridgeTowerModel`]. It is used to instantiate a
BridgeTower 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 bridgetower-base
[B... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowerconfig | #bridgetowerconfig | .md | 394_3 |
This is the configuration class to store the text configuration of a [`BridgeTowerModel`]. The default values here
are copied from RoBERTa. Instantiating a configuration with the defaults will yield a similar configuration to that
of the bridgetower-base [BridegTower/bridgetower-base](https://huggingface.co/BridgeTower... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowertextconfig | #bridgetowertextconfig | .md | 394_4 |
This is the configuration class to store the vision configuration of a [`BridgeTowerModel`]. Instantiating a
configuration with the defaults will yield a similar configuration to that of the bridgetower-base
[BridgeTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-base/) architecture.
Configurati... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowervisionconfig | #bridgetowervisionconfig | .md | 394_5 |
Constructs a BridgeTower image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{'shortest_ed... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowerimageprocessor | #bridgetowerimageprocessor | .md | 394_6 |
Constructs a BridgeTower processor which wraps a Roberta tokenizer and BridgeTower image processor into a single
processor.
[`BridgeTowerProcessor`] offers all the functionalities of [`BridgeTowerImageProcessor`] and
[`RobertaTokenizerFast`]. See the docstring of [`~BridgeTowerProcessor.__call__`] and
[`~BridgeTowerP... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowerprocessor | #bridgetowerprocessor | .md | 394_7 |
The bare BridgeTower Model transformer outputting BridgeTowerModelOutput object 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 relate... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowermodel | #bridgetowermodel | .md | 394_8 |
BridgeTower Model with a image-text contrastive head on top computing image-text contrastive loss.
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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowerforcontrastivelearning | #bridgetowerforcontrastivelearning | .md | 394_9 |
BridgeTower Model with a language modeling head on top as done during pretraining.
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
behavi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowerformaskedlm | #bridgetowerformaskedlm | .md | 394_10 |
BridgeTower Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the
[CLS] token) for image-to-text matching.
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 th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bridgetower.md | https://huggingface.co/docs/transformers/en/model_doc/bridgetower/#bridgetowerforimageandtextretrieval | #bridgetowerforimageandtextretrieval | .md | 394_11 |
<!--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/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/ | .md | 395_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=bart">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-bart-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/bart-large-mnli">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#bart | #bart | .md | 395_1 |
The Bart model was proposed in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation,
Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan
Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#overview | #overview | .md | 395_2 |
- BART is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left.
- Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is fed the original tokens (but has a mask to hide the future words like ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#usage-tips | #usage-tips | .md | 395_3 |
- Bart doesn't use `token_type_ids` for sequence classification. Use [`BartTokenizer`] or
[`~BartTokenizer.encode`] to get the proper splitting.
- The forward pass of [`BartModel`] will create the `decoder_input_ids` if they are not passed.
This is different than some other modeling APIs. A typical use case of this fea... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#implementation-notes | #implementation-notes | .md | 395_4 |
The `facebook/bart-base` and `facebook/bart-large` checkpoints can be used to fill multi-token masks.
```python
from transformers import BartForConditionalGeneration, BartTokenizer
model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", forced_bos_token_id=0)
tok = BartTokenizer.from_pretrained("... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#mask-filling | #mask-filling | .md | 395_5 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BART. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an exi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#resources | #resources | .md | 395_6 |
This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART
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 BART
[facebook/bart-large](https:... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#bartconfig | #bartconfig | .md | 395_7 |
Constructs a BART tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not: ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#barttokenizer | #barttokenizer | .md | 395_8 |
Construct a "fast" BART tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer,
using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginn... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#barttokenizerfast | #barttokenizerfast | .md | 395_9 |
The bare BART 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/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#bartmodel | #bartmodel | .md | 395_10 |
The BART Model with a language modeling head. Can be used for summarization.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#bartforconditionalgeneration | #bartforconditionalgeneration | .md | 395_11 |
Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input e... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#bartforsequenceclassification | #bartforsequenceclassification | .md | 395_12 |
BART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
li... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#bartforquestionanswering | #bartforquestionanswering | .md | 395_13 |
BART decoder 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 embeddings, p... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#bartforcausallm | #bartforcausallm | .md | 395_14 |
No docstring available for TFBartModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#tfbartmodel | #tfbartmodel | .md | 395_15 |
No docstring available for TFBartForConditionalGeneration
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#tfbartforconditionalgeneration | #tfbartforconditionalgeneration | .md | 395_16 |
No docstring available for TFBartForSequenceClassification
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#tfbartforsequenceclassification | #tfbartforsequenceclassification | .md | 395_17 |
No docstring available for FlaxBartModel
Methods: __call__
- encode
- decode | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#flaxbartmodel | #flaxbartmodel | .md | 395_18 |
No docstring available for FlaxBartForConditionalGeneration
Methods: __call__
- encode
- decode | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#flaxbartforconditionalgeneration | #flaxbartforconditionalgeneration | .md | 395_19 |
No docstring available for FlaxBartForSequenceClassification
Methods: __call__
- encode
- decode | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#flaxbartforsequenceclassification | #flaxbartforsequenceclassification | .md | 395_20 |
No docstring available for FlaxBartForQuestionAnswering
Methods: __call__
- encode
- decode | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#flaxbartforquestionanswering | #flaxbartforquestionanswering | .md | 395_21 |
No docstring available for FlaxBartForCausalLM
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bart.md | https://huggingface.co/docs/transformers/en/model_doc/bart/#flaxbartforcausallm | #flaxbartforcausallm | .md | 395_22 |
<!--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/dac.md | https://huggingface.co/docs/transformers/en/model_doc/dac/ | .md | 396_0 | |
The DAC model was proposed in [Descript Audio Codec: High-Fidelity Audio Compression with Improved RVQGAN](https://arxiv.org/abs/2306.06546) by Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, Kundan Kumar.
The Descript Audio Codec (DAC) model is a powerful tool for compressing audio data, making it hi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dac.md | https://huggingface.co/docs/transformers/en/model_doc/dac/#overview | #overview | .md | 396_1 |
The Descript Audio Codec (DAC) model is structured into three distinct stages:
1. Encoder Model: This stage compresses the input audio, reducing its size while retaining essential information.
2. Residual Vector Quantizer (RVQ) Model: Working in tandem with the encoder, this model quantizes the latent codes of the au... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dac.md | https://huggingface.co/docs/transformers/en/model_doc/dac/#model-structure | #model-structure | .md | 396_2 |
Here is a quick example of how to encode and decode an audio using this model:
```python
>>> from datasets import load_dataset, Audio
>>> from transformers import DacModel, AutoProcessor
>>> librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> model = DacMode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dac.md | https://huggingface.co/docs/transformers/en/model_doc/dac/#usage-example | #usage-example | .md | 396_3 |
This is the configuration class to store the configuration of an [`DacModel`]. It is used to instantiate a
Dac 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
[descript/dac_16khz](https://huggi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dac.md | https://huggingface.co/docs/transformers/en/model_doc/dac/#dacconfig | #dacconfig | .md | 396_4 |
Constructs an Dac feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information regarding those methods.
Args:
feature_size (`int`, *optional*, defaults to 1)... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dac.md | https://huggingface.co/docs/transformers/en/model_doc/dac/#dacfeatureextractor | #dacfeatureextractor | .md | 396_5 |
The DAC (Descript Audio Codec) 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 is also a PyTorch [torch.nn.Module](http... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dac.md | https://huggingface.co/docs/transformers/en/model_doc/dac/#dacmodel | #dacmodel | .md | 396_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/tapex.md | https://huggingface.co/docs/transformers/en/model_doc/tapex/ | .md | 397_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.
</... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapex.md | https://huggingface.co/docs/transformers/en/model_doc/tapex/#tapex | #tapex | .md | 397_1 |
The TAPEX model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu,
Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. TAPEX pre-trains a BART model to solve synthetic SQL queries, after
which it can be fine-tuned to answ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapex.md | https://huggingface.co/docs/transformers/en/model_doc/tapex/#overview | #overview | .md | 397_2 |
- TAPEX is a generative (seq2seq) model. One can directly plug in the weights of TAPEX into a BART model.
- TAPEX has checkpoints on the hub that are either pre-trained only, or fine-tuned on WTQ, SQA, WikiSQL and TabFact.
- Sentences + tables are presented to the model as `sentence + " " + linearized table`. The linea... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapex.md | https://huggingface.co/docs/transformers/en/model_doc/tapex/#usage-tips | #usage-tips | .md | 397_3 |
Below, we illustrate how to use TAPEX for table question answering. As one can see, one can directly plug in the weights of TAPEX into a BART model.
We use the [Auto API](auto), which will automatically instantiate the appropriate tokenizer ([`TapexTokenizer`]) and model ([`BartForConditionalGeneration`]) for us,
based... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapex.md | https://huggingface.co/docs/transformers/en/model_doc/tapex/#usage-inference | #usage-inference | .md | 397_4 |
Construct a TAPEX tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
This tokenizer can be used to flatten one or more table(s) and concatenate them with one or more related sentences
to be used by TAPEX models. The format that the TAPEX tokenizer creates is the following:
sentence col: col1 | col2 | col 3 ro... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tapex.md | https://huggingface.co/docs/transformers/en/model_doc/tapex/#tapextokenizer | #tapextokenizer | .md | 397_5 |
<!--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/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/ | .md | 398_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Ti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#efficientformer | #efficientformer | .md | 398_1 |
The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191)
by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren. EfficientFormer proposes a
dimension-consistent pure transformer that can be run... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#overview | #overview | .md | 398_2 |
- [Image classification task guide](../tasks/image_classification) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#documentation-resources | #documentation-resources | .md | 398_3 |
This is the configuration class to store the configuration of an [`EfficientFormerModel`]. It is used to
instantiate an EfficientFormer 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 Efficient... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#efficientformerconfig | #efficientformerconfig | .md | 398_4 |
Constructs a EfficientFormer image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#efficientformerimageprocessor | #efficientformerimageprocessor | .md | 398_5 |
The bare EfficientFormer Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#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/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#efficientformermodel | #efficientformermodel | .md | 398_6 |
EfficientFormer 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 [nn.Module](https://pytorch.org/docs/stable/nn.html#nn.Module) subclass. Use it as a
regular PyTorch Module and refer to the PyTorc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#efficientformerforimageclassification | #efficientformerforimageclassification | .md | 398_7 |
EfficientFormer 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.
<Tip warning={true}>
This model supports inference-only. Fine-tuning with dis... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#efficientformerforimageclassificationwithteacher | #efficientformerforimageclassificationwithteacher | .md | 398_8 |
No docstring available for TFEfficientFormerModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#tfefficientformermodel | #tfefficientformermodel | .md | 398_9 |
No docstring available for TFEfficientFormerForImageClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#tfefficientformerforimageclassification | #tfefficientformerforimageclassification | .md | 398_10 |
No docstring available for TFEfficientFormerForImageClassificationWithTeacher
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientformer.md | https://huggingface.co/docs/transformers/en/model_doc/efficientformer/#tfefficientformerforimageclassificationwithteacher | #tfefficientformerforimageclassificationwithteacher | .md | 398_11 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/madlad-400.md | https://huggingface.co/docs/transformers/en/model_doc/madlad-400/ | .md | 399_0 | |
MADLAD-400 models were released in the paper [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](MADLAD-400: A Multilingual And Document-Level Large Audited Dataset).
The abstract from the paper is the following:
*We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/madlad-400.md | https://huggingface.co/docs/transformers/en/model_doc/madlad-400/#overview | #overview | .md | 399_1 |
<!--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/mamba.md | https://huggingface.co/docs/transformers/en/model_doc/mamba/ | .md | 400_0 | |
The Mamba model was proposed in [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752) by Albert Gu and Tri Dao.
This model is a new paradigm architecture based on `state-space-models`. You can read more about the intuition behind these [here](https://srush.github.io/anno... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mamba.md | https://huggingface.co/docs/transformers/en/model_doc/mamba/#overview | #overview | .md | 400_1 |
```python
from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")
input_ids = tokenizer("Hey how are you doing?", return_tensors= "pt")["input_ids"]
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mamba.md | https://huggingface.co/docs/transformers/en/model_doc/mamba/#a-simple-generation-example | #a-simple-generation-example | .md | 400_2 |
The slow version is not very stable for training, and the fast one needs `float32`!
```python
from datasets import load_dataset
from trl import SFTTrainer
from peft import LoraConfig
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
model_id = "state-spaces/mamba-130m-hf"
tokenizer = Aut... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mamba.md | https://huggingface.co/docs/transformers/en/model_doc/mamba/#peft-finetuning | #peft-finetuning | .md | 400_3 |
This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA
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 MAMBA
[state-spaces/mamba-2.8b]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mamba.md | https://huggingface.co/docs/transformers/en/model_doc/mamba/#mambaconfig | #mambaconfig | .md | 400_4 |
The bare MAMBA 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/mamba.md | https://huggingface.co/docs/transformers/en/model_doc/mamba/#mambamodel | #mambamodel | .md | 400_5 |
The MAMBA 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 inpu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mamba.md | https://huggingface.co/docs/transformers/en/model_doc/mamba/#mambalmheadmodel | #mambalmheadmodel | .md | 400_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/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/ | .md | 401_0 | |
The CvT model was proposed in [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the [Vision Transformer (ViT)](vit) in performance and effici... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/#overview | #overview | .md | 401_1 |
- CvT models are regular Vision Transformers, but trained with convolutions. They outperform the [original model (ViT)](vit) when fine-tuned on ImageNet-1K and CIFAR-100.
- You can check out demo notebooks regarding inference as well as fine-tuning on custom data [here](https://github.com/NielsRogge/Transformers-Tutori... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/#usage-tips | #usage-tips | .md | 401_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CvT.
<PipelineTag pipeline="image-classification"/>
- [`CvtForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/#resources | #resources | .md | 401_3 |
This is the configuration class to store the configuration of a [`CvtModel`]. It is used to instantiate a CvT 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 CvT
[microsoft/cvt-13](https://hugg... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/#cvtconfig | #cvtconfig | .md | 401_4 |
The bare Cvt 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/cvt.md | https://huggingface.co/docs/transformers/en/model_doc/cvt/#cvtmodel | #cvtmodel | .md | 401_5 |
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