text stringlengths 5 58.6k | source stringclasses 470
values | url stringlengths 49 167 | source_section stringlengths 0 90 | file_type stringclasses 1
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ConvNextV2 Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all ma... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnextv2.md | https://huggingface.co/docs/transformers/en/model_doc/convnextv2/#convnextv2forimageclassification | #convnextv2forimageclassification | .md | 344_5 |
No docstring available for TFConvNextV2Model
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnextv2.md | https://huggingface.co/docs/transformers/en/model_doc/convnextv2/#tfconvnextv2model | #tfconvnextv2model | .md | 344_6 |
No docstring available for TFConvNextV2ForImageClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnextv2.md | https://huggingface.co/docs/transformers/en/model_doc/convnextv2/#tfconvnextv2forimageclassification | #tfconvnextv2forimageclassification | .md | 344_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/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/ | .md | 345_0 | |
The Donut model was proposed in [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by
Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
Donut consists of an image Transformer encoder and an autoregre... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#overview | #overview | .md | 345_1 |
- The quickest way to get started with Donut is by checking the [tutorial
notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Donut), which show how to use the model
at inference time as well as fine-tuning on custom data.
- Donut is always used within the [VisionEncoderDecoder](vision-encoder-d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#usage-tips | #usage-tips | .md | 345_2 |
Donut's [`VisionEncoderDecoder`] model accepts images as input and makes use of
[`~generation.GenerationMixin.generate`] to autoregressively generate text given the input image.
The [`DonutImageProcessor`] class is responsible for preprocessing the input image and
[`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`] dec... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#inference-examples | #inference-examples | .md | 345_3 |
We refer to the [tutorial notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Donut). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#training | #training | .md | 345_4 |
This is the configuration class to store the configuration of a [`DonutSwinModel`]. It is used to instantiate a
Donut 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 Donut
[naver-clova-ix/donut... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#donutswinconfig | #donutswinconfig | .md | 345_5 |
Constructs a Donut image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
Size of... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#donutimageprocessor | #donutimageprocessor | .md | 345_6 |
No docstring available for DonutFeatureExtractor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#donutfeatureextractor | #donutfeatureextractor | .md | 345_7 |
Constructs a Donut processor which wraps a Donut image processor and an XLMRoBERTa tokenizer into a single
processor.
[`DonutProcessor`] offers all the functionalities of [`DonutImageProcessor`] and
[`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]. See the [`~DonutProcessor.__call__`] and
[`~DonutProcessor.decode`] ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#donutprocessor | #donutprocessor | .md | 345_8 |
The bare Donut Swin Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general u... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/donut.md | https://huggingface.co/docs/transformers/en/model_doc/donut/#donutswinmodel | #donutswinmodel | .md | 345_9 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mluke.md | https://huggingface.co/docs/transformers/en/model_doc/mluke/ | .md | 346_0 | |
The mLUKE model was proposed in [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka. It's a multilingual extension
of the [LUKE model](https://arxiv.org/abs/2010.01057) trained on the basis of XLM-Ro... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mluke.md | https://huggingface.co/docs/transformers/en/model_doc/mluke/#overview | #overview | .md | 346_1 |
One can directly plug in the weights of mLUKE into a LUKE model, like so:
```python
from transformers import LukeModel
model = LukeModel.from_pretrained("studio-ousia/mluke-base")
```
Note that mLUKE has its own tokenizer, [`MLukeTokenizer`]. You can initialize it as follows:
```python
from transformers import M... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mluke.md | https://huggingface.co/docs/transformers/en/model_doc/mluke/#usage-tips | #usage-tips | .md | 346_2 |
Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mluke.md | https://huggingface.co/docs/transformers/en/model_doc/mluke/#mluketokenizer | #mluketokenizer | .md | 346_3 |
<!--Copyright 2021 NVIDIA Corporation and 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 b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/ | .md | 347_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/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbert | #qdqbert | .md | 347_1 |
The QDQBERT model can be referenced in [Integer Quantization for Deep Learning Inference: Principles and Empirical
Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius
Micikevicius.
The abstract from the paper is the following:
*Quantization techniques can ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#overview | #overview | .md | 347_2 |
- QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to (i) linear layer
inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.
- QDQBERT requires the dependency of [Pytorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/p... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#usage-tips | #usage-tips | .md | 347_3 |
QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to BERT by
`TensorQuantizer` in [Pytorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization). `TensorQuantizer` is the module
for quantizing tensors, with `QuantDescriptor` defining... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#set-default-quantizers | #set-default-quantizers | .md | 347_4 |
Calibration is the terminology of passing data samples to the quantizer and deciding the best scaling factors for
tensors. After setting up the tensor quantizers, one can use the following example to calibrate the model:
```python
>>> # Find the TensorQuantizer and enable calibration
>>> for name, module in model.nam... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#calibration | #calibration | .md | 347_5 |
The goal of exporting to ONNX is to deploy inference by [TensorRT](https://developer.nvidia.com/tensorrt). Fake
quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. After setting static member of
TensorQuantizer to use Pytorch’s own fake quantization functions, fake quantized model can b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#export-to-onnx | #export-to-onnx | .md | 347_6 |
- [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/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#resources | #resources | .md | 347_7 |
This is the configuration class to store the configuration of a [`QDQBertModel`]. It is used to instantiate an
QDQBERT 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 BERT
[google-bert/bert-bas... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertconfig | #qdqbertconfig | .md | 347_8 |
The bare QDQBERT 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 hea... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertmodel | #qdqbertmodel | .md | 347_9 |
QDQBERT Model with a `language modeling` head on top for CLM fine-tuning.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertlmheadmodel | #qdqbertlmheadmodel | .md | 347_10 |
QDQBERT Model with a `language modeling` head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [tor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertformaskedlm | #qdqbertformaskedlm | .md | 347_11 |
Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or savin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertforsequenceclassification | #qdqbertforsequenceclassification | .md | 347_12 |
Bert Model with a `next sentence prediction (classification)` head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertfornextsentenceprediction | #qdqbertfornextsentenceprediction | .md | 347_13 |
Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertformultiplechoice | #qdqbertformultiplechoice | .md | 347_14 |
QDQBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloadin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertfortokenclassification | #qdqbertfortokenclassification | .md | 347_15 |
QDQBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertforquestionanswering | #qdqbertforquestionanswering | .md | 347_16 |
<!--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/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/ | .md | 348_0 | |
The Aria model was proposed in [Aria: An Open Multimodal Native Mixture-of-Experts Model](https://huggingface.co/papers/2410.05993) by Li et al. from the Rhymes.AI team.
Aria is an open multimodal-native model with best-in-class performance across a wide range of multimodal, language, and coding tasks. It has a Mixtu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#overview | #overview | .md | 348_1 |
Here's how to use the model for vision tasks:
```python
import requests
import torch
from PIL import Image
from transformers import AriaProcessor, AriaForConditionalGeneration
model_id_or_path = "rhymes-ai/Aria"
model = AriaForConditionalGeneration.from_pretrained(
model_id_or_path, device_map="auto"
)
processor = ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#usage-tips | #usage-tips | .md | 348_2 |
A vision processor for the Aria model that handles image preprocessing.
Initialize the AriaImageProcessor.
Args:
image_mean (`list`, *optional*, defaults to [0.5, 0.5, 0.5]):
Mean values for normalization.
image_std (`list`, *optional*, defaults to [0.5, 0.5, 0.5]):
Standard deviation values for normalization.
max_im... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaimageprocessor | #ariaimageprocessor | .md | 348_3 |
AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer.
Args:
image_processor (`AriaImageProcessor`, *optional*):
The AriaImageProcessor to use for image preprocessing.
tokenizer (`PreTrainedTokenizerBase`, *optional*):
An instance of [`PreTrainedTokenizer... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaprocessor | #ariaprocessor | .md | 348_4 |
This class handles the configuration for the text component of the Aria model.
Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria
[rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture.
This class extends the LlamaConfig to include additiona... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | #ariatextconfig | .md | 348_5 |
This class handles the configuration for both vision and text components of the Aria model,
as well as additional parameters for image token handling and projector mapping.
Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria
[rhymes-ai/Aria](https://huggin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaconfig | #ariaconfig | .md | 348_6 |
The bare AriaText 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.)
Th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextmodel | #ariatextmodel | .md | 348_7 |
Aria model for causal language modeling tasks.
This class extends `LlamaForCausalLM` to incorporate the Mixture of Experts (MoE) approach,
allowing for more efficient and scalable language modeling.
Args:
config (`AriaTextConfig`):
Configuration object for the model. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextforcausallm | #ariatextforcausallm | .md | 348_8 |
Aria model for conditional generation tasks.
This model combines a vision tower, a multi-modal projector, and a language model
to perform tasks that involve both image and text inputs.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaforconditionalgeneration | #ariaforconditionalgeneration | .md | 348_9 |
<!--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/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/ | .md | 349_0 | |
The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
[`EncoderDecoderModel`] as proposed in [Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
The abstract from the paper... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#overview | #overview | .md | 349_1 |
The model can be used in combination with the [`EncoderDecoderModel`] to leverage two pretrained BERT checkpoints for
subsequent fine-tuning:
```python
>>> # leverage checkpoints for Bert2Bert model...
>>> # use BERT's cls token as BOS token and sep token as EOS token
>>> encoder = BertGenerationEncoder.from_pretrain... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#usage-examples-and-tips | #usage-examples-and-tips | .md | 349_2 |
This is the configuration class to store the configuration of a [`BertGenerationPreTrainedModel`]. It is used to
instantiate a BertGeneration 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 Ber... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | #bertgenerationconfig | .md | 349_3 |
Construct a BertGeneration tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
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`):
[Senten... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationtokenizer | #bertgenerationtokenizer | .md | 349_4 |
The bare BertGeneration 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, prun... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationencoder | #bertgenerationencoder | .md | 349_5 |
BertGeneration Model with a `language modeling` head on top for CLM fine-tuning.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationdecoder | #bertgenerationdecoder | .md | 349_6 |
<!--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/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/ | .md | 350_0 | |
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code. This model was deprecated due to security issues linked to `pickle.load`.
We recommend switching to more recent models for improved security.
In case you would still like to use `TransfoXL` in your exper... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transformer-xl | #transformer-xl | .md | 350_1 |
The Transformer-XL model was proposed in [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
Salakhutdinov. It's a causal (uni-directional) transformer with relative positioning (sinusoïd... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#overview | #overview | .md | 350_2 |
- Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right. The
original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
- Transformer-XL is one of the few models that has no sequence length limit.
- Same as a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#usage-tips | #usage-tips | .md | 350_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/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#resources | #resources | .md | 350_4 |
This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. It is
used to instantiate a Transformer-XL model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transfoxlconfig | #transfoxlconfig | .md | 350_5 |
Construct a Transformer-XL tokenizer adapted from Vocab class in [the original
code](https://github.com/kimiyoung/transformer-xl). The Transformer-XL tokenizer is a word-level tokenizer (no
sub-word tokenization).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users shou... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transfoxltokenizer | #transfoxltokenizer | .md | 350_6 |
[[autodoc]] models.deprecated.transfo_xl.modeling_transfo_xl.TransfoXLModelOutput: module 'transformers.models.deprecated' has no attribute 'transfo_xl'
[[autodoc]] models.deprecated.transfo_xl.modeling_transfo_xl.TransfoXLLMHeadModelOutput: module 'transformers.models.deprecated' has no attribute 'transfo_xl'
[[au... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transfoxl-specific-outputs | #transfoxl-specific-outputs | .md | 350_7 |
The bare Bert 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/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transfoxlmodel | #transfoxlmodel | .md | 350_8 |
The Transformer-XL Model with a language modeling head on top (adaptive softmax with weights tied to the adaptive
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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transfoxllmheadmodel | #transfoxllmheadmodel | .md | 350_9 |
The Transformer-XL Model transformer with a sequence classification head on top (linear layer).
[`TransfoXLForSequenceClassification`] uses the last token in order to do the classification, as other causal
models (e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transfoxlforsequenceclassification | #transfoxlforsequenceclassification | .md | 350_10 |
No docstring available for TFTransfoXLModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#tftransfoxlmodel | #tftransfoxlmodel | .md | 350_11 |
No docstring available for TFTransfoXLLMHeadModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#tftransfoxllmheadmodel | #tftransfoxllmheadmodel | .md | 350_12 |
No docstring available for TFTransfoXLForSequenceClassification
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#tftransfoxlforsequenceclassification | #tftransfoxlforsequenceclassification | .md | 350_13 |
No docstring available for AdaptiveEmbedding
No docstring available for TFAdaptiveEmbedding | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#internal-layers | #internal-layers | .md | 350_14 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deta.md | https://huggingface.co/docs/transformers/en/model_doc/deta/ | .md | 351_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/deta.md | https://huggingface.co/docs/transformers/en/model_doc/deta/#deta | #deta | .md | 351_1 |
The DETA model was proposed in [NMS Strikes Back](https://arxiv.org/abs/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
DETA (short for Detection Transformers with Assignment) improves [Deformable DETR](deformable_detr) by replacing the one-to-one bipartite Hungarian matching loss
w... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deta.md | https://huggingface.co/docs/transformers/en/model_doc/deta/#overview | #overview | .md | 351_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DETA.
- Demo notebooks for DETA can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETA).
- Scripts for finetuning [`DetaForObjectDetection`] with [`Trainer`] or [Accelerate](https:... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deta.md | https://huggingface.co/docs/transformers/en/model_doc/deta/#resources | #resources | .md | 351_3 |
This is the configuration class to store the configuration of a [`DetaModel`]. It is used to instantiate a DETA
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 DETA
[SenseTime/deformable-detr](... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deta.md | https://huggingface.co/docs/transformers/en/model_doc/deta/#detaconfig | #detaconfig | .md | 351_4 |
Constructs a Deformable DETR image processor.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the speci... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deta.md | https://huggingface.co/docs/transformers/en/model_doc/deta/#detaimageprocessor | #detaimageprocessor | .md | 351_5 |
The bare DETA Model (consisting of a backbone and encoder-decoder 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deta.md | https://huggingface.co/docs/transformers/en/model_doc/deta/#detamodel | #detamodel | .md | 351_6 |
DETA Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, 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 model (such as downloading or saving, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deta.md | https://huggingface.co/docs/transformers/en/model_doc/deta/#detaforobjectdetection | #detaforobjectdetection | .md | 351_7 |
<!--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/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/ | .md | 352_0 | |
StarCoder2 is a family of open LLMs for code and comes in 3 different sizes with 3B, 7B and 15B parameters. The flagship StarCoder2-15B model is trained on over 4 trillion tokens and 600+ programming languages from The Stack v2. All models use Grouped Query Attention, a context window of 16,384 tokens with a sliding wi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/#overview | #overview | .md | 352_1 |
The models are licensed under the [BigCode OpenRAIL-M v1 license agreement](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/#license | #license | .md | 352_2 |
The StarCoder2 models can be found in the [HuggingFace hub](https://huggingface.co/collections/bigcode/starcoder2-65de6da6e87db3383572be1a). You can find some examples for inference and fine-tuning in StarCoder2's [GitHub repo](https://github.com/bigcode-project/starcoder2).
These ready-to-use checkpoints can be down... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/#usage-tips | #usage-tips | .md | 352_3 |
This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a
Starcoder2 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 [bigcode/starcoder2-7... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/#starcoder2config | #starcoder2config | .md | 352_4 |
The bare Starcoder2 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/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/#starcoder2model | #starcoder2model | .md | 352_5 |
No docstring available for Starcoder2ForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/#starcoder2forcausallm | #starcoder2forcausallm | .md | 352_6 |
The Starcoder2 Model transformer with a sequence classification head on top (linear layer).
[`Starcoder2ForSequenceClassification`] 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 l... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/#starcoder2forsequenceclassification | #starcoder2forsequenceclassification | .md | 352_7 |
The Starcoder2 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/starcoder2.md | https://huggingface.co/docs/transformers/en/model_doc/starcoder2/#starcoder2fortokenclassification | #starcoder2fortokenclassification | .md | 352_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/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/ | .md | 353_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=electra">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-electra-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/electra_large_discriminator_squad2_512">
<img alt="Spaces" src="https://img.shiel... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electra | #electra | .md | 353_1 |
The ELECTRA model was proposed in the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than
Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ELECTRA is a new pretraining approach which trains two
transformer models: the generator and the discriminator. The generator's role is to replace tokens ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#overview | #overview | .md | 353_2 |
- ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The
only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller,
while the hidden size is larger. An additional projection layer (linear) is used to projec... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#usage-tips | #usage-tips | .md | 353_3 |
- [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/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#resources | #resources | .md | 353_4 |
This is the configuration class to store the configuration of a [`ElectraModel`] or a [`TFElectraModel`]. It is
used to instantiate a ELECTRA 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 ELE... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electraconfig | #electraconfig | .md | 353_5 |
Construct a Electra tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electratokenizer | #electratokenizer | .md | 353_6 |
Construct a "fast" ELECTRA tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Fil... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electratokenizerfast | #electratokenizerfast | .md | 353_7 |
models.electra.modeling_electra.ElectraForPreTrainingOutput
Output type of [`ElectraForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss of the ELECTRA objective.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Predicti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electra-specific-outputs | #electra-specific-outputs | .md | 353_8 |
The bare Electra Model transformer outputting raw hidden-states without any specific head on top. Identical to the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the hidden size and embedding size are different. Both the generator and discriminator checkpoints m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electramodel | #electramodel | .md | 353_9 |
Electra model with a binary classification head on top as used during pretraining for identifying generated tokens.
It is recommended to load the discriminator checkpoint into that model.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electraforpretraining | #electraforpretraining | .md | 353_10 |
ELECTRA Model with a `language modeling` head on top for CLM fine-tuning.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electraforcausallm | #electraforcausallm | .md | 353_11 |
Electra model with a language modeling head on top.
Even though both the discriminator and generator may be loaded into this model, the generator is the only model of
the two to have been trained for the masked language modeling task.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electraformaskedlm | #electraformaskedlm | .md | 353_12 |
ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or sa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electraforsequenceclassification | #electraforsequenceclassification | .md | 353_13 |
ELECTRA Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as download... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electraformultiplechoice | #electraformultiplechoice | .md | 353_14 |
Electra model with a token classification head on top.
Both the discriminator and generator may be loaded into this 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 in... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electrafortokenclassification | #electrafortokenclassification | .md | 353_15 |
ELECTRA Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/electra.md | https://huggingface.co/docs/transformers/en/model_doc/electra/#electraforquestionanswering | #electraforquestionanswering | .md | 353_16 |
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