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The Idefics3 model was proposed in [Building and better understanding vision-language models: insights and future directions](https://huggingface.co/papers/2408.12637) by Hugo Laurençon, Andrés Marafioti, Victor Sanh, and Léo Tronchon.
Idefics3 is an adaptation of the Idefics2 model with three main differences:
- I... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics3.md | https://huggingface.co/docs/transformers/en/model_doc/idefics3/#overview | #overview | .md | 192_1 |
Input images are processed either by upsampling (if resizing is enabled) or at their original resolution. The resizing behavior depends on two parameters: do_resize and size.
If `do_resize` is set to `True`, the model resizes images so that the longest edge is 4*364 pixels by default.
The default resizing behavior ca... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics3.md | https://huggingface.co/docs/transformers/en/model_doc/idefics3/#usage-tips | #usage-tips | .md | 192_2 |
This is the configuration class to store the configuration of a [`Idefics3Model`]. It is used to instantiate a
Idefics3 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 model of the Idefics3
[Hu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics3.md | https://huggingface.co/docs/transformers/en/model_doc/idefics3/#idefics3config | #idefics3config | .md | 192_3 |
This is the configuration class to store the configuration of a [`Idefics3VisionModel`]. It is used to instantiate a
Idefics3 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the SigLIP che... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics3.md | https://huggingface.co/docs/transformers/en/model_doc/idefics3/#idefics3visionconfig | #idefics3visionconfig | .md | 192_4 |
The Idefics3 Vision Transformer Model outputting raw image embedding.
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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics3.md | https://huggingface.co/docs/transformers/en/model_doc/idefics3/#idefics3visiontransformer | #idefics3visiontransformer | .md | 192_5 |
Idefics3 model consisting of a SIGLIP vision encoder and Llama3 language decoder
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 mod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics3.md | https://huggingface.co/docs/transformers/en/model_doc/idefics3/#idefics3model | #idefics3model | .md | 192_6 |
The Idefics3 Model with a language modeling head. It is made up a SigLIP vision encoder, 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 in... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/idefics3.md | https://huggingface.co/docs/transformers/en/model_doc/idefics3/#idefics3forconditionalgeneration | #idefics3forconditionalgeneration | .md | 192_7 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/ | .md | 193_0 | |
<a id='Overview'></a> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlm | #layoutlm | .md | 193_1 |
The LayoutLM model was proposed in the paper [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
Ming Zhou. It's a simple but effective pretraining method of text and layout for document image unde... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#overview | #overview | .md | 193_2 |
- In addition to *input_ids*, [`~transformers.LayoutLMModel.forward`] also expects the input `bbox`, which are
the bounding boxes (i.e. 2D-positions) of the input tokens. These can be obtained using an external OCR engine such
as Google's [Tesseract](https://github.com/tesseract-ocr/tesseract) (there's a [Python wrappe... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#usage-tips | #usage-tips | .md | 193_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLM. 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#resources | #resources | .md | 193_4 |
This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
LayoutLM 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 LayoutLM
[microsoft/layou... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlmconfig | #layoutlmconfig | .md | 193_5 |
Construct a LayoutLM 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`, *opti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlmtokenizer | #layoutlmtokenizer | .md | 193_6 |
Construct a "fast" LayoutLM 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`):
Fi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlmtokenizerfast | #layoutlmtokenizerfast | .md | 193_7 |
The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.
The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and
Ming Zhou. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlmmodel | #layoutlmmodel | .md | 193_8 |
LayoutLM Model with a `language modeling` head on top.
The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and
Ming Zhou.
This model is a PyTorch [torch.nn.Module](h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlmformaskedlm | #layoutlmformaskedlm | .md | 193_9 |
LayoutLM Model with a sequence classification head on top (a linear layer on top of the pooled output) e.g. for
document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Imag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlmforsequenceclassification | #layoutlmforsequenceclassification | .md | 193_10 |
LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
sequence labeling (information extraction) tasks such as the [FUNSD](https://guillaumejaume.github.io/FUNSD/)
dataset and the [SROIE](https://rrc.cvc.uab.es/?ch=13) dataset.
The LayoutLM model was propo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlmfortokenclassification | #layoutlmfortokenclassification | .md | 193_11 |
LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
[DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
start logits` and `span end logits`).
The LayoutLM model was proposed in [LayoutLM: Pre-training of Te... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#layoutlmforquestionanswering | #layoutlmforquestionanswering | .md | 193_12 |
No docstring available for TFLayoutLMModel | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#tflayoutlmmodel | #tflayoutlmmodel | .md | 193_13 |
No docstring available for TFLayoutLMForMaskedLM | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#tflayoutlmformaskedlm | #tflayoutlmformaskedlm | .md | 193_14 |
No docstring available for TFLayoutLMForSequenceClassification | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#tflayoutlmforsequenceclassification | #tflayoutlmforsequenceclassification | .md | 193_15 |
No docstring available for TFLayoutLMForTokenClassification | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#tflayoutlmfortokenclassification | #tflayoutlmfortokenclassification | .md | 193_16 |
No docstring available for TFLayoutLMForQuestionAnswering
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutlm/#tflayoutlmforquestionanswering | #tflayoutlmforquestionanswering | .md | 193_17 |
<!--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/table-transformer.md | https://huggingface.co/docs/transformers/en/model_doc/table-transformer/ | .md | 194_0 | |
The Table Transformer model was proposed in [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://arxiv.org/abs/2110.00061) by
Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/table-transformer.md | https://huggingface.co/docs/transformers/en/model_doc/table-transformer/#overview | #overview | .md | 194_1 |
<PipelineTag pipeline="object-detection"/>
- A demo notebook for the Table Transformer can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Table%20Transformer).
- It turns out padding of images is quite important for detection. An interesting Github thread with replies from the author... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/table-transformer.md | https://huggingface.co/docs/transformers/en/model_doc/table-transformer/#resources | #resources | .md | 194_2 |
This is the configuration class to store the configuration of a [`TableTransformerModel`]. It is used to
instantiate a Table Transformer 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 Table Tr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/table-transformer.md | https://huggingface.co/docs/transformers/en/model_doc/table-transformer/#tabletransformerconfig | #tabletransformerconfig | .md | 194_3 |
The bare Table Transformer 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 download... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/table-transformer.md | https://huggingface.co/docs/transformers/en/model_doc/table-transformer/#tabletransformermodel | #tabletransformermodel | .md | 194_4 |
Table Transformer 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 downloadin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/table-transformer.md | https://huggingface.co/docs/transformers/en/model_doc/table-transformer/#tabletransformerforobjectdetection | #tabletransformerforobjectdetection | .md | 194_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/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/ | .md | 195_0 | |
The LiLT model was proposed in [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
LiLT allows to combine any pre-trained RoBERTa text encoder with a lightweight Layout Transformer, to enab... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#overview | #overview | .md | 195_1 |
- To combine the Language-Independent Layout Transformer with a new RoBERTa checkpoint from the [hub](https://huggingface.co/models?search=roberta), refer to [this guide](https://github.com/jpWang/LiLT#or-generate-your-own-checkpoint-optional).
The script will result in `config.json` and `pytorch_model.bin` files being... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#usage-tips | #usage-tips | .md | 195_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LiLT.
- Demo notebooks for LiLT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT).
**Documentation resources**
- [Text classification task guide](../tasks/sequence_classifi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#resources | #resources | .md | 195_3 |
This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT
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 LiLT
[SCUT-DLVCLab/lilt-roberta-e... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltconfig | #liltconfig | .md | 195_4 |
The bare LiLT 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
et... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltmodel | #liltmodel | .md | 195_5 |
LiLT 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/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltforsequenceclassification | #liltforsequenceclassification | .md | 195_6 |
Lilt Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltfortokenclassification | #liltfortokenclassification | .md | 195_7 |
Lilt Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
l... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/lilt.md | https://huggingface.co/docs/transformers/en/model_doc/lilt/#liltforquestionanswering | #liltforquestionanswering | .md | 195_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/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/ | .md | 196_0 | |
The M2M100 model was proposed in [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky,
Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy
Liptch... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#overview | #overview | .md | 196_1 |
M2M100 is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation tasks. As the model is
multilingual it expects the sequences in a certain format: A special language id token is used as prefix in both the
source and target text. The source text format is `[lang_code] X [eos]`, where `lang_... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage-tips-and-examples | #usage-tips-and-examples | .md | 196_2 |
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#resources | #resources | .md | 196_3 |
This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
M2M100 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 M2M100
[facebook/m2m100_418M... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100config | #m2m100config | .md | 196_4 |
Construct an M2M100 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`):
Path to the vo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100tokenizer | #m2m100tokenizer | .md | 196_5 |
The bare M2M100 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100model | #m2m100model | .md | 196_6 |
The M2M100 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#m2m100forconditionalgeneration | #m2m100forconditionalgeneration | .md | 196_7 |
Flash Attention 2 is a faster, optimized version of the attention scores computation which relies on `cuda` kernels. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#using-flash-attention-2 | #using-flash-attention-2 | .md | 196_8 |
First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the [official documentation](https://github.com/Dao-AILab/flash-attention#installation-and-features).
Next, [install](https://github.com/Dao-AILab/flash-attention#installation-and-features) ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#installation | #installation | .md | 196_9 |
To load a model using Flash Attention 2, we can pass the argument `attn_implementation="flash_attention_2"` to [`.from_pretrained`](https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained). You can use either `torch.float16` or `torch.bfloat16` precision.
```py... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#usage | #usage | .md | 196_10 |
Below is an expected speedup diagram that compares pure inference time between the native implementation and the Flash Attention 2.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/visheratin/documentation-images/resolve/main/nllb-speedup.webp">
</div> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#expected-speedups | #expected-speedups | .md | 196_11 |
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/m2m_100.md | https://huggingface.co/docs/transformers/en/model_doc/m2m_100/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 196_12 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/ | .md | 197_0 | |
Bamba-9B is a decoder-only language model based on the [Mamba-2](https://github.com/state-spaces/mamba) architecture and is designed to handle a wide range of text generation tasks. It is trained from scratch using a two-stage training approach. In the first stage, the model is trained on 2 trillion tokens from the Dol... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#overview | #overview | .md | 197_1 |
| Model | Params | # Layers | Hidden Dim. | Attention Heads | GQA | KV Heads | Context Length | Tied Embeddings |
|-------------------|--------------|----------|-------------|-----------------|-----|----------|----------------|------------------|
| Bamba | 9B (9.78B) | 32 | 4096 | 32 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaconfig | #bambaconfig | .md | 197_2 |
Tips:
- The architecture is based on Mamba-2 models. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#usage-tips | #usage-tips | .md | 197_3 |
The bare Bamba 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambamodel | #bambamodel | .md | 197_4 |
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ibm-fms/Bamba-9B")
tokenizer = AutoTokenizer.from_pretrained("ibm-fms/Bamba-9B")
message = ["Mamba is a snake with following properties "]
inputs = tokenizer(message, return_tensors='pt', return_token... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bamba.md | https://huggingface.co/docs/transformers/en/model_doc/bamba/#bambaforcausallm | #bambaforcausallm | .md | 197_5 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/ | .md | 198_0 | |
The InstructBLIPVideo is an extension of the models proposed in [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
Instruc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#overview | #overview | .md | 198_1 |
- The model was trained by sampling 4 frames per video, so it's recommended to sample 4 frames
> [!NOTE]
> BLIP models after release v4.46 will raise warnings about adding `processor.num_query_tokens = {{num_query_tokens}}` and expand model embeddings layer to add special `<image>` token. It is strongly recommended t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#usage-tips | #usage-tips | .md | 198_2 |
[`InstructBlipVideoConfig`] is the configuration class to store the configuration of a
[`InstructBlipVideoForConditionalGeneration`]. It is used to instantiate a Instructblipvideo model according to the specified
arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configurat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoconfig | #instructblipvideoconfig | .md | 198_3 |
This is the configuration class to store the configuration of a [`InstructBlipVideoVisionModel`]. It is used to
instantiate a InstructBlipVideo vision encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration defaults will yield a similar configuration to that of the I... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionconfig | #instructblipvideovisionconfig | .md | 198_4 |
This is the configuration class to store the configuration of a [`InstructBlipVideoQFormerModel`]. It is used to
instantiate a InstructBlipVideo Querying Transformer (Q-Former) model according to the specified arguments, defining the
model architecture. Instantiating a configuration with the defaults will yield a simil... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformerconfig | #instructblipvideoqformerconfig | .md | 198_5 |
Constructs an InstructBLIPVideo processor which wraps a InstructBLIP image processor and a LLaMa/T5 tokenizer into a single
processor.
[`InstructBlipVideoProcessor`] offers all the functionalities of [`InstructBlipVideoImageProcessor`] and [`AutoTokenizer`]. See the
docstring of [`~InstructBlipVideoProcessor.__call__... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoprocessor | #instructblipvideoprocessor | .md | 198_6 |
Constructs a InstructBLIPVideo 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`, *optional*, defaults to `{"height": 384, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoimageprocessor | #instructblipvideoimageprocessor | .md | 198_7 |
No docstring available for InstructBlipVideoVisionModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideovisionmodel | #instructblipvideovisionmodel | .md | 198_8 |
Querying Transformer (Q-Former), used in InstructBlipVideo. Slightly modified from BLIP-2 as it also takes the
instruction as input.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoqformermodel | #instructblipvideoqformermodel | .md | 198_9 |
InstructBlipVideo Model for generating text given an image and an optional text prompt. The model consists of a vision
encoder, Querying Transformer (Q-Former) and a language model.
One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue
the prompt. Otherwis... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblipvideo.md | https://huggingface.co/docs/transformers/en/model_doc/instructblipvideo/#instructblipvideoforconditionalgeneration | #instructblipvideoforconditionalgeneration | .md | 198_10 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/ | .md | 199_0 | |
OWLv2 was proposed in [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2 scales up [OWL-ViT](owlvit) using self-training, which uses an existing detector to generate pseudo-box annotations on image-text pairs. This results in large ga... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#overview | #overview | .md | 199_1 |
OWLv2 is, just like its predecessor [OWL-ViT](owlvit), a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#usage-example | #usage-example | .md | 199_2 |
- A demo notebook on using OWLv2 for zero- and one-shot (image-guided) object detection can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/OWLv2).
- [Zero-shot object detection task guide](../tasks/zero_shot_object_detection)
<Tip>
The architecture of OWLv2 is identical to [OWL-ViT... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#resources | #resources | .md | 199_3 |
[`Owlv2Config`] is the configuration class to store the configuration of an [`Owlv2Model`]. It is used to
instantiate an OWLv2 model according to the specified arguments, defining the text model and vision model
configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the O... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2config | #owlv2config | .md | 199_4 |
This is the configuration class to store the configuration of an [`Owlv2TextModel`]. It is used to instantiate an
Owlv2 text encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Owlv2
[google/owlv... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textconfig | #owlv2textconfig | .md | 199_5 |
This is the configuration class to store the configuration of an [`Owlv2VisionModel`]. It is used to instantiate
an OWLv2 image encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the OWLv2
[google/o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionconfig | #owlv2visionconfig | .md | 199_6 |
Constructs an OWLv2 image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in
the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2imageprocessor | #owlv2imageprocessor | .md | 199_7 |
Owlv2Processor
Constructs an Owlv2 processor which wraps [`Owlv2ImageProcessor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`] into
a single processor that interits both the image processor and tokenizer functionalities. See the
[`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information.
Args:... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2processor | #owlv2processor | .md | 199_8 |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2model | #owlv2model | .md | 199_9 |
No docstring available for Owlv2TextModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2textmodel | #owlv2textmodel | .md | 199_10 |
No docstring available for Owlv2VisionModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2visionmodel | #owlv2visionmodel | .md | 199_11 |
No docstring available for Owlv2ForObjectDetection
Methods: forward
- image_guided_detection | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/owlv2.md | https://huggingface.co/docs/transformers/en/model_doc/owlv2/#owlv2forobjectdetection | #owlv2forobjectdetection | .md | 199_12 |
<!--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/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/ | .md | 200_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=funnel">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-funnel-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/funnel-transformer-small">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnel-transformer | #funnel-transformer | .md | 200_1 |
The Funnel Transformer model was proposed in the paper [Funnel-Transformer: Filtering out Sequential Redundancy for
Efficient Language Processing](https://arxiv.org/abs/2006.03236). It is a bidirectional transformer model, like
BERT, but with a pooling operation after each block of layers, a bit like in traditional con... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#overview | #overview | .md | 200_2 |
- Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers. This way, their length is divided by 2, which speeds up the computation of the next hidden states.
The base model therefore has a final sequence length that is a quarter of the original one. This model ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#usage-tips | #usage-tips | .md | 200_3 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multip... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#resources | #resources | .md | 200_4 |
This is the configuration class to store the configuration of a [`FunnelModel`] or a [`TFBertModel`]. It is used to
instantiate a Funnel Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelconfig | #funnelconfig | .md | 200_5 |
Construct a Funnel Transformer 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 (`bo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funneltokenizer | #funneltokenizer | .md | 200_6 |
Construct a "fast" Funnel Transformer 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 (... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funneltokenizerfast | #funneltokenizerfast | .md | 200_7 |
models.funnel.modeling_funnel.FunnelForPreTrainingOutput
Output type of [`FunnelForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss of the ELECTRA-style objective.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Predic... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnel-specific-outputs | #funnel-specific-outputs | .md | 200_8 |
The base Funnel Transformer Model transformer outputting raw hidden-states without upsampling head (also called
decoder) or any task-specific head on top.
The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Language Processing](https://arxiv.org/abs/2006... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelbasemodel | #funnelbasemodel | .md | 200_9 |
The bare Funnel Transformer Model transformer outputting raw hidden-states without any specific head on top.
The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelmodel | #funnelmodel | .md | 200_10 |
No docstring available for FunnelForPreTraining
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelmodelforpretraining | #funnelmodelforpretraining | .md | 200_11 |
Funnel Transformer Model with a `language modeling` head on top.
The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
This model inherits from [`P... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelformaskedlm | #funnelformaskedlm | .md | 200_12 |
Funnel Transformer Model with a sequence classification/regression head on top (two linear layer on top of the
first timestep of the last hidden state) e.g. for GLUE tasks.
The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Language Processing](https://... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelforsequenceclassification | #funnelforsequenceclassification | .md | 200_13 |
Funnel Transformer Model with a multiple choice classification head on top (two linear layer on top of the first
timestep of the last hidden state, and a softmax) e.g. for RocStories/SWAG tasks.
The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Languag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelformultiplechoice | #funnelformultiplechoice | .md | 200_14 |
Funnel Transformer 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.
The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Language Processing](https://arxiv.org... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelfortokenclassification | #funnelfortokenclassification | .md | 200_15 |
Funnel Transformer 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`).
The Funnel Transformer model was proposed in [Funnel-Transformer: Filtering out Sequential Redund... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#funnelforquestionanswering | #funnelforquestionanswering | .md | 200_16 |
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