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RoCBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/roc_bert.md | https://huggingface.co/docs/transformers/en/model_doc/roc_bert/#rocbertforquestionanswering | #rocbertforquestionanswering | .md | 281_12 |
<!--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/swiftformer.md | https://huggingface.co/docs/transformers/en/model_doc/swiftformer/ | .md | 282_0 | |
The SwiftFormer model was proposed in [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
The SwiftFormer paper introduces a nov... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swiftformer.md | https://huggingface.co/docs/transformers/en/model_doc/swiftformer/#overview | #overview | .md | 282_1 |
This is the configuration class to store the configuration of a [`SwiftFormerModel`]. It is used to instantiate an
SwiftFormer 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 SwiftFormer
[MBZUA... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swiftformer.md | https://huggingface.co/docs/transformers/en/model_doc/swiftformer/#swiftformerconfig | #swiftformerconfig | .md | 282_2 |
The bare SwiftFormer Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general u... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swiftformer.md | https://huggingface.co/docs/transformers/en/model_doc/swiftformer/#swiftformermodel | #swiftformermodel | .md | 282_3 |
SwiftFormer Model transformer with an image classification head on top (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 matter related to general usage and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swiftformer.md | https://huggingface.co/docs/transformers/en/model_doc/swiftformer/#swiftformerforimageclassification | #swiftformerforimageclassification | .md | 282_4 |
No docstring available for TFSwiftFormerModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swiftformer.md | https://huggingface.co/docs/transformers/en/model_doc/swiftformer/#tfswiftformermodel | #tfswiftformermodel | .md | 282_5 |
No docstring available for TFSwiftFormerForImageClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swiftformer.md | https://huggingface.co/docs/transformers/en/model_doc/swiftformer/#tfswiftformerforimageclassification | #tfswiftformerforimageclassification | .md | 282_6 |
<!--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/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/ | .md | 283_0 | |
The SeamlessM4T-v2 model was proposed in [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team from Meta AI.
SeamlessM4T-v2 is a collection of models designed... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#overview | #overview | .md | 283_1 |
In the following example, we'll load an Arabic audio sample and an English text sample and convert them into Russian speech and French text.
First, load the processor and a checkpoint of the model:
```python
>>> from transformers import AutoProcessor, SeamlessM4Tv2Model
>>> processor = AutoProcessor.from_pretraine... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#usage | #usage | .md | 283_2 |
[`SeamlessM4Tv2Model`] can *seamlessly* generate text or speech with few or no changes. Let's target Russian voice translation:
```python
>>> audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
>>> audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#speech | #speech | .md | 283_3 |
Similarly, you can generate translated text from audio files or from text with the same model. You only have to pass `generate_speech=False` to [`SeamlessM4Tv2Model.generate`].
This time, let's translate to French.
```python
>>> # from audio
>>> output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#text | #text | .md | 283_4 |
[`SeamlessM4Tv2Model`] is transformers top level model to generate speech and text, but you can also use dedicated models that perform the task without additional components, thus reducing the memory footprint.
For example, you can replace the audio-to-audio generation snippet with the model dedicated to the S2ST task,... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#1-use-dedicated-models | #1-use-dedicated-models | .md | 283_5 |
You have the possibility to change the speaker used for speech synthesis with the `speaker_id` argument. Some `speaker_id` works better than other for some languages! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#2-change-the-speaker-identity | #2-change-the-speaker-identity | .md | 283_6 |
You can use different [generation strategies](../generation_strategies) for text generation, e.g `.generate(input_ids=input_ids, text_num_beams=4, text_do_sample=True)` which will perform multinomial beam-search decoding on the text model. Note that speech generation only supports greedy - by default - or multinomial s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#3-change-the-generation-strategy | #3-change-the-generation-strategy | .md | 283_7 |
Use `return_intermediate_token_ids=True` with [`SeamlessM4Tv2Model`] to return both speech and text ! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#4-generate-speech-and-text-at-the-same-time | #4-generate-speech-and-text-at-the-same-time | .md | 283_8 |
SeamlessM4T-v2 features a versatile architecture that smoothly handles the sequential generation of text and speech. This setup comprises two sequence-to-sequence (seq2seq) models. The first model translates the input modality into translated text, while the second model generates speech tokens, known as "unit tokens,"... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#model-architecture | #model-architecture | .md | 283_9 |
The architecture of this new version differs from the first in a few aspects: | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#difference-with-seamlessm4t-v1 | #difference-with-seamlessm4t-v1 | .md | 283_10 |
The second seq2seq model, named text-to-unit model, is now non-auto regressive, meaning that it computes units in a **single forward pass**. This achievement is made possible by:
- the use of **character-level embeddings**, meaning that each character of the predicted translated text has its own embeddings, which are t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#improvements-on-the-second-pass-model | #improvements-on-the-second-pass-model | .md | 283_11 |
The speech encoder, which is used during the first-pass generation process to predict the translated text, differs mainly from the previous speech encoder through these mechanisms:
- the use of chunked attention mask to prevent attention across chunks, ensuring that each position attends only to positions within its ow... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#difference-in-the-speech-encoder | #difference-in-the-speech-encoder | .md | 283_12 |
Here's how the generation process works:
- Input text or speech is processed through its specific encoder.
- A decoder creates text tokens in the desired language.
- If speech generation is required, the second seq2seq model, generates unit tokens in an non auto-regressive way.
- These unit tokens are then passed thr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#generation-process | #generation-process | .md | 283_13 |
The original SeamlessM4Tv2 Model transformer which can be used for every tasks available (S2ST, S2TT, T2TT, T2ST).
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 relate... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#seamlessm4tv2model | #seamlessm4tv2model | .md | 283_14 |
The text-to-speech SeamlessM4Tv2 Model transformer which can be used for T2ST.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
P... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#seamlessm4tv2fortexttospeech | #seamlessm4tv2fortexttospeech | .md | 283_15 |
The speech-to-speech SeamlessM4Tv2 Model transformer which can be used for S2ST.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#seamlessm4tv2forspeechtospeech | #seamlessm4tv2forspeechtospeech | .md | 283_16 |
The text-to-text SeamlessM4Tv2 Model transformer which can be used for T2TT.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Par... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#seamlessm4tv2fortexttotext | #seamlessm4tv2fortexttotext | .md | 283_17 |
The speech-to-text SeamlessM4Tv2 Model transformer which can be used for S2TT.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
P... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#seamlessm4tv2forspeechtotext | #seamlessm4tv2forspeechtotext | .md | 283_18 |
This is the configuration class to store the configuration of a [`~SeamlessM4Tv2Model`]. It is used to instantiate
an SeamlessM4Tv2 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 SeamlessM4Tv2... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t_v2.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2/#seamlessm4tv2config | #seamlessm4tv2config | .md | 283_19 |
<!--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/vit_msn.md | https://huggingface.co/docs/transformers/en/model_doc/vit_msn/ | .md | 284_0 | |
The ViTMSN model was proposed in [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes,
Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas. The paper presents a joint-embedding architecture to ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_msn.md | https://huggingface.co/docs/transformers/en/model_doc/vit_msn/#overview | #overview | .md | 284_1 |
- MSN (masked siamese networks) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training
objective is to match the prototypes assigned to the unmasked views of the images to that of the masked views of the same images.
- The authors have only released pre-trained weights of the backb... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_msn.md | https://huggingface.co/docs/transformers/en/model_doc/vit_msn/#usage-tips | #usage-tips | .md | 284_2 |
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_msn.md | https://huggingface.co/docs/transformers/en/model_doc/vit_msn/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 284_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT MSN.
<PipelineTag pipeline="image-classification"/>
- [`ViTMSNForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classif... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_msn.md | https://huggingface.co/docs/transformers/en/model_doc/vit_msn/#resources | #resources | .md | 284_4 |
This is the configuration class to store the configuration of a [`ViTMSNModel`]. It is used to instantiate an ViT
MSN 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 ViT
[facebook/vit_msn_base]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_msn.md | https://huggingface.co/docs/transformers/en/model_doc/vit_msn/#vitmsnconfig | #vitmsnconfig | .md | 284_5 |
The bare ViTMSN Model outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_msn.md | https://huggingface.co/docs/transformers/en/model_doc/vit_msn/#vitmsnmodel | #vitmsnmodel | .md | 284_6 |
ViTMSN Model with an image classification head on top 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 matter related to general usage and
behavior.
Parame... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_msn.md | https://huggingface.co/docs/transformers/en/model_doc/vit_msn/#vitmsnforimageclassification | #vitmsnforimageclassification | .md | 284_7 |
<!--
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 agreed to in writing, software distributed under the License... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmoe.md | https://huggingface.co/docs/transformers/en/model_doc/olmoe/ | .md | 285_0 | |
The OLMoE model was proposed in [OLMoE: Open Mixture-of-Experts Language Models](https://arxiv.org/abs/2409.02060) by Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert, Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, Da... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmoe.md | https://huggingface.co/docs/transformers/en/model_doc/olmoe/#overview | #overview | .md | 285_1 |
This is the configuration class to store the configuration of a [`OlmoeModel`]. It is used to instantiate an OLMoE
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 [allenai/OLMoE-1B-7B-0924](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmoe.md | https://huggingface.co/docs/transformers/en/model_doc/olmoe/#olmoeconfig | #olmoeconfig | .md | 285_2 |
The bare Olmoe 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/olmoe.md | https://huggingface.co/docs/transformers/en/model_doc/olmoe/#olmoemodel | #olmoemodel | .md | 285_3 |
No docstring available for OlmoeForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmoe.md | https://huggingface.co/docs/transformers/en/model_doc/olmoe/#olmoeforcausallm | #olmoeforcausallm | .md | 285_4 |
<!--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/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/ | .md | 286_0 | |
The OneFormer model was proposed in [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi. OneFormer is a universal image segmentation framework that can be trained on a single panoptic datase... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#overview | #overview | .md | 286_1 |
- OneFormer requires two inputs during inference: *image* and *task token*.
- During training, OneFormer only uses panoptic annotations.
- If you want to train the model in a distributed environment across multiple nodes, then one should update the
`get_num_masks` function inside in the `OneFormerLoss` class of `model... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#usage-tips | #usage-tips | .md | 286_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with OneFormer.
- Demo notebooks regarding inference + fine-tuning on custom data can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/OneFormer).
If you're interested in submitting a re... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#resources | #resources | .md | 286_3 |
models.oneformer.modeling_oneformer.OneFormerModelOutput
Class for outputs of [`OneFormerModel`]. This class returns all the needed hidden states to compute the logits.
Args:
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hid... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#oneformer-specific-outputs | #oneformer-specific-outputs | .md | 286_4 |
This is the configuration class to store the configuration of a [`OneFormerModel`]. It is used to instantiate a
OneFormer 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 OneFormer
[shi-labs/one... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#oneformerconfig | #oneformerconfig | .md | 286_5 |
Constructs a OneFormer image processor. The image processor can be used to prepare image(s), task input(s) and
optional text inputs and targets for the model.
This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
refer to this superclass for more information r... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#oneformerimageprocessor | #oneformerimageprocessor | .md | 286_6 |
Constructs an OneFormer processor which wraps [`OneFormerImageProcessor`] and
[`CLIPTokenizer`]/[`CLIPTokenizerFast`] into a single processor that inherits both the image processor and
tokenizer functionalities.
Args:
image_processor ([`OneFormerImageProcessor`]):
The image processor is a required input.
tokenizer ([... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#oneformerprocessor | #oneformerprocessor | .md | 286_7 |
The bare OneFormer Model outputting raw hidden-states without any specific head on top.
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#oneformermodel | #oneformermodel | .md | 286_8 |
OneFormer Model for instance, semantic and panoptic image segmentation.
This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a
regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
Parameters:
co... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/oneformer.md | https://huggingface.co/docs/transformers/en/model_doc/oneformer/#oneformerforuniversalsegmentation | #oneformerforuniversalsegmentation | .md | 286_9 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew.md | https://huggingface.co/docs/transformers/en/model_doc/sew/ | .md | 287_0 | |
SEW (Squeezed and Efficient Wav2Vec) was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training
for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q.
Weinberger, Yoav Artzi.
The abstract from the paper is the following:
*This paper is... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew.md | https://huggingface.co/docs/transformers/en/model_doc/sew/#overview | #overview | .md | 287_1 |
- SEW is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- SEWForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded using
[`Wav2Vec2CTCTokenizer`]. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew.md | https://huggingface.co/docs/transformers/en/model_doc/sew/#usage-tips | #usage-tips | .md | 287_2 |
- [Audio classification task guide](../tasks/audio_classification)
- [Automatic speech recognition task guide](../tasks/asr) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew.md | https://huggingface.co/docs/transformers/en/model_doc/sew/#resources | #resources | .md | 287_3 |
This is the configuration class to store the configuration of a [`SEWModel`]. It is used to instantiate a SEW model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the SEW
[asapp/sew-tiny-100k](https://h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew.md | https://huggingface.co/docs/transformers/en/model_doc/sew/#sewconfig | #sewconfig | .md | 287_4 |
The bare SEW Model transformer outputting raw hidden-states without any specific head on top.
SEW was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech
Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger,
Yoav Artzi.
T... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew.md | https://huggingface.co/docs/transformers/en/model_doc/sew/#sewmodel | #sewmodel | .md | 287_5 |
SEW Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
SEW was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech
Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger,
Yoav Artzi.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew.md | https://huggingface.co/docs/transformers/en/model_doc/sew/#sewforctc | #sewforctc | .md | 287_6 |
SEW Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB
Keyword Spotting.
SEW was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech
Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu H... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sew.md | https://huggingface.co/docs/transformers/en/model_doc/sew/#sewforsequenceclassification | #sewforsequenceclassification | .md | 287_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/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/ | .md | 288_0 | |
The AltCLIP model was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679v2) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu. AltCLIP
(Altering the Language Encoder in CLIP) is a neural network trained on a varie... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#overview | #overview | .md | 288_1 |
The usage of AltCLIP is very similar to the CLIP. the difference between CLIP is the text encoder. Note that we use bidirectional attention instead of casual attention
and we take the [CLS] token in XLM-R to represent text embedding.
AltCLIP is a multi-modal vision and language model. It can be used for image-text si... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#usage-tips-and-example | #usage-tips-and-example | .md | 288_2 |
This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an
AltCLIP 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 AltCLIP
[BAAI/AltCLIP](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#altclipconfig | #altclipconfig | .md | 288_3 |
This is the configuration class to store the configuration of a [`AltCLIPTextModel`]. It is used to instantiate a
AltCLIP text 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 AltCLIP
[BAAI/AltC... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#altcliptextconfig | #altcliptextconfig | .md | 288_4 |
This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an
AltCLIP 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 AltCLIP
[BAAI/AltCLIP](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#altclipvisionconfig | #altclipvisionconfig | .md | 288_5 |
Constructs a AltCLIP processor which wraps a CLIP image processor and a XLM-Roberta tokenizer into a single
processor.
[`AltCLIPProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`XLMRobertaTokenizerFast`]. See
the [`~AltCLIPProcessor.__call__`] and [`~AltCLIPProcessor.decode`] for more informa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#altclipprocessor | #altclipprocessor | .md | 288_6 |
No docstring available for AltCLIPModel
Methods: forward
- get_text_features
- get_image_features | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#altclipmodel | #altclipmodel | .md | 288_7 |
No docstring available for AltCLIPTextModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#altcliptextmodel | #altcliptextmodel | .md | 288_8 |
No docstring available for AltCLIPVisionModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/altclip.md | https://huggingface.co/docs/transformers/en/model_doc/altclip/#altclipvisionmodel | #altclipvisionmodel | .md | 288_9 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/ | .md | 289_0 | |
The PaliGemma model was proposed in [PaliGemma – Google's Cutting-Edge Open Vision Language Model](https://huggingface.co/blog/paligemma) by Google. It is a 3B vision-language model composed by a [SigLIP](siglip) vision encoder and a [Gemma](gemma) language decoder linked by a multimodal linear projection. It cuts an i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#overview | #overview | .md | 289_1 |
- PaliGemma is not meant for conversational use, and it works best when fine-tuning to a specific use case. Some downstream tasks on which PaliGemma can be fine-tuned include image captioning, visual question answering (VQA), object detection, referring expression segmentation and document understanding.
- One can use ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#usage-tips | #usage-tips | .md | 289_2 |
The model can accept a single or multiple images. According to the [paper](https://arxiv.org/abs/2407.07726v1), the checkpoint PaliGemma can transfer to tasks which take multiple images as input. NLVR2 is one such task, which asks one question about two images, and requires looking at both to give the correct answer. H... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#usage-example | #usage-example | .md | 289_3 |
```python
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
model_id = "google/paligemma-3b-mix-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
prompt = "What is on the flower?"
image_file = "https://huggingface.co/da... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#single-image-inference | #single-image-inference | .md | 289_4 |
```python
model_id = "google/paligemma-3b-ft-nlvr2-448" # checkpoint tuned for multiple images
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
processor = PaliGemmaProcessor.from_pretrained(model_id)
prompt = "answer en Which of the two pictures shows a snowman, first or second?"
stop_sign_image =... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#multi-image-inference | #multi-image-inference | .md | 289_5 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with PaliGemma. 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#resources | #resources | .md | 289_6 |
This is the configuration class to store the configuration of a [`PaliGemmaForConditionalGeneration`]. It is used to instantiate an
PaliGemmamodel according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Pali... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#paligemmaconfig | #paligemmaconfig | .md | 289_7 |
Constructs a PaliGemma processor which wraps a PaliGemma image processor and a PaliGemma tokenizer into a single processor.
[`PaliGemmaProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`GemmaTokenizerFast`]. See the
[`~PaliGemmaProcessor.__call__`] and [`~PaliGemmaProcessor.decode`] for more... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#paligemmaprocessor | #paligemmaprocessor | .md | 289_8 |
The PALIGEMMA model which consists of a vision backbone and a language model.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/paligemma.md | https://huggingface.co/docs/transformers/en/model_doc/paligemma/#paligemmaforconditionalgeneration | #paligemmaforconditionalgeneration | .md | 289_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/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/ | .md | 290_0 | |
The [`EncoderDecoderModel`] can be used to initialize a sequence-to-sequence model with any
pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
was s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#overview | #overview | .md | 290_1 |
[`EncoderDecoderModel`] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [`BertModel`] configuration for the encoder and the default [`BertForCausalLM`] configuration for the decoder.
```python
>>> from transformers import BertConfig,... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#randomly-initializing-encoderdecodermodel-from-model-configurations | #randomly-initializing-encoderdecodermodel-from-model-configurations | .md | 290_2 |
[`EncoderDecoderModel`] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained auto-encoding model, *e.g.* BERT, can serve as the encoder and both pretrained auto-encoding models, *e.g.* BERT, pretrained causal language models, *e.g.* GPT2, as well as the p... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#initialising-encoderdecodermodel-from-a-pretrained-encoder-and-a-pretrained-decoder | #initialising-encoderdecodermodel-from-a-pretrained-encoder-and-a-pretrained-decoder | .md | 290_3 |
To load fine-tuned checkpoints of the `EncoderDecoderModel` class, [`EncoderDecoderModel`] provides the `from_pretrained(...)` method just like any other model architecture in Transformers.
To perform inference, one uses the [`generate`] method, which allows to autoregressively generate text. This method supports var... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#loading-an-existing-encoderdecodermodel-checkpoint-and-perform-inference | #loading-an-existing-encoderdecodermodel-checkpoint-and-perform-inference | .md | 290_4 |
[`TFEncoderDecoderModel.from_pretrained`] currently doesn't support initializing the model from a
pytorch checkpoint. Passing `from_pt=True` to this method will throw an exception. If there are only pytorch
checkpoints for a particular encoder-decoder model, a workaround is:
```python
>>> # a workaround to load from ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#loading-a-pytorch-checkpoint-into-tfencoderdecodermodel | #loading-a-pytorch-checkpoint-into-tfencoderdecodermodel | .md | 290_5 |
Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model.
As you can see, only 2 inputs are required for the model in order to compute a loss: `input_ids` (which are the
`input_ids` of the encoded input sequence) and `labels` (which are the `input_ids` of the encoded
target... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#training | #training | .md | 290_6 |
[`EncoderDecoderConfig`] is the configuration class to store the configuration of a [`EncoderDecoderModel`]. It is
used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder
configs.
Configuration objects inherit from [`PretrainedConfig`] and can be used to con... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#encoderdecoderconfig | #encoderdecoderconfig | .md | 290_7 |
This class can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the
encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via
[`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`]
functi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#encoderdecodermodel | #encoderdecodermodel | .md | 290_8 |
No docstring available for TFEncoderDecoderModel
Methods: call
- from_encoder_decoder_pretrained
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#tfencoderdecodermodel | #tfencoderdecodermodel | .md | 290_9 |
No docstring available for FlaxEncoderDecoderModel
Methods: __call__
- from_encoder_decoder_pretrained
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/encoder-decoder/#flaxencoderdecodermodel | #flaxencoderdecodermodel | .md | 290_10 |
<!--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/colpali.md | https://huggingface.co/docs/transformers/en/model_doc/colpali/ | .md | 291_0 | |
The *ColPali* model was proposed in [ColPali: Efficient Document Retrieval with Vision Language Models](https://doi.org/10.48550/arXiv.2407.01449) by **Manuel Faysse***, **Hugues Sibille***, **Tony Wu***, Bilel Omrani, Gautier Viaud, Céline Hudelot, Pierre Colombo (* denotes equal contribution). Work lead by ILLUIN Tec... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/colpali.md | https://huggingface.co/docs/transformers/en/model_doc/colpali/#overview | #overview | .md | 291_1 |
- The *ColPali* arXiv paper can be found [here](https://doi.org/10.48550/arXiv.2407.01449). 📄
- The official blog post detailing ColPali can be found [here](https://huggingface.co/blog/manu/colpali). 📝
- The original model implementation code for the ColPali model and for the `colpali-engine` package can be found [he... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/colpali.md | https://huggingface.co/docs/transformers/en/model_doc/colpali/#resources | #resources | .md | 291_2 |
This example demonstrates how to use *ColPali* to embed both queries and images, calculate their similarity scores, and identify the most relevant matches. For a specific query, you can retrieve the top-k most similar images by selecting the ones with the highest similarity scores.
```python
import torch
from PIL imp... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/colpali.md | https://huggingface.co/docs/transformers/en/model_doc/colpali/#usage | #usage | .md | 291_3 |
Configuration class to store the configuration of a [`ColPaliForRetrieval`]. It is used to instantiate an instance
of `ColPaliForRetrieval` according to the specified arguments, defining the model architecture following the methodology
from the "ColPali: Efficient Document Retrieval with Vision Language Models" paper. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/colpali.md | https://huggingface.co/docs/transformers/en/model_doc/colpali/#colpaliconfig | #colpaliconfig | .md | 291_4 |
Constructs a ColPali processor which wraps a PaliGemmaProcessor and special methods to process images and queries, as
well as to compute the late-interaction retrieval score.
[`ColPaliProcessor`] offers all the functionalities of [`PaliGemmaProcessor`]. See the [`~PaliGemmaProcessor.__call__`]
for more information. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/colpali.md | https://huggingface.co/docs/transformers/en/model_doc/colpali/#colpaliprocessor | #colpaliprocessor | .md | 291_5 |
In our proposed ColPali approach, we leverage VLMs to construct efficient multi-vector embeddings directly
from document images (“screenshots”) for document retrieval. We train the model to maximize the similarity
between these document embeddings and the corresponding query embeddings, using the late interaction metho... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/colpali.md | https://huggingface.co/docs/transformers/en/model_doc/colpali/#colpaliforretrieval | #colpaliforretrieval | .md | 291_6 |
<!--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/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/ | .md | 292_0 | |
The SigLIP model was proposed in [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer. SigLIP proposes to replace the loss function used in [CLIP](clip) by a simple pairwise sigmoid loss. This results in better performance in ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#overview | #overview | .md | 292_1 |
- Usage of SigLIP is similar to [CLIP](clip). The main difference is the training loss, which does not require a global view of all the pairwise similarities of images and texts within a batch. One needs to apply the sigmoid activation function to the logits, rather than the softmax.
- Training is supported but does no... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#usage-tips | #usage-tips | .md | 292_2 |
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