title stringlengths 0 82 | url stringlengths 46 131 | markdown stringlengths 193 178k | html stringlengths 243 3.7M | crawlDate stringlengths 24 24 |
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REALM | https://huggingface.co/docs/transformers/model_doc/realm | ## [](#overview)Overview
The REALM model was proposed in [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang. It’s a retrieval-augmented language model that firstly retrieves documents from a textual knowle... | <!DOCTYPE html><html class=""><head>
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RetriBERT | https://huggingface.co/docs/transformers/model_doc/retribert | Transformers documentation
Natural Language Processing
Performance and scalability
Reinforcement learning models
## [](#overview)Overview
The RetriBERT model was proposed in the blog post [Explain Anything Like I’m Five: A Model for Open Domain Long Form Question Answering](https://yjernite.github.io/lfqa.html). R... | <!DOCTYPE html><html class=""><head>
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FSMT | https://huggingface.co/docs/transformers/model_doc/fsmt | **DISCLAIMER:** If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title) and assign @stas00.
## [](#overview)Overview
FSMT (FairSeq MachineTranslation) models were introduced in [Facebook FAIR’s WMT19 News Translation T... | <!DOCTYPE html><html class=""><head>
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Masked language modeling | https://huggingface.co/docs/transformers/tasks/masked_language_modeling | Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally. This means the model has full access to the tokens on the left and right. Masked language modeling is great for tasks that require a good contextual understanding of an entire sequence. BERT is an example... | <!DOCTYPE html><html class=""><head>
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Multiple choice | https://huggingface.co/docs/transformers/tasks/multiple_choice | A multiple choice task is similar to question answering, except several candidate answers are provided along with a context and the model is trained to select the correct answer.
This guide will show you how to:
1. Finetune [BERT](https://huggingface.co/bert-base-uncased) on the `regular` configuration of the [SWAG]... | <!DOCTYPE html><html class=""><head>
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https://huggingface.co/docs/transformers/parallelism | The documentation page PARALLELISM doesn’t exist in v4.30.0, but exists on the main version. Click [here](/docs/transformers/main/en/parallelism) to redirect to the main version of the documentation. | <html><head></head><body>The documentation page PARALLELISM doesn’t exist in v4.30.0, but exists on the main version. Click <a href="/docs/transformers/main/en/parallelism">here</a> to redirect to the main version of the documentation.</body></html> | 2023-06-27T19:55:00.199Z | |
Speech Encoder Decoder Models | https://huggingface.co/docs/transformers/model_doc/speech-encoder-decoder | The [SpeechEncoderDecoderModel](/docs/transformers/v4.30.0/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel) can be used to initialize a speech-to-text model with any pretrained speech autoencoding model as the encoder (_e.g._ [Wav2Vec2](wav2vec2), [Hubert](hubert)) and any pretrained autoregr... | <!DOCTYPE html><html class=""><head>
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https://huggingface.co/docs/transformers/tasks/question-answering | The documentation page TASKS/QUESTION-ANSWERING doesn’t exist in v4.30.0, but exists on the main version. Click [here](/docs/transformers/main/en/tasks/question-answering) to redirect to the main version of the documentation. | <html><head></head><body>The documentation page TASKS/QUESTION-ANSWERING doesn’t exist in v4.30.0, but exists on the main version. Click <a href="/docs/transformers/main/en/tasks/question-answering">here</a> to redirect to the main version of the documentation.</body></html> | 2023-06-27T19:55:00.613Z | |
VisionTextDualEncoder | https://huggingface.co/docs/transformers/model_doc/vision-text-dual-encoder | ## [](#overview)Overview
The [VisionTextDualEncoderModel](/docs/transformers/v4.30.0/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel) can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model as the vision encoder (_e.g._ [ViT](vit), [BEiT](b... | <!DOCTYPE html><html class=""><head>
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https://huggingface.co/docs/transformers/tasks/video-classification | The documentation page TASKS/VIDEO-CLASSIFICATION doesn’t exist in v4.30.0, but exists on the main version. Click [here](/docs/transformers/main/en/tasks/video-classification) to redirect to the main version of the documentation. | <html><head></head><body>The documentation page TASKS/VIDEO-CLASSIFICATION doesn’t exist in v4.30.0, but exists on the main version. Click <a href="/docs/transformers/main/en/tasks/video-classification">here</a> to redirect to the main version of the documentation.</body></html> | 2023-06-27T19:55:01.055Z | |
https://huggingface.co/docs/transformers/model_doc/data2vec-text | The documentation page MODEL\_DOC/DATA2VEC-TEXT doesn’t exist in v4.30.0, but exists on the main version. Click [here](/docs/transformers/main/en/model_doc/data2vec-text) to redirect to the main version of the documentation. | <html><head></head><body>The documentation page MODEL_DOC/DATA2VEC-TEXT doesn’t exist in v4.30.0, but exists on the main version. Click <a href="/docs/transformers/main/en/model_doc/data2vec-text">here</a> to redirect to the main version of the documentation.</body></html> | 2023-06-27T19:55:01.063Z | |
Vision Encoder Decoder Models | https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder | ## [](#overview)Overview
The [VisionEncoderDecoderModel](/docs/transformers/v4.30.0/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel) can be used to initialize an image-to-text model with any pretrained Transformer-based vision model as the encoder (_e.g._ [ViT](vit), [BEiT](beit), [DeiT](dei... | <!DOCTYPE html><html class=""><head>
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SEW-D | https://huggingface.co/docs/transformers/model_doc/sew-d | ## [](#overview)Overview
SEW-D (Squeezed and Efficient Wav2Vec with Disentangled attention) 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 abs... | <!DOCTYPE html><html class=""><head>
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https://huggingface.co/docs/transformers/model_doc/data2vec-audio | The documentation page MODEL\_DOC/DATA2VEC-AUDIO doesn’t exist in v4.30.0, but exists on the main version. Click [here](/docs/transformers/main/en/model_doc/data2vec-audio) to redirect to the main version of the documentation. | <html><head></head><body>The documentation page MODEL_DOC/DATA2VEC-AUDIO doesn’t exist in v4.30.0, but exists on the main version. Click <a href="/docs/transformers/main/en/model_doc/data2vec-audio">here</a> to redirect to the main version of the documentation.</body></html> | 2023-06-27T19:55:01.729Z | |
https://huggingface.co/docs/transformers/model_doc/sequence_classification.mdx | The documentation page MODEL\_DOC/SEQUENCE\_CLASSIFICATION.MDX doesn’t exist in v4.30.0, but exists on the main version. Click [here](/docs/transformers/main/en/model_doc/sequence_classification.mdx) to redirect to the main version of the documentation. | <html><head></head><body>The documentation page MODEL_DOC/SEQUENCE_CLASSIFICATION.MDX doesn’t exist in v4.30.0, but exists on the main version. Click <a href="/docs/transformers/main/en/model_doc/sequence_classification.mdx">here</a> to redirect to the main version of the documentation.</body></html> | 2023-06-27T19:55:01.737Z | |
Translation | https://huggingface.co/docs/transformers/tasks/translation | Translation converts a sequence of text from one language to another. It is one of several tasks you can formulate as a sequence-to-sequence problem, a powerful framework for returning some output from an input, like translation or summarization. Translation systems are commonly used for translation between different l... | <!DOCTYPE html><html class=""><head>
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<meta property="fb:... | 2023-06-27T19:55:02.120Z |
Monocular depth estimation | https://huggingface.co/docs/transformers/tasks/monocular_depth_estimation | Monocular depth estimation is a computer vision task that involves predicting the depth information of a scene from a single image. In other words, it is the process of estimating the distance of objects in a scene from a single camera viewpoint.
Monocular depth estimation has various applications, including 3D recons... | <!DOCTYPE html><html class=""><head>
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Document Question Answering | https://huggingface.co/docs/transformers/tasks/document_question_answering | Document Question Answering, also referred to as Document Visual Question Answering, is a task that involves providing answers to questions posed about document images. The input to models supporting this task is typically a combination of an image and a question, and the output is an answer expressed in natural langua... | <!DOCTYPE html><html class=""><head>
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<meta property="fb:... | 2023-06-27T19:55:02.581Z |
https://huggingface.co/docs/transformers/model_doc/data2vec-vision | The documentation page MODEL\_DOC/DATA2VEC-VISION doesn’t exist in v4.30.0, but exists on the main version. Click [here](/docs/transformers/main/en/model_doc/data2vec-vision) to redirect to the main version of the documentation. | <html><head></head><body>The documentation page MODEL_DOC/DATA2VEC-VISION doesn’t exist in v4.30.0, but exists on the main version. Click <a href="/docs/transformers/main/en/model_doc/data2vec-vision">here</a> to redirect to the main version of the documentation.</body></html> | 2023-06-27T19:55:02.602Z | |
https://huggingface.co/docs/transformers/tasks/training#train-a-tensorflow-model-with-keras | The documentation page TASKS/TRAINING doesn’t exist in v4.30.0, but exists on the main version. Click [here](/docs/transformers/main/en/tasks/training) to redirect to the main version of the documentation. | <html><head></head><body>The documentation page TASKS/TRAINING doesn’t exist in v4.30.0, but exists on the main version. Click <a href="/docs/transformers/main/en/tasks/training">here</a> to redirect to the main version of the documentation.</body></html> | 2023-06-27T19:55:02.609Z | |
Video classification | https://huggingface.co/docs/transformers/tasks/video_classification | Video classification is the task of assigning a label or class to an entire video. Videos are expected to have only one class for each video. Video classification models take a video as input and return a prediction about which class the video belongs to. These models can be used to categorize what a video is all about... | <!DOCTYPE html><html class=""><head>
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<meta name="description" content="We’re on a journey to advance and democratize artificial intelligence through open source and open science.">
<meta property="fb:... | 2023-06-27T19:55:02.891Z |
Image captioning | https://huggingface.co/docs/transformers/tasks/image_captioning | Image captioning is the task of predicting a caption for a given image. Common real world applications of it include aiding visually impaired people that can help them navigate through different situations. Therefore, image captioning helps to improve content accessibility for people by describing images to them.
This... | <!DOCTYPE html><html class=""><head>
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<meta property="fb:... | 2023-06-27T19:55:03.225Z |
Zero-shot object detection | https://huggingface.co/docs/transformers/tasks/zero_shot_object_detection | Traditionally, models used for [object detection](object_detection) require labeled image datasets for training, and are limited to detecting the set of classes from the training data.
Zero-shot object detection is supported by the [OWL-ViT](../model_doc/owlvit) model which uses a different approach. OWL-ViT is an ope... | <!DOCTYPE html><html class=""><head>
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Zero-shot image classification | https://huggingface.co/docs/transformers/tasks/zero_shot_image_classification | Zero-shot image classification is a task that involves classifying images into different categories using a model that was not explicitly trained on data containing labeled examples from those specific categories.
Traditionally, image classification requires training a model on a specific set of labeled images, and th... | <!DOCTYPE html><html class=""><head>
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<meta property="fb:... | 2023-06-27T19:55:03.696Z |
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