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No docstring available for OlmoForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo.md | https://huggingface.co/docs/transformers/en/model_doc/olmo/#olmoforcausallm | #olmoforcausallm | .md | 183_4 |
<!--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/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/ | .md | 184_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=xlm-roberta">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm--roberta-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/xlm-roberta-base">
<img alt="Spaces" src="https://img.shields.io/badge/%... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlm-roberta | #xlm-roberta | .md | 184_1 |
The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#overview | #overview | .md | 184_2 |
- XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
not require `lang` tensors to understand which language is used, and should be able to determine the correct
language from the input ids.
- Uses RoBERTa tricks on the XLM approach, but does not use the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#usage-tips | #usage-tips | .md | 184_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with XLM-RoBERTa. 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#resources | #resources | .md | 184_4 |
This is the configuration class to store the configuration of a [`XLMRobertaModel`] or a [`TFXLMRobertaModel`]. It
is used to instantiate a XLM-RoBERTa model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertaconfig | #xlmrobertaconfig | .md | 184_5 |
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. 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_f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertatokenizer | #xlmrobertatokenizer | .md | 184_6 |
Construct a "fast" XLM-RoBERTa tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which c... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertatokenizerfast | #xlmrobertatokenizerfast | .md | 184_7 |
The bare XLM-RoBERTa 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertamodel | #xlmrobertamodel | .md | 184_8 |
XLM-RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This mode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertaforcausallm | #xlmrobertaforcausallm | .md | 184_9 |
XLM-RoBERTa Model with a `language modeling` head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertaformaskedlm | #xlmrobertaformaskedlm | .md | 184_10 |
XLM-RoBERTa 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 o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertaforsequenceclassification | #xlmrobertaforsequenceclassification | .md | 184_11 |
XLM-RoBERTa Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as down... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertaformultiplechoice | #xlmrobertaformultiplechoice | .md | 184_12 |
XLM-RoBERTa 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 downlo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertafortokenclassification | #xlmrobertafortokenclassification | .md | 184_13 |
XLM-RoBERTa 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 method... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#xlmrobertaforquestionanswering | #xlmrobertaforquestionanswering | .md | 184_14 |
No docstring available for TFXLMRobertaModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#tfxlmrobertamodel | #tfxlmrobertamodel | .md | 184_15 |
No docstring available for TFXLMRobertaForCausalLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#tfxlmrobertaforcausallm | #tfxlmrobertaforcausallm | .md | 184_16 |
No docstring available for TFXLMRobertaForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#tfxlmrobertaformaskedlm | #tfxlmrobertaformaskedlm | .md | 184_17 |
No docstring available for TFXLMRobertaForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#tfxlmrobertaforsequenceclassification | #tfxlmrobertaforsequenceclassification | .md | 184_18 |
No docstring available for TFXLMRobertaForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#tfxlmrobertaformultiplechoice | #tfxlmrobertaformultiplechoice | .md | 184_19 |
No docstring available for TFXLMRobertaForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#tfxlmrobertafortokenclassification | #tfxlmrobertafortokenclassification | .md | 184_20 |
No docstring available for TFXLMRobertaForQuestionAnswering
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#tfxlmrobertaforquestionanswering | #tfxlmrobertaforquestionanswering | .md | 184_21 |
No docstring available for FlaxXLMRobertaModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#flaxxlmrobertamodel | #flaxxlmrobertamodel | .md | 184_22 |
No docstring available for FlaxXLMRobertaForCausalLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#flaxxlmrobertaforcausallm | #flaxxlmrobertaforcausallm | .md | 184_23 |
No docstring available for FlaxXLMRobertaForMaskedLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#flaxxlmrobertaformaskedlm | #flaxxlmrobertaformaskedlm | .md | 184_24 |
No docstring available for FlaxXLMRobertaForSequenceClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#flaxxlmrobertaforsequenceclassification | #flaxxlmrobertaforsequenceclassification | .md | 184_25 |
No docstring available for FlaxXLMRobertaForMultipleChoice
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#flaxxlmrobertaformultiplechoice | #flaxxlmrobertaformultiplechoice | .md | 184_26 |
No docstring available for FlaxXLMRobertaForTokenClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#flaxxlmrobertafortokenclassification | #flaxxlmrobertafortokenclassification | .md | 184_27 |
No docstring available for FlaxXLMRobertaForQuestionAnswering
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-roberta.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-roberta/#flaxxlmrobertaforquestionanswering | #flaxxlmrobertaforquestionanswering | .md | 184_28 |
<!--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/dit.md | https://huggingface.co/docs/transformers/en/model_doc/dit/ | .md | 185_0 | |
DiT was proposed in [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
DiT applies the self-supervised objective of [BEiT](beit) (BERT pre-training of Image Transformers) to 42 million document images,... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dit.md | https://huggingface.co/docs/transformers/en/model_doc/dit/#overview | #overview | .md | 185_1 |
One can directly use the weights of DiT with the AutoModel API:
```python
from transformers import AutoModel
model = AutoModel.from_pretrained("microsoft/dit-base")
```
This will load the model pre-trained on masked image modeling. Note that this won't include the language modeling head on top, used to predict vis... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dit.md | https://huggingface.co/docs/transformers/en/model_doc/dit/#usage-tips | #usage-tips | .md | 185_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DiT.
<PipelineTag pipeline="image-classification"/>
- [`BeitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classificatio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/dit.md | https://huggingface.co/docs/transformers/en/model_doc/dit/#resources | #resources | .md | 185_3 |
<!--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/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/ | .md | 186_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Ti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#tvlt | #tvlt | .md | 186_1 |
The TVLT model was proposed in [TVLT: Textless Vision-Language Transformer](https://arxiv.org/abs/2209.14156)
by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal (the first three authors contributed equally). The Textless Vision-Language Transformer (TVLT) is a model that uses raw visual and audio inputs for vision-and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#overview | #overview | .md | 186_2 |
- TVLT is a model that takes both `pixel_values` and `audio_values` as input. One can use [`TvltProcessor`] to prepare data for the model.
This processor wraps an image processor (for the image/video modality) and an audio feature extractor (for the audio modality) into one.
- TVLT is trained with images/videos and aud... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#usage-tips | #usage-tips | .md | 186_3 |
This is the configuration class to store the configuration of a [`TvltModel`]. It is used to instantiate a TVLT
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 TVLT
[ZinengTang/tvlt-base](https... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#tvltconfig | #tvltconfig | .md | 186_4 |
Constructs a TVLT processor which wraps a TVLT image processor and TVLT feature extractor into a single processor.
[`TvltProcessor`] offers all the functionalities of [`TvltImageProcessor`] and [`TvltFeatureExtractor`]. See the
docstring of [`~TvltProcessor.__call__`] for more information.
Args:
image_processor (`T... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#tvltprocessor | #tvltprocessor | .md | 186_5 |
Constructs a TVLT image processor.
This processor can be used to prepare either videos or images for the model by converting images to 1-frame videos.
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
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#tvltimageprocessor | #tvltimageprocessor | .md | 186_6 |
Constructs a TVLT audio feature extractor. This feature extractor can be used to prepare audios for the model.
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.
Args:
spectrogra... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#tvltfeatureextractor | #tvltfeatureextractor | .md | 186_7 |
The bare TVLT Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#tvltmodel | #tvltmodel | .md | 186_8 |
The TVLT Model transformer with the decoder on top for self-supervised pre-training.
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/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#tvltforpretraining | #tvltforpretraining | .md | 186_9 |
Tvlt Model transformer with a classifier head on top (an MLP on top of the final hidden state of the [CLS] token)
for audiovisual classification tasks, e.g. CMU-MOSEI Sentiment Analysis and Audio to Video Retrieval.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/tvlt.md | https://huggingface.co/docs/transformers/en/model_doc/tvlt/#tvltforaudiovisualclassification | #tvltforaudiovisualclassification | .md | 186_10 |
<!--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/time_series_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/time_series_transformer/ | .md | 187_0 | |
The Time Series Transformer model is a vanilla encoder-decoder Transformer for time series forecasting.
This model was contributed by [kashif](https://huggingface.co/kashif). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/time_series_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/time_series_transformer/#overview | #overview | .md | 187_1 |
- Similar to other models in the library, [`TimeSeriesTransformerModel`] is the raw Transformer without any head on top, and [`TimeSeriesTransformerForPrediction`]
adds a distribution head on top of the former, which can be used for time-series forecasting. Note that this is a so-called probabilistic forecasting model,... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/time_series_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/time_series_transformer/#usage-tips | #usage-tips | .md | 187_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. 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 existing reso... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/time_series_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/time_series_transformer/#resources | #resources | .md | 187_3 |
This is the configuration class to store the configuration of a [`TimeSeriesTransformerModel`]. It is used to
instantiate a Time Series 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 t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/time_series_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/time_series_transformer/#timeseriestransformerconfig | #timeseriestransformerconfig | .md | 187_4 |
The bare Time Series Transformer 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 h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/time_series_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/time_series_transformer/#timeseriestransformermodel | #timeseriestransformermodel | .md | 187_5 |
The Time Series Transformer Model with a distribution head on top for time-series forecasting.
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/time_series_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/time_series_transformer/#timeseriestransformerforprediction | #timeseriestransformerforprediction | .md | 187_6 |
<!--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/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/ | .md | 188_0 | |
The GPTNeo model was released in the [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) repository by Sid
Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like causal language model trained on the
[Pile](https://pile.eleuther.ai/) dataset.
The architecture is similar to GPT2 except t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#overview | #overview | .md | 188_1 |
The `generate()` method can be used to generate text using GPT Neo model.
```python
>>> from transformers import GPTNeoForCausalLM, GPT2Tokenizer
>>> model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
>>> prompt = (
... "... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#usage-example | #usage-example | .md | 188_2 |
First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature, and make sure your hardware is compatible with Flash-Attention 2. More details are available [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2) concerning the installati... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#combining-gpt-neo-and-flash-attention-2 | #combining-gpt-neo-and-flash-attention-2 | .md | 188_3 |
Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `EleutherAI/gpt-neo-2.7B` checkpoint and the Flash Attention 2 version of the model.
Note that for GPT-Neo it is not possible to train / run on very long context as the max [position embeddings... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#expected-speedups | #expected-speedups | .md | 188_4 |
- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#resources | #resources | .md | 188_5 |
This is the configuration class to store the configuration of a [`GPTNeoModel`]. It is used to instantiate a GPT
Neo 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 GPTNeo
[EleutherAI/gpt-neo-1... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoconfig | #gptneoconfig | .md | 188_6 |
The bare GPT Neo Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning hea... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneomodel | #gptneomodel | .md | 188_7 |
The GPT Neo Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the in... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforcausallm | #gptneoforcausallm | .md | 188_8 |
The GPT-Neo Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the gen... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforquestionanswering | #gptneoforquestionanswering | .md | 188_9 |
The GPTNeo Model transformer with a sequence classification head on top (linear layer).
[`GPTNeoForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last toke... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforsequenceclassification | #gptneoforsequenceclassification | .md | 188_10 |
GPT Neo model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloadin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneofortokenclassification | #gptneofortokenclassification | .md | 188_11 |
No docstring available for FlaxGPTNeoModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#flaxgptneomodel | #flaxgptneomodel | .md | 188_12 |
No docstring available for FlaxGPTNeoForCausalLM
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#flaxgptneoforcausallm | #flaxgptneoforcausallm | .md | 188_13 |
<!--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/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/ | .md | 189_0 | |
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan
Salakhutdinov, Abdelrahman Mohamed.
The abstract from the paper is the following:
*Sel... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#overview | #overview | .md | 189_1 |
- Hubert is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- Hubert model was 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/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#usage-tips | #usage-tips | .md | 189_2 |
Flash Attention 2 is an faster, optimized version of the model. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#using-flash-attention-2 | #using-flash-attention-2 | .md | 189_3 |
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). If your hardware is not compatible with Flash Attention 2, you can still benefit from atte... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#installation | #installation | .md | 189_4 |
Below is an expected speedup diagram comparing the pure inference time between the native implementation in transformers of `facebook/hubert-large-ls960-ft`, the flash-attention-2 and the sdpa (scale-dot-product-attention) version. We show the average speedup obtained on the `librispeech_asr` `clean` validation split: ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#usage | #usage | .md | 189_5 |
Below is an expected speedup diagram comparing the pure inference time between the native implementation in transformers of the `facebook/hubert-large-ls960-ft` model and the flash-attention-2 and sdpa (scale-dot-product-attention) versions. . We show the average speedup obtained on the `librispeech_asr` `clean` valida... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#expected-speedups | #expected-speedups | .md | 189_6 |
- [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/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#resources | #resources | .md | 189_7 |
This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an
Hubert 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 Hubert
[facebook/hubert-base... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | #hubertconfig | .md | 189_8 |
The bare Hubert Model transformer outputting raw hidden-states without any specific head on top.
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden
Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia,
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertmodel | #hubertmodel | .md | 189_9 |
Hubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden
Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhot... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertforctc | #hubertforctc | .md | 189_10 |
Hubert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting.
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden
Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertforsequenceclassification | #hubertforsequenceclassification | .md | 189_11 |
No docstring available for TFHubertModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#tfhubertmodel | #tfhubertmodel | .md | 189_12 |
No docstring available for TFHubertForCTC
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#tfhubertforctc | #tfhubertforctc | .md | 189_13 |
<!--Copyright 2024 The Qwen Team and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by app... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/ | .md | 190_0 | |
Qwen2 is the new model series of large language models from the Qwen team. Previously, we released the Qwen series, including Qwen2-0.5B, Qwen2-1.5B, Qwen2-7B, Qwen2-57B-A14B, Qwen2-72B, Qwen2-Audio, etc. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#overview | #overview | .md | 190_1 |
Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#model-details | #model-details | .md | 190_2 |
`Qwen2-7B` and `Qwen2-7B-Instruct` can be found on the [Huggingface Hub](https://huggingface.co/Qwen)
In the following, we demonstrate how to use `Qwen2-7B-Instruct` for the inference. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` for this purpose.
`... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#usage-tips | #usage-tips | .md | 190_3 |
This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen2-7B-beta [Qwen/Qwen2-7B-beta](... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | #qwen2config | .md | 190_4 |
Construct a Qwen2 tokenizer. Based on byte-level Byte-Pair-Encoding.
Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import Q... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizer | #qwen2tokenizer | .md | 190_5 |
Construct a "fast" Qwen2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
be encoded differently whether it is at the beginning of the sentence (without spa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizerfast | #qwen2tokenizerfast | .md | 190_6 |
The bare Qwen2 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/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2model | #qwen2model | .md | 190_7 |
No docstring available for Qwen2ForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2forcausallm | #qwen2forcausallm | .md | 190_8 |
The Qwen2 Model transformer with a sequence classification head on top (linear layer).
[`Qwen2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2forsequenceclassification | #qwen2forsequenceclassification | .md | 190_9 |
The Qwen2 Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2fortokenclassification | #qwen2fortokenclassification | .md | 190_10 |
The Qwen2 Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the gener... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2forquestionanswering | #qwen2forquestionanswering | .md | 190_11 |
<!--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/zoedepth.md | https://huggingface.co/docs/transformers/en/model_doc/zoedepth/ | .md | 191_0 | |
The ZoeDepth model was proposed in [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://arxiv.org/abs/2302.12288) by Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, Matthias Müller. ZoeDepth extends the [DPT](dpt) framework for metric (also called absolute) depth estimation. ZoeDepth... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zoedepth.md | https://huggingface.co/docs/transformers/en/model_doc/zoedepth/#overview | #overview | .md | 191_1 |
- ZoeDepth is an absolute (also called metric) depth estimation model, unlike DPT which is a relative depth estimation model. This means that ZoeDepth is able to estimate depth in metric units like meters.
The easiest to perform inference with ZoeDepth is by leveraging the [pipeline API](../main_classes/pipelines.md)... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zoedepth.md | https://huggingface.co/docs/transformers/en/model_doc/zoedepth/#usage-tips | #usage-tips | .md | 191_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ZoeDepth.
- A demo notebook regarding inference with ZoeDepth models can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ZoeDepth). 🌎 | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zoedepth.md | https://huggingface.co/docs/transformers/en/model_doc/zoedepth/#resources | #resources | .md | 191_3 |
This is the configuration class to store the configuration of a [`ZoeDepthForDepthEstimation`]. It is used to instantiate an ZoeDepth
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 ZoeDepth
[I... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zoedepth.md | https://huggingface.co/docs/transformers/en/model_doc/zoedepth/#zoedepthconfig | #zoedepthconfig | .md | 191_4 |
Constructs a ZoeDepth image processor.
Args:
do_pad (`bool`, *optional*, defaults to `True`):
Whether to apply pad the input.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overidden by `do_rescale` in
`preprocess`.
rescale_factor (`in... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zoedepth.md | https://huggingface.co/docs/transformers/en/model_doc/zoedepth/#zoedepthimageprocessor | #zoedepthimageprocessor | .md | 191_5 |
ZoeDepth model with one or multiple metric depth estimation head(s) 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.
Par... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zoedepth.md | https://huggingface.co/docs/transformers/en/model_doc/zoedepth/#zoedepthfordepthestimation | #zoedepthfordepthestimation | .md | 191_6 |
<!--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/idefics3.md | https://huggingface.co/docs/transformers/en/model_doc/idefics3/ | .md | 192_0 |
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