text stringlengths 5 58.6k | source stringclasses 470
values | url stringlengths 49 167 | source_section stringlengths 0 90 | file_type stringclasses 1
value | id stringlengths 3 6 |
|---|---|---|---|---|---|
The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#ctrlmodel | #ctrlmodel | .md | 326_7 |
The CTRL 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 input... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#ctrllmheadmodel | #ctrllmheadmodel | .md | 326_8 |
The CTRL Model transformer with a sequence classification head on top (linear layer).
[`CTRLForSequenceClassification`] 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. If a ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#ctrlforsequenceclassification | #ctrlforsequenceclassification | .md | 326_9 |
No docstring available for TFCTRLModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#tfctrlmodel | #tfctrlmodel | .md | 326_10 |
No docstring available for TFCTRLLMHeadModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#tfctrllmheadmodel | #tfctrllmheadmodel | .md | 326_11 |
No docstring available for TFCTRLForSequenceClassification
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ctrl.md | https://huggingface.co/docs/transformers/en/model_doc/ctrl/#tfctrlforsequenceclassification | #tfctrlforsequenceclassification | .md | 326_12 |
<!--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/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/ | .md | 327_0 | |
The GIT model was proposed in [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by
Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang. GIT is a decoder-only Transformer
that leverages [CLIP](clip)'s vision enco... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#overview | #overview | .md | 327_1 |
- GIT is implemented in a very similar way to GPT-2, the only difference being that the model is also conditioned on `pixel_values`. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#usage-tips | #usage-tips | .md | 327_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GIT.
- Demo notebooks regarding inference + fine-tuning GIT on custom data can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/GIT).
- See also: [Causal language modeling task guide]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#resources | #resources | .md | 327_3 |
This is the configuration class to store the configuration of a [`GitVisionModel`]. It is used to instantiate a GIT
vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the vision encoder of th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#gitvisionconfig | #gitvisionconfig | .md | 327_4 |
The vision model from CLIP, used in GIT, without any head or projection 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 mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#gitvisionmodel | #gitvisionmodel | .md | 327_5 |
This is the configuration class to store the configuration of a [`GitModel`]. It is used to instantiate a GIT 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 GIT
[microsoft/git-base](https://hu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#gitconfig | #gitconfig | .md | 327_6 |
Constructs a GIT processor which wraps a CLIP image processor and a BERT tokenizer into a single processor.
[`GitProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BertTokenizerFast`]. See the
[`~GitProcessor.__call__`] and [`~GitProcessor.decode`] for more information.
Args:
image_processor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#gitprocessor | #gitprocessor | .md | 327_7 |
The bare GIT Model transformer consisting of a CLIP image encoder and text decoder 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 sa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#gitmodel | #gitmodel | .md | 327_8 |
GIT Model with a `language modeling` head on top for autoregressive language modeling.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/git.md | https://huggingface.co/docs/transformers/en/model_doc/git/#gitforcausallm | #gitforcausallm | .md | 327_9 |
<!--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/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/ | .md | 328_0 | |
The CLAP model was proposed in [Large Scale Contrastive Language-Audio pretraining with
feature fusion and keyword-to-caption augmentation](https://arxiv.org/pdf/2211.06687.pdf) by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
CLAP (Contrastive Language-Audio Pretraining) is a ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#overview | #overview | .md | 328_1 |
[`ClapConfig`] is the configuration class to store the configuration of a [`ClapModel`]. It is used to instantiate
a CLAP model according to the specified arguments, defining the text model and audio model configs. Instantiating a
configuration with the defaults will yield a similar configuration to that of the CLAP
[l... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#clapconfig | #clapconfig | .md | 328_2 |
This is the configuration class to store the configuration of a [`ClapTextModel`]. It is used to instantiate a CLAP
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 CLAP
[calp-hsat-fused](https:... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#claptextconfig | #claptextconfig | .md | 328_3 |
This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a
CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the audio encoder of the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#clapaudioconfig | #clapaudioconfig | .md | 328_4 |
Constructs a CLAP feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information regarding those methods.
This class extracts mel-filter bank features from raw... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#clapfeatureextractor | #clapfeatureextractor | .md | 328_5 |
Constructs a CLAP processor which wraps a CLAP feature extractor and a RoBerta tokenizer into a single processor.
[`ClapProcessor`] offers all the functionalities of [`ClapFeatureExtractor`] and [`RobertaTokenizerFast`]. See the
[`~ClapProcessor.__call__`] and [`~ClapProcessor.decode`] for more information.
Args:
f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#clapprocessor | #clapprocessor | .md | 328_6 |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#clapmodel | #clapmodel | .md | 328_7 |
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#claptextmodel | #claptextmodel | .md | 328_8 |
CLAP Text Model with a projection layer on top (a linear layer on top of the pooled output).
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/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#claptextmodelwithprojection | #claptextmodelwithprojection | .md | 328_9 |
No docstring available for ClapAudioModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#clapaudiomodel | #clapaudiomodel | .md | 328_10 |
CLAP Audio Model with a projection layer on top (a linear layer on top of the pooled output).
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
e... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clap.md | https://huggingface.co/docs/transformers/en/model_doc/clap/#clapaudiomodelwithprojection | #clapaudiomodelwithprojection | .md | 328_11 |
<!--Copyright 2025 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/moonshine.md | https://huggingface.co/docs/transformers/en/model_doc/moonshine/ | .md | 329_0 | |
The Moonshine model was proposed in [Moonshine: Speech Recognition for Live Transcription and Voice Commands
](https://arxiv.org/abs/2410.15608) by Nat Jeffries, Evan King, Manjunath Kudlur, Guy Nicholson, James Wang, Pete Warden.
The abstract from the paper is the following:
*This paper introduces Moonshine, a fam... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moonshine.md | https://huggingface.co/docs/transformers/en/model_doc/moonshine/#overview | #overview | .md | 329_1 |
- [Automatic speech recognition task guide](../tasks/asr) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moonshine.md | https://huggingface.co/docs/transformers/en/model_doc/moonshine/#resources | #resources | .md | 329_2 |
This is the configuration class to store the configuration of a [`MoonshineModel`]. It is used to instantiate a Moonshine
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 Moonshine
[UsefulSensor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moonshine.md | https://huggingface.co/docs/transformers/en/model_doc/moonshine/#moonshineconfig | #moonshineconfig | .md | 329_3 |
The bare Moonshine 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.)
T... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moonshine.md | https://huggingface.co/docs/transformers/en/model_doc/moonshine/#moonshinemodel | #moonshinemodel | .md | 329_4 |
The Moonshine Model with a language modeling head. Can be used for automatic speech recognition.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/moonshine.md | https://huggingface.co/docs/transformers/en/model_doc/moonshine/#moonshineforconditionalgeneration | #moonshineforconditionalgeneration | .md | 329_5 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/ | .md | 330_0 | |
The CLVP (Contrastive Language-Voice Pretrained Transformer) model was proposed in [Better speech synthesis through scaling](https://arxiv.org/abs/2305.07243) by James Betker.
The abstract from the paper is the following:
*In recent years, the field of image generation has been revolutionized by the application of ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#overview | #overview | .md | 330_1 |
1. CLVP is an integral part of the Tortoise TTS model.
2. CLVP can be used to compare different generated speech candidates with the provided text, and the best speech tokens are forwarded to the diffusion model.
3. The use of the [`ClvpModelForConditionalGeneration.generate()`] method is strongly recommended for torto... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#usage-tips | #usage-tips | .md | 330_2 |
- The [`ClvpTokenizer`] tokenizes the text input, and the [`ClvpFeatureExtractor`] extracts the log mel-spectrogram from the desired audio.
- [`ClvpConditioningEncoder`] takes those text tokens and audio representations and converts them into embeddings conditioned on the text and audio.
- The [`ClvpForCausalLM`] uses ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#brief-explanation | #brief-explanation | .md | 330_3 |
[`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It
is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and
decoder model configs. Instantiating a configuration with the defaults will yield a simi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpconfig | #clvpconfig | .md | 330_4 |
This is the configuration class to store the configuration of a [`ClvpEncoder`]. It is used to instantiate a CLVP
text or CLVP speech encoder according to the specified arguments. Instantiating a configuration with the defaults
will yield a similar configuration to that of the encoder of the CLVP
[susnato/clvp_dev](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpencoderconfig | #clvpencoderconfig | .md | 330_5 |
This is the configuration class to store the configuration of a [`ClvpDecoder`]. It is used to instantiate a CLVP
Decoder 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 Decoder part of the CLV... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpdecoderconfig | #clvpdecoderconfig | .md | 330_6 |
Construct a CLVP tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvptokenizer | #clvptokenizer | .md | 330_7 |
Constructs a CLVP feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information regarding those methods.
This class extracts log-mel-spectrogram features from... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpfeatureextractor | #clvpfeatureextractor | .md | 330_8 |
Constructs a CLVP processor which wraps a CLVP Feature Extractor and a CLVP Tokenizer into a single processor.
[`ClvpProcessor`] offers all the functionalities of [`ClvpFeatureExtractor`] and [`ClvpTokenizer`]. See the
[`~ClvpProcessor.__call__`], [`~ClvpProcessor.decode`] and [`~ClvpProcessor.batch_decode`] for more... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpprocessor | #clvpprocessor | .md | 330_9 |
The composite CLVP model with a text encoder, speech encoder and speech decoder model.The speech decoder model generates the speech_ids from the text and the text encoder and speech encoder workstogether to filter out the best speech_ids.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpmodelforconditionalgeneration | #clvpmodelforconditionalgeneration | .md | 330_10 |
The CLVP decoder model with a language modelling 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 PyTorc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpforcausallm | #clvpforcausallm | .md | 330_11 |
The bare Clvp decoder model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpmodel | #clvpmodel | .md | 330_12 |
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`ClvpEncoderLayer`].
Args:
config: ClvpConfig | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpencoder | #clvpencoder | .md | 330_13 |
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`ClvpDecoderLayer`] | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clvp.md | https://huggingface.co/docs/transformers/en/model_doc/clvp/#clvpdecoder | #clvpdecoder | .md | 330_14 |
<!--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/recurrent_gemma.md | https://huggingface.co/docs/transformers/en/model_doc/recurrent_gemma/ | .md | 331_0 | |
The Recurrent Gemma model was proposed in [RecurrentGemma: Moving Past Transformers for Efficient Open Language Models](https://storage.googleapis.com/deepmind-media/gemma/recurrentgemma-report.pdf) by the Griffin, RLHF and Gemma Teams of Google.
The abstract from the paper is the following:
*We introduce Recurrent... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/recurrent_gemma.md | https://huggingface.co/docs/transformers/en/model_doc/recurrent_gemma/#overview | #overview | .md | 331_1 |
This is the configuration class to store the configuration of a [`RecurrentGemmaModel`]. It is used to instantiate a RecurrentGemma
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 RecurrentGemm... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/recurrent_gemma.md | https://huggingface.co/docs/transformers/en/model_doc/recurrent_gemma/#recurrentgemmaconfig | #recurrentgemmaconfig | .md | 331_2 |
The bare RecurrentGemma Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/recurrent_gemma.md | https://huggingface.co/docs/transformers/en/model_doc/recurrent_gemma/#recurrentgemmamodel | #recurrentgemmamodel | .md | 331_3 |
No docstring available for RecurrentGemmaForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/recurrent_gemma.md | https://huggingface.co/docs/transformers/en/model_doc/recurrent_gemma/#recurrentgemmaforcausallm | #recurrentgemmaforcausallm | .md | 331_4 |
<!--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/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/ | .md | 332_0 | |
The DETR model was proposed in [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR
consists of a convolutional backbone followed by an encoder-decoder Transformer which can ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#overview | #overview | .md | 332_1 |
Here's a TLDR explaining how [`~transformers.DetrForObjectDetection`] works:
First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
ResNet-50/ResNet-101). Let's assume we also add a batch dimension. This means that the input to the backbone is a
tensor of shape `(batch_siz... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#how-detr-works | #how-detr-works | .md | 332_2 |
- DETR uses so-called **object queries** to detect objects in an image. The number of queries determines the maximum
number of objects that can be detected in a single image, and is set to 100 by default (see parameter
`num_queries` of [`~transformers.DetrConfig`]). Note that it's good to have some slack (in COCO, the
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#usage-tips | #usage-tips | .md | 332_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DETR.
<PipelineTag pipeline="object-detection"/>
- All example notebooks illustrating fine-tuning [`DetrForObjectDetection`] and [`DetrForSegmentation`] on a custom dataset can be found [here](https://github.com/N... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#resources | #resources | .md | 332_4 |
This is the configuration class to store the configuration of a [`DetrModel`]. It is used to instantiate a DETR
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 DETR
[facebook/detr-resnet-50](ht... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#detrconfig | #detrconfig | .md | 332_5 |
Constructs a Detr image processor.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's `(height, width)` dimensions to the specified `siz... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#detrimageprocessor | #detrimageprocessor | .md | 332_6 |
Constructs a fast Detr image processor.
Args:
format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's `(height, width)` dimensions t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#detrimageprocessorfast | #detrimageprocessorfast | .md | 332_7 |
No docstring available for DetrFeatureExtractor
Methods: __call__
- post_process_object_detection
- post_process_semantic_segmentation
- post_process_instance_segmentation
- post_process_panoptic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#detrfeatureextractor | #detrfeatureextractor | .md | 332_8 |
models.detr.modeling_detr.DetrModelOutput
Base class for outputs of the DETR encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone through a layernorm. This is useful when train... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#detr-specific-outputs | #detr-specific-outputs | .md | 332_9 |
The bare DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without
any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#detrmodel | #detrmodel | .md | 332_10 |
DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks
such as COCO detection.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#detrforobjectdetection | #detrforobjectdetection | .md | 332_11 |
DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top, for tasks
such as COCO panoptic.
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, resi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/detr.md | https://huggingface.co/docs/transformers/en/model_doc/detr/#detrforsegmentation | #detrforsegmentation | .md | 332_12 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/ | .md | 333_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=xlnet">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlnet-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/xlnet-base-cased">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%2... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnet | #xlnet | .md | 333_1 |
The XLNet model was proposed in [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov,
Quoc V. Le. XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#overview | #overview | .md | 333_2 |
- The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
- Due to the difficulty of training a fully auto-regressive model over various factorization order, XLNet is pretrained
using only a sub-set of the output tokens as target which are selected with the `target_mappin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#usage-tips | #usage-tips | .md | 333_3 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Multiple choice task guide](../tasks/multiple_choi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#resources | #resources | .md | 333_4 |
This is the configuration class to store the configuration of a [`XLNetModel`] or a [`TFXLNetModel`]. It is used to
instantiate a XLNet 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
[xlnet/xl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnetconfig | #xlnetconfig | .md | 333_5 |
Construct an XLNet tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnettokenizer | #xlnettokenizer | .md | 333_6 |
Construct a "fast" XLNet tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnettokenizerfast | #xlnettokenizerfast | .md | 333_7 |
models.xlnet.modeling_xlnet.XLNetModelOutput
Output type of [`XLNetModel`].
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_predict, hidden_size)`):
Sequence of hidden-states at the last layer of the model.
`num_predict` corresponds to `target_mapping.shape[1]`. If `target_mapping` is `Non... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnet-specific-outputs | #xlnet-specific-outputs | .md | 333_8 |
The bare XLNet Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnetmodel | #xlnetmodel | .md | 333_9 |
XLNet Model 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 input embeddings, pr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnetlmheadmodel | #xlnetlmheadmodel | .md | 333_10 |
XLNet Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.
for GLUE tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnetforsequenceclassification | #xlnetforsequenceclassification | .md | 333_11 |
XLNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RACE/SWAG tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnetformultiplechoice | #xlnetformultiplechoice | .md | 333_12 |
XLNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnetfortokenclassification | #xlnetfortokenclassification | .md | 333_13 |
XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnetforquestionansweringsimple | #xlnetforquestionansweringsimple | .md | 333_14 |
XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#xlnetforquestionanswering | #xlnetforquestionanswering | .md | 333_15 |
No docstring available for TFXLNetModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#tfxlnetmodel | #tfxlnetmodel | .md | 333_16 |
No docstring available for TFXLNetLMHeadModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#tfxlnetlmheadmodel | #tfxlnetlmheadmodel | .md | 333_17 |
No docstring available for TFXLNetForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#tfxlnetforsequenceclassification | #tfxlnetforsequenceclassification | .md | 333_18 |
No docstring available for TFXLNetForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#tfxlnetformultiplechoice | #tfxlnetformultiplechoice | .md | 333_19 |
No docstring available for TFXLNetForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#tfxlnetfortokenclassification | #tfxlnetfortokenclassification | .md | 333_20 |
No docstring available for TFXLNetForQuestionAnsweringSimple
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlnet/#tfxlnetforquestionansweringsimple | #tfxlnetforquestionansweringsimple | .md | 333_21 |
<!--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/ijepa.md | https://huggingface.co/docs/transformers/en/model_doc/ijepa/ | .md | 334_0 | |
The I-JEPA model was proposed in [Image-based Joint-Embedding Predictive Architecture](https://arxiv.org/abs/2301.08243) by Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas.
I-JEPA is a self-supervised learning method that predicts the representati... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ijepa.md | https://huggingface.co/docs/transformers/en/model_doc/ijepa/#overview | #overview | .md | 334_1 |
Here is how to use this model for image feature extraction:
```python
import requests
import torch
from PIL import Image
from torch.nn.functional import cosine_similarity
from transformers import AutoModel, AutoProcessor
url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
url_2 = "http://images.cocodat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ijepa.md | https://huggingface.co/docs/transformers/en/model_doc/ijepa/#how-to-use | #how-to-use | .md | 334_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with I-JEPA.
<PipelineTag pipeline="image-classification"/>
- [`IJepaForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classific... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ijepa.md | https://huggingface.co/docs/transformers/en/model_doc/ijepa/#resources | #resources | .md | 334_3 |
This is the configuration class to store the configuration of a [`IJepaModel`]. It is used to instantiate an IJEPA
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 I-JEPA
[google/ijepa-base-patc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ijepa.md | https://huggingface.co/docs/transformers/en/model_doc/ijepa/#ijepaconfig | #ijepaconfig | .md | 334_4 |
The bare IJepa 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ijepa.md | https://huggingface.co/docs/transformers/en/model_doc/ijepa/#ijepamodel | #ijepamodel | .md | 334_5 |
IJepa Model transformer with an image classification head on top (a linear layer on top of the final hidden states)
e.g. for ImageNet.
<Tip>
Note that it's possible to fine-tune IJepa on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in the forward of ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ijepa.md | https://huggingface.co/docs/transformers/en/model_doc/ijepa/#ijepaforimageclassification | #ijepaforimageclassification | .md | 334_6 |
<!--Copyright 2024 The GLM & ZhipuAI 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 requir... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/glm.md | https://huggingface.co/docs/transformers/en/model_doc/glm/ | .md | 335_0 | |
The GLM Model was proposed
in [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://arxiv.org/html/2406.12793v1)
by GLM Team, THUDM & ZhipuAI.
The abstract from the paper is the following:
*We introduce ChatGLM, an evolving family of large language models that we have been developing... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/glm.md | https://huggingface.co/docs/transformers/en/model_doc/glm/#overview | #overview | .md | 335_1 |
`GLM-4` can be found on the [Huggingface Hub](https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7)
In the following, we demonstrate how to use `glm-4-9b-chat` 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/glm.md | https://huggingface.co/docs/transformers/en/model_doc/glm/#usage-tips | #usage-tips | .md | 335_2 |
This is the configuration class to store the configuration of a [`GlmModel`]. It is used to instantiate an Glm
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 Glm-4-9b-chat.
e.g. [THUDM/glm-4-9... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/glm.md | https://huggingface.co/docs/transformers/en/model_doc/glm/#glmconfig | #glmconfig | .md | 335_3 |
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