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 |
|---|---|---|---|---|---|
FNet Model with a `next sentence prediction (classification)` head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Param... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/fnet.md | https://huggingface.co/docs/transformers/en/model_doc/fnet/#fnetfornextsentenceprediction | #fnetfornextsentenceprediction | .md | 165_10 |
FNet 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch docum... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/fnet.md | https://huggingface.co/docs/transformers/en/model_doc/fnet/#fnetforsequenceclassification | #fnetforsequenceclassification | .md | 165_11 |
FNet 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/fnet.md | https://huggingface.co/docs/transformers/en/model_doc/fnet/#fnetformultiplechoice | #fnetformultiplechoice | .md | 165_12 |
FNet 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/fnet.md | https://huggingface.co/docs/transformers/en/model_doc/fnet/#fnetfortokenclassification | #fnetfortokenclassification | .md | 165_13 |
FNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/fnet.md | https://huggingface.co/docs/transformers/en/model_doc/fnet/#fnetforquestionanswering | #fnetforquestionanswering | .md | 165_14 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/ | .md | 166_0 | |
The UniSpeech model was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael
Zeng, Xuedong Huang .
The abstract from the paper is the following:
*In this pap... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#overview | #overview | .md | 166_1 |
- UniSpeech is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please
use [`Wav2Vec2Processor`] for the feature extraction.
- UniSpeech model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
decoded using [`Wav2Vec2CTCTok... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#usage-tips | #usage-tips | .md | 166_2 |
- [Audio classification task guide](../tasks/audio_classification)
- [Automatic speech recognition task guide](../tasks/asr) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#resources | #resources | .md | 166_3 |
This is the configuration class to store the configuration of a [`UniSpeechModel`]. It is used to instantiate an
UniSpeech 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 UniSpeech
[microsoft/u... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#unispeechconfig | #unispeechconfig | .md | 166_4 |
models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
Output type of [`UniSpeechForPreTrainingOutput`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when model is in train mode, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the contrastive loss (L_m) and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#unispeech-specific-outputs | #unispeech-specific-outputs | .md | 166_5 |
The bare UniSpeech Model transformer outputting raw hidden-states without any specific head on top.
UniSpeech was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled
Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei,
Mi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#unispeechmodel | #unispeechmodel | .md | 166_6 |
UniSpeech Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
UniSpeech was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled
Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#unispeechforctc | #unispeechforctc | .md | 166_7 |
UniSpeech Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting.
UniSpeech was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled
Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, K... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#unispeechforsequenceclassification | #unispeechforsequenceclassification | .md | 166_8 |
UniSpeech Model with a vector-quantization module and ctc loss for pre-training.
UniSpeech was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled
Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei,
Michael Zeng, Xuedong... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/unispeech.md | https://huggingface.co/docs/transformers/en/model_doc/unispeech/#unispeechforpretraining | #unispeechforpretraining | .md | 166_9 |
<!--Copyright 2022 The HuggingFace Team and The OpenBMB 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpmant.md | https://huggingface.co/docs/transformers/en/model_doc/cpmant/ | .md | 167_0 | |
CPM-Ant is an open-source Chinese pre-trained language model (PLM) with 10B parameters. It is also the first milestone of the live training process of CPM-Live. The training process is cost-effective and environment-friendly. CPM-Ant also achieves promising results with delta tuning on the CUGE benchmark. Besides the f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpmant.md | https://huggingface.co/docs/transformers/en/model_doc/cpmant/#overview | #overview | .md | 167_1 |
- A tutorial on [CPM-Live](https://github.com/OpenBMB/CPM-Live/tree/cpm-ant/cpm-live). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpmant.md | https://huggingface.co/docs/transformers/en/model_doc/cpmant/#resources | #resources | .md | 167_2 |
This is the configuration class to store the configuration of a [`CpmAntModel`]. It is used to instantiate an
CPMAnt 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 CPMAnt
[openbmb/cpm-ant-10b]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpmant.md | https://huggingface.co/docs/transformers/en/model_doc/cpmant/#cpmantconfig | #cpmantconfig | .md | 167_3 |
Construct a CPMAnt tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
bod_token (`str`, *optional*, defaults to `"<d>"`):
The beginning of document token.
eod_token (`str`, *optional*, defaults to `"</d>"`):
The end of document token.
bos_token (`str`, *optional*... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpmant.md | https://huggingface.co/docs/transformers/en/model_doc/cpmant/#cpmanttokenizer | #cpmanttokenizer | .md | 167_4 |
The bare CPMAnt Model outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpmant.md | https://huggingface.co/docs/transformers/en/model_doc/cpmant/#cpmantmodel | #cpmantmodel | .md | 167_5 |
The CPMAnt Model with a language modeling head on top (linear layer with weights tied to the input embeddings).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpmant.md | https://huggingface.co/docs/transformers/en/model_doc/cpmant/#cpmantforcausallm | #cpmantforcausallm | .md | 167_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/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/ | .md | 168_0 | |
The [`SpeechEncoderDecoderModel`] can be used to initialize a speech-to-text model
with any pretrained speech autoencoding model as the encoder (*e.g.* [Wav2Vec2](wav2vec2), [Hubert](hubert)) and any pretrained autoregressive model as the decoder.
The effectiveness of initializing speech-sequence-to-text-sequence mod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/#speech-encoder-decoder-models | #speech-encoder-decoder-models | .md | 168_1 |
[`SpeechEncoderDecoderModel`] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [`Wav2Vec2Model`] configuration for the encoder
and the default [`BertForCausalLM`] configuration for the decoder.
```python
>>> from transformers import B... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/#randomly-initializing-speechencoderdecodermodel-from-model-configurations | #randomly-initializing-speechencoderdecodermodel-from-model-configurations | .md | 168_2 |
[`SpeechEncoderDecoderModel`] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained Transformer-based speech model, *e.g.* [Wav2Vec2](wav2vec2), [Hubert](hubert) can serve as the encoder and both pretrained auto-encoding models, *e.g.* BERT, pretrained cau... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/#initialising-speechencoderdecodermodel-from-a-pretrained-encoder-and-a-pretrained-decoder | #initialising-speechencoderdecodermodel-from-a-pretrained-encoder-and-a-pretrained-decoder | .md | 168_3 |
To load fine-tuned checkpoints of the `SpeechEncoderDecoderModel` class, [`SpeechEncoderDecoderModel`] provides the `from_pretrained(...)` method just like any other model architecture in Transformers.
To perform inference, one uses the [`generate`] method, which allows to autoregressively generate text. This method ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/#loading-an-existing-speechencoderdecodermodel-checkpoint-and-perform-inference | #loading-an-existing-speechencoderdecodermodel-checkpoint-and-perform-inference | .md | 168_4 |
Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model on a dataset of (speech, text) pairs.
As you can see, only 2 inputs are required for the model in order to compute a loss: `input_values` (which are the
speech inputs) and `labels` (which are the `input_ids` of the en... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/#training | #training | .md | 168_5 |
[`SpeechEncoderDecoderConfig`] is the configuration class to store the configuration of a
[`SpeechEncoderDecoderModel`]. It is used to instantiate an Encoder Decoder model according to the specified
arguments, defining the encoder and decoder configs.
Configuration objects inherit from [`PretrainedConfig`] and can be... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/#speechencoderdecoderconfig | #speechencoderdecoderconfig | .md | 168_6 |
This class can be used to initialize a speech-sequence-to-text-sequence model with any pretrained speech
autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is
loaded via [`~AutoModel.from_pretrained`] function and the decoder is loaded via
[`~AutoModelForCausalLM.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/#speechencoderdecodermodel | #speechencoderdecodermodel | .md | 168_7 |
No docstring available for FlaxSpeechEncoderDecoderModel
Methods: __call__
- from_encoder_decoder_pretrained | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech-encoder-decoder.md | https://huggingface.co/docs/transformers/en/model_doc/speech-encoder-decoder/#flaxspeechencoderdecodermodel | #flaxspeechencoderdecodermodel | .md | 168_8 |
<!--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/mimi.md | https://huggingface.co/docs/transformers/en/model_doc/mimi/ | .md | 169_0 | |
The Mimi model was proposed in [Moshi: a speech-text foundation model for real-time dialogue](https://kyutai.org/Moshi.pdf) by Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave and Neil Zeghidour. Mimi is a high-fidelity audio codec model developed by the Kyutai te... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mimi.md | https://huggingface.co/docs/transformers/en/model_doc/mimi/#overview | #overview | .md | 169_1 |
Here is a quick example of how to encode and decode an audio using this model:
```python
>>> from datasets import load_dataset, Audio
>>> from transformers import MimiModel, AutoFeatureExtractor
>>> librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> # load ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mimi.md | https://huggingface.co/docs/transformers/en/model_doc/mimi/#usage-example | #usage-example | .md | 169_2 |
This is the configuration class to store the configuration of an [`MimiModel`]. It is used to instantiate a
Mimi 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
[kyutai/mimi](https://huggingfac... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mimi.md | https://huggingface.co/docs/transformers/en/model_doc/mimi/#mimiconfig | #mimiconfig | .md | 169_3 |
The Mimi neural audio codec model.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https:/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mimi.md | https://huggingface.co/docs/transformers/en/model_doc/mimi/#mimimodel | #mimimodel | .md | 169_4 |
<!--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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/ | .md | 170_0 | |
The MMS model was proposed in [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516)
by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alex... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#overview | #overview | .md | 170_1 |
The ASR model checkpoints can be found here : [mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102), [mms-1b-l1107](https://huggingface.co/facebook/mms-1b-l1107), [mms-1b-all](https://huggingface.co/facebook/mms-1b-all). For best accuracy, use the `mms-1b-all` model.
Tips:
- All ASR models accept a float ar... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#automatic-speech-recognition-asr | #automatic-speech-recognition-asr | .md | 170_2 |
By default MMS loads adapter weights for English. If you want to load adapter weights of another language
make sure to specify `target_lang=<your-chosen-target-lang>` as well as `"ignore_mismatched_sizes=True`.
The `ignore_mismatched_sizes=True` keyword has to be passed to allow the language model head to be resized ac... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#loading | #loading | .md | 170_3 |
Next, let's look at how we can run MMS in inference and change adapter layers after having called [`~PretrainedModel.from_pretrained`]
First, we load audio data in different languages using the [Datasets](https://github.com/huggingface/datasets).
```py
from datasets import load_dataset, Audio
# English
stream_data =... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#inference | #inference | .md | 170_4 |
MMS-TTS uses the same model architecture as VITS, which was added to 🤗 Transformers in v4.33. MMS trains a separate
model checkpoint for each of the 1100+ languages in the project. All available checkpoints can be found on the Hugging
Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=face... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#speech-synthesis-tts | #speech-synthesis-tts | .md | 170_5 |
To use the MMS model, first update to the latest version of the Transformers library:
```bash
pip install --upgrade transformers accelerate
```
Since the flow-based model in VITS is non-deterministic, it is good practice to set a seed to ensure reproducibility of
the outputs.
- For languages with a Roman alphabet... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#inference | #inference | .md | 170_6 |
Different LID models are available based on the number of languages they can recognize - [126](https://huggingface.co/facebook/mms-lid-126), [256](https://huggingface.co/facebook/mms-lid-256), [512](https://huggingface.co/facebook/mms-lid-512), [1024](https://huggingface.co/facebook/mms-lid-1024), [2048](https://huggin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#language-identification-lid | #language-identification-lid | .md | 170_7 |
First, we install transformers and some other libraries
```bash
pip install torch accelerate datasets[audio]
pip install --upgrade transformers
````
Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz.
```py
from datasets import load_dataset, Audio
# Engl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#inference | #inference | .md | 170_8 |
Pretrained models are available for two different sizes - [300M](https://huggingface.co/facebook/mms-300m) ,
[1Bil](https://huggingface.co/facebook/mms-1b).
<Tip>
The MMS for ASR architecture is based on the Wav2Vec2 model, refer to [Wav2Vec2's documentation page](wav2vec2) for further
details on how to finetune wi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mms.md | https://huggingface.co/docs/transformers/en/model_doc/mms/#audio-pretrained-models | #audio-pretrained-models | .md | 170_9 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bort.md | https://huggingface.co/docs/transformers/en/model_doc/bort/ | .md | 171_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we do not 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: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.
<... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bort.md | https://huggingface.co/docs/transformers/en/model_doc/bort/#bort | #bort | .md | 171_1 |
The BORT model was proposed in [Optimal Subarchitecture Extraction for BERT](https://arxiv.org/abs/2010.10499) by
Adrian de Wynter and Daniel J. Perry. It is an optimal subset of architectural parameters for the BERT, which the
authors refer to as "Bort".
The abstract from the paper is the following:
*We extract an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bort.md | https://huggingface.co/docs/transformers/en/model_doc/bort/#overview | #overview | .md | 171_2 |
- BORT's model architecture is based on BERT, refer to [BERT's documentation page](bert) for the
model's API reference as well as usage examples.
- BORT uses the RoBERTa tokenizer instead of the BERT tokenizer, refer to [RoBERTa's documentation page](roberta) for the tokenizer's API reference as well as usage examples.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bort.md | https://huggingface.co/docs/transformers/en/model_doc/bort/#usage-tips | #usage-tips | .md | 171_3 |
<!--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/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/ | .md | 172_0 | |
The Emu3 model was proposed in [Emu3: Next-Token Prediction is All You Need](https://arxiv.org/abs/2409.18869) by Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, Yingli Zhao, Yulong Ao, Xuebin Min, Tao Li, Boya Wu, Bo Zhao, Bowen Zhang, Liang... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#overview | #overview | .md | 172_1 |
Here's how to load the model and perform inference in half-precision (`torch.bfloat16`) to generate textual output from text or text and image inputs:
```python
from transformers import Emu3Processor, Emu3ForConditionalGeneration
import torch
from PIL import Image
import requests
processor = Emu3Processor.from_pretr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#text-generation-inference | #text-generation-inference | .md | 172_2 |
Emu3 can also generate images from textual input. Here is how you can do it:
```python
processor = Emu3Processor.from_pretrained("BAAI/Emu3-Gen-hf")
model = Emu3ForConditionalGeneration.from_pretrained("BAAI/Emu3-Gen-hf", torch_dtype="bfloat16", device_map="auto", attn_implementation="flash_attention_2")
inputs = p... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#image-generation-inference | #image-generation-inference | .md | 172_3 |
This is the configuration class to store the configuration of a [`Emu3Model`]. It is used to instantiate a
emu3 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
[Emu3-community/Emu3-Chat-hf](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3config | #emu3config | .md | 172_4 |
This is the configuration class to store the configuration of a [`Emu3VQVAE`]. It is used to instantiate an VQ-VAE
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a configuration to the VQ model presented in Emu3 paper.
Configurat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3vqvaeconfig | #emu3vqvaeconfig | .md | 172_5 |
This is the configuration class to store the configuration of a [`Emu3TextModel`]. It is used to instantiate a
emu3 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
[Emu3-community/Emu3-Chat-hf]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3textconfig | #emu3textconfig | .md | 172_6 |
Constructs a Emu3 processor which wraps a Emu3 image processor and a GPT2 tokenizer into a single
processor.
[`Emu3Processor`] offers all the functionalities of [`Emu3ImageProcessor`] and [`GPT2TokenizerFast`].
See the [`~Emu3Processor.__call__`] and [`~Emu3Processor.decode`] for more information.
Args:
image_proce... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3processor | #emu3processor | .md | 172_7 |
Constructs a Emu3 image processor that dynamically resizes images based on the original images.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use wh... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3imageprocessor | #emu3imageprocessor | .md | 172_8 |
The VQ-VAE model used in Emu3 for encoding/decoding images into discrete tokens.
This model follows the "Make-a-scene: Scene-based text-to-image generation with human priors" paper from
[ Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman](https://arxiv.org/abs/2203.13131).
This mode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3vqvae | #emu3vqvae | .md | 172_9 |
The bare Emu3Text 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.)
Th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3textmodel | #emu3textmodel | .md | 172_10 |
No docstring available for Emu3ForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3forcausallm | #emu3forcausallm | .md | 172_11 |
No docstring available for Emu3ForConditionalGeneration
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/emu3.md | https://huggingface.co/docs/transformers/en/model_doc/emu3/#emu3forconditionalgeneration | #emu3forconditionalgeneration | .md | 172_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/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/ | .md | 173_0 | |
The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, Kurt W. Keutzer. It's a
bidirectional transformer similar to the BERT model. The key difference between the BERT... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#overview | #overview | .md | 173_1 |
- SqueezeBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
rather than the left.
- SqueezeBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
efficient at predicting masked tokens and at NLU in general, but ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#usage-tips | #usage-tips | .md | 173_2 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multip... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#resources | #resources | .md | 173_3 |
This is the configuration class to store the configuration of a [`SqueezeBertModel`]. It is used to instantiate a
SqueezeBERT 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 SqueezeBERT
[squeez... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezebertconfig | #squeezebertconfig | .md | 173_4 |
Construct a SqueezeBERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezeberttokenizer | #squeezeberttokenizer | .md | 173_5 |
Construct a "fast" SqueezeBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezeberttokenizerfast | #squeezeberttokenizerfast | .md | 173_6 |
The bare SqueezeBERT Model transformer outputting raw hidden-states without any specific head on top.
The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural
networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezebertmodel | #squeezebertmodel | .md | 173_7 |
SqueezeBERT Model with a `language modeling` head on top.
The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural
networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W.
Keutzer
This model inherits from [`Pre... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezebertformaskedlm | #squeezebertformaskedlm | .md | 173_8 |
SqueezeBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural
networks?](https://arxiv.org/abs/2006.11316) by Forrest N.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezebertforsequenceclassification | #squeezebertforsequenceclassification | .md | 173_9 |
SqueezeBERT 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.
The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural
networks?](https://arxiv.org/abs/2006.11316) by F... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezebertformultiplechoice | #squeezebertformultiplechoice | .md | 173_10 |
SqueezeBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.
The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural
networks?](https://arxiv.org/abs/2006.11316) by For... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezebertfortokenclassification | #squeezebertfortokenclassification | .md | 173_11 |
SqueezeBERT 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`).
The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/squeezebert.md | https://huggingface.co/docs/transformers/en/model_doc/squeezebert/#squeezebertforquestionanswering | #squeezebertforquestionanswering | .md | 173_12 |
<!--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/wav2vec2_phoneme.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2_phoneme/ | .md | 174_0 | |
The Wav2Vec2Phoneme model was proposed in [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al.,
2021](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
The abstract from the paper is the following:
*Recent progress in self-training, self-supervised pretraining a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2_phoneme.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2_phoneme/#overview | #overview | .md | 174_1 |
- Wav2Vec2Phoneme uses the exact same architecture as Wav2Vec2
- Wav2Vec2Phoneme is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- Wav2Vec2Phoneme model was trained using connectionist temporal classification (CTC) so the model output has to be
decoded using [`Wav2Ve... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2_phoneme.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2_phoneme/#usage-tips | #usage-tips | .md | 174_2 |
Constructs a Wav2Vec2PhonemeCTC tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
bos_token (`str`, *optional*, defaults ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/wav2vec2_phoneme.md | https://huggingface.co/docs/transformers/en/model_doc/wav2vec2_phoneme/#wav2vec2phonemectctokenizer | #wav2vec2phonemectctokenizer | .md | 174_3 |
<!--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/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/ | .md | 175_0 | |
The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by
Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. Big... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#overview | #overview | .md | 175_1 |
- For an in-detail explanation on how BigBird's attention works, see [this blog post](https://huggingface.co/blog/big-bird).
- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
**original_full** is advised as there is no benefit in using **block_sparse** a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#usage-tips | #usage-tips | .md | 175_2 |
- [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)
- [Masked language modeling task guide](../tasks/mask... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#resources | #resources | .md | 175_3 |
This is the configuration class to store the configuration of a [`BigBirdModel`]. It is used to instantiate an
BigBird 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 BigBird
[google/bigbird-ro... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdconfig | #bigbirdconfig | .md | 175_4 |
Construct a BigBird 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/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdtokenizer | #bigbirdtokenizer | .md | 175_5 |
Construct a "fast" BigBird 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/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdtokenizerfast | #bigbirdtokenizerfast | .md | 175_6 |
models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput
Output type of [`BigBirdForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbird-specific-outputs | #bigbird-specific-outputs | .md | 175_7 |
The bare BigBird 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) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdmodel | #bigbirdmodel | .md | 175_8 |
No docstring available for BigBirdForPreTraining
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdforpretraining | #bigbirdforpretraining | .md | 175_9 |
BigBird Model with a `language modeling` head on top for CLM fine-tuning.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parame... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdforcausallm | #bigbirdforcausallm | .md | 175_10 |
BigBird Model with a `language modeling` head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`BigB... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdformaskedlm | #bigbirdformaskedlm | .md | 175_11 |
BigBird 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch do... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdforsequenceclassification | #bigbirdforsequenceclassification | .md | 175_12 |
BigBird 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the P... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdformultiplechoice | #bigbirdformultiplechoice | .md | 175_13 |
BigBird 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyT... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdfortokenclassification | #bigbirdfortokenclassification | .md | 175_14 |
BigBird Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-clas... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#bigbirdforquestionanswering | #bigbirdforquestionanswering | .md | 175_15 |
No docstring available for FlaxBigBirdModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#flaxbigbirdmodel | #flaxbigbirdmodel | .md | 175_16 |
No docstring available for FlaxBigBirdForPreTraining
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#flaxbigbirdforpretraining | #flaxbigbirdforpretraining | .md | 175_17 |
No docstring available for FlaxBigBirdForCausalLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#flaxbigbirdforcausallm | #flaxbigbirdforcausallm | .md | 175_18 |
No docstring available for FlaxBigBirdForMaskedLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#flaxbigbirdformaskedlm | #flaxbigbirdformaskedlm | .md | 175_19 |
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