text stringlengths 5 58.6k | source stringclasses 470
values | url stringlengths 49 167 | source_section stringlengths 0 90 | file_type stringclasses 1
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No docstring available for TFFunnelBaseModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#tffunnelbasemodel | #tffunnelbasemodel | .md | 200_17 |
No docstring available for TFFunnelModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#tffunnelmodel | #tffunnelmodel | .md | 200_18 |
No docstring available for TFFunnelForPreTraining
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#tffunnelmodelforpretraining | #tffunnelmodelforpretraining | .md | 200_19 |
No docstring available for TFFunnelForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#tffunnelformaskedlm | #tffunnelformaskedlm | .md | 200_20 |
No docstring available for TFFunnelForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#tffunnelforsequenceclassification | #tffunnelforsequenceclassification | .md | 200_21 |
No docstring available for TFFunnelForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#tffunnelformultiplechoice | #tffunnelformultiplechoice | .md | 200_22 |
No docstring available for TFFunnelForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#tffunnelfortokenclassification | #tffunnelfortokenclassification | .md | 200_23 |
No docstring available for TFFunnelForQuestionAnswering
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/funnel.md | https://huggingface.co/docs/transformers/en/model_doc/funnel/#tffunnelforquestionanswering | #tffunnelforquestionanswering | .md | 200_24 |
<!--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/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/ | .md | 201_0 | |
The Llama2 model was proposed in [LLaMA: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargav... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#overview | #overview | .md | 201_1 |
<Tip warning={true}>
The `Llama2` models were trained using `bfloat16`, but the original inference uses `float16`. The checkpoints uploaded on the Hub use `torch_dtype = 'float16'`, which will be
used by the `AutoModel` API to cast the checkpoints from `torch.float32` to `torch.float16`.
The `dtype` of the online w... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#usage-tips | #usage-tips | .md | 201_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LLaMA2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an e... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#resources | #resources | .md | 201_3 |
This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
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 LLaMA-7B.
Configuration obje... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#llamaconfig | #llamaconfig | .md | 201_4 |
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model.
Args:
vocab_file (`str`):
Path to the vocabulary file.
unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
The unknown token. A token... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#llamatokenizer | #llamatokenizer | .md | 201_5 |
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and no normalization.
```python
>>> from transformers import LlamaTokenizerFast
>>> tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
>>> tokenizer.encode("Hello this is a test")
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#llamatokenizerfast | #llamatokenizerfast | .md | 201_6 |
The bare LLaMA Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#llamamodel | #llamamodel | .md | 201_7 |
No docstring available for LlamaForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#llamaforcausallm | #llamaforcausallm | .md | 201_8 |
The LLaMa Model transformer with a sequence classification head on top (linear layer).
[`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llama2.md | https://huggingface.co/docs/transformers/en/model_doc/llama2/#llamaforsequenceclassification | #llamaforsequenceclassification | .md | 201_9 |
<!--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/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/ | .md | 202_0 | |
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
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/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#m-ctc-t | #m-ctc-t | .md | 202_1 |
The M-CTC-T model was proposed in [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#overview | #overview | .md | 202_2 |
The PyTorch version of this model is only available in torch 1.9 and higher. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#usage-tips | #usage-tips | .md | 202_3 |
- [Automatic speech recognition task guide](../tasks/asr) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#resources | #resources | .md | 202_4 |
This is the configuration class to store the configuration of a [`MCTCTModel`]. It is used to instantiate an
M-CTC-T 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 M-CTC-T
[speechbrain/m-ctc-t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#mctctconfig | #mctctconfig | .md | 202_5 |
Constructs a M-CTC-T 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
code has been adapted from Flashlight's C++ cod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#mctctfeatureextractor | #mctctfeatureextractor | .md | 202_6 |
Constructs a MCTCT processor which wraps a MCTCT feature extractor and a MCTCT tokenizer into a single processor.
[`MCTCTProcessor`] offers all the functionalities of [`MCTCTFeatureExtractor`] and [`AutoTokenizer`]. See the
[`~MCTCTProcessor.__call__`] and [`~MCTCTProcessor.decode`] for more information.
Args:
feat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#mctctprocessor | #mctctprocessor | .md | 202_7 |
The bare M-CTC-T 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/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#mctctmodel | #mctctmodel | .md | 202_8 |
MCTCT Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
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 us... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mctct.md | https://huggingface.co/docs/transformers/en/model_doc/mctct/#mctctforctc | #mctctforctc | .md | 202_9 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/ | .md | 203_0 | |
Note that [`BlenderbotSmallModel`] and
[`BlenderbotSmallForConditionalGeneration`] are only used in combination with the checkpoint
[facebook/blenderbot-90M](https://huggingface.co/facebook/blenderbot-90M). Larger Blenderbot checkpoints should
instead be used with [`BlenderbotModel`] and
[`BlenderbotForConditionalGener... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#blenderbot-small | #blenderbot-small | .md | 203_1 |
The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://arxiv.org/pdf/2004.13637.pdf) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.
The abstract of the pa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#overview | #overview | .md | 203_2 |
Blenderbot Small is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#usage-tips | #usage-tips | .md | 203_3 |
- [Causal language modeling task guide](../tasks/language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#resources | #resources | .md | 203_4 |
This is the configuration class to store the configuration of a [`BlenderbotSmallModel`]. It is used to instantiate
an BlenderbotSmall 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 Blenderbot... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#blenderbotsmallconfig | #blenderbotsmallconfig | .md | 203_5 |
Constructs a Blenderbot-90M tokenizer based on BPE (Byte-Pair-Encoding)
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
the superclass for more information regarding methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
merges_file (`s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#blenderbotsmalltokenizer | #blenderbotsmalltokenizer | .md | 203_6 |
Construct a "fast" BlenderbotSmall tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
<frameworkcontent>
<pt> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#blenderbotsmalltokenizerfast | #blenderbotsmalltokenizerfast | .md | 203_7 |
The bare BlenderbotSmall 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/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#blenderbotsmallmodel | #blenderbotsmallmodel | .md | 203_8 |
The BlenderbotSmall Model with a language modeling head. Can be used for summarization.
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/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#blenderbotsmallforconditionalgeneration | #blenderbotsmallforconditionalgeneration | .md | 203_9 |
No docstring available for BlenderbotSmallForCausalLM
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#blenderbotsmallforcausallm | #blenderbotsmallforcausallm | .md | 203_10 |
No docstring available for TFBlenderbotSmallModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#tfblenderbotsmallmodel | #tfblenderbotsmallmodel | .md | 203_11 |
No docstring available for TFBlenderbotSmallForConditionalGeneration
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#tfblenderbotsmallforconditionalgeneration | #tfblenderbotsmallforconditionalgeneration | .md | 203_12 |
No docstring available for FlaxBlenderbotSmallModel
Methods: __call__
- encode
- decode | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#flaxblenderbotsmallmodel | #flaxblenderbotsmallmodel | .md | 203_13 |
No docstring available for FlaxBlenderbotSmallForConditionalGeneration
Methods: __call__
- encode
- decode
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/blenderbot-small.md | https://huggingface.co/docs/transformers/en/model_doc/blenderbot-small/#flaxblenderbotforconditionalgeneration | #flaxblenderbotforconditionalgeneration | .md | 203_14 |
<!--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/vitmatte.md | https://huggingface.co/docs/transformers/en/model_doc/vitmatte/ | .md | 204_0 | |
The ViTMatte model was proposed in [Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang.
ViTMatte leverages plain [Vision Transformers](vit) for the task of image matting, which is the process of accurately estim... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vitmatte.md | https://huggingface.co/docs/transformers/en/model_doc/vitmatte/#overview | #overview | .md | 204_1 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViTMatte.
- A demo notebook regarding inference with [`VitMatteForImageMatting`], including background replacement, can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ViTMatte).
<... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vitmatte.md | https://huggingface.co/docs/transformers/en/model_doc/vitmatte/#resources | #resources | .md | 204_2 |
This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to
instantiate a ViTMatte 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 ViTMatte
[hustvl/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vitmatte.md | https://huggingface.co/docs/transformers/en/model_doc/vitmatte/#vitmatteconfig | #vitmatteconfig | .md | 204_3 |
Constructs a ViTMatte image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vitmatte.md | https://huggingface.co/docs/transformers/en/model_doc/vitmatte/#vitmatteimageprocessor | #vitmatteimageprocessor | .md | 204_4 |
ViTMatte framework leveraging any vision backbone e.g. for ADE20k, CityScapes.
Parameters:
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
b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vitmatte.md | https://huggingface.co/docs/transformers/en/model_doc/vitmatte/#vitmatteforimagematting | #vitmatteforimagematting | .md | 204_5 |
<!--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/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/ | .md | 205_0 | |
The BLOOM model has been proposed with its various versions through the [BigScience Workshop](https://bigscience.huggingface.co/). BigScience is inspired by other open science initiatives where researchers have pooled their time and resources to collectively achieve a higher impact.
The architecture of BLOOM is essenti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#overview | #overview | .md | 205_1 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BLOOM. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an ex... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#resources | #resources | .md | 205_2 |
This is the configuration class to store the configuration of a [`BloomModel`]. It is used to instantiate a Bloom
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to the Bloom architecture
[bigscience/bloom](h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#bloomconfig | #bloomconfig | .md | 205_3 |
Construct a "fast" Bloom tokenizer (backed by HuggingFace's *tokenizers* library). 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 s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#bloomtokenizerfast | #bloomtokenizerfast | .md | 205_4 |
The bare Bloom 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 etc.)
This m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#bloommodel | #bloommodel | .md | 205_5 |
The Bloom 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 inpu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#bloomforcausallm | #bloomforcausallm | .md | 205_6 |
The Bloom Model transformer with a sequence classification head on top (linear layer).
[`BloomForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last token.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#bloomforsequenceclassification | #bloomforsequenceclassification | .md | 205_7 |
Bloom 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/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#bloomfortokenclassification | #bloomfortokenclassification | .md | 205_8 |
The BLOOM Model transformer 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 gene... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#bloomforquestionanswering | #bloomforquestionanswering | .md | 205_9 |
No docstring available for FlaxBloomModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#flaxbloommodel | #flaxbloommodel | .md | 205_10 |
No docstring available for FlaxBloomForCausalLM
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bloom.md | https://huggingface.co/docs/transformers/en/model_doc/bloom/#flaxbloomforcausallm | #flaxbloomforcausallm | .md | 205_11 |
<!--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_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/ | .md | 206_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Ti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#speech2text2 | #speech2text2 | .md | 206_1 |
The Speech2Text2 model is used together with [Wav2Vec2](wav2vec2) for Speech Translation models proposed in
[Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by
Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
Speech2Text2 is a *d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#overview | #overview | .md | 206_2 |
- Speech2Text2 achieves state-of-the-art results on the CoVoST Speech Translation dataset. For more information, see
the [official models](https://huggingface.co/models?other=speech2text2) .
- Speech2Text2 is always used within the [SpeechEncoderDecoder](speech-encoder-decoder) framework.
- Speech2Text2's tokenizer is ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#usage-tips | #usage-tips | .md | 206_3 |
Speech2Text2's [`SpeechEncoderDecoderModel`] model accepts raw waveform input values from speech and
makes use of [`~generation.GenerationMixin.generate`] to translate the input speech
autoregressively to the target language.
The [`Wav2Vec2FeatureExtractor`] class is responsible for preprocessing the input speech and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#inference | #inference | .md | 206_4 |
- [Causal language modeling task guide](../tasks/language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#resources | #resources | .md | 206_5 |
This is the configuration class to store the configuration of a [`Speech2Text2ForCausalLM`]. It is used to
instantiate an Speech2Text2 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 Speech2Tex... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#speech2text2config | #speech2text2config | .md | 206_6 |
Constructs a Speech2Text2Tokenizer.
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 to `"<s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#speech2texttokenizer | #speech2texttokenizer | .md | 206_7 |
Constructs a Speech2Text2 processor which wraps a Speech2Text2 feature extractor and a Speech2Text2 tokenizer into
a single processor.
[`Speech2Text2Processor`] offers all the functionalities of [`AutoFeatureExtractor`] and [`Speech2Text2Tokenizer`].
See the [`~Speech2Text2Processor.__call__`] and [`~Speech2Text2Proc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#speech2text2processor | #speech2text2processor | .md | 206_8 |
The Speech2Text2 Decoder with a language modeling head. Can be used as the decoder part of [`EncoderDecoderModel`] and [`SpeechEncoderDecoder`].
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or savin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/speech_to_text_2.md | https://huggingface.co/docs/transformers/en/model_doc/speech_to_text_2/#speech2text2forcausallm | #speech2text2forcausallm | .md | 206_9 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutxlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutxlm/ | .md | 207_0 | |
LayoutXLM was proposed in [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha
Zhang, Furu Wei. It's a multilingual extension of the [LayoutLMv2 model](https://arxiv.o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutxlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutxlm/#overview | #overview | .md | 207_1 |
One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like so:
```python
from transformers import LayoutLMv2Model
model = LayoutLMv2Model.from_pretrained("microsoft/layoutxlm-base")
```
Note that LayoutXLM has its own tokenizer, based on
[`LayoutXLMTokenizer`]/[`LayoutXLMTokenizerFast`]. You c... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutxlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutxlm/#usage-tips-and-examples | #usage-tips-and-examples | .md | 207_2 |
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutxlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutxlm/#layoutxlmtokenizer | #layoutxlmtokenizer | .md | 207_3 |
Construct a "fast" LayoutXLM tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which con... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutxlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutxlm/#layoutxlmtokenizerfast | #layoutxlmtokenizerfast | .md | 207_4 |
Constructs a LayoutXLM processor which combines a LayoutXLM image processor and a LayoutXLM tokenizer into a single
processor.
[`LayoutXLMProcessor`] offers all the functionalities you need to prepare data for the model.
It first uses [`LayoutLMv2ImageProcessor`] to resize document images to a fixed size, and optio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/layoutxlm.md | https://huggingface.co/docs/transformers/en/model_doc/layoutxlm/#layoutxlmprocessor | #layoutxlmprocessor | .md | 207_5 |
<!--Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/ | .md | 208_0 | |
The MegatronBERT model was proposed in [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model
Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley,
Jared Casper and Bryan Catanzaro.
The abstract from the paper is the following:
*Recent... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#overview | #overview | .md | 208_1 |
We have provided pretrained [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m) checkpoints
for use to evaluate or finetuning downstream tasks.
To access these checkpoints, first [sign up](https://ngc.nvidia.com/signup) for and setup the NVIDIA GPU Cloud (NGC)
Registry CLI. Further documentat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#usage-tips | #usage-tips | .md | 208_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/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#resources | #resources | .md | 208_3 |
This is the configuration class to store the configuration of a [`MegatronBertModel`]. It is used to instantiate a
MEGATRON_BERT 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 MEGATRON_BERT
[n... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertconfig | #megatronbertconfig | .md | 208_4 |
The bare MegatronBert 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, prunin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertmodel | #megatronbertmodel | .md | 208_5 |
MegatronBert Model with a `language modeling` head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertformaskedlm | #megatronbertformaskedlm | .md | 208_6 |
MegatronBert Model with a `language modeling` head on top for CLM fine-tuning.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This mod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertforcausallm | #megatronbertforcausallm | .md | 208_7 |
MegatronBert Model with a `next sentence prediction (classification)` head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertfornextsentenceprediction | #megatronbertfornextsentenceprediction | .md | 208_8 |
MegatronBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
`next sentence prediction (classification)` head.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertforpretraining | #megatronbertforpretraining | .md | 208_9 |
MegatronBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertforsequenceclassification | #megatronbertforsequenceclassification | .md | 208_10 |
MegatronBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output
and a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as dow... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertformultiplechoice | #megatronbertformultiplechoice | .md | 208_11 |
MegatronBert 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 downl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertfortokenclassification | #megatronbertfortokenclassification | .md | 208_12 |
MegatronBert 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 metho... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron-bert.md | https://huggingface.co/docs/transformers/en/model_doc/megatron-bert/#megatronbertforquestionanswering | #megatronbertforquestionanswering | .md | 208_13 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/ | .md | 209_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Ti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/#xlm-prophetnet | #xlm-prophetnet | .md | 209_1 |
The XLM-ProphetNet model was proposed in [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
Zhang, Ming Zhou on 13 Jan, 2020.
XLM-ProphetNet is an encoder-decoder model and can... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/#overview | #overview | .md | 209_2 |
- [Causal language modeling task guide](../tasks/language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/#resources | #resources | .md | 209_3 |
This is the configuration class to store the configuration of a [`XLMProphetNetModel`]. It is used to instantiate a
XLMProphetNet 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 XLMProphetNet
[... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/#xlmprophetnetconfig | #xlmprophetnetconfig | .md | 209_4 |
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/#xlmprophetnettokenizer | #xlmprophetnettokenizer | .md | 209_5 |
The bare XLMProphetNet 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/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/#xlmprophetnetmodel | #xlmprophetnetmodel | .md | 209_6 |
The standalone encoder part of the XLMProphetNetModel.
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.)
Original ProphetNet code can be fo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/#xlmprophetnetencoder | #xlmprophetnetencoder | .md | 209_7 |
The standalone decoder part of the XLMProphetNetModel.
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.)
Original ProphetNet code can be fo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-prophetnet.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-prophetnet/#xlmprophetnetdecoder | #xlmprophetnetdecoder | .md | 209_8 |
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