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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|---|---|---|---|---|---|
The bare Glm 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 mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/glm.md | https://huggingface.co/docs/transformers/en/model_doc/glm/#glmmodel | #glmmodel | .md | 335_4 |
No docstring available for GlmForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/glm.md | https://huggingface.co/docs/transformers/en/model_doc/glm/#glmforcausallm | #glmforcausallm | .md | 335_5 |
The Glm Model transformer with a sequence classification head on top (linear layer).
[`GlmForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/glm.md | https://huggingface.co/docs/transformers/en/model_doc/glm/#glmforsequenceclassification | #glmforsequenceclassification | .md | 335_6 |
The Glm Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/glm.md | https://huggingface.co/docs/transformers/en/model_doc/glm/#glmfortokenclassification | #glmfortokenclassification | .md | 335_7 |
<!--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/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/ | .md | 336_0 | |
This page provides code and pre-trained weights for Transformer protein language models from Meta AI's Fundamental
AI Research Team, providing the state-of-the-art ESMFold and ESM-2, and the previously released ESM-1b and ESM-1v.
Transformer protein language models were introduced in the paper [Biological structure and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#overview | #overview | .md | 336_1 |
- ESM models are trained with a masked language modeling (MLM) objective.
- The HuggingFace port of ESMFold uses portions of the [openfold](https://github.com/aqlaboratory/openfold) library. The `openfold` library is licensed under the Apache License 2.0. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#usage-tips | #usage-tips | .md | 336_2 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Masked language modeling task guide](../tasks/masked_language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#resources | #resources | .md | 336_3 |
This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM 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 ESM
[facebook/esm-1b](https://huggi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#esmconfig | #esmconfig | .md | 336_4 |
Constructs an ESM tokenizer.
Methods: build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
<frameworkcontent>
<pt> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#esmtokenizer | #esmtokenizer | .md | 336_5 |
The bare ESM Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
e... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#esmmodel | #esmmodel | .md | 336_6 |
ESM 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 [torch.n... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#esmformaskedlm | #esmformaskedlm | .md | 336_7 |
ESM 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 or saving... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#esmforsequenceclassification | #esmforsequenceclassification | .md | 336_8 |
ESM 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 or... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#esmfortokenclassification | #esmfortokenclassification | .md | 336_9 |
ESMForProteinFolding is the HuggingFace port of the original ESMFold model. It consists of an ESM-2 "stem" followed
by a protein folding "head", although unlike most other output heads, this "head" is similar in size and runtime to
the rest of the model combined! It outputs a dictionary containing predicted structural ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#esmforproteinfolding | #esmforproteinfolding | .md | 336_10 |
No docstring available for TFEsmModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#tfesmmodel | #tfesmmodel | .md | 336_11 |
No docstring available for TFEsmForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#tfesmformaskedlm | #tfesmformaskedlm | .md | 336_12 |
No docstring available for TFEsmForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#tfesmforsequenceclassification | #tfesmforsequenceclassification | .md | 336_13 |
No docstring available for TFEsmForTokenClassification
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/esm.md | https://huggingface.co/docs/transformers/en/model_doc/esm/#tfesmfortokenclassification | #tfesmfortokenclassification | .md | 336_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/pvt.md | https://huggingface.co/docs/transformers/en/model_doc/pvt/ | .md | 337_0 | |
The PVT model was proposed in
[Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122)
by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. The PVT is a type of
vision transformer that utilizes a p... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pvt.md | https://huggingface.co/docs/transformers/en/model_doc/pvt/#overview | #overview | .md | 337_1 |
This is the configuration class to store the configuration of a [`PvtModel`]. It is used to instantiate an Pvt
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 Pvt
[Xrenya/pvt-tiny-224](https://... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pvt.md | https://huggingface.co/docs/transformers/en/model_doc/pvt/#pvtconfig | #pvtconfig | .md | 337_2 |
Constructs a PVT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `{"h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pvt.md | https://huggingface.co/docs/transformers/en/model_doc/pvt/#pvtimageprocessor | #pvtimageprocessor | .md | 337_3 |
Pvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.
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 PyTor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pvt.md | https://huggingface.co/docs/transformers/en/model_doc/pvt/#pvtforimageclassification | #pvtforimageclassification | .md | 337_4 |
The bare Pvt encoder 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
behavior... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pvt.md | https://huggingface.co/docs/transformers/en/model_doc/pvt/#pvtmodel | #pvtmodel | .md | 337_5 |
<!--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/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/ | .md | 338_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=reformer">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-reformer-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/reformer-crime-and-punishment">
<img alt="Spaces" src="https://img.shields.io/b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformer | #reformer | .md | 338_1 |
The Reformer model was proposed in the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451.pdf) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
The abstract from the paper is the following:
*Large Transformer models routinely achieve state-of-the-art results on a number of tasks but train... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#overview | #overview | .md | 338_2 |
- Reformer does **not** work with *torch.nn.DataParallel* due to a bug in PyTorch, see [issue #36035](https://github.com/pytorch/pytorch/issues/36035).
- Use Axial position encoding (see below for more details). It’s a mechanism to avoid having a huge positional encoding matrix (when the sequence length is very big) by... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#usage-tips | #usage-tips | .md | 338_3 |
Axial Positional Encodings were first implemented in Google's [trax library](https://github.com/google/trax/blob/4d99ad4965bab1deba227539758d59f0df0fef48/trax/layers/research/position_encodings.py#L29)
and developed by the authors of this model's paper. In models that are treating very long input sequences, the
convent... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#axial-positional-encodings | #axial-positional-encodings | .md | 338_4 |
In Locality sensitive hashing (LSH) self attention the key and query projection weights are tied. Therefore, the key
query embedding vectors are also tied. LSH self attention uses the locality sensitive hashing mechanism proposed in
[Practical and Optimal LSH for Angular Distance](https://arxiv.org/abs/1509.02897) to a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#lsh-self-attention | #lsh-self-attention | .md | 338_5 |
Local self attention is essentially a "normal" self attention layer with key, query and value projections, but is
chunked so that in each chunk of length `config.local_chunk_length` the query embedding vectors only attends to
the key embedding vectors in its chunk and to the key embedding vectors of `config.local_num_c... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#local-self-attention | #local-self-attention | .md | 338_6 |
During training, we must ensure that the sequence length is set to a value that can be divided by the least common
multiple of `config.lsh_chunk_length` and `config.local_chunk_length` and that the parameters of the Axial
Positional Encodings are correctly set as described above. Reformer is very memory efficient so th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#training | #training | .md | 338_7 |
- [Text classification task guide](../tasks/sequence_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#resources | #resources | .md | 338_8 |
This is the configuration class to store the configuration of a [`ReformerModel`]. It is used to instantiate a
Reformer 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 ReFormer
[google/reformer... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerconfig | #reformerconfig | .md | 338_9 |
Construct a Reformer 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`):
[SentencePie... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizer | #reformertokenizer | .md | 338_10 |
Construct a "fast" Reformer 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformertokenizerfast | #reformertokenizerfast | .md | 338_11 |
The bare Reformer Model transformer outputting raw hidden-stateswithout any specific head on top.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits from [`PreTrainedModel`]. Check the superclass docume... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformermodel | #reformermodel | .md | 338_12 |
Reformer Model with a `language modeling` head on top.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformermodelwithlmhead | #reformermodelwithlmhead | .md | 338_13 |
Reformer Model with a `language modeling` head on top.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerformaskedlm | #reformerformaskedlm | .md | 338_14 |
Reformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev,
Łukasz Kaiser, Anselm Levskaya.
This model inherits fr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerforsequenceclassification | #reformerforsequenceclassification | .md | 338_15 |
Reformer Model with a span classification head on top for extractive question-answering tasks like SQuAD / TriviaQA
( a linear layer on top of hidden-states output to compute `span start logits` and `span end logits`.
Reformer was proposed in [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/reformer.md | https://huggingface.co/docs/transformers/en/model_doc/reformer/#reformerforquestionanswering | #reformerforquestionanswering | .md | 338_16 |
<!--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/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/ | .md | 339_0 | |
The CamemBERT model was proposed in [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by
[Louis Martin](https://huggingface.co/louismartin), [Benjamin Muller](https://huggingface.co/benjamin-mlr), [Pedro Javier Ortiz Suárez](https://huggingface.co/pjox), Yoann Dupont, Laurent Romary, Éric Vil... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#overview | #overview | .md | 339_1 |
- [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/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#resources | #resources | .md | 339_2 |
This is the configuration class to store the configuration of a [`CamembertModel`] or a [`TFCamembertModel`]. It is
used to instantiate a Camembert model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertconfig | #camembertconfig | .md | 339_3 |
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Construct a CamemBERT 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 regardi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizer | #camemberttokenizer | .md | 339_4 |
Construct a "fast" CamemBERT 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/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camemberttokenizerfast | #camemberttokenizerfast | .md | 339_5 |
The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertmodel | #camembertmodel | .md | 339_6 |
CamemBERT 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 model ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertforcausallm | #camembertforcausallm | .md | 339_7 |
CamemBERT 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 [t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertformaskedlm | #camembertformaskedlm | .md | 339_8 |
CamemBERT 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 or ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertforsequenceclassification | #camembertforsequenceclassification | .md | 339_9 |
CamemBERT 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 downlo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertformultiplechoice | #camembertformultiplechoice | .md | 339_10 |
CamemBERT 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 download... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertfortokenclassification | #camembertfortokenclassification | .md | 339_11 |
CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#camembertforquestionanswering | #camembertforquestionanswering | .md | 339_12 |
No docstring available for TFCamembertModel | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#tfcamembertmodel | #tfcamembertmodel | .md | 339_13 |
No docstring available for TFCamembertForCausalLM | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#tfcamembertforcausallm | #tfcamembertforcausallm | .md | 339_14 |
No docstring available for TFCamembertForMaskedLM | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#tfcamembertformaskedlm | #tfcamembertformaskedlm | .md | 339_15 |
No docstring available for TFCamembertForSequenceClassification | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#tfcamembertforsequenceclassification | #tfcamembertforsequenceclassification | .md | 339_16 |
No docstring available for TFCamembertForMultipleChoice | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#tfcamembertformultiplechoice | #tfcamembertformultiplechoice | .md | 339_17 |
No docstring available for TFCamembertForTokenClassification | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#tfcamembertfortokenclassification | #tfcamembertfortokenclassification | .md | 339_18 |
No docstring available for TFCamembertForQuestionAnswering
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/camembert.md | https://huggingface.co/docs/transformers/en/model_doc/camembert/#tfcamembertforquestionanswering | #tfcamembertforquestionanswering | .md | 339_19 |
<!--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/gpt_neox_japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neox_japanese/ | .md | 340_0 | |
We introduce GPT-NeoX-Japanese, which is an autoregressive language model for Japanese, trained on top of [https://github.com/EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).
Japanese is a unique language with its large vocabulary and a combination of hiragana, katakana, and kanji writing scripts.
To addre... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neox_japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neox_japanese/#overview | #overview | .md | 340_1 |
The `generate()` method can be used to generate text using GPT NeoX Japanese model.
```python
>>> from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseTokenizer
>>> model = GPTNeoXJapaneseForCausalLM.from_pretrained("abeja/gpt-neox-japanese-2.7b")
>>> tokenizer = GPTNeoXJapaneseTokenizer.from_pretrain... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neox_japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neox_japanese/#usage-example | #usage-example | .md | 340_2 |
- [Causal language modeling task guide](../tasks/language_modeling) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neox_japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neox_japanese/#resources | #resources | .md | 340_3 |
This is the configuration class to store the configuration of a [`GPTNeoXModelJapanese`]. It is used to instantiate
a GPTNeoX 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 GPTNeoXJapanese
[ab... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neox_japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neox_japanese/#gptneoxjapaneseconfig | #gptneoxjapaneseconfig | .md | 340_4 |
This tokenizer inherits from [`PreTrainedTokenizer`] and is based on Japanese special Sub-Word-Encoding that is
used in this repository (https://github.com/tanreinama/Japanese-BPEEncoder_V2). Check the repository for details.
Japanese has a relatively large vocabulary and there is no separation between words. Furthermo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neox_japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neox_japanese/#gptneoxjapanesetokenizer | #gptneoxjapanesetokenizer | .md | 340_5 |
The bare GPTNeoXJapanese 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 gene... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neox_japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neox_japanese/#gptneoxjapanesemodel | #gptneoxjapanesemodel | .md | 340_6 |
GPTNeoXJapanese Model with a `language modeling` head on top for Classifier Model 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neox_japanese.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neox_japanese/#gptneoxjapaneseforcausallm | #gptneoxjapaneseforcausallm | .md | 340_7 |
<!--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/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/ | .md | 341_0 | |
The MRA model was proposed in [Multi Resolution Analysis (MRA) for Approximate Self-Attention](https://arxiv.org/abs/2207.10284) by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, and Vikas Singh.
The abstract from the paper is the following:
*Transformers have emerged as a preferred model for many tasks in... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/#overview | #overview | .md | 341_1 |
This is the configuration class to store the configuration of a [`MraModel`]. It is used to instantiate an MRA
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 Mra
[uw-madison/mra-base-512-4](ht... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/#mraconfig | #mraconfig | .md | 341_2 |
The bare MRA 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 usage an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/#mramodel | #mramodel | .md | 341_3 |
MRA 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 ([`MraConfi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/#mraformaskedlm | #mraformaskedlm | .md | 341_4 |
MRA 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 document... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/#mraforsequenceclassification | #mraforsequenceclassification | .md | 341_5 |
MRA 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 PyTorch... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/#mraformultiplechoice | #mraformultiplechoice | .md | 341_6 |
MRA 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 PyTorch d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/#mrafortokenclassification | #mrafortokenclassification | .md | 341_7 |
MRA 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. Use... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mra.md | https://huggingface.co/docs/transformers/en/model_doc/mra/#mraforquestionanswering | #mraforquestionanswering | .md | 341_8 |
<!--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/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/ | .md | 342_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/spaces/sweetcocoa/pop2piano">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#pop2piano | #pop2piano | .md | 342_1 |
The Pop2Piano model was proposed in [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi and Kyogu Lee.
Piano covers of pop music are widely enjoyed, but generating them from music is not a trivial task. It requires great
expertise with playing piano as well as knowing... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#overview | #overview | .md | 342_2 |
* To use Pop2Piano, you will need to install the 🤗 Transformers library, as well as the following third party modules:
```bash
pip install pretty-midi==0.2.9 essentia==2.1b6.dev1034 librosa scipy
```
Please note that you may need to restart your runtime after installation.
* Pop2Piano is an Encoder-Decoder based model... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#usage-tips | #usage-tips | .md | 342_3 |
- Example using HuggingFace Dataset:
```python
>>> from datasets import load_dataset
>>> from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor
>>> model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano")
>>> processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#examples | #examples | .md | 342_4 |
This is the configuration class to store the configuration of a [`Pop2PianoForConditionalGeneration`]. It is used
to instantiate a Pop2PianoForConditionalGeneration model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configura... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#pop2pianoconfig | #pop2pianoconfig | .md | 342_5 |
No docstring available for Pop2PianoFeatureExtractor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#pop2pianofeatureextractor | #pop2pianofeatureextractor | .md | 342_6 |
Pop2Piano 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 [tor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#pop2pianoforconditionalgeneration | #pop2pianoforconditionalgeneration | .md | 342_7 |
No docstring available for Pop2PianoTokenizer
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#pop2pianotokenizer | #pop2pianotokenizer | .md | 342_8 |
No docstring available for Pop2PianoProcessor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pop2piano.md | https://huggingface.co/docs/transformers/en/model_doc/pop2piano/#pop2pianoprocessor | #pop2pianoprocessor | .md | 342_9 |
<!--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/falcon_mamba.md | https://huggingface.co/docs/transformers/en/model_doc/falcon_mamba/ | .md | 343_0 | |
The FalconMamba model was proposed by TII UAE (Technology Innovation Institute) in their release.
The abstract from the paper is the following:
*We present FalconMamba, a new base large language model based on the novel Mamba architecture. FalconMamba is trained on 5.8 trillion tokens with carefully selected data m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon_mamba.md | https://huggingface.co/docs/transformers/en/model_doc/falcon_mamba/#overview | #overview | .md | 343_1 |
Below we demonstrate how to use the model:
```python
from transformers import FalconMambaForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
model = FalconMambaForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b")
input_ids = tokenizer("Hey how are you doing?"... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon_mamba.md | https://huggingface.co/docs/transformers/en/model_doc/falcon_mamba/#usage | #usage | .md | 343_2 |
This is the configuration class to store the configuration of a [`FalconMambaModel`]. It is used to instantiate a FALCON_MAMBA
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 FALCON_MAMBA
[tiiu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon_mamba.md | https://huggingface.co/docs/transformers/en/model_doc/falcon_mamba/#falconmambaconfig | #falconmambaconfig | .md | 343_3 |
The bare FALCONMAMBA Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon_mamba.md | https://huggingface.co/docs/transformers/en/model_doc/falcon_mamba/#falconmambamodel | #falconmambamodel | .md | 343_4 |
The FALCONMAMBA 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 th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon_mamba.md | https://huggingface.co/docs/transformers/en/model_doc/falcon_mamba/#falconmambalmheadmodel | #falconmambalmheadmodel | .md | 343_5 |
<!--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/convnextv2.md | https://huggingface.co/docs/transformers/en/model_doc/convnextv2/ | .md | 344_0 | |
The ConvNeXt V2 model was proposed in [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
ConvNeXt V2 is a pure convolutional model (ConvNet), inspired by the design... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnextv2.md | https://huggingface.co/docs/transformers/en/model_doc/convnextv2/#overview | #overview | .md | 344_1 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXt V2.
<PipelineTag pipeline="image-classification"/>
- [`ConvNextV2ForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnextv2.md | https://huggingface.co/docs/transformers/en/model_doc/convnextv2/#resources | #resources | .md | 344_2 |
This is the configuration class to store the configuration of a [`ConvNextV2Model`]. It is used to instantiate an
ConvNeXTV2 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 ConvNeXTV2
[facebook... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnextv2.md | https://huggingface.co/docs/transformers/en/model_doc/convnextv2/#convnextv2config | #convnextv2config | .md | 344_3 |
The bare ConvNextV2 model outputting raw features without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnextv2.md | https://huggingface.co/docs/transformers/en/model_doc/convnextv2/#convnextv2model | #convnextv2model | .md | 344_4 |
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