text stringlengths 5 58.6k | source stringclasses 470
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Construct a ConvBERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *opti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#convberttokenizer | #convberttokenizer | .md | 247_6 |
Construct a "fast" ConvBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Fi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#convberttokenizerfast | #convberttokenizerfast | .md | 247_7 |
The bare ConvBERT 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 usa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#convbertmodel | #convbertmodel | .md | 247_8 |
ConvBERT 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 ([`Con... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#convbertformaskedlm | #convbertformaskedlm | .md | 247_9 |
ConvBERT 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 d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#convbertforsequenceclassification | #convbertforsequenceclassification | .md | 247_10 |
ConvBERT 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#convbertformultiplechoice | #convbertformultiplechoice | .md | 247_11 |
ConvBERT 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 Py... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#convbertfortokenclassification | #convbertfortokenclassification | .md | 247_12 |
ConvBERT 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-cla... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#convbertforquestionanswering | #convbertforquestionanswering | .md | 247_13 |
No docstring available for TFConvBertModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#tfconvbertmodel | #tfconvbertmodel | .md | 247_14 |
No docstring available for TFConvBertForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#tfconvbertformaskedlm | #tfconvbertformaskedlm | .md | 247_15 |
No docstring available for TFConvBertForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#tfconvbertforsequenceclassification | #tfconvbertforsequenceclassification | .md | 247_16 |
No docstring available for TFConvBertForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#tfconvbertformultiplechoice | #tfconvbertformultiplechoice | .md | 247_17 |
No docstring available for TFConvBertForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#tfconvbertfortokenclassification | #tfconvbertfortokenclassification | .md | 247_18 |
No docstring available for TFConvBertForQuestionAnswering
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convbert.md | https://huggingface.co/docs/transformers/en/model_doc/convbert/#tfconvbertforquestionanswering | #tfconvbertforquestionanswering | .md | 247_19 |
<!--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/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/ | .md | 248_0 | |
SAM (Segment Anything Model) was proposed in [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
The model can be used to predict segmen... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#overview | #overview | .md | 248_1 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SAM.
- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/segment_anything.ipynb) for using the model.
- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/automatic... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#resources | #resources | .md | 248_2 |
SlimSAM, a pruned version of SAM, was proposed in [0.1% Data Makes Segment Anything Slim](https://arxiv.org/abs/2312.05284) by Zigeng Chen et al. SlimSAM reduces the size of the SAM models considerably while maintaining the same performance.
Checkpoints can be found on the [hub](https://huggingface.co/models?other=sl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#slimsam | #slimsam | .md | 248_3 |
One can combine [Grounding DINO](grounding-dino) with SAM for text-based mask generation as introduced in [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://arxiv.org/abs/2401.14159). You can refer to this [demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Ground... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#grounded-sam | #grounded-sam | .md | 248_4 |
[`SamConfig`] is the configuration class to store the configuration of a [`SamModel`]. It is used to instantiate a
SAM model according to the specified arguments, defining the vision model, prompt-encoder model and mask decoder
configs. Instantiating a configuration with the defaults will yield a similar configuration ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#samconfig | #samconfig | .md | 248_5 |
This is the configuration class to store the configuration of a [`SamVisionModel`]. It is used to instantiate a SAM
vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
defaults will yield a similar configuration to that of the SAM ViT-h
[facebook/sam-vit-h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#samvisionconfig | #samvisionconfig | .md | 248_6 |
This is the configuration class to store the configuration of a [`SamMaskDecoder`]. It is used to instantiate a SAM
mask decoder to the specified arguments, defining the model architecture. Instantiating a configuration defaults
will yield a similar configuration to that of the SAM-vit-h
[facebook/sam-vit-huge](https:/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#sammaskdecoderconfig | #sammaskdecoderconfig | .md | 248_7 |
This is the configuration class to store the configuration of a [`SamPromptEncoder`]. The [`SamPromptEncoder`]
module is used to encode the input 2D points and bounding boxes. Instantiating a configuration defaults will yield
a similar configuration to that of the SAM-vit-h
[facebook/sam-vit-huge](https://huggingface.c... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#sampromptencoderconfig | #sampromptencoderconfig | .md | 248_8 |
Constructs a SAM processor which wraps a SAM image processor and an 2D points & Bounding boxes processor into a
single processor.
[`SamProcessor`] offers all the functionalities of [`SamImageProcessor`]. See the docstring of
[`~SamImageProcessor.__call__`] for more information.
Args:
image_processor (`SamImageProce... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#samprocessor | #samprocessor | .md | 248_9 |
Constructs a SAM image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `{"longest_edge": 1024}`):
Size... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#samimageprocessor | #samimageprocessor | .md | 248_10 |
Segment Anything Model (SAM) for generating segmentation masks, given an input image and optional 2D location and bounding boxes.
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/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#sammodel | #sammodel | .md | 248_11 |
No docstring available for TFSamModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/sam.md | https://huggingface.co/docs/transformers/en/model_doc/sam/#tfsammodel | #tfsammodel | .md | 248_12 |
<!--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/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/ | .md | 249_0 | |
Falcon is a class of causal decoder-only models built by [TII](https://www.tii.ae/). The largest Falcon checkpoints
have been trained on >=1T tokens of text, with a particular emphasis on the [RefinedWeb](https://arxiv.org/abs/2306.01116)
corpus. They are made available under the Apache 2.0 license.
Falcon's architec... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/#overview | #overview | .md | 249_1 |
<Tip>
Falcon models were initially added to the Hugging Face Hub as custom code checkpoints. However, Falcon is now fully
supported in the Transformers library. If you fine-tuned a model from a custom code checkpoint, we recommend converting
your checkpoint to the new in-library format, as this should give significan... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/#converting-custom-checkpoints | #converting-custom-checkpoints | .md | 249_2 |
This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
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
[tiiuae/falcon-7b](https://hu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/#falconconfig | #falconconfig | .md | 249_3 |
The bare Falcon 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/#falconmodel | #falconmodel | .md | 249_4 |
The Falcon 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 inp... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/#falconforcausallm | #falconforcausallm | .md | 249_5 |
The Falcon Model transformer with a sequence classification head on top (linear layer).
[`FalconForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last toke... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/#falconforsequenceclassification | #falconforsequenceclassification | .md | 249_6 |
Falcon 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/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/#falconfortokenclassification | #falconfortokenclassification | .md | 249_7 |
The Falcon 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 gen... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/falcon.md | https://huggingface.co/docs/transformers/en/model_doc/falcon/#falconforquestionanswering | #falconforquestionanswering | .md | 249_8 |
<!--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/barthez.md | https://huggingface.co/docs/transformers/en/model_doc/barthez/ | .md | 250_0 | |
The BARThez model was proposed in [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
2020.
The abstract of the paper:
*Inductive transfer learning, enabled by self-supervised learning, hav... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/barthez.md | https://huggingface.co/docs/transformers/en/model_doc/barthez/#overview | #overview | .md | 250_1 |
- BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check:
[examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/barthez.md | https://huggingface.co/docs/transformers/en/model_doc/barthez/#resources | #resources | .md | 250_2 |
Adapted from [`CamembertTokenizer`] and [`BartTokenizer`]. Construct a BARThez 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 regardin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/barthez.md | https://huggingface.co/docs/transformers/en/model_doc/barthez/#bartheztokenizer | #bartheztokenizer | .md | 250_3 |
Adapted from [`CamembertTokenizer`] and [`BartTokenizer`]. Construct a "fast" BARThez tokenizer. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more informati... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/barthez.md | https://huggingface.co/docs/transformers/en/model_doc/barthez/#bartheztokenizerfast | #bartheztokenizerfast | .md | 250_4 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/ | .md | 251_0 | |
The LUKE model was proposed in [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda and Yuji Matsumoto.
It is based on RoBERTa and adds entity embeddings as well as an entity-aware self-attentio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#overview | #overview | .md | 251_1 |
- This implementation is the same as [`RobertaModel`] with the addition of entity embeddings as well
as an entity-aware self-attention mechanism, which improves performance on tasks involving reasoning about entities.
- LUKE treats entities as input tokens; therefore, it takes `entity_ids`, `entity_attention_mask`,
`en... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#usage-tips | #usage-tips | .md | 251_2 |
- [A demo notebook on how to fine-tune [`LukeForEntityPairClassification`] for relation classification](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LUKE)
- [Notebooks showcasing how you to reproduce the results as reported in the paper with the HuggingFace implementation of LUKE](https://github.com... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#resources | #resources | .md | 251_3 |
This is the configuration class to store the configuration of a [`LukeModel`]. It is used to instantiate a LUKE
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 LUKE
[studio-ousia/luke-base](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukeconfig | #lukeconfig | .md | 251_4 |
Constructs a LUKE tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```pyth... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#luketokenizer | #luketokenizer | .md | 251_5 |
The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities 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 th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukemodel | #lukemodel | .md | 251_6 |
The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and
masked entity prediction.
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, re... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukeformaskedlm | #lukeformaskedlm | .md | 251_7 |
The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity
token) for entity classification tasks, such as Open Entity.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukeforentityclassification | #lukeforentityclassification | .md | 251_8 |
The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity
tokens) for entity pair classification tasks, such as TACRED.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukeforentitypairclassification | #lukeforentitypairclassification | .md | 251_9 |
The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks
such as named entity recognition.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukeforentityspanclassification | #lukeforentityspanclassification | .md | 251_10 |
The LUKE 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 s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukeforsequenceclassification | #lukeforsequenceclassification | .md | 251_11 |
The LUKE 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 downloa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukeformultiplechoice | #lukeformultiplechoice | .md | 251_12 |
The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To
solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this
class.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukefortokenclassification | #lukefortokenclassification | .md | 251_13 |
The LUKE 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 t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/luke.md | https://huggingface.co/docs/transformers/en/model_doc/luke/#lukeforquestionanswering | #lukeforquestionanswering | .md | 251_14 |
<!--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/focalnet.md | https://huggingface.co/docs/transformers/en/model_doc/focalnet/ | .md | 252_0 | |
The FocalNet model was proposed in [Focal Modulation Networks](https://arxiv.org/abs/2203.11926) by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
FocalNets completely replace self-attention (used in models like [ViT](vit) and [Swin](swin)) by a focal modulation mechanism for modeling token interactions ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/focalnet.md | https://huggingface.co/docs/transformers/en/model_doc/focalnet/#overview | #overview | .md | 252_1 |
This is the configuration class to store the configuration of a [`FocalNetModel`]. It is used to instantiate a
FocalNet 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 FocalNet
[microsoft/focal... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/focalnet.md | https://huggingface.co/docs/transformers/en/model_doc/focalnet/#focalnetconfig | #focalnetconfig | .md | 252_2 |
The bare FocalNet Model outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behav... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/focalnet.md | https://huggingface.co/docs/transformers/en/model_doc/focalnet/#focalnetmodel | #focalnetmodel | .md | 252_3 |
FocalNet Model with a decoder on top for masked image modeling.
This follows the same implementation as in [SimMIM](https://arxiv.org/abs/2111.09886).
<Tip>
Note that we provide a script to pre-train this model on custom data in our [examples
directory](https://github.com/huggingface/transformers/tree/main/exampl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/focalnet.md | https://huggingface.co/docs/transformers/en/model_doc/focalnet/#focalnetformaskedimagemodeling | #focalnetformaskedimagemodeling | .md | 252_4 |
FocalNet Model with an image classification head on top (a linear layer on top of the pooled output) 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 PyTorch documentation for all matter... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/focalnet.md | https://huggingface.co/docs/transformers/en/model_doc/focalnet/#focalnetforimageclassification | #focalnetforimageclassification | .md | 252_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/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/ | .md | 253_0 | |
ERNIE is a series of powerful models proposed by baidu, especially in Chinese tasks,
including [ERNIE1.0](https://arxiv.org/abs/1904.09223), [ERNIE2.0](https://ojs.aaai.org/index.php/AAAI/article/view/6428),
[ERNIE3.0](https://arxiv.org/abs/2107.02137), [ERNIE-Gram](https://arxiv.org/abs/2010.12148), [ERNIE-health](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#overview | #overview | .md | 253_1 |
Take `ernie-1.0-base-zh` as an example:
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh")
model = AutoModel.from_pretrained("nghuyong/ernie-1.0-base-zh")
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#usage-example | #usage-example | .md | 253_2 |
| Model Name | Language | Description |
|:-------------------:|:--------:|:-------------------------------:|
| ernie-1.0-base-zh | Chinese | Layer:12, Heads:12, Hidden:768 |
| ernie-2.0-base-en | English | Layer:12, Heads:12, Hidden:768 |
| ernie-2.0-large-en | English | Layer:24,... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#model-checkpoints | #model-checkpoints | .md | 253_3 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/mask... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#resources | #resources | .md | 253_4 |
This is the configuration class to store the configuration of a [`ErnieModel`] or a [`TFErnieModel`]. It is used to
instantiate a ERNIE 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 ERNIE
[ng... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#ernieconfig | #ernieconfig | .md | 253_5 |
models.ernie.modeling_ernie.ErnieForPreTrainingOutput
Output type of [`ErnieForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
predictio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#ernie-specific-outputs | #ernie-specific-outputs | .md | 253_6 |
The bare Ernie Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#erniemodel | #erniemodel | .md | 253_7 |
Ernie 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 downloa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#ernieforpretraining | #ernieforpretraining | .md | 253_8 |
Ernie 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 is a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#ernieforcausallm | #ernieforcausallm | .md | 253_9 |
Ernie 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#ernieformaskedlm | #ernieformaskedlm | .md | 253_10 |
Ernie 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 model ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#erniefornextsentenceprediction | #erniefornextsentenceprediction | .md | 253_11 |
Ernie 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 savi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#ernieforsequenceclassification | #ernieforsequenceclassification | .md | 253_12 |
Ernie 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 downloadin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#ernieformultiplechoice | #ernieformultiplechoice | .md | 253_13 |
Ernie 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/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#erniefortokenclassification | #erniefortokenclassification | .md | 253_14 |
Ernie Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ernie.md | https://huggingface.co/docs/transformers/en/model_doc/ernie/#ernieforquestionanswering | #ernieforquestionanswering | .md | 253_15 |
<!--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/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/ | .md | 254_0 | |
The CLIP model was proposed in [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh,
Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#overview | #overview | .md | 254_1 |
CLIP is a multi-modal vision and language model. It can be used for image-text similarity and for zero-shot image
classification. CLIP uses a ViT like transformer to get visual features and a causal language model to get the text
features. Both the text and visual features are then projected to a latent space with iden... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#usage-tips-and-example | #usage-tips-and-example | .md | 254_2 |
First, make sure to install the latest version of Flash Attention 2.
```bash
pip install -U flash-attn --no-build-isolation
```
Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#combining-clip-and-flash-attention-2 | #combining-clip-and-flash-attention-2 | .md | 254_3 |
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 254_4 |
On a local benchmark (NVIDIA A10G, PyTorch 2.3.1+cu121) with `float16`, we saw the following speedups during inference for `"openai/clip-vit-large-patch14"` checkpoint ([code](https://gist.github.com/qubvel/ac691a54e54f9fae8144275f866a7ff8)): | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#expected-speedups-with-flash-attention-and-sdpa | #expected-speedups-with-flash-attention-and-sdpa | .md | 254_5 |
| Num text labels | Eager (s/iter) | FA2 (s/iter) | FA2 speedup | SDPA (s/iter) | SDPA speedup |
|------------------:|-----------------:|---------------:|--------------:|----------------:|---------------:|
| 4 | 0.009 | 0.012 | 0.737 | 0.007 | 1... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#cliptextmodel | #cliptextmodel | .md | 254_6 |
| Image batch size | Eager (s/iter) | FA2 (s/iter) | FA2 speedup | SDPA (s/iter) | SDPA speedup |
|-------------------:|-----------------:|---------------:|--------------:|----------------:|---------------:|
| 1 | 0.016 | 0.013 | 1.247 | 0.012 | ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipvisionmodel | #clipvisionmodel | .md | 254_7 |
| Image batch size | Num text labels | Eager (s/iter) | FA2 (s/iter) | FA2 speedup | SDPA (s/iter) | SDPA speedup |
|-------------------:|------------------:|-----------------:|---------------:|--------------:|----------------:|---------------:|
| 1 | 4 | 0.025 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipmodel | #clipmodel | .md | 254_8 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIP.
- [Fine tuning CLIP with Remote Sensing (Satellite) images and captions](https://huggingface.co/blog/fine-tune-clip-rsicd), a blog post about how to fine-tune CLIP with [RSICD dataset](https://github.com/20152... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#resources | #resources | .md | 254_9 |
[`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate
a CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating
a configuration with the defaults will yield a similar configuration to that of the CLIP
[... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipconfig | #clipconfig | .md | 254_10 |
This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP
text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the text encoder of the CL... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#cliptextconfig | #cliptextconfig | .md | 254_11 |
This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a
CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder of ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipvisionconfig | #clipvisionconfig | .md | 254_12 |
Construct a CLIP tokenizer. Based on byte-level Byte-Pair-Encoding.
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`):
Path to the vocabulary file.
merges_file (`s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#cliptokenizer | #cliptokenizer | .md | 254_13 |
Construct a "fast" CLIP tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#cliptokenizerfast | #cliptokenizerfast | .md | 254_14 |
Constructs a CLIP image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
Size of ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipimageprocessor | #clipimageprocessor | .md | 254_15 |
No docstring available for CLIPFeatureExtractor | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipfeatureextractor | #clipfeatureextractor | .md | 254_16 |
Constructs a CLIP processor which wraps a CLIP image processor and a CLIP tokenizer into a single processor.
[`CLIPProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`CLIPTokenizerFast`]. See the
[`~CLIPProcessor.__call__`] and [`~CLIPProcessor.decode`] for more information.
Args:
image_proce... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipprocessor | #clipprocessor | .md | 254_17 |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipmodel | #clipmodel | .md | 254_18 |
The text model from CLIP without any head or projection on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#cliptextmodel | #cliptextmodel | .md | 254_19 |
CLIP Text Model with a projection layer on top (a linear layer on top of the pooled output).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
et... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#cliptextmodelwithprojection | #cliptextmodelwithprojection | .md | 254_20 |
CLIP Vision Model with a projection layer on top (a linear layer on top of the pooled output).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clip.md | https://huggingface.co/docs/transformers/en/model_doc/clip/#clipvisionmodelwithprojection | #clipvisionmodelwithprojection | .md | 254_21 |
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