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AutoModel
This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the [`~AutoModel.from_pretrained`] class method or the [`~AutoModel.from_config`] class
method.
This class cannot be instantiated directly using `__init__()` (throws an error).
ForDocumentQuestionAnswering | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/auto.md | https://huggingface.co/docs/transformers/en/model_doc/auto/#automodelfordocumentquestionanswering | #automodelfordocumentquestionanswering | .md | 212_78 |
No docstring available for TFAutoModelForDocumentQuestionAnswering | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/auto.md | https://huggingface.co/docs/transformers/en/model_doc/auto/#tfautomodelfordocumentquestionanswering | #tfautomodelfordocumentquestionanswering | .md | 212_79 |
AutoModel
This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the [`~AutoModel.from_pretrained`] class method or the [`~AutoModel.from_config`] class
method.
This class cannot be instantiated directly using `__init__()` (throws an error).
ForVisualQuestionAnswering | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/auto.md | https://huggingface.co/docs/transformers/en/model_doc/auto/#automodelforvisualquestionanswering | #automodelforvisualquestionanswering | .md | 212_80 |
AutoModel
This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the [`~AutoModel.from_pretrained`] class method or the [`~AutoModel.from_config`] class
method.
This class cannot be instantiated directly using `__init__()` (throws an error).
ForVision2Seq | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/auto.md | https://huggingface.co/docs/transformers/en/model_doc/auto/#automodelforvision2seq | #automodelforvision2seq | .md | 212_81 |
No docstring available for TFAutoModelForVision2Seq | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/auto.md | https://huggingface.co/docs/transformers/en/model_doc/auto/#tfautomodelforvision2seq | #tfautomodelforvision2seq | .md | 212_82 |
No docstring available for FlaxAutoModelForVision2Seq | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/auto.md | https://huggingface.co/docs/transformers/en/model_doc/auto/#flaxautomodelforvision2seq | #flaxautomodelforvision2seq | .md | 212_83 |
AutoModel
This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the [`~AutoModel.from_pretrained`] class method or the [`~AutoModel.from_config`] class
method.
This class cannot be instantiated directly using `__init__()` (throws an error).
ForImageTextToText | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/auto.md | https://huggingface.co/docs/transformers/en/model_doc/auto/#automodelforimagetexttotext | #automodelforimagetexttotext | .md | 212_84 |
<!--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 agreed to in writing, software distributed under the License is distributed on
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/ | .md | 213_0 |
|
The InstructBLIP model was proposed in [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
InstructBLIP leverages the [BLIP-2](blip2) architecture for visual instruction tuning.
The abstract from the paper is the following:
*General-purpose language models that can solve various language-domain tasks have emerged driven by the pre-training and instruction-tuning pipeline. However, building general-purpose vision-language models is challenging due to the increased task discrepancy introduced by the additional visual input. Although vision-language pre-training has been widely studied, vision-language instruction tuning remains relatively less explored. In this paper, we conduct a systematic and comprehensive study on vision-language instruction tuning based on the pre-trained BLIP-2 models. We gather a wide variety of 26 publicly available datasets, transform them into instruction tuning format and categorize them into two clusters for held-in instruction tuning and held-out zero-shot evaluation. Additionally, we introduce instruction-aware visual feature extraction, a crucial method that enables the model to extract informative features tailored to the given instruction. The resulting InstructBLIP models achieve state-of-the-art zero-shot performance across all 13 held-out datasets, substantially outperforming BLIP-2 and the larger Flamingo. Our models also lead to state-of-the-art performance when finetuned on individual downstream tasks (e.g., 90.7% accuracy on ScienceQA IMG). Furthermore, we qualitatively demonstrate the advantages of InstructBLIP over concurrent multimodal models.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/instructblip_architecture.jpg"
alt="drawing" width="600"/>
<small> InstructBLIP architecture. Taken from the <a href="https://arxiv.org/abs/2305.06500">original paper.</a> </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/salesforce/LAVIS/tree/main/projects/instructblip). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#overview | #overview | .md | 213_1 |
InstructBLIP uses the same architecture as [BLIP-2](blip2) with a tiny but important difference: it also feeds the text prompt (instruction) to the Q-Former.
> [!NOTE]
> BLIP models after release v4.46 will raise warnings about adding `processor.num_query_tokens = {{num_query_tokens}}` and expand model embeddings layer to add special `<image>` token. It is strongly recommended to add the attributes to the processor if you own the model checkpoint, or open a PR if it is not owned by you. Adding these attributes means that BLIP will add the number of query tokens required per image and expand the text with as many `<image>` placeholders as there will be query tokens. Usually it is around 500 tokens per image, so make sure that the text is not truncated as otherwise there wil be failure when merging the embeddings.
The attributes can be obtained from model config, as `model.config.num_query_tokens` and model embeddings expansion can be done by following [this link](https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#usage-tips | #usage-tips | .md | 213_2 |
[`InstructBlipConfig`] is the configuration class to store the configuration of a
[`InstructBlipForConditionalGeneration`]. It is used to instantiate a InstructBLIP model according to the specified
arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configuration with
the defaults will yield a similar configuration to that of the InstructBLIP
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVisionConfig`].
qformer_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipQFormerConfig`].
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize any [`PretrainedConfig`].
num_query_tokens (`int`, *optional*, defaults to 32):
The number of query tokens passed through the Transformer.
image_token_index (`int`, *optional*):
Token index of special image token.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import (
... InstructBlipVisionConfig,
... InstructBlipQFormerConfig,
... OPTConfig,
... InstructBlipConfig,
... InstructBlipForConditionalGeneration,
... )
>>> # Initializing a InstructBlipConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipConfig()
>>> # Initializing a InstructBlipForConditionalGeneration (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a InstructBlipConfig from a InstructBlipVisionConfig, InstructBlipQFormerConfig and any PretrainedConfig
>>> # Initializing InstructBLIP vision, InstructBLIP Q-Former and language model configurations
>>> vision_config = InstructBlipVisionConfig()
>>> qformer_config = InstructBlipQFormerConfig()
>>> text_config = OPTConfig()
>>> config = InstructBlipConfig.from_text_vision_configs(vision_config, qformer_config, text_config)
```
Methods: from_vision_qformer_text_configs | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#instructblipconfig | #instructblipconfig | .md | 213_3 |
This is the configuration class to store the configuration of a [`InstructBlipVisionModel`]. It is used to
instantiate a InstructBLIP vision encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration defaults will yield a similar configuration to that of the InstructBLIP
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 1408):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 6144):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 39):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported. to 1e-5): The epsilon used by the layer
normalization layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 1e-10):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries and values in the self-attention layers.
Example:
```python
>>> from transformers import InstructBlipVisionConfig, InstructBlipVisionModel
>>> # Initializing a InstructBlipVisionConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVisionConfig()
>>> # Initializing a InstructBlipVisionModel (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#instructblipvisionconfig | #instructblipvisionconfig | .md | 213_4 |
This is the configuration class to store the configuration of a [`InstructBlipQFormerModel`]. It is used to
instantiate a InstructBLIP Querying Transformer (Q-Former) 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 InstructBLIP [Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5)
architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs.
Read the documentation from [`PretrainedConfig`] for more information.
Note that [`InstructBlipQFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling the model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Token id used for padding sequences.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
cross_attention_frequency (`int`, *optional*, defaults to 2):
The frequency of adding cross-attention to the Transformer layers.
encoder_hidden_size (`int`, *optional*, defaults to 1408):
The hidden size of the hidden states for cross-attention.
Examples:
```python
>>> from transformers import InstructBlipQFormerConfig, InstructBlipQFormerModel
>>> # Initializing a InstructBLIP Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipQFormerConfig()
>>> # Initializing a model (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipQFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#instructblipqformerconfig | #instructblipqformerconfig | .md | 213_5 |
Constructs an InstructBLIP processor which wraps a BLIP image processor and a LLaMa/T5 tokenizer into a single
processor.
[`InstructBlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the
docstring of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information.
Args:
image_processor (`BlipImageProcessor`):
An instance of [`BlipImageProcessor`]. The image processor is a required input.
tokenizer (`AutoTokenizer`):
An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
qformer_tokenizer (`AutoTokenizer`):
An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
num_query_tokens (`int`, *optional*):"
Number of tokens used by the Qformer as queries, should be same as in model's config. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#instructblipprocessor | #instructblipprocessor | .md | 213_6 |
No docstring available for InstructBlipVisionModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#instructblipvisionmodel | #instructblipvisionmodel | .md | 213_7 |
Querying Transformer (Q-Former), used in InstructBLIP. Slightly modified from BLIP-2 as it also takes the
instruction as input.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#instructblipqformermodel | #instructblipqformermodel | .md | 213_8 |
InstructBLIP Model for generating text given an image and an optional text prompt. The model consists of a vision
encoder, Querying Transformer (Q-Former) and a language model.
One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue
the prompt. Otherwise, the language model starts generating text from the [BOS] (beginning-of-sequence) token.
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#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.
Parameters:
config ([`InstructBlipConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
- generate | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/instructblip.md | https://huggingface.co/docs/transformers/en/model_doc/instructblip/#instructblipforconditionalgeneration | #instructblipforconditionalgeneration | .md | 213_9 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/ | .md | 214_0 |
|
The Qwen2-Audio is the new model series of large audio-language models from the Qwen team. Qwen2-Audio is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. We introduce two distinct audio interaction modes:
* voice chat: users can freely engage in voice interactions with Qwen2-Audio without text input
* audio analysis: users could provide audio and text instructions for analysis during the interaction
It was proposed in [Qwen2-Audio Technical Report](https://arxiv.org/abs/2407.10759) by Yunfei Chu, Jin Xu, Qian Yang, Haojie Wei, Xipin Wei, Zhifang Guo, Yichong Leng, Yuanjun Lv, Jinzheng He, Junyang Lin, Chang Zhou, Jingren Zhou.
The abstract from the paper is the following:
*We introduce the latest progress of Qwen-Audio, a large-scale audio-language model called Qwen2-Audio, which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. In contrast to complex hierarchical tags, we have simplified the pre-training process by utilizing natural language prompts for different data and tasks, and have further expanded the data volume. We have boosted the instruction-following capability of Qwen2-Audio and implemented two distinct audio interaction modes for voice chat and audio analysis. In the voice chat mode, users can freely engage in voice interactions with Qwen2-Audio without text input. In the audio analysis mode, users could provide audio and text instructions for analysis during the interaction. Note that we do not use any system prompts to switch between voice chat and audio analysis modes. Qwen2-Audio is capable of intelligently comprehending the content within audio and following voice commands to respond appropriately. For instance, in an audio segment that simultaneously contains sounds, multi-speaker conversations, and a voice command, Qwen2-Audio can directly understand the command and provide an interpretation and response to the audio. Additionally, DPO has optimized the model's performance in terms of factuality and adherence to desired behavior. According to the evaluation results from AIR-Bench, Qwen2-Audio outperformed previous SOTAs, such as Gemini-1.5-pro, in tests focused on audio-centric instruction-following capabilities. Qwen2-Audio is open-sourced with the aim of fostering the advancement of the multi-modal language community. * | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#overview | #overview | .md | 214_1 |
`Qwen2-Audio-7B` and `Qwen2-Audio-7B-Instruct` can be found on the [Huggingface Hub](https://huggingface.co/Qwen) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#usage-tips | #usage-tips | .md | 214_2 |
```python
from io import BytesIO
from urllib.request import urlopen
import librosa
from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
model = Qwen2AudioForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B", trust_remote_code=True, device_map="auto")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B", trust_remote_code=True)
prompt = "<|audio_bos|><|AUDIO|><|audio_eos|>Generate the caption in English:"
url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/glass-breaking-151256.mp3"
audio, sr = librosa.load(BytesIO(urlopen(url).read()), sr=processor.feature_extractor.sampling_rate)
inputs = processor(text=prompt, audios=audio, return_tensors="pt").to(model.device)
generate_ids = model.generate(**inputs, max_length=256)
generate_ids = generate_ids[:, inputs.input_ids.size(1):]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
# We can also omit the audio_bos and audio_eos tokens
prompt = "<|AUDIO|>Generate the caption in English:"
inputs = processor(text=prompt, audios=audio, return_tensors="pt").to(model.device)
generate_ids = model.generate(**inputs, max_length=256)
generate_ids = generate_ids[:, inputs.input_ids.size(1):]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
```
In the following, we demonstrate how to use `Qwen2-Audio-7B-Instruct` for the inference, supporting both voice chat and audio analysis modes. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` for this purpose. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#inference | #inference | .md | 214_3 |
In the voice chat mode, users can freely engage in voice interactions with Qwen2-Audio without text input:
```python
from io import BytesIO
from urllib.request import urlopen
import librosa
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct")
model = Qwen2AudioForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct", device_map="auto")
conversation = [
{"role": "user", "content": [
{"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/guess_age_gender.wav"},
]},
{"role": "assistant", "content": "Yes, the speaker is female and in her twenties."},
{"role": "user", "content": [
{"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/translate_to_chinese.wav"},
]},
]
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios = []
for message in conversation:
if isinstance(message["content"], list):
for ele in message["content"]:
if ele["type"] == "audio":
audios.append(librosa.load(
BytesIO(urlopen(ele['audio_url']).read()),
sr=processor.feature_extractor.sampling_rate)[0]
)
inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True)
inputs.input_ids = inputs.input_ids.to("cuda")
generate_ids = model.generate(**inputs, max_length=256)
generate_ids = generate_ids[:, inputs.input_ids.size(1):]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#voice-chat-inference | #voice-chat-inference | .md | 214_4 |
In the audio analysis, users could provide both audio and text instructions for analysis:
```python
from io import BytesIO
from urllib.request import urlopen
import librosa
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct")
model = Qwen2AudioForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct", device_map="auto")
conversation = [
{'role': 'system', 'content': 'You are a helpful assistant.'},
{"role": "user", "content": [
{"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"},
{"type": "text", "text": "What's that sound?"},
]},
{"role": "assistant", "content": "It is the sound of glass shattering."},
{"role": "user", "content": [
{"type": "text", "text": "What can you do when you hear that?"},
]},
{"role": "assistant", "content": "Stay alert and cautious, and check if anyone is hurt or if there is any damage to property."},
{"role": "user", "content": [
{"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/1272-128104-0000.flac"},
{"type": "text", "text": "What does the person say?"},
]},
]
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios = []
for message in conversation:
if isinstance(message["content"], list):
for ele in message["content"]:
if ele["type"] == "audio":
audios.append(
librosa.load(
BytesIO(urlopen(ele['audio_url']).read()),
sr=processor.feature_extractor.sampling_rate)[0]
)
inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True)
inputs.input_ids = inputs.input_ids.to("cuda")
generate_ids = model.generate(**inputs, max_length=256)
generate_ids = generate_ids[:, inputs.input_ids.size(1):]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#audio-analysis-inference | #audio-analysis-inference | .md | 214_5 |
We also support batch inference:
```python
from io import BytesIO
from urllib.request import urlopen
import librosa
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct")
model = Qwen2AudioForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct", device_map="auto")
conversation1 = [
{"role": "user", "content": [
{"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"},
{"type": "text", "text": "What's that sound?"},
]},
{"role": "assistant", "content": "It is the sound of glass shattering."},
{"role": "user", "content": [
{"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/f2641_0_throatclearing.wav"},
{"type": "text", "text": "What can you hear?"},
]}
]
conversation2 = [
{"role": "user", "content": [
{"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/1272-128104-0000.flac"},
{"type": "text", "text": "What does the person say?"},
]},
]
conversations = [conversation1, conversation2]
text = [processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) for conversation in conversations]
audios = []
for conversation in conversations:
for message in conversation:
if isinstance(message["content"], list):
for ele in message["content"]:
if ele["type"] == "audio":
audios.append(
librosa.load(
BytesIO(urlopen(ele['audio_url']).read()),
sr=processor.feature_extractor.sampling_rate)[0]
)
inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True)
inputs['input_ids'] = inputs['input_ids'].to("cuda")
inputs.input_ids = inputs.input_ids.to("cuda")
generate_ids = model.generate(**inputs, max_length=256)
generate_ids = generate_ids[:, inputs.input_ids.size(1):]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#batch-inference | #batch-inference | .md | 214_6 |
This is the configuration class to store the configuration of a [`Qwen2AudioForConditionalGeneration`]. It is used to instantiate an
Qwen2-Audio 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 Qwen2-Audio.
e.g. [Qwen/Qwen2-Audio-7B](https://huggingface.co/Qwen/Qwen2-Audio-7B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
audio_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
The config object or dictionary of the audio backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
The config object or dictionary of the text backbone.
audio_token_index (`int`, *optional*, defaults to 151646):
The image token index to encode the image prompt.
Example:
```python
>>> from transformers import Qwen2AudioForConditionalGeneration, Qwen2AudioConfig, Qwen2AudioEncoderConfig, Qwen2Config
>>> # Initializing a Qwen2AudioEncoder config
>>> audio_config = Qwen2AudioEncoderConfig()
>>> # Initializing a Qwen2 config
>>> text_config = Qwen2Config()
>>> # Initializing a Qwen2Audio configuration
>>> configuration = Qwen2AudioConfig(audio_config, text_config)
>>> # Initializing a model from the qwen2-audio style configuration
>>> model = Qwen2AudioForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
This is the configuration class to store the configuration of a [`Qwen2AudioEncoder`]. It is used to instantiate a
Qwen2-Audio audio encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the audio encoder of the Qwen2-Audio
architecture.
e.g. [Qwen/Qwen2-Audio-7B](https://huggingface.co/Qwen/Qwen2-Audio-7B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_mel_bins (`int`, *optional*, defaults to 128):
Number of mel features used per input features. Should correspond to the value used in the
`Qwen2AudioProcessor` class.
encoder_layers (`int`, *optional*, defaults to 32):
Number of encoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 20):
Number of attention heads for each attention layer in the Transformer encoder.
encoder_ffn_dim (`int`, *optional*, defaults to 5120):
Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
d_model (`int`, *optional*, defaults to 1280):
Dimensionality of the layers.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_function (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
max_source_positions (`int`, *optional*, defaults to 1500):
The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
Example:
```python
>>> from transformers import Qwen2AudioEncoderConfig, Qwen2AudioEncoder
>>> # Initializing a Qwen2AudioEncoderConfig
>>> configuration = Qwen2AudioEncoderConfig()
>>> # Initializing a Qwen2AudioEncoder (with random weights)
>>> model = Qwen2AudioEncoder(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#qwen2audioconfig | #qwen2audioconfig | .md | 214_7 |
Constructs a Qwen2Audio processor which wraps a Qwen2Audio feature extractor and a Qwen2Audio tokenizer into a single processor.
[`Qwen2AudioProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`Qwen2TokenizerFast`]. See the
[`~Qwen2AudioProcessor.__call__`] and [`~Qwen2AudioProcessor.decode`] for more information.
Args:
feature_extractor ([`WhisperFeatureExtractor`], *optional*):
The feature extractor is a required input.
tokenizer ([`Qwen2TokenizerFast`], *optional*):
The tokenizer is a required input.
chat_template (`Optional[str]`, *optional*):
The Jinja template to use for formatting the conversation. If not provided, the default chat template
is used.
audio_token (`str`, *optional*, defaults to `"<|AUDIO|>"`):
The token to use for audio tokens.
audio_bos_token (`str`, *optional*, defaults to `"<|audio_bos|>"`):
The token to use for audio bos tokens.
audio_eos_token (`str`, *optional*, defaults to `"<|audio_eos|>"`):
The token to use for audio eos tokens. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#qwen2audioprocessor | #qwen2audioprocessor | .md | 214_8 |
The QWEN2AUDIO model which consists of a audio backbone and a language model.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://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.
Parameters:
config ([`Qwen2AudioConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_audio.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_audio/#qwen2audioforconditionalgeneration | #qwen2audioforconditionalgeneration | .md | 214_9 |
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/ | .md | 215_0 |
|
The MPNet model was proposed in [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
masked language modeling and permuted language modeling for natural language understanding.
The abstract from the paper is the following:
*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
BERT, XLNet, RoBERTa) under the same model setting.*
The original code can be found [here](https://github.com/microsoft/MPNet). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#overview | #overview | .md | 215_1 |
MPNet doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
separate your segments with the separation token `tokenizer.sep_token` (or `[sep]`). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#usage-tips | #usage-tips | .md | 215_2 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#resources | #resources | .md | 215_3 |
This is the configuration class to store the configuration of a [`MPNetModel`] or a [`TFMPNetModel`]. It is used to
instantiate a MPNet 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 MPNet
[microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30527):
Vocabulary size of the MPNet model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MPNetModel`] or [`TFMPNetModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
The number of buckets to use for each attention layer.
Examples:
```python
>>> from transformers import MPNetModel, MPNetConfig
>>> # Initializing a MPNet mpnet-base style configuration
>>> configuration = MPNetConfig()
>>> # Initializing a model from the mpnet-base style configuration
>>> model = MPNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnetconfig | #mpnetconfig | .md | 215_4 |
This tokenizer inherits from [`BertTokenizer`] which contains most of the methods. Users should refer to the
superclass for more information regarding methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic tokenization before WordPiece.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.
Methods: build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnettokenizer | #mpnettokenizer | .md | 215_5 |
Construct a "fast" MPNet 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`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
<frameworkcontent>
<pt> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnettokenizerfast | #mpnettokenizerfast | .md | 215_6 |
The bare MPNet 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
etc.)
This model is also 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.
Parameters:
config ([`MPNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnetmodel | #mpnetmodel | .md | 215_7 |
No docstring available for MPNetForMaskedLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnetformaskedlm | #mpnetformaskedlm | .md | 215_8 |
MPNet 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, resizing the input embeddings, pruning heads
etc.)
This model is also 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.
Parameters:
config ([`MPNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnetforsequenceclassification | #mpnetforsequenceclassification | .md | 215_9 |
MPNet 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 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#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.
Parameters:
config ([`MPNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnetformultiplechoice | #mpnetformultiplechoice | .md | 215_10 |
MPNet 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 saving, resizing the input embeddings, pruning heads
etc.)
This model is also 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.
Parameters:
config ([`MPNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnetfortokenclassification | #mpnetfortokenclassification | .md | 215_11 |
MPNet 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
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#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.
Parameters:
config ([`MPNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#mpnetforquestionanswering | #mpnetforquestionanswering | .md | 215_12 |
No docstring available for TFMPNetModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#tfmpnetmodel | #tfmpnetmodel | .md | 215_13 |
No docstring available for TFMPNetForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#tfmpnetformaskedlm | #tfmpnetformaskedlm | .md | 215_14 |
No docstring available for TFMPNetForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#tfmpnetforsequenceclassification | #tfmpnetforsequenceclassification | .md | 215_15 |
No docstring available for TFMPNetForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#tfmpnetformultiplechoice | #tfmpnetformultiplechoice | .md | 215_16 |
No docstring available for TFMPNetForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#tfmpnetfortokenclassification | #tfmpnetfortokenclassification | .md | 215_17 |
No docstring available for TFMPNetForQuestionAnswering
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mpnet.md | https://huggingface.co/docs/transformers/en/model_doc/mpnet/#tfmpnetforquestionanswering | #tfmpnetforquestionanswering | .md | 215_18 |
<!--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 agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/ | .md | 216_0 |
|
The ConvNeXT model was proposed in [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.
The abstract from the paper is the following:
*The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.
A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers
(e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide
variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive
biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design
of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models
dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy
and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.jpg"
alt="drawing" width="600"/>
<small> ConvNeXT architecture. Taken from the <a href="https://arxiv.org/abs/2201.03545">original paper</a>.</small>
This model was contributed by [nielsr](https://huggingface.co/nielsr). TensorFlow version of the model was contributed by [ariG23498](https://github.com/ariG23498),
[gante](https://github.com/gante), and [sayakpaul](https://github.com/sayakpaul) (equal contribution). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#overview | #overview | .md | 216_1 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXT.
<PipelineTag pipeline="image-classification"/>
- [`ConvNextForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#resources | #resources | .md | 216_2 |
This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
ConvNeXT 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 ConvNeXT
[facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
patch_size (`int`, *optional*, defaults to 4):
Patch size to use in the patch embedding layer.
num_stages (`int`, *optional*, defaults to 4):
The number of stages in the model.
hidden_sizes (`List[int]`, *optional*, defaults to [96, 192, 384, 768]):
Dimensionality (hidden size) at each stage.
depths (`List[int]`, *optional*, defaults to [3, 3, 9, 3]):
Depth (number of blocks) for each stage.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
layer_scale_init_value (`float`, *optional*, defaults to 1e-6):
The initial value for the layer scale.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The drop rate for stochastic depth.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
Example:
```python
>>> from transformers import ConvNextConfig, ConvNextModel
>>> # Initializing a ConvNext convnext-tiny-224 style configuration
>>> configuration = ConvNextConfig()
>>> # Initializing a model (with random weights) from the convnext-tiny-224 style configuration
>>> model = ConvNextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#convnextconfig | #convnextconfig | .md | 216_3 |
No docstring available for ConvNextFeatureExtractor | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#convnextfeatureextractor | #convnextfeatureextractor | .md | 216_4 |
Constructs a ConvNeXT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden
by `do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 384}`):
Resolution of the output image after `resize` is applied. If `size["shortest_edge"]` >= 384, the image is
resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will
be matched to `int(size["shortest_edge"]/crop_pct)`, after which the image is cropped to
`(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. Can
be overriden by `size` in the `preprocess` method.
crop_pct (`float` *optional*, defaults to 224 / 256):
Percentage of the image to crop. Only has an effect if `do_resize` is `True` and size < 384. Can be
overriden by `crop_pct` in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overriden by `resample` in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in
the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overriden by `rescale_factor` in the `preprocess`
method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Methods: preprocess
<frameworkcontent>
<pt> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#convnextimageprocessor | #convnextimageprocessor | .md | 216_5 |
The bare ConvNext 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.
Parameters:
config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#convnextmodel | #convnextmodel | .md | 216_6 |
ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
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.
Parameters:
config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#convnextforimageclassification | #convnextforimageclassification | .md | 216_7 |
No docstring available for TFConvNextModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#tfconvnextmodel | #tfconvnextmodel | .md | 216_8 |
No docstring available for TFConvNextForImageClassification
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/convnext.md | https://huggingface.co/docs/transformers/en/model_doc/convnext/#tfconvnextforimageclassification | #tfconvnextforimageclassification | .md | 216_9 |
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/ | .md | 217_0 |
|
The SegFormer model was proposed in [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping
Luo. The model consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great
results on image segmentation benchmarks such as ADE20K and Cityscapes.
The abstract from the paper is the following:
*We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with
lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel
hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding,
thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution
differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from
different layers, and thus combining both local attention and global attention to render powerful representations. We
show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our
approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance
and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters,
being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on
Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C.*
The figure below illustrates the architecture of SegFormer. Taken from the [original paper](https://arxiv.org/abs/2105.15203).
<img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/segformer_architecture.png"/>
This model was contributed by [nielsr](https://huggingface.co/nielsr). The TensorFlow version
of the model was contributed by [sayakpaul](https://huggingface.co/sayakpaul). The original code can be found [here](https://github.com/NVlabs/SegFormer). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#overview | #overview | .md | 217_1 |
- SegFormer consists of a hierarchical Transformer encoder, and a lightweight all-MLP decoder head.
[`SegformerModel`] is the hierarchical Transformer encoder (which in the paper is also referred to
as Mix Transformer or MiT). [`SegformerForSemanticSegmentation`] adds the all-MLP decoder head on
top to perform semantic segmentation of images. In addition, there's
[`SegformerForImageClassification`] which can be used to - you guessed it - classify images. The
authors of SegFormer first pre-trained the Transformer encoder on ImageNet-1k to classify images. Next, they throw
away the classification head, and replace it by the all-MLP decode head. Next, they fine-tune the model altogether on
ADE20K, Cityscapes and COCO-stuff, which are important benchmarks for semantic segmentation. All checkpoints can be
found on the [hub](https://huggingface.co/models?other=segformer).
- The quickest way to get started with SegFormer is by checking the [example notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SegFormer) (which showcase both inference and
fine-tuning on custom data). One can also check out the [blog post](https://huggingface.co/blog/fine-tune-segformer) introducing SegFormer and illustrating how it can be fine-tuned on custom data.
- TensorFlow users should refer to [this repository](https://github.com/deep-diver/segformer-tf-transformers) that shows off-the-shelf inference and fine-tuning.
- One can also check out [this interactive demo on Hugging Face Spaces](https://huggingface.co/spaces/chansung/segformer-tf-transformers)
to try out a SegFormer model on custom images.
- SegFormer works on any input size, as it pads the input to be divisible by `config.patch_sizes`.
- One can use [`SegformerImageProcessor`] to prepare images and corresponding segmentation maps
for the model. Note that this image processor is fairly basic and does not include all data augmentations used in
the original paper. The original preprocessing pipelines (for the ADE20k dataset for instance) can be found [here](https://github.com/NVlabs/SegFormer/blob/master/local_configs/_base_/datasets/ade20k_repeat.py). The most
important preprocessing step is that images and segmentation maps are randomly cropped and padded to the same size,
such as 512x512 or 640x640, after which they are normalized.
- One additional thing to keep in mind is that one can initialize [`SegformerImageProcessor`] with
`do_reduce_labels` set to `True` or `False`. In some datasets (like ADE20k), the 0 index is used in the annotated
segmentation maps for background. However, ADE20k doesn't include the "background" class in its 150 labels.
Therefore, `do_reduce_labels` is used to reduce all labels by 1, and to make sure no loss is computed for the
background class (i.e. it replaces 0 in the annotated maps by 255, which is the *ignore_index* of the loss function
used by [`SegformerForSemanticSegmentation`]). However, other datasets use the 0 index as
background class and include this class as part of all labels. In that case, `do_reduce_labels` should be set to
`False`, as loss should also be computed for the background class.
- As most models, SegFormer comes in different sizes, the details of which can be found in the table below
(taken from Table 7 of the [original paper](https://arxiv.org/abs/2105.15203)).
| **Model variant** | **Depths** | **Hidden sizes** | **Decoder hidden size** | **Params (M)** | **ImageNet-1k Top 1** |
| :---------------: | ------------- | ------------------- | :---------------------: | :------------: | :-------------------: |
| MiT-b0 | [2, 2, 2, 2] | [32, 64, 160, 256] | 256 | 3.7 | 70.5 |
| MiT-b1 | [2, 2, 2, 2] | [64, 128, 320, 512] | 256 | 14.0 | 78.7 |
| MiT-b2 | [3, 4, 6, 3] | [64, 128, 320, 512] | 768 | 25.4 | 81.6 |
| MiT-b3 | [3, 4, 18, 3] | [64, 128, 320, 512] | 768 | 45.2 | 83.1 |
| MiT-b4 | [3, 8, 27, 3] | [64, 128, 320, 512] | 768 | 62.6 | 83.6 |
| MiT-b5 | [3, 6, 40, 3] | [64, 128, 320, 512] | 768 | 82.0 | 83.8 |
Note that MiT in the above table refers to the Mix Transformer encoder backbone introduced in SegFormer. For
SegFormer's results on the segmentation datasets like ADE20k, refer to the [paper](https://arxiv.org/abs/2105.15203). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#usage-tips | #usage-tips | .md | 217_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SegFormer.
<PipelineTag pipeline="image-classification"/>
- [`SegformerForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- [Image classification task guide](../tasks/image_classification)
Semantic segmentation:
- [`SegformerForSemanticSegmentation`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/semantic-segmentation).
- A blog on fine-tuning SegFormer on a custom dataset can be found [here](https://huggingface.co/blog/fine-tune-segformer).
- More demo notebooks on SegFormer (both inference + fine-tuning on a custom dataset) can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SegFormer).
- [`TFSegformerForSemanticSegmentation`] is supported by this [example notebook](https://github.com/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb).
- [Semantic segmentation task guide](../tasks/semantic_segmentation)
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#resources | #resources | .md | 217_3 |
This is the configuration class to store the configuration of a [`SegformerModel`]. It is used to instantiate an
SegFormer 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 SegFormer
[nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_encoder_blocks (`int`, *optional*, defaults to 4):
The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
depths (`List[int]`, *optional*, defaults to `[2, 2, 2, 2]`):
The number of layers in each encoder block.
sr_ratios (`List[int]`, *optional*, defaults to `[8, 4, 2, 1]`):
Sequence reduction ratios in each encoder block.
hidden_sizes (`List[int]`, *optional*, defaults to `[32, 64, 160, 256]`):
Dimension of each of the encoder blocks.
patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3, 3]`):
Patch size before each encoder block.
strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Stride before each encoder block.
num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
Number of attention heads for each attention layer in each block of the Transformer encoder.
mlp_ratios (`List[int]`, *optional*, defaults to `[4, 4, 4, 4]`):
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability before the classification head.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
drop_path_rate (`float`, *optional*, defaults to 0.1):
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
decoder_hidden_size (`int`, *optional*, defaults to 256):
The dimension of the all-MLP decode head.
semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
The index that is ignored by the loss function of the semantic segmentation model.
Example:
```python
>>> from transformers import SegformerModel, SegformerConfig
>>> # Initializing a SegFormer nvidia/segformer-b0-finetuned-ade-512-512 style configuration
>>> configuration = SegformerConfig()
>>> # Initializing a model from the nvidia/segformer-b0-finetuned-ade-512-512 style configuration
>>> model = SegformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#segformerconfig | #segformerconfig | .md | 217_4 |
No docstring available for SegformerFeatureExtractor
Methods: __call__
- post_process_semantic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#segformerfeatureextractor | #segformerfeatureextractor | .md | 217_5 |
Constructs a Segformer 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[str, int]` *optional*, defaults to `{"height": 512, "width": 512}`):
Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
`preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
do_reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the
`preprocess` method.
Methods: preprocess
- post_process_semantic_segmentation
<frameworkcontent>
<pt> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#segformerimageprocessor | #segformerimageprocessor | .md | 217_6 |
The bare SegFormer encoder (Mix-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 and
behavior.
Parameters:
config ([`SegformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#segformermodel | #segformermodel | .md | 217_7 |
No docstring available for SegformerDecodeHead
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#segformerdecodehead | #segformerdecodehead | .md | 217_8 |
SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden
states) 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 related to general usage and
behavior.
Parameters:
config ([`SegformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#segformerforimageclassification | #segformerforimageclassification | .md | 217_9 |
SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.
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 ([`SegformerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#segformerforsemanticsegmentation | #segformerforsemanticsegmentation | .md | 217_10 |
No docstring available for TFSegformerDecodeHead
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#tfsegformerdecodehead | #tfsegformerdecodehead | .md | 217_11 |
No docstring available for TFSegformerModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#tfsegformermodel | #tfsegformermodel | .md | 217_12 |
No docstring available for TFSegformerForImageClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#tfsegformerforimageclassification | #tfsegformerforimageclassification | .md | 217_13 |
No docstring available for TFSegformerForSemanticSegmentation
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/segformer.md | https://huggingface.co/docs/transformers/en/model_doc/segformer/#tfsegformerforsemanticsegmentation | #tfsegformerforsemanticsegmentation | .md | 217_14 |
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|
The XLS-R model was proposed in [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman
Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
The abstract from the paper is the following:
*This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0.
We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128
languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range
of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation
benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into
English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as
VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107
language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform
English-only pretraining when translating English speech into other languages, a setting which favors monolingual
pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.*
Relevant checkpoints can be found under https://huggingface.co/models?other=xls_r.
The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xls_r.md | https://huggingface.co/docs/transformers/en/model_doc/xls_r/#overview | #overview | .md | 218_1 |
- XLS-R is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- XLS-R model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using
[`Wav2Vec2CTCTokenizer`].
<Tip>
XLS-R's architecture is based on the Wav2Vec2 model, refer to [Wav2Vec2's documentation page](wav2vec2) for API reference.
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xls_r.md | https://huggingface.co/docs/transformers/en/model_doc/xls_r/#usage-tips | #usage-tips | .md | 218_2 |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-v.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-v/ | .md | 219_0 |
|
XLM-V is multilingual language model with a one million token vocabulary trained on 2.5TB of data from Common Crawl (same as XLM-R).
It was introduced in the [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://arxiv.org/abs/2301.10472)
paper by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer and Madian Khabsa.
From the abstract of the XLM-V paper:
*Large multilingual language models typically rely on a single vocabulary shared across 100+ languages.
As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged.
This vocabulary bottleneck limits the representational capabilities of multilingual models like XLM-R.
In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by
de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity
to achieve sufficient coverage for each individual language. Tokenizations using our vocabulary are typically
more semantically meaningful and shorter compared to XLM-R. Leveraging this improved vocabulary, we train XLM-V,
a multilingual language model with a one million token vocabulary. XLM-V outperforms XLM-R on every task we
tested on ranging from natural language inference (XNLI), question answering (MLQA, XQuAD, TyDiQA), and
named entity recognition (WikiAnn) to low-resource tasks (Americas NLI, MasakhaNER).*
This model was contributed by [stefan-it](https://huggingface.co/stefan-it), including detailed experiments with XLM-V on downstream tasks.
The experiments repository can be found [here](https://github.com/stefan-it/xlm-v-experiments). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-v.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-v/#overview | #overview | .md | 219_1 |
- XLM-V is compatible with the XLM-RoBERTa model architecture, only model weights from [`fairseq`](https://github.com/facebookresearch/fairseq)
library had to be converted.
- The `XLMTokenizer` implementation is used to load the vocab and performs tokenization.
A XLM-V (base size) model is available under the [`facebook/xlm-v-base`](https://huggingface.co/facebook/xlm-v-base) identifier.
<Tip>
XLM-V architecture is the same as XLM-RoBERTa, refer to [XLM-RoBERTa documentation](xlm-roberta) for API reference, and examples.
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm-v.md | https://huggingface.co/docs/transformers/en/model_doc/xlm-v/#usage-tips | #usage-tips | .md | 219_2 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/ | .md | 220_0 |
|
The RemBERT model was proposed in [Rethinking Embedding Coupling in Pre-trained Language Models](https://arxiv.org/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, Melvin Johnson, Sebastian Ruder.
The abstract from the paper is the following:
*We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art
pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to
significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By
reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on
standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that
allocating additional capacity to the output embedding provides benefits to the model that persist through the
fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger
output embeddings prevent the model's last layers from overspecializing to the pre-training task and encourage
Transformer representations to be more general and more transferable to other tasks and languages. Harnessing these
findings, we are able to train models that achieve strong performance on the XTREME benchmark without increasing the
number of parameters at the fine-tuning stage.* | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#overview | #overview | .md | 220_1 |
For fine-tuning, RemBERT can be thought of as a bigger version of mBERT with an ALBERT-like factorization of the
embedding layer. The embeddings are not tied in pre-training, in contrast with BERT, which enables smaller input
embeddings (preserved during fine-tuning) and bigger output embeddings (discarded at fine-tuning). The tokenizer is
also similar to the Albert one rather than the BERT one. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#usage-tips | #usage-tips | .md | 220_2 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#resources | #resources | .md | 220_3 |
This is the configuration class to store the configuration of a [`RemBertModel`]. It is used to instantiate an
RemBERT 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 RemBERT
[google/rembert](https://huggingface.co/google/rembert) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 250300):
Vocabulary size of the RemBERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`RemBertModel`] or [`TFRemBertModel`]. Vocabulary size of the model.
Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of
[`RemBertModel`].
hidden_size (`int`, *optional*, defaults to 1152):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 18):
Number of attention heads for each attention layer in the Transformer encoder.
input_embedding_size (`int`, *optional*, defaults to 256):
Dimensionality of the input embeddings.
output_embedding_size (`int`, *optional*, defaults to 1664):
Dimensionality of the output embeddings.
intermediate_size (`int`, *optional*, defaults to 4608):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0):
The dropout ratio for the attention probabilities.
classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the classifier layer when fine-tuning.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`RemBertModel`] or [`TFRemBertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
Example:
```python
>>> from transformers import RemBertModel, RemBertConfig
>>> # Initializing a RemBERT rembert style configuration
>>> configuration = RemBertConfig()
>>> # Initializing a model from the rembert style configuration
>>> model = RemBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertconfig | #rembertconfig | .md | 220_4 |
Construct a RemBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
bos_token (`str`, *optional*, defaults to `"[CLS]"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
Attributes:
sp_model (`SentencePieceProcessor`):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
Methods: build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#remberttokenizer | #remberttokenizer | .md | 220_5 |
Construct a "fast" RemBert tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
remove_space (`bool`, *optional*, defaults to `True`):
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
keep_accents (`bool`, *optional*, defaults to `False`):
Whether or not to keep accents when tokenizing.
bos_token (`str`, *optional*, defaults to `"[CLS]"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the `sep_token`.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
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/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#remberttokenizerfast | #remberttokenizerfast | .md | 220_6 |
The bare RemBERT 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 and
behavior.
Parameters:
config ([`RemBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertmodel | #rembertmodel | .md | 220_7 |
RemBERT Model with a `language modeling` head on top for CLM fine-tuning.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RemBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertforcausallm | #rembertforcausallm | .md | 220_8 |
RemBERT 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 ([`RemBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertformaskedlm | #rembertformaskedlm | .md | 220_9 |
RemBERT 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 documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RemBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertforsequenceclassification | #rembertforsequenceclassification | .md | 220_10 |
RemBERT 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 documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RemBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertformultiplechoice | #rembertformultiplechoice | .md | 220_11 |
RemBERT 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 documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RemBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertfortokenclassification | #rembertfortokenclassification | .md | 220_12 |
RemBERT 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
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RemBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertforquestionanswering | #rembertforquestionanswering | .md | 220_13 |
No docstring available for TFRemBertModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertmodel | #tfrembertmodel | .md | 220_14 |
No docstring available for TFRemBertForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertformaskedlm | #tfrembertformaskedlm | .md | 220_15 |
No docstring available for TFRemBertForCausalLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertforcausallm | #tfrembertforcausallm | .md | 220_16 |
No docstring available for TFRemBertForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertforsequenceclassification | #tfrembertforsequenceclassification | .md | 220_17 |
No docstring available for TFRemBertForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertformultiplechoice | #tfrembertformultiplechoice | .md | 220_18 |
No docstring available for TFRemBertForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertfortokenclassification | #tfrembertfortokenclassification | .md | 220_19 |
No docstring available for TFRemBertForQuestionAnswering
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertforquestionanswering | #tfrembertforquestionanswering | .md | 220_20 |
<!--Copyright 2024 The Qwen Team and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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|
Qwen2MoE is the new model series of large language models from the Qwen team. Previously, we released the Qwen series, including Qwen-72B, Qwen-1.8B, Qwen-VL, Qwen-Audio, etc. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#overview | #overview | .md | 221_1 |
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