source stringclasses 470
values | url stringlengths 49 167 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertforquestionanswering | .md | QDQBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods th... | 347_16_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertforquestionanswering | .md | 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 ma... | 347_16_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qdqbert.md | https://huggingface.co/docs/transformers/en/model_doc/qdqbert/#qdqbertforquestionanswering | .md | and behavior.
Parameters:
config ([`QDQBertConfig`]): 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.
Meth... | 347_16_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/ | .md | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 348_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/ | .md | 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 ... | 348_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#overview | .md | The Aria model was proposed in [Aria: An Open Multimodal Native Mixture-of-Experts Model](https://huggingface.co/papers/2410.05993) by Li et al. from the Rhymes.AI team.
Aria is an open multimodal-native model with best-in-class performance across a wide range of multimodal, language, and coding tasks. It has a Mixtu... | 348_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#overview | .md | *Information comes in diverse modalities. Multimodal native AI models are essential to integrate real-world information and deliver comprehensive understanding. While proprietary multimodal native models exist, their lack of openness imposes obstacles for adoptions, let alone adaptations. To fill this gap, we introduce... | 348_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#overview | .md | performance across a wide range of multimodal, language, and coding tasks. Aria is a mixture-of-expert model with 3.9B and 3.5B activated parameters per visual token and text token, respectively. It outperforms Pixtral-12B and Llama3.2-11B, and is competitive against the best proprietary models on various multimodal ta... | 348_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#overview | .md | which progressively equips the model with strong capabilities in language understanding, multimodal understanding, long context window, and instruction following. We open-source the model weights along with a codebase that facilitates easy adoptions and adaptations of Aria in real-world applications.* | 348_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#overview | .md | This model was contributed by [m-ric](https://huggingface.co/m-ric).
The original code can be found [here](https://github.com/rhymes-ai/Aria). | 348_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#usage-tips | .md | Here's how to use the model for vision tasks:
```python
import requests
import torch
from PIL import Image
from transformers import AriaProcessor, AriaForConditionalGeneration
model_id_or_path = "rhymes-ai/Aria"
model = AriaForConditionalGeneration.from_pretrained(
model_id_or_path, device_map="auto"
)
processor = ... | 348_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#usage-tips | .md | image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"text": "what is the image?", "type": "text"},
],
}
]
text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processo... | 348_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#usage-tips | .md | output = model.generate(
**inputs,
max_new_tokens=15,
stop_strings=["<|im_end|>"],
tokenizer=processor.tokenizer,
do_sample=True,
temperature=0.9,
)
output_ids = output[0][inputs["input_ids"].shape[1]:]
response = processor.decode(output_ids, skip_special_tokens=True)
``` | 348_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaimageprocessor | .md | A vision processor for the Aria model that handles image preprocessing.
Initialize the AriaImageProcessor.
Args:
image_mean (`list`, *optional*, defaults to [0.5, 0.5, 0.5]):
Mean values for normalization.
image_std (`list`, *optional*, defaults to [0.5, 0.5, 0.5]):
Standard deviation values for normalization.
max_im... | 348_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaimageprocessor | .md | Maximum image size.
min_image_size (`int`, *optional*, defaults to 336):
Minimum image size.
split_resolutions (`list`, *optional*, defaults to a list of optimal,resolutions as tuples):
The optimal resolutions for splitting the image.
split_image (`bool`, *optional*, defaults to `False`):
Whether to split the image.
do... | 348_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaimageprocessor | .md | Whether to convert the image to RGB.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image.
resample (PILImageResampling, *optional*, defaults to `BICUBIC`):
The resampling filter to use if resizing the image. | 348_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaprocessor | .md | AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer.
Args:
image_processor (`AriaImageProcessor`, *optional*):
The AriaImageProcessor to use for image preprocessing.
tokenizer (`PreTrainedTokenizerBase`, *optional*):
An instance of [`PreTrainedTokenizer... | 348_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaprocessor | .md | chat_template (`str`, *optional*):
A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.
size_conversion (`Dict`, *optional*):
A dictionary indicating size conversions for images. | 348_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | This class handles the configuration for the text component of the Aria model.
Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria
[rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture.
This class extends the LlamaConfig to include additiona... | 348_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LlamaModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size... | 348_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value head... | 348_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all t... | 348_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maxi... | 348_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | Llama 2 up to 4096, CodeLlama up to 16384.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool... | 348_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 2):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental... | 348_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *op... | 348_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_pos... | 348_5_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply... | 348_5_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the atte... | 348_5_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | `factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interp... | 348_5_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads di... | 348_5_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | `long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):... | 348_5_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | `high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *opt... | 348_5_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | The dropout ratio for the attention probabilities.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
head_dim (`int`, *optional*):
The attention head dimension. If None, it will default to hidden_size // num_heads
moe_num_experts (`in... | 348_5_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextconfig | .md | moe_topk (`int`, *optional*, defaults to 2):
The number of top experts to route to for each token.
moe_num_shared_experts (`int`, *optional*, defaults to 2):
The number of shared experts. | 348_5_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaconfig | .md | This class handles the configuration for both vision and text components of the Aria model,
as well as additional parameters for image token handling and projector mapping.
Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria
[rhymes-ai/Aria](https://huggin... | 348_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaconfig | .md | 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 (`AriaVisionConfig` or `dict`, *optional*):
Configuration for the vision component.
vision_feature_layer (`int`, *optional*, ... | 348_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaconfig | .md | text_config (`AriaTextConfig` or `dict`, *optional*):
Configuration for the text component.
projector_patch_to_query_dict (`dict`, *optional*):
Mapping of patch sizes to query dimensions.
image_token_index (`int`, *optional*, defaults to 9):
Index used to represent image tokens.
initializer_range (`float`, *optional*, ... | 348_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaconfig | .md | Attributes:
model_type (`str`):
Type of the model, set to `"aria"`.
image_token_index (`int`):
Index used to represent image tokens.
projector_patch_to_query_dict (`dict`):
Mapping of patch sizes to query dimensions.
vision_config (`AriaVisionConfig`):
Configuration for the vision component.
text_config (`AriaTextConfi... | 348_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextmodel | .md | The bare AriaText Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
Th... | 348_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextmodel | .md | 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 ([`AriaTextConfig`]):
Model configuration class w... | 348_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextmodel | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`AriaTextDecoderLayer`]
Args:
config: AriaTextConfig | 348_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariatextforcausallm | .md | Aria model for causal language modeling tasks.
This class extends `LlamaForCausalLM` to incorporate the Mixture of Experts (MoE) approach,
allowing for more efficient and scalable language modeling.
Args:
config (`AriaTextConfig`):
Configuration object for the model. | 348_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaforconditionalgeneration | .md | Aria model for conditional generation tasks.
This model combines a vision tower, a multi-modal projector, and a language model
to perform tasks that involve both image and text inputs.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for a... | 348_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaforconditionalgeneration | .md | 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 ma... | 348_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/aria.md | https://huggingface.co/docs/transformers/en/model_doc/aria/#ariaforconditionalgeneration | .md | config (`AriaConfig`):
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 | 348_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/ | .md | <!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 349_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/ | .md | 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 ... | 349_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#overview | .md | The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
[`EncoderDecoderModel`] as proposed in [Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
The abstract from the paper... | 349_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#overview | .md | *Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By
warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple
benchmarks while saving significant amounts of compute time. So far the focus has been mainly ... | 349_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#overview | .md | Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We
developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT,
GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the uti... | 349_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#overview | .md | encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation,
Text Summarization, Sentence Splitting, and Sentence Fusion.*
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The original code can be
found [here](https://... | 349_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#usage-examples-and-tips | .md | The model can be used in combination with the [`EncoderDecoderModel`] to leverage two pretrained BERT checkpoints for
subsequent fine-tuning:
```python
>>> # leverage checkpoints for Bert2Bert model...
>>> # use BERT's cls token as BOS token and sep token as EOS token
>>> encoder = BertGenerationEncoder.from_pretrain... | 349_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#usage-examples-and-tips | .md | >>> # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
>>> decoder = BertGenerationDecoder.from_pretrained(
... "google-bert/bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102
... )
>>> bert2bert = EncoderDecoderModel(encoder... | 349_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#usage-examples-and-tips | .md | >>> # create tokenizer...
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
>>> input_ids = tokenizer(
... "This is a long article to summarize", add_special_tokens=False, return_tensors="pt"
... ).input_ids
>>> labels = tokenizer("This is a short summary", return_tensors="pt").input_... | 349_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#usage-examples-and-tips | .md | >>> # train...
>>> loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
>>> loss.backward()
```
Pretrained [`EncoderDecoderModel`] are also directly available in the model hub, e.g.:
```python
>>> # instantiate sentence fusion model
>>> sentence_fuser = EncoderDecoderModel.from_pretra... | 349_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#usage-examples-and-tips | .md | >>> input_ids = tokenizer(
... "This is the first sentence. This is the second sentence.", add_special_tokens=False, return_tensors="pt"
... ).input_ids
>>> outputs = sentence_fuser.generate(input_ids) | 349_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#usage-examples-and-tips | .md | >>> outputs = sentence_fuser.generate(input_ids)
>>> print(tokenizer.decode(outputs[0]))
```
Tips:
- [`BertGenerationEncoder`] and [`BertGenerationDecoder`] should be used in
combination with [`EncoderDecoder`].
- For summarization, sentence splitting, sentence fusion and translation, no special tokens are require... | 349_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | This is the configuration class to store the configuration of a [`BertGenerationPreTrainedModel`]. It is used to
instantiate a BertGeneration 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 Ber... | 349_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | 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 50358):
Vocabulary size of the BERT model. Defines the number of different token... | 349_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | `inputs_ids` passed when calling [`BertGeneration`].
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults t... | 349_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | intermediate_size (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often called 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, `"... | 349_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | `"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 probabili... | 349_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | 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 ... | 349_3_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 2):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
position_embedding_type (`str`, *optional*, defau... | 349_3_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | 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 inform... | 349_3_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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`.
Examples:
```python
>>> from tran... | 349_3_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationconfig | .md | >>> # Initializing a BertGeneration config
>>> configuration = BertGenerationConfig()
>>> # Initializing a model (with random weights) from the config
>>> model = BertGenerationEncoder(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | 349_3_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationtokenizer | .md | Construct a BertGeneration 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`):
[Senten... | 349_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationtokenizer | .md | contains the vocabulary necessary to instantiate a tokenizer.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The begin of sequence token.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in t... | 349_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationtokenizer | .md | token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
sep_token (`str`, *optional*, defaults to `"<::::>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
se... | 349_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationtokenizer | .md | token of a sequence built with special tokens.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable... | 349_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationtokenizer | .md | - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backwa... | 349_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationencoder | .md | The bare BertGeneration 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, prun... | 349_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationencoder | .md | 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 ([`BertGenerationConfig`]): Model configuration c... | 349_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationencoder | .md | 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... | 349_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationencoder | .md | 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.
This model should be used wh... | 349_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationencoder | .md | This model should be used when leveraging Bert or Roberta checkpoints for the [`EncoderDecoderModel`] class as
described in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461)
by Sascha Rothe, Shashi Narayan, and Aliaksei Severyn.
To behave as an decoder the model need... | 349_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationencoder | .md | 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 | 349_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationdecoder | .md | BertGeneration Model with a `language modeling` head on top for CLM fine-tuning.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This m... | 349_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationdecoder | .md | 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 ([`BertGenerationConfig`]): Model configuration c... | 349_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bert-generation.md | https://huggingface.co/docs/transformers/en/model_doc/bert-generation/#bertgenerationdecoder | .md | 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 | 349_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/ | .md | <!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 350_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/ | .md | 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 ... | 350_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transformer-xl | .md | <Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code. This model was deprecated due to security issues linked to `pickle.load`.
We recommend switching to more recent models for improved security.
In case you would still like to use `TransfoXL` in your exper... | 350_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transformer-xl | .md | You will need to set the environment variable `TRUST_REMOTE_CODE` to `True` in order to allow the
usage of `pickle.load()`:
```python
import os
from transformers import TransfoXLTokenizer, TransfoXLLMHeadModel | 350_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transformer-xl | .md | os.environ["TRUST_REMOTE_CODE"] = "True"
checkpoint = 'transfo-xl/transfo-xl-wt103'
revision = '40a186da79458c9f9de846edfaea79c412137f97' | 350_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transformer-xl | .md | tokenizer = TransfoXLTokenizer.from_pretrained(checkpoint, revision=revision)
model = TransfoXLLMHeadModel.from_pretrained(checkpoint, revision=revision)
```
If you run into any issues running this model, please reinstall the last version that supported this model: v4.35.0.
You can do so by running the following comm... | 350_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transformer-xl | .md | </Tip>
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=transfo-xl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-transfo--xl-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/transfo-xl-wt103">
<img alt="Spaces" src="https://img.shields.io/... | 350_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#overview | .md | The Transformer-XL model was proposed in [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
Salakhutdinov. It's a causal (uni-directional) transformer with relative positioning (sinusoïd... | 350_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#overview | .md | inputs and outputs (tied).
The abstract from the paper is the following:
*Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the
setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency
beyond a f... | 350_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#overview | .md | beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a
novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the
context fragmentation problem. As a result, Transformer-XL learns dependency that i... | 350_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#overview | .md | longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+
times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of
bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Wo... | 350_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#overview | .md | coherent, novel text articles with thousands of tokens.*
This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/kimiyoung/transformer-xl). | 350_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#usage-tips | .md | - Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right. The
original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
- Transformer-XL is one of the few models that has no sequence length limit. | 350_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#usage-tips | .md | - Transformer-XL is one of the few models that has no sequence length limit.
- Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular RNNs with two consecutive inputs). In this context, a segment is a number of consecutive tokens (for instance 512) that may... | 350_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#usage-tips | .md | - Basically, the hidden states of the previous segment are concatenated to the current input to compute the attention scores. This allows the model to pay attention to information that was in the previous segment as well as the current one. By stacking multiple attention layers, the receptive field can be increased to ... | 350_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#usage-tips | .md | - This changes the positional embeddings to positional relative embeddings (as the regular positional embeddings would give the same results in the current input and the current hidden state at a given position) and needs to make some adjustments in the way attention scores are computed.
<Tip warning={true}>
Transf... | 350_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#resources | .md | - [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling) | 350_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/transfo-xl.md | https://huggingface.co/docs/transformers/en/model_doc/transfo-xl/#transfoxlconfig | .md | This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. It is
used to instantiate a Transformer-XL model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that... | 350_5_0 |
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