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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneomodel | .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 | 188_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforcausallm | .md | The GPT Neo Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the in... | 188_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforcausallm | .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 ([`GPTNeoConfig`]): Model configuration class wit... | 188_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforcausallm | .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 | 188_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforquestionanswering | .md | The GPT-Neo Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the gen... | 188_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforquestionanswering | .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... | 188_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforquestionanswering | .md | and behavior.
Parameters:
config ([`GPTNeoConfig`]): 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.
Metho... | 188_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforsequenceclassification | .md | The GPTNeo Model transformer with a sequence classification head on top (linear layer).
[`GPTNeoForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last toke... | 188_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforsequenceclassification | .md | `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (ta... | 188_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforsequenceclassification | .md | This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#to... | 188_10_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneoforsequenceclassification | .md | and behavior.
Parameters:
config ([`GPTNeoConfig`]): 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.
Metho... | 188_10_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneofortokenclassification | .md | GPT Neo 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 downloadin... | 188_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneofortokenclassification | .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 ([`GPTNeoConfig`]): Model configuration class wit... | 188_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#gptneofortokenclassification | .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
</pt>
<jax> | 188_11_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#flaxgptneomodel | .md | No docstring available for FlaxGPTNeoModel
Methods: __call__ | 188_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt_neo.md | https://huggingface.co/docs/transformers/en/model_doc/gpt_neo/#flaxgptneoforcausallm | .md | No docstring available for FlaxGPTNeoForCausalLM
Methods: __call__
</jax>
</frameworkcontent> | 188_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/ | .md | <!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 189_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/ | .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 ... | 189_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#overview | .md | Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan
Salakhutdinov, Abdelrahman Mohamed.
The abstract from the paper is the following:
*Sel... | 189_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#overview | .md | *Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are
multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training
phase, and (3) sound units have variable lengths with no explicit segmentation. To deal... | 189_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#overview | .md | propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an
offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our
approach is applying the prediction loss over the masked regions only, which forces the m... | 189_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#overview | .md | acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised
clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means
teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either... | 189_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#overview | .md | state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h,
10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER
reduction on the more challenging dev-other and test-other evaluation subsets.*
This ... | 189_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#usage-tips | .md | - Hubert is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- Hubert model was fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
using [`Wav2Vec2CTCTokenizer`]. | 189_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#using-flash-attention-2 | .md | Flash Attention 2 is an faster, optimized version of the model. | 189_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#installation | .md | First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the [official documentation](https://github.com/Dao-AILab/flash-attention#installation-and-features). If your hardware is not compatible with Flash Attention 2, you can still benefit from atte... | 189_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#installation | .md | Next, [install](https://github.com/Dao-AILab/flash-attention#installation-and-features) the latest version of Flash Attention 2:
```bash
pip install -U flash-attn --no-build-isolation
``` | 189_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#usage | .md | Below is an expected speedup diagram comparing the pure inference time between the native implementation in transformers of `facebook/hubert-large-ls960-ft`, the flash-attention-2 and the sdpa (scale-dot-product-attention) version. We show the average speedup obtained on the `librispeech_asr` `clean` validation split: ... | 189_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#usage | .md | model = Wav2Vec2Model.from_pretrained("facebook/hubert-large-ls960-ft", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to(device)
...
``` | 189_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#expected-speedups | .md | Below is an expected speedup diagram comparing the pure inference time between the native implementation in transformers of the `facebook/hubert-large-ls960-ft` model and the flash-attention-2 and sdpa (scale-dot-product-attention) versions. . We show the average speedup obtained on the `librispeech_asr` `clean` valida... | 189_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#resources | .md | - [Audio classification task guide](../tasks/audio_classification)
- [Automatic speech recognition task guide](../tasks/asr) | 189_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an
Hubert 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 Hubert
[facebook/hubert-base... | 189_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | 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 32):
Vocabulary size of the Hubert model. Defines the number of different tokens that can be rep... | 189_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | `inputs_ids` passed when calling [`HubertModel`]. Vocabulary size of the model. Defines the different
tokens that can be represented by the *inputs_ids* passed to the forward method of [`HubertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidde... | 189_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | 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" (i.e., feed-forward) layer in the Tran... | 189_8_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | 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(`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
activation_dropo... | 189_8_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | The dropout ratio for activations inside the fully connected layer.
attention_dropout(`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`Wav2Vec2ForCTC`].
layerdrop (`... | 189_8_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
details.
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)... | 189_8_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolu... | 189_8_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_proj_layer_norm (`bool`, *optional*, defaults to `True`):
Whether to apply LayerNorm to the output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-l... | 189_8_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* ... | 189_8_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | conv_stride (`Tuple[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]`, *option... | 189_8_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_... | 189_8_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
conv_pos_batch_norm (`bool`, *optional*, defaults to `False`):
Whet... | 189_8_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | Whether to use batch norm instead of weight norm in conv_pos
do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
Whether do apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
True` corresponds to applying layer norm before the attention layer, whereas `do_stable_laye... | 189_8_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*,... | 189_8_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
ma... | 189_8_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`i... | 189_8_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and ... | 189_8_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overl... | 189_8_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. O... | 189_8_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`HubertForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `to... | 189_8_20 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`HubertForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`HubertForSeq... | 189_8_21 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | Dimensionality of the projection before token mean-pooling for classification.
Example:
```python
>>> from transformers import HubertModel, HubertConfig | 189_8_22 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertconfig | .md | >>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration
>>> configuration = HubertConfig()
>>> # Initializing a model from the facebook/hubert-base-ls960 style configuration
>>> model = HubertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
<framew... | 189_8_23 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertmodel | .md | The bare Hubert Model transformer outputting raw hidden-states without any specific head on top.
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden
Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia,
... | 189_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertmodel | .md | 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 etc.).
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 PyTo... | 189_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertmodel | .md | behavior.
Parameters:
config ([`HubertConfig`]): 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: ... | 189_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertforctc | .md | Hubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden
Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhot... | 189_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertforctc | .md | 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 etc.).
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 PyTo... | 189_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertforctc | .md | behavior.
Parameters:
config ([`HubertConfig`]): 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: ... | 189_10_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertforsequenceclassification | .md | Hubert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting.
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden
Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte... | 189_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertforsequenceclassification | .md | Ruslan Salakhutdinov, Abdelrahman Mohamed.
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 etc.).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.n... | 189_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#hubertforsequenceclassification | .md | behavior.
Parameters:
config ([`HubertConfig`]): 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: ... | 189_11_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#tfhubertmodel | .md | No docstring available for TFHubertModel
Methods: call | 189_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/hubert.md | https://huggingface.co/docs/transformers/en/model_doc/hubert/#tfhubertforctc | .md | No docstring available for TFHubertForCTC
Methods: call
</tf>
</frameworkcontent> | 189_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/ | .md | <!--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 app... | 190_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/ | .md | 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... | 190_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#overview | .md | Qwen2 is the new model series of large language models from the Qwen team. Previously, we released the Qwen series, including Qwen2-0.5B, Qwen2-1.5B, Qwen2-7B, Qwen2-57B-A14B, Qwen2-72B, Qwen2-Audio, etc. | 190_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#model-details | .md | Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention an... | 190_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#usage-tips | .md | `Qwen2-7B` and `Qwen2-7B-Instruct` can be found on the [Huggingface Hub](https://huggingface.co/Qwen)
In the following, we demonstrate how to use `Qwen2-7B-Instruct` for the inference. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` for this purpose.
`... | 190_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#usage-tips | .md | >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-7B-Instruct", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
>>> prompt = "Give me a short introduction to large language model."
>>> messages = [{"role": "user", "content": prompt}]
>>> text = tokenizer.apply_ch... | 190_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#usage-tips | .md | >>> model_inputs = tokenizer([text], return_tensors="pt").to(device)
>>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
>>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
>>> response = tokenizer.batch_... | 190_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen2-7B-beta [Qwen/Qwen2-7B-beta](... | 190_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | 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 151936):
Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be ... | 190_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 22016):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int... | 190_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | num_key_value_heads (`int`, *optional*, defaults to 32):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`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) ... | 190_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str` or `function`... | 190_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
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`, *optiona... | 190_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | The epsilon used by the rms normalization layers.
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`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the mode... | 190_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | 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_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:... | 190_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | 'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained len... | 190_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | 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 attention
computation. If unspecified, it defaults to value recommended by the implementation, us... | 190_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | `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 interpolation (only) in the linear
ramp function. I... | 190_4_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | `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 divided by 2
`long_factor` (`List[float]`, *optiona... | 190_4_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | `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*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *opti... | 190_4_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 28):
The number of layers t... | 190_4_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import Qwen2Model, Qwen2Config | 190_4_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2config | .md | >>> # Initializing a Qwen2 style configuration
>>> configuration = Qwen2Config()
>>> # Initializing a model from the Qwen2-7B style configuration
>>> model = Qwen2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | 190_4_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizer | .md | Construct a Qwen2 tokenizer. Based on byte-level Byte-Pair-Encoding.
Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import Q... | 190_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizer | .md | >>> tokenizer(" Hello world")["input_ids"]
[21927, 1879]
```
This is expected.
You should not use GPT2Tokenizer instead, because of the different pretokenization rules.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more inform... | 190_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizer | .md | Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
unk_token (`str`, *optional*, defaults t... | 190_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizer | .md | token instead.
bos_token (`str`, *optional*):
The beginning of sequence token. Not applicable for this tokenizer.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The token used for padding, for example when batching s... | 190_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizer | .md | clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not the model should cleanup the spaces that were added when splitting the input text during the
tokenization process. Not applicable to this tokenizer, since tokenization does not add spaces.
split_special_tokens (`bool`, *optional*, de... | 190_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizer | .md | Whether or not the special tokens should be split during the tokenization process. The default behavior is
to not split special tokens. This means that if `<|endoftext|>` is the `eos_token`, then `tokenizer.tokenize("<|endoftext|>") =
['<|endoftext|>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokeniz... | 190_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizerfast | .md | Construct a "fast" Qwen2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
be encoded differently whether it is at the beginning of the sentence (without spa... | 190_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizerfast | .md | >>> tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen-tokenizer")
>>> tokenizer("Hello world")["input_ids"]
[9707, 1879] | 190_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizerfast | .md | >>> tokenizer(" Hello world")["input_ids"]
[21927, 1879]
```
This is expected.
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`, *optional*):
Path to the vocab... | 190_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizerfast | .md | Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
tokenizer_file (`str`, *optional*):
Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
contains everything needed to load the tokenizer.
unk_token (`str`, *optional*, defaults ... | 190_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2/#qwen2tokenizerfast | .md | token instead. Not applicable to this tokenizer.
bos_token (`str`, *optional*):
The beginning of sequence token. Not applicable for this tokenizer.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The token used for pa... | 190_6_4 |
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