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/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertforquestionanswering | .md | RemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-clas... | 220_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#rembertforquestionanswering | .md | behavior.
Parameters:
config ([`RemBertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods:... | 220_13_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertmodel | .md | No docstring available for TFRemBertModel
Methods: call | 220_14_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertformaskedlm | .md | No docstring available for TFRemBertForMaskedLM
Methods: call | 220_15_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertforcausallm | .md | No docstring available for TFRemBertForCausalLM
Methods: call | 220_16_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertforsequenceclassification | .md | No docstring available for TFRemBertForSequenceClassification
Methods: call | 220_17_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertformultiplechoice | .md | No docstring available for TFRemBertForMultipleChoice
Methods: call | 220_18_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertfortokenclassification | .md | No docstring available for TFRemBertForTokenClassification
Methods: call | 220_19_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/rembert.md | https://huggingface.co/docs/transformers/en/model_doc/rembert/#tfrembertforquestionanswering | .md | No docstring available for TFRemBertForQuestionAnswering
Methods: call
</tf>
</frameworkcontent> | 220_20_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/ | .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... | 221_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/ | .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... | 221_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#overview | .md | Qwen2MoE is the new model series of large language models from the Qwen team. Previously, we released the Qwen series, including Qwen-72B, Qwen-1.8B, Qwen-VL, Qwen-Audio, etc. | 221_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#model-details | .md | Qwen2MoE 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. Qwen2MoE has the following architectural choices:
- Qwen2MoE is based on the Transformer architecture with SwiGLU activation, attention QKV bias... | 221_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#model-details | .md | - Qwen2MoE employs Mixture of Experts (MoE) architecture, where the models are upcycled from dense language models. For instance, `Qwen1.5-MoE-A2.7B` is upcycled from `Qwen-1.8B`. It has 14.3B parameters in total and 2.7B activated parameters during runtime, while it achieves comparable performance with `Qwen1.5-7B`, w... | 221_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#usage-tips | .md | `Qwen1.5-MoE-A2.7B` and `Qwen1.5-MoE-A2.7B-Chat` can be found on the [Huggingface Hub](https://huggingface.co/Qwen)
In the following, we demonstrate how to use `Qwen1.5-MoE-A2.7B-Chat` for the inference. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` fo... | 221_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#usage-tips | .md | >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B-Chat", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B-Chat")
>>> prompt = "Give me a short introduction to large language model."
>>> messages = [{"role": "user", "content": prompt}]
>>> text = tokenize... | 221_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#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_... | 221_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .md | This is the configuration class to store the configuration of a [`Qwen2MoeModel`]. It is used to instantiate a
Qwen2MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen1.5-MoE-A2.7B" [Qwen/Qwen... | 221_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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 Qwen2MoE model. Defines the number of different tokens that can ... | 221_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .md | hidden_size (`int`, *optional*, defaults to 2048):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 5632):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`... | 221_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .md | num_key_value_heads (`int`, *optional*, defaults to 16):
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) ... | 221_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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`... | 221_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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... | 221_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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... | 221_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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:... | 221_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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... | 221_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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... | 221_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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... | 221_4_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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... | 221_4_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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... | 221_4_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .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... | 221_4_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .md | attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
decoder_sparse_step (`int`, *optional*, defaults to 1):
The frequency of the MoE layer.
moe_intermediate_size (`int`, *optional*, defaults to 1408):
Intermediate size of the routed expert.
shared_expert_intermed... | 221_4_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .md | Intermediate size of the shared expert.
num_experts_per_tok (`int`, *optional*, defaults to 4):
Number of selected experts.
num_experts (`int`, *optional*, defaults to 60):
Number of routed experts.
norm_topk_prob (`bool`, *optional*, defaults to `False`):
Whether to normalize the topk probabilities.
output_router_logi... | 221_4_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .md | Whether or not the router logits should be returned by the model. Enabeling this will also
allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.
mlp_only_layers (`List[int]`, *... | 221_4_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .md | The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
```python
>>> from transformers import Qwen2MoeModel, Qwen2MoeConfig | 221_4_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeconfig | .md | >>> # Initializing a Qwen2MoE style configuration
>>> configuration = Qwen2MoeConfig()
>>> # Initializing a model from the Qwen1.5-MoE-A2.7B" style configuration
>>> model = Qwen2MoeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | 221_4_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moemodel | .md | The bare Qwen2MoE 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... | 221_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moemodel | .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 ([`Qwen2MoeConfig`]):
Model configuration class w... | 221_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moemodel | .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 [`Qwen2MoeDecoderLayer`]
Args:
config: Qwen2MoeConfig
Methods: forward | 221_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeforcausallm | .md | No docstring available for Qwen2MoeForCausalLM
Methods: forward | 221_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeforsequenceclassification | .md | The Qwen2MoE Model transformer with a sequence classification head on top (linear layer).
[`Qwen2MoeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last ... | 221_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeforsequenceclassification | .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... | 221_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeforsequenceclassification | .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... | 221_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeforsequenceclassification | .md | and behavior.
Parameters:
config ([`Qwen2MoeConfig`]):
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.
Met... | 221_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moefortokenclassification | .md | The Qwen2MoE Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (s... | 221_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moefortokenclassification | .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... | 221_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moefortokenclassification | .md | 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 | 221_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeforquestionanswering | .md | The Qwen2MoE 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 ge... | 221_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeforquestionanswering | .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... | 221_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/qwen2_moe.md | https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe/#qwen2moeforquestionanswering | .md | 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 | 221_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/ | .md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 222_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/ | .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.
--> | 222_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#overview | .md | The SeamlessM4T model was proposed in [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team from Meta AI.
This is the **version 1** release of the model. For the updated **version 2** release, refer to... | 222_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#overview | .md | SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text.
SeamlessM4T enables multiple tasks without relying on separate models:
- Speech-to-speech translation (S2ST)
- Speech-to-text... | 222_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#overview | .md | - Text-to-speech translation (T2ST)
- Text-to-text translation (T2TT)
- Automatic speech recognition (ASR)
[`SeamlessM4TModel`] can perform all the above tasks, but each task also has its own dedicated sub-model.
The abstract from the paper is the following: | 222_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#overview | .md | *What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-speech translation models have yet to achieve similar strides. More s... | 222_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#overview | .md | rely on cascaded systems that perform translation progressively, putting high-performing unified systems out of reach. To address these gaps, we introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automat... | 222_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#overview | .md | this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations. Filtered and combined with human-labeled and pseudo-labeled data, we developed the first multilingual syst... | 222_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#overview | .md | and text. On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in s... | 222_1_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#overview | .md | Tested for robustness, our system performs better against background noises and speaker variations in speech-to-text tasks compared to the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and added toxicity to assess translation safety. Finally, all contributions in this work are open-sourced and... | 222_1_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#usage | .md | First, load the processor and a checkpoint of the model:
```python
>>> from transformers import AutoProcessor, SeamlessM4TModel | 222_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#usage | .md | >>> processor = AutoProcessor.from_pretrained("facebook/hf-seamless-m4t-medium")
>>> model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-medium")
```
You can seamlessly use this model on text or on audio, to generated either translated text or translated audio.
Here is how to use the processor to pro... | 222_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#usage | .md | ```python
>>> # let's load an audio sample from an Arabic speech corpus
>>> from datasets import load_dataset
>>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True)
>>> audio_sample = next(iter(dataset))["audio"] | 222_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#usage | .md | >>> # now, process it
>>> audio_inputs = processor(audios=audio_sample["array"], return_tensors="pt")
>>> # now, process some English test as well
>>> text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")
``` | 222_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#speech | .md | [`SeamlessM4TModel`] can *seamlessly* generate text or speech with few or no changes. Let's target Russian voice translation:
```python
>>> audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
>>> audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cp... | 222_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#text | .md | Similarly, you can generate translated text from audio files or from text with the same model. You only have to pass `generate_speech=False` to [`SeamlessM4TModel.generate`].
This time, let's translate to French.
```python
>>> # from audio
>>> output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_sp... | 222_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#text | .md | >>> # from text
>>> output_tokens = model.generate(**text_inputs, tgt_lang="fra", generate_speech=False)
>>> translated_text_from_text = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
``` | 222_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#1-use-dedicated-models | .md | [`SeamlessM4TModel`] is transformers top level model to generate speech and text, but you can also use dedicated models that perform the task without additional components, thus reducing the memory footprint.
For example, you can replace the audio-to-audio generation snippet with the model dedicated to the S2ST task, t... | 222_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#1-use-dedicated-models | .md | ```python
>>> from transformers import SeamlessM4TForSpeechToSpeech
>>> model = SeamlessM4TForSpeechToSpeech.from_pretrained("facebook/hf-seamless-m4t-medium")
```
Or you can replace the text-to-text generation snippet with the model dedicated to the T2TT task, you only have to remove `generate_speech=False`.
```py... | 222_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#1-use-dedicated-models | .md | >>> model = SeamlessM4TForTextToText.from_pretrained("facebook/hf-seamless-m4t-medium")
```
Feel free to try out [`SeamlessM4TForSpeechToText`] and [`SeamlessM4TForTextToSpeech`] as well. | 222_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#2-change-the-speaker-identity | .md | You have the possibility to change the speaker used for speech synthesis with the `spkr_id` argument. Some `spkr_id` works better than other for some languages! | 222_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#3-change-the-generation-strategy | .md | You can use different [generation strategies](./generation_strategies) for speech and text generation, e.g `.generate(input_ids=input_ids, text_num_beams=4, speech_do_sample=True)` which will successively perform beam-search decoding on the text model, and multinomial sampling on the speech model. | 222_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#4-generate-speech-and-text-at-the-same-time | .md | Use `return_intermediate_token_ids=True` with [`SeamlessM4TModel`] to return both speech and text ! | 222_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#model-architecture | .md | SeamlessM4T features a versatile architecture that smoothly handles the sequential generation of text and speech. This setup comprises two sequence-to-sequence (seq2seq) models. The first model translates the input modality into translated text, while the second model generates speech tokens, known as "unit tokens," fr... | 222_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#model-architecture | .md | Each modality has its own dedicated encoder with a unique architecture. Additionally, for speech output, a vocoder inspired by the [HiFi-GAN](https://arxiv.org/abs/2010.05646) architecture is placed on top of the second seq2seq model.
Here's how the generation process works:
- Input text or speech is processed thro... | 222_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#model-architecture | .md | - Input text or speech is processed through its specific encoder.
- A decoder creates text tokens in the desired language.
- If speech generation is required, the second seq2seq model, following a standard encoder-decoder structure, generates unit tokens.
- These unit tokens are then passed through the final vocoder to... | 222_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#model-architecture | .md | - These unit tokens are then passed through the final vocoder to produce the actual speech.
This model was contributed by [ylacombe](https://huggingface.co/ylacombe). The original code can be found [here](https://github.com/facebookresearch/seamless_communication). | 222_9_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tmodel | .md | The original SeamlessM4T Model transformer which can be used for every tasks available (S2ST, S2TT, T2TT, T2ST).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related ... | 222_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tmodel | .md | behavior.
Parameters:
config ([`~SeamlessM4TConfig`]): 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.
cur... | 222_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tfortexttospeech | .md | The text-to-speech SeamlessM4T Model transformer which can be used for T2ST.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Par... | 222_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tfortexttospeech | .md | behavior.
Parameters:
config ([`~SeamlessM4TConfig`]): 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.
Met... | 222_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tforspeechtospeech | .md | The speech-to-speech SeamlessM4T Model transformer which can be used for S2ST.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
P... | 222_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tforspeechtospeech | .md | behavior.
Parameters:
config ([`~SeamlessM4TConfig`]): 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.
Met... | 222_12_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tfortexttotext | .md | The text-to-text SeamlessM4T Model transformer which can be used for T2TT.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Param... | 222_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tfortexttotext | .md | behavior.
Parameters:
config ([`~SeamlessM4TConfig`]): 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.
Met... | 222_13_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tforspeechtotext | .md | The speech-to-text SeamlessM4T Model transformer which can be used for S2TT.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Par... | 222_14_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tforspeechtotext | .md | behavior.
Parameters:
config ([`~SeamlessM4TConfig`]): 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.
Met... | 222_14_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | This is the configuration class to store the configuration of a [`~SeamlessM4TModel`]. It is used to instantiate an
SeamlessM4T 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 SeamlessM4T
["fac... | 222_15_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | ["facebook/hf-seamless-m4t-medium"](https://huggingface.co/"facebook/hf-seamless-m4t-medium") 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*... | 222_15_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | Vocabulary size of the SeamlessM4T model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`~SeamlessM4TModel`], [`~SeamlessM4TForTextToSpeech`] or
[`~SeamlessM4TForTextToText`].
t2u_vocab_size (`int`, *optional*, defaults to 10082):
Unit vocabulary size of the Sea... | 222_15_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | represented by the `inputs_ids` passed when calling the Text-To-Units sub-model of [`~SeamlessM4TModel`],
[`~SeamlessM4TForSpeechToSpeech`] or [`~SeamlessM4TForTextToSpeech`].
> Parameters shared across sub-models
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the "intermediate" layers in the ... | 222_15_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values att... | 222_15_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model text encoder and decoder might ever be used with. Typically set
this to something large just in case (e.g., 512 or 1024 or 2048).
is_encoder_decoder (`bool`, *optional*, defaults to `True`):
Whether the model is u... | 222_15_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | Whether the model is used as an encoder/decoder or not.
encoder_layerdrop (`float`, *optional*, defaults to 0.05):
The LayerDrop probability for the encoders. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.05):
The LayerDrop probab... | 222_15_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | for more details.
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the decoder and feed-forward layers. If string,
`"gelu"`, `"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1)... | 222_15_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all attention layers.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all activation layers in the model.
scale_embedding (`bool`, *optional*, defaults to `True`):
Scale embeddings by diving by s... | 222_15_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | encoder_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer text encoder.
encoder_ffn_dim (`int`, *optional*, defaults to 8192):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text encoder.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Num... | 222_15_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | decoder_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer text decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 8192):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text decoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Num... | 222_15_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | decoder_start_token_id (`int`, *optional*, defaults to 3):
If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token. Only
applied in the text decoder.
max_new_tokens (`int`, *optional*, defaults to 256):
The maximum numbers of text tokens to generate, ignoring the number of to... | 222_15_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/seamless_m4t.md | https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t/#seamlessm4tconfig | .md | pad_token_id (`int`, *optional*, defaults to 0):
The id of the _padding_ text token. Only applied to the text-decoder model.
bos_token_id (`int`, *optional*, defaults to 2):
The id of the _beginning-of-stream_ text token. Only applied to the text-decoder model.
eos_token_id (`int`, *optional*, defaults to 3):
The id of... | 222_15_12 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.