| from transformers import PretrainedConfig | |
| class XLMRobertaRefSegConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`XLMRobertaModel`] or a [`TFXLMRobertaModel`]. It | |
| is used to instantiate a XLM-RoBERTa 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 XLMRoBERTa | |
| [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 30522): | |
| Vocabulary size of the XLM-RoBERTa model. Defines the number of different tokens that can be represented by | |
| the `inputs_ids` passed when calling [`XLMRobertaModel`] or [`TFXLMRobertaModel`]. | |
| hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| num_hidden_layers (`int`, *optional*, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (`int`, *optional*, defaults to 3072): | |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
| `"relu"`, `"silu"` and `"gelu_new"` are supported. | |
| hidden_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout ratio for the attention probabilities. | |
| max_position_embeddings (`int`, *optional*, defaults to 512): | |
| The maximum sequence length that this model might ever be used with. Typically set this to something large | |
| just in case (e.g., 512 or 1024 or 2048). | |
| type_vocab_size (`int`, *optional*, defaults to 2): | |
| The vocabulary size of the `token_type_ids` passed when calling [`XLMRobertaModel`] or | |
| [`TFXLMRobertaModel`]. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| layer_norm_eps (`float`, *optional*, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| position_embedding_type (`str`, *optional*, defaults to `"absolute"`): | |
| Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For | |
| positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to | |
| [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). | |
| For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models | |
| with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). | |
| is_decoder (`bool`, *optional*, defaults to `False`): | |
| Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. | |
| use_cache (`bool`, *optional*, defaults to `True`): | |
| Whether or not the model should return the last key/values attentions (not used by all models). Only | |
| relevant if `config.is_decoder=True`. | |
| classifier_dropout (`float`, *optional*): | |
| The dropout ratio for the classification head. | |
| Examples: | |
| ```python | |
| >>> from transformers import XLMRobertaConfig, XLMRobertaModel | |
| >>> # Initializing a XLM-RoBERTa xlm-roberta-base style configuration | |
| >>> configuration = XLMRobertaConfig() | |
| >>> # Initializing a model (with random weights) from the xlm-roberta-base style configuration | |
| >>> model = XLMRobertaModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "xlm-roberta" | |
| def __init__( | |
| self, | |
| vocab_size=250002, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=514, | |
| type_vocab_size=1, | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-12, | |
| pad_token_id=1, | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| position_embedding_type="absolute", | |
| use_cache=True, | |
| classifier_dropout=None, | |
| num_labels_first=29, | |
| num_labels_second=2, | |
| alpha=1.0, | |
| **kwargs | |
| ): | |
| super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_act = hidden_act | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.position_embedding_type = position_embedding_type | |
| self.use_cache = use_cache | |
| self.classifier_dropout = classifier_dropout | |
| self.num_labels_first = num_labels_first | |
| self.num_labels_second = num_labels_second | |
| self.alpha = alpha | |
| super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |