229 lines
12 KiB
Python
229 lines
12 KiB
Python
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# coding=utf-8
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# Copyright 2020 Google Research and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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TAPAS configuration. Based on the BERT configuration with added parameters.
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Hyperparameters are taken from run_task_main.py and hparam_utils.py of the original implementation. URLS:
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- https://github.com/google-research/tapas/blob/master/tapas/run_task_main.py
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- https://github.com/google-research/tapas/blob/master/tapas/utils/hparam_utils.py
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"""
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from ...configuration_utils import PretrainedConfig
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from ..deprecated._archive_maps import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class TapasConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`TapasModel`]. It is used to instantiate a TAPAS
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the TAPAS
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[google/tapas-base-finetuned-sqa](https://huggingface.co/google/tapas-base-finetuned-sqa) architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Hyperparameters additional to BERT are taken from run_task_main.py and hparam_utils.py of the original
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implementation. Original implementation available at https://github.com/google-research/tapas/tree/master.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the TAPAS model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`TapasModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"swish"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 1024):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_sizes (`List[int]`, *optional*, defaults to `[3, 256, 256, 2, 256, 256, 10]`):
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The vocabulary sizes of the `token_type_ids` passed when calling [`TapasModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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positive_label_weight (`float`, *optional*, defaults to 10.0):
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Weight for positive labels.
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num_aggregation_labels (`int`, *optional*, defaults to 0):
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The number of aggregation operators to predict.
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aggregation_loss_weight (`float`, *optional*, defaults to 1.0):
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Importance weight for the aggregation loss.
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use_answer_as_supervision (`bool`, *optional*):
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Whether to use the answer as the only supervision for aggregation examples.
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answer_loss_importance (`float`, *optional*, defaults to 1.0):
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Importance weight for the regression loss.
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use_normalized_answer_loss (`bool`, *optional*, defaults to `False`):
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Whether to normalize the answer loss by the maximum of the predicted and expected value.
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huber_loss_delta (`float`, *optional*):
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Delta parameter used to calculate the regression loss.
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temperature (`float`, *optional*, defaults to 1.0):
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Value used to control (OR change) the skewness of cell logits probabilities.
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aggregation_temperature (`float`, *optional*, defaults to 1.0):
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Scales aggregation logits to control the skewness of probabilities.
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use_gumbel_for_cells (`bool`, *optional*, defaults to `False`):
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Whether to apply Gumbel-Softmax to cell selection.
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use_gumbel_for_aggregation (`bool`, *optional*, defaults to `False`):
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Whether to apply Gumbel-Softmax to aggregation selection.
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average_approximation_function (`string`, *optional*, defaults to `"ratio"`):
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Method to calculate the expected average of cells in the weak supervision case. One of `"ratio"`,
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`"first_order"` or `"second_order"`.
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cell_selection_preference (`float`, *optional*):
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Preference for cell selection in ambiguous cases. Only applicable in case of weak supervision for
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aggregation (WTQ, WikiSQL). If the total mass of the aggregation probabilities (excluding the "NONE"
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operator) is higher than this hyperparameter, then aggregation is predicted for an example.
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answer_loss_cutoff (`float`, *optional*):
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Ignore examples with answer loss larger than cutoff.
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max_num_rows (`int`, *optional*, defaults to 64):
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Maximum number of rows.
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max_num_columns (`int`, *optional*, defaults to 32):
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Maximum number of columns.
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average_logits_per_cell (`bool`, *optional*, defaults to `False`):
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Whether to average logits per cell.
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select_one_column (`bool`, *optional*, defaults to `True`):
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Whether to constrain the model to only select cells from a single column.
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allow_empty_column_selection (`bool`, *optional*, defaults to `False`):
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Whether to allow not to select any column.
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init_cell_selection_weights_to_zero (`bool`, *optional*, defaults to `False`):
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Whether to initialize cell selection weights to 0 so that the initial probabilities are 50%.
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reset_position_index_per_cell (`bool`, *optional*, defaults to `True`):
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Whether to restart position indexes at every cell (i.e. use relative position embeddings).
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disable_per_token_loss (`bool`, *optional*, defaults to `False`):
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Whether to disable any (strong or weak) supervision on cells.
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aggregation_labels (`Dict[int, label]`, *optional*):
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The aggregation labels used to aggregate the results. For example, the WTQ models have the following
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aggregation labels: `{0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}`
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no_aggregation_label_index (`int`, *optional*):
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If the aggregation labels are defined and one of these labels represents "No aggregation", this should be
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set to its index. For example, the WTQ models have the "NONE" aggregation label at index 0, so that value
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should be set to 0 for these models.
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Example:
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```python
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>>> from transformers import TapasModel, TapasConfig
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>>> # Initializing a default (SQA) Tapas configuration
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>>> configuration = TapasConfig()
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>>> # Initializing a model from the configuration
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>>> model = TapasModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "tapas"
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=1024,
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type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10],
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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positive_label_weight=10.0,
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num_aggregation_labels=0,
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aggregation_loss_weight=1.0,
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use_answer_as_supervision=None,
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answer_loss_importance=1.0,
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use_normalized_answer_loss=False,
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huber_loss_delta=None,
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temperature=1.0,
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aggregation_temperature=1.0,
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use_gumbel_for_cells=False,
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use_gumbel_for_aggregation=False,
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average_approximation_function="ratio",
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cell_selection_preference=None,
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answer_loss_cutoff=None,
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max_num_rows=64,
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max_num_columns=32,
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average_logits_per_cell=False,
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select_one_column=True,
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allow_empty_column_selection=False,
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init_cell_selection_weights_to_zero=False,
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reset_position_index_per_cell=True,
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disable_per_token_loss=False,
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aggregation_labels=None,
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no_aggregation_label_index=None,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, **kwargs)
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# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_sizes = type_vocab_sizes
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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# Fine-tuning task hyperparameters
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self.positive_label_weight = positive_label_weight
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self.num_aggregation_labels = num_aggregation_labels
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self.aggregation_loss_weight = aggregation_loss_weight
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self.use_answer_as_supervision = use_answer_as_supervision
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self.answer_loss_importance = answer_loss_importance
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self.use_normalized_answer_loss = use_normalized_answer_loss
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self.huber_loss_delta = huber_loss_delta
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self.temperature = temperature
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self.aggregation_temperature = aggregation_temperature
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self.use_gumbel_for_cells = use_gumbel_for_cells
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self.use_gumbel_for_aggregation = use_gumbel_for_aggregation
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self.average_approximation_function = average_approximation_function
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self.cell_selection_preference = cell_selection_preference
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self.answer_loss_cutoff = answer_loss_cutoff
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self.max_num_rows = max_num_rows
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self.max_num_columns = max_num_columns
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self.average_logits_per_cell = average_logits_per_cell
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self.select_one_column = select_one_column
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self.allow_empty_column_selection = allow_empty_column_selection
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self.init_cell_selection_weights_to_zero = init_cell_selection_weights_to_zero
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self.reset_position_index_per_cell = reset_position_index_per_cell
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self.disable_per_token_loss = disable_per_token_loss
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# Aggregation hyperparameters
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self.aggregation_labels = aggregation_labels
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self.no_aggregation_label_index = no_aggregation_label_index
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if isinstance(self.aggregation_labels, dict):
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self.aggregation_labels = {int(k): v for k, v in aggregation_labels.items()}
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