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train | WikiTablesSemanticParser._get_neighbor_indices | This method returns the indices of each entity's neighbors. A tensor
is accepted as a parameter for copying purposes.
Parameters
----------
worlds : ``List[WikiTablesWorld]``
num_entities : ``int``
tensor : ``torch.Tensor``
Used for copying the constructed li... | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | def _get_neighbor_indices(worlds: List[WikiTablesWorld],
num_entities: int,
tensor: torch.Tensor) -> torch.LongTensor:
"""
This method returns the indices of each entity's neighbors. A tensor
is accepted as a parameter for copying purpo... | def _get_neighbor_indices(worlds: List[WikiTablesWorld],
num_entities: int,
tensor: torch.Tensor) -> torch.LongTensor:
"""
This method returns the indices of each entity's neighbors. A tensor
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train | WikiTablesSemanticParser._get_type_vector | Produces a tensor with shape ``(batch_size, num_entities)`` that encodes each entity's
type. In addition, a map from a flattened entity index to type is returned to combine
entity type operations into one method.
Parameters
----------
worlds : ``List[WikiTablesWorld]``
n... | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | def _get_type_vector(worlds: List[WikiTablesWorld],
num_entities: int,
tensor: torch.Tensor) -> Tuple[torch.LongTensor, Dict[int, int]]:
"""
Produces a tensor with shape ``(batch_size, num_entities)`` that encodes each entity's
type. In addition,... | def _get_type_vector(worlds: List[WikiTablesWorld],
num_entities: int,
tensor: torch.Tensor) -> Tuple[torch.LongTensor, Dict[int, int]]:
"""
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train | WikiTablesSemanticParser._get_linking_probabilities | Produces the probability of an entity given a question word and type. The logic below
separates the entities by type since the softmax normalization term sums over entities
of a single type.
Parameters
----------
worlds : ``List[WikiTablesWorld]``
linking_scores : ``torc... | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | def _get_linking_probabilities(self,
worlds: List[WikiTablesWorld],
linking_scores: torch.FloatTensor,
question_mask: torch.LongTensor,
entity_type_dict: Dict[int, int]) -> torch.F... | def _get_linking_probabilities(self,
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train | WikiTablesSemanticParser.get_metrics | We track three metrics here:
1. dpd_acc, which is the percentage of the time that our best output action sequence is
in the set of action sequences provided by DPD. This is an easy-to-compute lower bound
on denotation accuracy for the set of examples where we actually have DPD outp... | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | def get_metrics(self, reset: bool = False) -> Dict[str, float]:
"""
We track three metrics here:
1. dpd_acc, which is the percentage of the time that our best output action sequence is
in the set of action sequences provided by DPD. This is an easy-to-compute lower bound
... | def get_metrics(self, reset: bool = False) -> Dict[str, float]:
"""
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1. dpd_acc, which is the percentage of the time that our best output action sequence is
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train | WikiTablesSemanticParser._create_grammar_state | This method creates the LambdaGrammarStatelet object that's used for decoding. Part of
creating that is creating the `valid_actions` dictionary, which contains embedded
representations of all of the valid actions. So, we create that here as well.
The way we represent the valid expansions is a... | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | def _create_grammar_state(self,
world: WikiTablesWorld,
possible_actions: List[ProductionRule],
linking_scores: torch.Tensor,
entity_types: torch.Tensor) -> LambdaGrammarStatelet:
"""
... | def _create_grammar_state(self,
world: WikiTablesWorld,
possible_actions: List[ProductionRule],
linking_scores: torch.Tensor,
entity_types: torch.Tensor) -> LambdaGrammarStatelet:
"""
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train | WikiTablesSemanticParser._compute_validation_outputs | Does common things for validation time: computing logical form accuracy (which is expensive
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This doesn't return anything; instead it `modifies` the given ``outputs`` dictionary, and
calls metrics on ``sel... | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | def _compute_validation_outputs(self,
actions: List[List[ProductionRule]],
best_final_states: Mapping[int, Sequence[GrammarBasedState]],
world: List[WikiTablesWorld],
example_l... | def _compute_validation_outputs(self,
actions: List[List[ProductionRule]],
best_final_states: Mapping[int, Sequence[GrammarBasedState]],
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train | WikiTablesSemanticParser.decode | This method overrides ``Model.decode``, which gets called after ``Model.forward``, at test
time, to finalize predictions. This is (confusingly) a separate notion from the "decoder"
in "encoder/decoder", where that decoder logic lives in the ``TransitionFunction``.
This method trims the output ... | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
This method overrides ``Model.decode``, which gets called after ``Model.forward``, at test
time, to finalize predictions. This is (confusingly) a separate notion from the "decoder"
in "encoder/decoder... | def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
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train | LinkingCoverageTransitionFunction._get_linked_logits_addition | Gets the logits of desired terminal actions yet to be produced by the decoder, and
returns them for the decoder to add to the prior action logits, biasing the model towards
predicting missing linked actions. | allennlp/state_machines/transition_functions/linking_coverage_transition_function.py | def _get_linked_logits_addition(checklist_state: ChecklistStatelet,
action_ids: List[int],
action_logits: torch.Tensor) -> torch.Tensor:
"""
Gets the logits of desired terminal actions yet to be produced by the decoder, and
... | def _get_linked_logits_addition(checklist_state: ChecklistStatelet,
action_ids: List[int],
action_logits: torch.Tensor) -> torch.Tensor:
"""
Gets the logits of desired terminal actions yet to be produced by the decoder, and
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train | BasicTransitionFunction.attend_on_question | Given a query (which is typically the decoder hidden state), compute an attention over the
output of the question encoder, and return a weighted sum of the question representations
given this attention. We also return the attention weights themselves.
This is a simple computation, but we have ... | allennlp/state_machines/transition_functions/basic_transition_function.py | def attend_on_question(self,
query: torch.Tensor,
encoder_outputs: torch.Tensor,
encoder_output_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Given a query (which is typically the decoder hidden state), comp... | def attend_on_question(self,
query: torch.Tensor,
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train | ActionSpaceWalker._walk | Walk over action space to collect completed paths of at most ``self._max_path_length`` steps. | allennlp/semparse/action_space_walker.py | def _walk(self) -> None:
"""
Walk over action space to collect completed paths of at most ``self._max_path_length`` steps.
"""
# Buffer of NTs to expand, previous actions
incomplete_paths = [([str(type_)], [f"{START_SYMBOL} -> {type_}"]) for type_ in
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"""
Walk over action space to collect completed paths of at most ``self._max_path_length`` steps.
"""
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train | Batch._check_types | Check that all the instances have the same types. | allennlp/data/dataset.py | def _check_types(self) -> None:
"""
Check that all the instances have the same types.
"""
all_instance_fields_and_types: List[Dict[str, str]] = [{k: v.__class__.__name__
for k, v in x.fields.items()}
... | def _check_types(self) -> None:
"""
Check that all the instances have the same types.
"""
all_instance_fields_and_types: List[Dict[str, str]] = [{k: v.__class__.__name__
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train | Batch.get_padding_lengths | Gets the maximum padding lengths from all ``Instances`` in this batch. Each ``Instance``
has multiple ``Fields``, and each ``Field`` could have multiple things that need padding.
We look at all fields in all instances, and find the max values for each (field_name,
padding_key) pair, returning t... | allennlp/data/dataset.py | def get_padding_lengths(self) -> Dict[str, Dict[str, int]]:
"""
Gets the maximum padding lengths from all ``Instances`` in this batch. Each ``Instance``
has multiple ``Fields``, and each ``Field`` could have multiple things that need padding.
We look at all fields in all instances, and ... | def get_padding_lengths(self) -> Dict[str, Dict[str, int]]:
"""
Gets the maximum padding lengths from all ``Instances`` in this batch. Each ``Instance``
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train | Batch.as_tensor_dict | This method converts this ``Batch`` into a set of pytorch Tensors that can be passed
through a model. In order for the tensors to be valid tensors, all ``Instances`` in this
batch need to be padded to the same lengths wherever padding is necessary, so we do that
first, then we combine all of th... | allennlp/data/dataset.py | def as_tensor_dict(self,
padding_lengths: Dict[str, Dict[str, int]] = None,
verbose: bool = False) -> Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]]:
# This complex return type is actually predefined elsewhere as a DataArray,
# but we can't use it b... | def as_tensor_dict(self,
padding_lengths: Dict[str, Dict[str, int]] = None,
verbose: bool = False) -> Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]]:
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train | get_strings_from_utterance | Based on the current utterance, return a dictionary where the keys are the strings in
the database that map to lists of the token indices that they are linked to. | allennlp/semparse/worlds/atis_world.py | def get_strings_from_utterance(tokenized_utterance: List[Token]) -> Dict[str, List[int]]:
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"""
string_linking_scores: Dict[str, List[i... | def get_strings_from_utterance(tokenized_utterance: List[Token]) -> Dict[str, List[int]]:
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"""
We create a new ``Grammar`` object from the one in ``AtisSqlTableContext``, that also
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all_numbers: Set[str],
number_linking_scores: Dict[str, Tuple[str, str, List[int]]],
get_number_linking_dict: Callable[[str, List[Token]],
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train | AtisWorld.all_possible_actions | Return a sorted list of strings representing all possible actions
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Return a sorted list of strings representing all possible actions
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"""
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train | make_app | Creates a Flask app that serves up a simple configuration wizard. | allennlp/service/config_explorer.py | def make_app(include_packages: Sequence[str] = ()) -> Flask:
"""
Creates a Flask app that serves up a simple configuration wizard.
"""
# Load modules
for package_name in include_packages:
import_submodules(package_name)
app = Flask(__name__) # pylint: disable=invalid-name
@app.err... | def make_app(include_packages: Sequence[str] = ()) -> Flask:
"""
Creates a Flask app that serves up a simple configuration wizard.
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train | train_model_from_args | Just converts from an ``argparse.Namespace`` object to string paths. | allennlp/commands/train.py | def train_model_from_args(args: argparse.Namespace):
"""
Just converts from an ``argparse.Namespace`` object to string paths.
"""
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train | train_model_from_file | A wrapper around :func:`train_model` which loads the params from a file.
Parameters
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parameter_filename : ``str``
A json parameter file specifying an AllenNLP experiment.
serialization_dir : ``str``
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train | train_model | Trains the model specified in the given :class:`Params` object, using the data and training
parameters also specified in that object, and saves the results in ``serialization_dir``.
Parameters
----------
params : ``Params``
A parameter object specifying an AllenNLP Experiment.
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recover: bool = False,
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cache_directory: str = None,
cache_prefix: str = None) -> Model:
"""
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serialization_dir: str,
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train | _prf_divide | Performs division and handles divide-by-zero.
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"""Performs division and handles divide-by-zero.
On zero-division, sets the corresponding result elements to zero.
"""
result = numerator / denominator
mask = denominator == 0.0
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"""Performs division and handles divide-by-zero.
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result = numerator / denominator
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The###DET dog###NN ate###V the###DET apple###NN
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"""
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The###DET dog###NN ate###V the###DET apple###NN
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train | pop_max_vocab_size | max_vocab_size limits the size of the vocabulary, not including the @@UNKNOWN@@ token.
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train | Vocabulary.set_from_file | If you already have a vocabulary file for a trained model somewhere, and you really want to
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train | Model._load | Instantiates an already-trained model, based on the experiment
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"""
Returns an agenda that can be used guide search.
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train | WikiTablesLanguage.last | Takes an expression that evaluates to a list of rows, and returns the last one in that
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train | WikiTablesLanguage.mode_string | Takes a list of rows and a column and returns the most frequent values (one or more) under
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train | WikiTablesLanguage.mode_number | Takes a list of rows and a column and returns the most frequent value under
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train | WikiTablesLanguage.mode_date | Takes a list of rows and a column and returns the most frequent value under
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train | WikiTablesLanguage.max_date | Takes a list of rows and a column and returns the max of the values under that column in
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train | WikiTablesLanguage.max_number | Takes a list of rows and a column and returns the max of the values under that column in
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train | WikiTablesLanguage.average | Takes a list of rows and a column and returns the mean of the values under that column in
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train | WikiTablesLanguage.diff | Takes a two rows and a number column and returns the difference between the values under
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return 0.0 ... | def diff(self, first_row: List[Row], second_row: List[Row], column: NumberColumn) -> Number:
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Takes a two rows and a number column and returns the difference between the values under
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is the result of applying one or more functions on the table rows), the method returns -1. | allennlp/semparse/domain_languages/wikitables_language.py | def _get_row_index(self, row: Row) -> int:
"""
Takes a row and returns its index in the full list of rows. If the row does not occur in the
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train | World.is_terminal | This function will be called on nodes of a logical form tree, which are either non-terminal
symbols that can be expanded or terminal symbols that must be leaf nodes. Returns ``True``
if the given symbol is a terminal symbol. | allennlp/semparse/worlds/world.py | def is_terminal(self, symbol: str) -> bool:
"""
This function will be called on nodes of a logical form tree, which are either non-terminal
symbols that can be expanded or terminal symbols that must be leaf nodes. Returns ``True``
if the given symbol is a terminal symbol.
"""
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"""
This function will be called on nodes of a logical form tree, which are either non-terminal
symbols that can be expanded or terminal symbols that must be leaf nodes. Returns ``True``
if the given symbol is a terminal symbol.
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train | World.get_paths_to_root | For a given action, returns at most ``max_num_paths`` paths to the root (production with
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beam_size: int = 30,
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train | World.get_multi_match_mapping | Returns a mapping from each `MultiMatchNamedBasicType` to all the `NamedBasicTypes` that it
matches. | allennlp/semparse/worlds/world.py | def get_multi_match_mapping(self) -> Dict[Type, List[Type]]:
"""
Returns a mapping from each `MultiMatchNamedBasicType` to all the `NamedBasicTypes` that it
matches.
"""
if self._multi_match_mapping is None:
self._multi_match_mapping = {}
basic_types = sel... | def get_multi_match_mapping(self) -> Dict[Type, List[Type]]:
"""
Returns a mapping from each `MultiMatchNamedBasicType` to all the `NamedBasicTypes` that it
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train | World.parse_logical_form | Takes a logical form as a string, maps its tokens using the mapping and returns a parsed expression.
Parameters
----------
logical_form : ``str``
Logical form to parse
remove_var_function : ``bool`` (optional)
``var`` is a special function that some languages use... | allennlp/semparse/worlds/world.py | def parse_logical_form(self,
logical_form: str,
remove_var_function: bool = True) -> Expression:
"""
Takes a logical form as a string, maps its tokens using the mapping and returns a parsed expression.
Parameters
----------
l... | def parse_logical_form(self,
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Takes a logical form as a string, maps its tokens using the mapping and returns a parsed expression.
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train | World.get_action_sequence | Returns the sequence of actions (as strings) that resulted in the given expression. | allennlp/semparse/worlds/world.py | def get_action_sequence(self, expression: Expression) -> List[str]:
"""
Returns the sequence of actions (as strings) that resulted in the given expression.
"""
# Starting with the type of the whole expression
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"""
Returns the sequence of actions (as strings) that resulted in the given expression.
"""
# Starting with the type of the whole expression
return self._get_transitions(expression,
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train | World.get_logical_form | Takes an action sequence and constructs a logical form from it. This is useful if you want
to get a logical form from a decoded sequence of actions generated by a transition based
semantic parser.
Parameters
----------
action_sequence : ``List[str]``
The sequence of ... | allennlp/semparse/worlds/world.py | def get_logical_form(self,
action_sequence: List[str],
add_var_function: bool = True) -> str:
"""
Takes an action sequence and constructs a logical form from it. This is useful if you want
to get a logical form from a decoded sequence of actions ... | def get_logical_form(self,
action_sequence: List[str],
add_var_function: bool = True) -> str:
"""
Takes an action sequence and constructs a logical form from it. This is useful if you want
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train | World._construct_node_from_actions | Given a current node in the logical form tree, and a list of actions in an action sequence,
this method fills in the children of the current node from the action sequence, then
returns whatever actions are left.
For example, we could get a node with type ``c``, and an action sequence that begin... | allennlp/semparse/worlds/world.py | def _construct_node_from_actions(self,
current_node: Tree,
remaining_actions: List[List[str]],
add_var_function: bool) -> List[List[str]]:
"""
Given a current node in the logical form tree, and... | def _construct_node_from_actions(self,
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train | World._infer_num_arguments | Takes a type signature and infers the number of arguments the corresponding function takes.
Examples:
e -> 0
<r,e> -> 1
<e,<e,t>> -> 2
<b,<<b,#1>,<#1,b>>> -> 3 | allennlp/semparse/worlds/world.py | def _infer_num_arguments(cls, type_signature: str) -> int:
"""
Takes a type signature and infers the number of arguments the corresponding function takes.
Examples:
e -> 0
<r,e> -> 1
<e,<e,t>> -> 2
<b,<<b,#1>,<#1,b>>> -> 3
"""
if no... | def _infer_num_arguments(cls, type_signature: str) -> int:
"""
Takes a type signature and infers the number of arguments the corresponding function takes.
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e -> 0
<r,e> -> 1
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train | World._process_nested_expression | ``nested_expression`` is the result of parsing a logical form in Lisp format.
We process it recursively and return a string in the format that NLTK's ``LogicParser``
would understand. | allennlp/semparse/worlds/world.py | def _process_nested_expression(self, nested_expression) -> str:
"""
``nested_expression`` is the result of parsing a logical form in Lisp format.
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expression_is_li... | def _process_nested_expression(self, nested_expression) -> str:
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train | World._add_name_mapping | Utility method to add a name and its translation to the local name mapping, and the corresponding
signature, if available to the local type signatures. This method also updates the reverse name
mapping. | allennlp/semparse/worlds/world.py | def _add_name_mapping(self, name: str, translated_name: str, name_type: Type = None):
"""
Utility method to add a name and its translation to the local name mapping, and the corresponding
signature, if available to the local type signatures. This method also updates the reverse name
mapp... | def _add_name_mapping(self, name: str, translated_name: str, name_type: Type = None):
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train | AugmentedLstm.forward | Parameters
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inputs : PackedSequence, required.
A tensor of shape (batch_size, num_timesteps, input_size)
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initial_state : Tuple[torch.Tensor, torch.Tensor], optional, (default = None)
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inputs: PackedSequence,
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Parameters
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inputs : PackedSequence, required.
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train | WikiTablesSempreExecutor._create_sempre_executor | Creates a server running SEMPRE that we can send logical forms to for evaluation. This
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to clean up the process when our program exits. | allennlp/semparse/executors/wikitables_sempre_executor.py | def _create_sempre_executor(self) -> None:
"""
Creates a server running SEMPRE that we can send logical forms to for evaluation. This
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"""
Creates a server running SEMPRE that we can send logical forms to for evaluation. This
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train | Scorer.b_cubed | Averaged per-mention precision and recall.
<https://pdfs.semanticscholar.org/cfe3/c24695f1c14b78a5b8e95bcbd1c666140fd1.pdf> | allennlp/training/metrics/conll_coref_scores.py | def b_cubed(clusters, mention_to_gold):
"""
Averaged per-mention precision and recall.
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"""
numerator, denominator = 0, 0
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"""
Averaged per-mention precision and recall.
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numerator, denominator = 0, 0
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train | Scorer.muc | Counts the mentions in each predicted cluster which need to be re-allocated in
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Counts the mentions in each predicted cluster which need to be re-allocated in
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<http://aclweb.org/anthology/M/M95/M95-1005.pdf>
"""
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Counts the mentions in each predicted cluster which need to be re-allocated in
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train | Scorer.phi4 | Subroutine for ceafe. Computes the mention F measure between gold and
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"""
Subroutine for ceafe. Computes the mention F measure between gold and
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train | Scorer.ceafe | Computes the Constrained EntityAlignment F-Measure (CEAF) for evaluating coreference.
Gold and predicted mentions are aligned into clusterings which maximise a metric - in
this case, the F measure between gold and predicted clusters.
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Computes the Constrained EntityAlignment F-Measure (CEAF) for evaluating coreference.
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Computes the Constrained EntityAlignment F-Measure (CEAF) for evaluating coreference.
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train | GrammarStatelet.take_action | Takes an action in the current grammar state, returning a new grammar state with whatever
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Takes an action in the current grammar state, returning a new grammar state with whatever
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train | sparse_clip_norm | Clips gradient norm of an iterable of parameters.
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Supports sparse gradients.
Parameters
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parameters : ``(Iterable[torch.Tensor])``
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The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
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Parameters
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train | move_optimizer_to_cuda | Move the optimizer state to GPU, if necessary.
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train | get_batch_size | Returns the size of the batch dimension. Assumes a well-formed batch,
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Returns the size of the batch dimension. Assumes a well-formed batch,
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train | time_to_str | Convert seconds past Epoch to human readable string. | allennlp/training/util.py | def time_to_str(timestamp: int) -> str:
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Convert seconds past Epoch to human readable string.
"""
datetimestamp = datetime.datetime.fromtimestamp(timestamp)
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Convert seconds past Epoch to human readable string.
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train | str_to_time | Convert human readable string to datetime.datetime. | allennlp/training/util.py | def str_to_time(time_str: str) -> datetime.datetime:
"""
Convert human readable string to datetime.datetime.
"""
pieces: Any = [int(piece) for piece in time_str.split('-')]
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train | datasets_from_params | Load all the datasets specified by the config.
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params : ``Params``
cache_directory : ``str``, optional
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Load all the datasets specified by the config.
Parameters
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Load all the datasets specified by the config.
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train | create_serialization_dir | This function creates the serialization directory if it doesn't exist. If it already exists
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Parameters
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params: ``Params``
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train | data_parallel | Performs a forward pass using multiple GPUs. This is a simplification
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interface. | allennlp/training/util.py | def data_parallel(batch_group: List[TensorDict],
model: Model,
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"""
Performs a forward pass using multiple GPUs. This is a simplification
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train | rescale_gradients | Performs gradient rescaling. Is a no-op if gradient rescaling is not enabled. | allennlp/training/util.py | def rescale_gradients(model: Model, grad_norm: Optional[float] = None) -> Optional[float]:
"""
Performs gradient rescaling. Is a no-op if gradient rescaling is not enabled.
"""
if grad_norm:
parameters_to_clip = [p for p in model.parameters()
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"""
Performs gradient rescaling. Is a no-op if gradient rescaling is not enabled.
"""
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train | get_metrics | Gets the metrics but sets ``"loss"`` to
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the ``"loss"`` metric is "average loss per batch". | allennlp/training/util.py | def get_metrics(model: Model, total_loss: float, num_batches: int, reset: bool = False) -> Dict[str, float]:
"""
Gets the metrics but sets ``"loss"`` to
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metrics = model.get_metrics(reset=... | def get_metrics(model: Model, total_loss: float, num_batches: int, reset: bool = False) -> Dict[str, float]:
"""
Gets the metrics but sets ``"loss"`` to
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train | parse_requirements | Parse all dependencies out of the requirements.txt file. | scripts/check_requirements_and_setup.py | def parse_requirements() -> Tuple[PackagesType, PackagesType, Set[str]]:
"""Parse all dependencies out of the requirements.txt file."""
essential_packages: PackagesType = {}
other_packages: PackagesType = {}
duplicates: Set[str] = set()
with open("requirements.txt", "r") as req_file:
section... | def parse_requirements() -> Tuple[PackagesType, PackagesType, Set[str]]:
"""Parse all dependencies out of the requirements.txt file."""
essential_packages: PackagesType = {}
other_packages: PackagesType = {}
duplicates: Set[str] = set()
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train | parse_setup | Parse all dependencies out of the setup.py script. | scripts/check_requirements_and_setup.py | def parse_setup() -> Tuple[PackagesType, PackagesType, Set[str], Set[str]]:
"""Parse all dependencies out of the setup.py script."""
essential_packages: PackagesType = {}
test_packages: PackagesType = {}
essential_duplicates: Set[str] = set()
test_duplicates: Set[str] = set()
with open('setup.p... | def parse_setup() -> Tuple[PackagesType, PackagesType, Set[str], Set[str]]:
"""Parse all dependencies out of the setup.py script."""
essential_packages: PackagesType = {}
test_packages: PackagesType = {}
essential_duplicates: Set[str] = set()
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train | enumerate_spans | Given a sentence, return all token spans within the sentence. Spans are `inclusive`.
Additionally, you can provide a maximum and minimum span width, which will be used
to exclude spans outside of this range.
Finally, you can provide a function mapping ``List[T] -> bool``, which will
be applied to every... | allennlp/data/dataset_readers/dataset_utils/span_utils.py | def enumerate_spans(sentence: List[T],
offset: int = 0,
max_span_width: int = None,
min_span_width: int = 1,
filter_function: Callable[[List[T]], bool] = None) -> List[Tuple[int, int]]:
"""
Given a sentence, return all token spans w... | def enumerate_spans(sentence: List[T],
offset: int = 0,
max_span_width: int = None,
min_span_width: int = 1,
filter_function: Callable[[List[T]], bool] = None) -> List[Tuple[int, int]]:
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train | bio_tags_to_spans | Given a sequence corresponding to BIO tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
Ill-formed spans are also included (i.e those which do not start with a "B-LABEL"),
as otherwise it is possible to get a perfect precision score whilst still predicting... | allennlp/data/dataset_readers/dataset_utils/span_utils.py | def bio_tags_to_spans(tag_sequence: List[str],
classes_to_ignore: List[str] = None) -> List[TypedStringSpan]:
"""
Given a sequence corresponding to BIO tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
Ill-formed spans are also in... | def bio_tags_to_spans(tag_sequence: List[str],
classes_to_ignore: List[str] = None) -> List[TypedStringSpan]:
"""
Given a sequence corresponding to BIO tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
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train | iob1_tags_to_spans | Given a sequence corresponding to IOB1 tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
Ill-formed spans are also included (i.e., those where "B-LABEL" is not preceded
by "I-LABEL" or "B-LABEL").
Parameters
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tag_sequence : List[str]... | allennlp/data/dataset_readers/dataset_utils/span_utils.py | def iob1_tags_to_spans(tag_sequence: List[str],
classes_to_ignore: List[str] = None) -> List[TypedStringSpan]:
"""
Given a sequence corresponding to IOB1 tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
Ill-formed spans are also... | def iob1_tags_to_spans(tag_sequence: List[str],
classes_to_ignore: List[str] = None) -> List[TypedStringSpan]:
"""
Given a sequence corresponding to IOB1 tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
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train | bioul_tags_to_spans | Given a sequence corresponding to BIOUL tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
Ill-formed spans are not allowed and will raise ``InvalidTagSequence``.
This function works properly when the spans are unlabeled (i.e., your labels are
simply "B... | allennlp/data/dataset_readers/dataset_utils/span_utils.py | def bioul_tags_to_spans(tag_sequence: List[str],
classes_to_ignore: List[str] = None) -> List[TypedStringSpan]:
"""
Given a sequence corresponding to BIOUL tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
Ill-formed spans are n... | def bioul_tags_to_spans(tag_sequence: List[str],
classes_to_ignore: List[str] = None) -> List[TypedStringSpan]:
"""
Given a sequence corresponding to BIOUL tags, extracts spans.
Spans are inclusive and can be of zero length, representing a single word span.
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