class SimpleClassifier(Model):
def __init__(self, vocab: Vocabulary, embedder: TextFieldEmbedder,
encoder: Seq2VecEncoder):
super().__init__(vocab)
self.embedder = embedder
self.encoder = encoder
num_labels = vocab.get_vocab_size("labels")
self.classifier = torch.nn.Linear(encoder.get_output_dim(), num_labels)
self.accuracy = CategoricalAccuracy()
self.macrof1 = FBetaMeasure(average='macro')
self.microf1 = FBetaMeasure(average='micro')
self.weightedf1 = FBetaMeasure(average='weighted')
def forward(self, text: Dict[str, torch.Tensor],
label: torch.Tensor) -> Dict[str, torch.Tensor]:
# Shape: (batch_size, num_tokens, embedding_dim)
embedded_text = self.embedder(text)
# Shape: (batch_size, num_tokens)
mask = util.get_text_field_mask(text)
# Shape: (batch_size, encoding_dim)
encoded_text = self.encoder(embedded_text, mask)
# Shape: (batch_size, num_labels)
logits = self.classifier(encoded_text)
self.accuracy(logits, label)
self.macrof1(logits, label)
self.microf1(logits, label)
self.weightedf1(logits, label)
# Shape: (batch_size, num_labels)
probs = torch.nn.functional.softmax(logits)
# Shape: (1,)
loss = torch.nn.functional.cross_entropy(logits, label)
return {'loss': loss, 'probs': probs}
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
result_macro = self.macrof1.get_metric(reset)
result_micro = self.microf1.get_metric(reset)
result_weighted = self.weightedf1.get_metric(reset)
return {
"accuracy": self.accuracy.get_metric(reset),
"macrof1_precision": result_macro["precision"],
"macrof1_recall": result_macro["recall"],
"macrof1_fscore": result_macro["fscore"],
"microf1_precision": result_micro["precision"],
"microf1_recall": result_micro["recall"],
"microf1_fscore": result_macro["fscore"],
"weightedf1_precision": result_weighted["precision"],
"weightedf1_recall": result_weighted["recall"],
"weightedf1_fscore": result_weighted["fscore"]
}
class TransformerSrlSpan(SrlBert):
"""
# Parameters
vocab : `Vocabulary`, required
A Vocabulary, required in order to compute sizes for input/output projections.
model : `Union[str, AutoModel]`, required.
A string describing the BERT model to load or an already constructed AutoModel.
initializer : `InitializerApplicator`, optional (default=`InitializerApplicator()`)
Used to initialize the model parameters.
label_smoothing : `float`, optional (default = `0.0`)
Whether or not to use label smoothing on the labels when computing cross entropy loss.
ignore_span_metric : `bool`, optional (default = `False`)
Whether to calculate span loss, which is irrelevant when predicting BIO for Open Information Extraction.
srl_eval_path : `str`, optional (default=`DEFAULT_SRL_EVAL_PATH`)
The path to the srl-eval.pl script. By default, will use the srl-eval.pl included with allennlp,
which is located at allennlp/tools/srl-eval.pl . If `None`, srl-eval.pl is not used.
"""
def __init__(
self,
vocab: Vocabulary,
bert_model: Union[str, AutoModel],
embedding_dropout: float = 0.0,
initializer: InitializerApplicator = InitializerApplicator(),
label_smoothing: float = None,
ignore_span_metric: bool = False,
srl_eval_path: str = DEFAULT_SRL_EVAL_PATH,
inventory: str = "verbatlas",
**kwargs,
) -> None:
# bypass SrlBert constructor
Model.__init__(self, vocab, **kwargs)
self.transformer = AutoModel.from_pretrained(bert_model)
self.frame_criterion = nn.CrossEntropyLoss()
if inventory == "verbatlas":
# add missing frame labels
frame_list = load_label_list(FRAME_LIST_PATH)
self.vocab.add_tokens_to_namespace(frame_list, "frames_labels")
# add missing role labels
role_list = load_label_list(ROLE_LIST_PATH)
self.vocab.add_tokens_to_namespace(role_list, "labels")
self.num_classes = self.vocab.get_vocab_size("labels")
self.frame_num_classes = self.vocab.get_vocab_size("frames_labels")
if srl_eval_path is not None:
# For the span based evaluation, we don't want to consider labels
# for verb, because the verb index is provided to the model.
self.span_metric = SrlEvalScorer(srl_eval_path,
ignore_classes=["V"])
else:
self.span_metric = None
self.f1_frame_metric = FBetaMeasure(average="micro")
self.tag_projection_layer = nn.Linear(
self.transformer.config.hidden_size, self.num_classes)
self.frame_projection_layer = nn.Linear(
self.transformer.config.hidden_size, self.frame_num_classes)
self.embedding_dropout = nn.Dropout(p=embedding_dropout)
self._label_smoothing = label_smoothing
self.ignore_span_metric = ignore_span_metric
initializer(self)
def forward( # type: ignore
self,
tokens: TextFieldTensors,
verb_indicator: torch.Tensor,
frame_indicator: torch.Tensor,
metadata: List[Any],
tags: torch.LongTensor = None,
frame_tags: torch.LongTensor = None,
):
"""
# Parameters
tokens : `TextFieldTensors`, required
The output of `TextField.as_array()`, which should typically be passed directly to a
`TextFieldEmbedder`. For this model, this must be a `SingleIdTokenIndexer` which
indexes wordpieces from the BERT vocabulary.
verb_indicator: `torch.LongTensor`, required.
An integer `SequenceFeatureField` representation of the position of the verb
in the sentence. This should have shape (batch_size, num_tokens) and importantly, can be
all zeros, in the case that the sentence has no verbal predicate.
frame_indicator: torch.LongTensor, required.
An integer ``SequenceFeatureField`` representation of the position of the frame
in the sentence. This should have shape (batch_size, num_tokens). Similar to verb_indicator,
but handles bert wordpiece tokenizer by cosnidering a frame only the first subtoken.
tags : `torch.LongTensor`, optional (default = `None`)
A torch tensor representing the sequence of integer gold class labels
of shape `(batch_size, num_tokens)`
frame_tags : torch.LongTensor, optional (default = None)
A torch tensor representing the gold frames
of shape ``(batch_size, num_tokens)``
metadata : `List[Dict[str, Any]]`, optional, (default = `None`)
metadata containg the original words in the sentence, the verb to compute the
frame for, and start offsets for converting wordpieces back to a sequence of words,
under 'words', 'verb' and 'offsets' keys, respectively.
# Returns
An output dictionary consisting of:
logits : `torch.FloatTensor`
A tensor of shape `(batch_size, num_tokens, tag_vocab_size)` representing
unnormalised log probabilities of the tag classes.
class_probabilities : `torch.FloatTensor`
A tensor of shape `(batch_size, num_tokens, tag_vocab_size)` representing
a distribution of the tag classes per word.
loss : `torch.FloatTensor`, optional
A scalar loss to be optimised.
"""
mask = get_text_field_mask(tokens)
input_ids = util.get_token_ids_from_text_field_tensors(tokens)
bert_embeddings, _ = self.transformer(
input_ids=input_ids,
token_type_ids=verb_indicator,
attention_mask=mask,
return_dict=False,
)
# extract embeddings
embedded_text_input = self.embedding_dropout(bert_embeddings)
frame_embeddings = embedded_text_input[frame_indicator == 1]
# get sizes
batch_size, sequence_length, _ = embedded_text_input.size()
# outputs
logits = self.tag_projection_layer(embedded_text_input)
frame_logits = self.frame_projection_layer(frame_embeddings)
reshaped_log_probs = logits.view(-1, self.num_classes)
class_probabilities = F.softmax(reshaped_log_probs, dim=-1).view(
[batch_size, sequence_length, self.num_classes])
frame_probabilities = F.softmax(frame_logits, dim=-1)
# We need to retain the mask in the output dictionary
# so that we can crop the sequences to remove padding
# when we do viterbi inference in self.make_output_human_readable.
output_dict = {
"logits": logits,
"frame_logits": frame_logits,
"class_probabilities": class_probabilities,
"frame_probabilities": frame_probabilities,
"mask": mask,
}
# We add in the offsets here so we can compute the un-wordpieced tags.
words, verbs, offsets = zip(*[(x["words"], x["verb"], x["offsets"])
for x in metadata])
lemmas = [l for x in metadata for l in x["lemmas"]]
output_dict["words"] = list(words)
output_dict["lemma"] = list(lemmas)
output_dict["verb"] = list(verbs)
output_dict["wordpiece_offsets"] = list(offsets)
if tags is not None:
# compute role loss
role_loss = sequence_cross_entropy_with_logits(
logits, tags, mask, label_smoothing=self._label_smoothing)
# compute frame loss
frame_tags_filtered = frame_tags[frame_indicator == 1]
frame_loss = self.frame_criterion(frame_logits,
frame_tags_filtered)
if not self.ignore_span_metric and self.span_metric is not None and not self.training:
batch_verb_indices = [
example_metadata["verb_index"]
for example_metadata in metadata
]
batch_sentences = [
example_metadata["words"] for example_metadata in metadata
]
# Get the BIO tags from make_output_human_readable()
batch_bio_predicted_tags = self.make_output_human_readable(
output_dict).pop("tags")
from allennlp_models.structured_prediction.models.srl import (
convert_bio_tags_to_conll_format, )
batch_conll_predicted_tags = [
convert_bio_tags_to_conll_format(tags)
for tags in batch_bio_predicted_tags
]
batch_bio_gold_tags = [
example_metadata["gold_tags"]
for example_metadata in metadata
]
batch_conll_gold_tags = [
convert_bio_tags_to_conll_format(tags)
for tags in batch_bio_gold_tags
]
self.span_metric(
batch_verb_indices,
batch_sentences,
batch_conll_predicted_tags,
batch_conll_gold_tags,
)
self.f1_frame_metric(frame_logits, frame_tags_filtered)
output_dict["frame_loss"] = frame_loss
output_dict["role_loss"] = role_loss
output_dict["loss"] = (role_loss + frame_loss) / 2
return output_dict
def decode_frames(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
# frame prediction
frame_probabilities = output_dict["frame_probabilities"]
frame_predictions = frame_probabilities.argmax(
dim=-1).cpu().data.numpy()
output_dict["frame_tags"] = [
self.vocab.get_token_from_index(f, namespace="frames_labels")
for f in frame_predictions
]
output_dict["frame_scores"] = [
fp[f] for f, fp in zip(frame_predictions, frame_probabilities)
]
return output_dict
@overrides
def make_output_human_readable(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
output_dict = self.decode_frames(output_dict)
output_dict = super().make_output_human_readable(output_dict)
return output_dict
@overrides
def get_metrics(self, reset: bool = False):
if self.ignore_span_metric:
# Return an empty dictionary if ignoring the
# span metric
return {}
else:
metric_dict = self.span_metric.get_metric(reset=reset)
frame_metric_dict = self.f1_frame_metric.get_metric(reset=reset)
# This can be a lot of metrics, as there are 3 per class.
# we only really care about the overall metrics, so we filter for them here.
metric_dict_filtered = {
x.split("-")[0] + "_role": y
for x, y in metric_dict.items() if "overall" in x
}
frame_metric_dict = {
x + "_frame": y
for x, y in frame_metric_dict.items()
}
return {**metric_dict_filtered, **frame_metric_dict}
def _get_label_tokens(self, namespace: str = "labels"):
return self.vocab.get_token_to_index_vocabulary(namespace).keys()
def _get_label_ids(self, namespace: str = "labels"):
return self.vocab.get_index_to_token_vocabulary(namespace).keys()
default_predictor = "transformer_srl"
class BasicClassifierF1(Model):
def __init__(
self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
seq2vec_encoder: Seq2VecEncoder,
seq2seq_encoder: Seq2SeqEncoder = None,
dropout: float = None,
num_labels: int = None,
label_namespace: str = "labels",
loss: str = None, # focal_loss
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None,
) -> None:
super().__init__(vocab, regularizer)
self._text_field_embedder = text_field_embedder
if seq2seq_encoder:
self._seq2seq_encoder = seq2seq_encoder
else:
self._seq2seq_encoder = None
self._seq2vec_encoder = seq2vec_encoder
self._classifier_input_dim = self._seq2vec_encoder.get_output_dim()
if dropout:
self._dropout = torch.nn.Dropout(dropout)
else:
self._dropout = None
self._label_namespace = label_namespace
if num_labels:
self._num_labels = num_labels
else:
self._num_labels = vocab.get_vocab_size(
namespace=self._label_namespace)
self._classification_layer = torch.nn.Linear(
self._classifier_input_dim, self._num_labels)
self._accuracy = CategoricalAccuracy()
if loss is None:
self._loss = torch.nn.CrossEntropyLoss()
elif loss == 'focal_loss':
self._loss = FocalLoss(alpha=0.25,
num_classes=self._num_labels) # focal loss
elif loss == 'cross_entropy_loss':
self._loss = torch.nn.CrossEntropyLoss()
else:
raise ValueError('wrong loss type')
self._f1_measure = FBetaMeasure()
initializer(self)
def forward(
self, # type: ignore
tokens: Dict[str, torch.LongTensor],
label: torch.IntTensor = None) -> Dict[str, torch.Tensor]:
embedded_text = self._text_field_embedder(tokens)
mask = get_text_field_mask(tokens).float()
if self._seq2seq_encoder:
embedded_text = self._seq2seq_encoder(embedded_text, mask=mask)
embedded_text = self._seq2vec_encoder(embedded_text, mask=mask)
if self._dropout:
embedded_text = self._dropout(embedded_text)
logits = self._classification_layer(embedded_text)
probs = F.softmax(logits, dim=-1)
output_dict = {"logits": logits, "probs": probs}
if label is not None:
loss = self._loss(logits, label.long().view(-1))
output_dict["loss"] = loss
self._accuracy(logits, label)
self._f1_measure(logits, label)
return output_dict
@overrides
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does a simple argmax over the probabilities, converts index to string label, and
add ``"label"`` key to the dictionary with the result.
"""
predictions = output_dict["probs"]
if predictions.dim() == 2:
predictions_list = [
predictions[i] for i in range(predictions.shape[0])
]
else:
predictions_list = [predictions]
labels = []
for prediction in predictions_list:
label_idx = prediction.argmax(dim=-1).item()
label_str = self.vocab.get_index_to_token_vocabulary(
self._label_namespace).get(label_idx, str(label_idx))
labels.append(label_str)
output_dict["label"] = labels
return output_dict
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
f1_dict = self._f1_measure.get_metric(reset)
output = {'accuracy': self._accuracy.get_metric(reset=reset)}
counter = 0
for precision, recall, fscore in zip(f1_dict['precision'],
f1_dict['recall'],
f1_dict['fscore']):
output[str(counter) + '_precision'] = precision
output[str(counter) + '_recall'] = recall
output[str(counter) + '_fscore'] = fscore
counter += 1
return output
class TransformerSrlDependency(Model):
"""
# Parameters
vocab : `Vocabulary`, required
A Vocabulary, required in order to compute sizes for input/output projections.
model : `Union[str, AutoModel]`, required.
A string describing the BERT model to load or an already constructed AutoModel.
initializer : `InitializerApplicator`, optional (default=`InitializerApplicator()`)
Used to initialize the model parameters.
label_smoothing : `float`, optional (default = `0.0`)
Whether or not to use label smoothing on the labels when computing cross entropy loss.
ignore_span_metric : `bool`, optional (default = `False`)
Whether to calculate span loss, which is irrelevant when predicting BIO for Open Information Extraction.
srl_eval_path : `str`, optional (default=`DEFAULT_SRL_EVAL_PATH`)
The path to the srl-eval.pl script. By default, will use the srl-eval.pl included with allennlp,
which is located at allennlp/tools/srl-eval.pl . If `None`, srl-eval.pl is not used.
"""
def __init__(
self,
vocab: Vocabulary,
model_name: Union[str, AutoModel],
embedding_dropout: float = 0.0,
initializer: InitializerApplicator = InitializerApplicator(),
label_smoothing: float = None,
ignore_span_metric: bool = False,
srl_eval_path: str = DEFAULT_SRL_EVAL_PATH,
restrict_frames: bool = False,
restrict_roles: bool = False,
**kwargs,
) -> None:
# bypass SrlBert constructor
Model.__init__(self, vocab, **kwargs)
self.lemma_frame_dict = load_lemma_frame(LEMMA_FRAME_PATH)
self.frame_role_dict = load_role_frame(FRAME_ROLE_PATH)
self.restrict_frames = restrict_frames
self.restrict_roles = restrict_roles
if isinstance(model_name, str):
self.transformer = AutoModel.from_pretrained(model_name)
else:
self.transformer = model_name
# loss
self.role_criterion = nn.CrossEntropyLoss(ignore_index=0)
self.frame_criterion = nn.CrossEntropyLoss()
# number of classes
self.num_classes = self.vocab.get_vocab_size("labels")
self.frame_num_classes = self.vocab.get_vocab_size("frames_labels")
# metrics
role_set = self.vocab.get_token_to_index_vocabulary("labels")
role_set_filter = [v for k, v in role_set.items() if k != "O"]
self.f1_role_metric = FBetaMeasure(average="micro", labels=role_set_filter)
self.f1_frame_metric = FBetaMeasure(average="micro")
# output layer
self.tag_projection_layer = nn.Linear(self.transformer.config.hidden_size, self.num_classes)
self.frame_projection_layer = nn.Linear(
self.transformer.config.hidden_size, self.frame_num_classes
)
self.embedding_dropout = nn.Dropout(p=embedding_dropout)
self._label_smoothing = label_smoothing
initializer(self)
def forward( # type: ignore
self,
tokens: TextFieldTensors,
verb_indicator: torch.Tensor,
frame_indicator: torch.Tensor,
metadata: List[Any],
tags: torch.LongTensor = None,
frame_tags: torch.LongTensor = None,
):
"""
# Parameters
tokens : `TextFieldTensors`, required
The output of `TextField.as_array()`, which should typically be passed directly to a
`TextFieldEmbedder`. For this model, this must be a `SingleIdTokenIndexer` which
indexes wordpieces from the BERT vocabulary.
verb_indicator: `torch.LongTensor`, required.
An integer `SequenceFeatureField` representation of the position of the verb
in the sentence. This should have shape (batch_size, num_tokens) and importantly, can be
all zeros, in the case that the sentence has no verbal predicate.
tags : `torch.LongTensor`, optional (default = `None`)
A torch tensor representing the sequence of integer gold class labels
of shape `(batch_size, num_tokens)`
frame_tags : torch.LongTensor, optional (default = None)
A torch tensor representing the gold frames
of shape ``(batch_size, num_tokens)``
metadata : `List[Dict[str, Any]]`, optional, (default = `None`)
metadata containg the original words in the sentence, the verb to compute the
frame for, and start offsets for converting wordpieces back to a sequence of words,
under 'words', 'verb' and 'offsets' keys, respectively.
# Returns
An output dictionary consisting of:
logits : `torch.FloatTensor`
A tensor of shape `(batch_size, num_tokens, tag_vocab_size)` representing
unnormalised log probabilities of the tag classes.
class_probabilities : `torch.FloatTensor`
A tensor of shape `(batch_size, num_tokens, tag_vocab_size)` representing
a distribution of the tag classes per word.
loss : `torch.FloatTensor`, optional
A scalar loss to be optimised.
"""
mask = get_text_field_mask(tokens)
bert_embeddings, _ = self.transformer(
input_ids=util.get_token_ids_from_text_field_tensors(tokens),
token_type_ids=verb_indicator,
attention_mask=mask,
)
# extract embeddings
embedded_text_input = self.embedding_dropout(bert_embeddings)
frame_embeddings = embedded_text_input[frame_indicator == 1]
# get sizes
batch_size, sequence_length, _ = embedded_text_input.size()
# outputs
logits = self.tag_projection_layer(embedded_text_input)
frame_logits = self.frame_projection_layer(frame_embeddings)
reshaped_log_probs = logits.view(-1, self.num_classes)
role_probabilities = F.softmax(reshaped_log_probs, dim=-1).view(
[batch_size, sequence_length, self.num_classes]
)
frame_probabilities = F.softmax(frame_logits, dim=-1)
# We need to retain the mask in the output dictionary
# so that we can crop the sequences to remove padding
# when we do viterbi inference in self.make_output_human_readable.
output_dict = {
"logits": logits,
"frame_logits": frame_logits,
"role_probabilities": role_probabilities,
"frame_probabilities": frame_probabilities,
"mask": mask,
}
# We add in the offsets here so we can compute the un-wordpieced tags.
words, verbs = zip(*[(x["words"], x["verb"]) for x in metadata])
lemmas = [l for x in metadata for l in x["lemmas"]]
output_dict["words"] = list(words)
output_dict["verb"] = list(verbs)
output_dict["lemma"] = list(lemmas)
if tags is not None:
# compute role loss
# role_loss = sequence_cross_entropy_with_logits(
# logits, tags, mask, label_smoothing=self._label_smoothing
# )
role_loss = self.role_criterion(logits.view(-1, self.num_classes), tags.view(-1))
# compute frame loss
frame_tags_filtered = frame_tags[frame_indicator == 1]
frame_loss = self.frame_criterion(frame_logits, frame_tags_filtered)
self.f1_role_metric(role_probabilities, tags)
self.f1_frame_metric(frame_logits, frame_tags_filtered)
output_dict["frame_loss"] = frame_loss
output_dict["role_loss"] = role_loss
output_dict["loss"] = (role_loss + frame_loss) / 2
return output_dict
def decode_frames(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
# frame prediction
frame_probabilities = output_dict["frame_probabilities"]
if self.restrict:
frame_probabilities = frame_probabilities.cpu().data.numpy()
lemmas = output_dict["lemma"]
candidate_labels = [self.lemma_frame_dict.get(l, []) for l in lemmas]
# clear candidates from unknowns
label_set = set(k for k in self._get_label_tokens("frames_labels"))
candidate_labels_ids = [
[
self.vocab.get_token_index(l, namespace="frames_labels")
for l in cl
if l in label_set
]
for cl in candidate_labels
]
frame_predictions = []
for cl, fp in zip(candidate_labels_ids, frame_probabilities):
# restrict candidates from verbatlas inventory
fp_candidates = np.take(fp, cl)
if fp_candidates.size > 0:
frame_predictions.append(cl[fp_candidates.argmax(axis=-1)])
else:
frame_predictions.append(fp.argmax(axis=-1))
else:
frame_predictions = frame_probabilities.argmax(dim=-1).cpu().data.numpy()
output_dict["frame_tags"] = [
self.vocab.get_token_from_index(f, namespace="frames_labels") for f in frame_predictions
]
output_dict["frame_scores"] = [
fp[f] for f, fp in zip(frame_predictions, frame_probabilities)
]
return output_dict
@overrides
def make_output_human_readable(
self, output_dict: Dict[str, torch.Tensor], restrict: bool = True
) -> Dict[str, torch.Tensor]:
output_dict = self.decode_frames(output_dict)
# if self.restrict:
# output_dict = self._mask_args(output_dict)
# output_dict = super().make_output_human_readable(output_dict)
roles_probabilities = output_dict["role_probabilities"]
roles_predictions = roles_probabilities.argmax(dim=-1).cpu().data.numpy()
output_dict["tags"] = [
[self.vocab.get_token_from_index(r, namespace="labels") for r in roles]
for roles in roles_predictions
]
return output_dict
def _mask_args(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
class_probs = output_dict["class_probabilities"]
device = get_device_of(class_probs)
lemmas = output_dict["lemma"]
frames = output_dict["frame_tags"]
candidate_mask = torch.ones_like(class_probs, dtype=torch.bool).to(device)
for i, (l, f) in enumerate(zip(lemmas, frames)):
candidates = self.frame_role_dict.get((l, f), [])
if candidates:
canidate_ids = [
self.vocab.get_token_index(r, namespace="labels") for r in candidates
]
canidate_ids = torch.tensor(canidate_ids).to(device)
canidate_ids = canidate_ids.repeat(candidate_mask.shape[1], 1)
candidate_mask[i].scatter_(1, canidate_ids, False)
else:
candidate_mask[i].fill_(False)
class_probs.masked_fill_(candidate_mask, 0)
return output_dict
@overrides
def get_metrics(self, reset: bool = False):
role_metric_dict = self.f1_role_metric.get_metric(reset=reset)
frame_metric_dict = self.f1_frame_metric.get_metric(reset=reset)
# This can be a lot of metrics, as there are 3 per class.
# we only really care about the overall metrics, so we filter for them here.
# metric_dict_filtered = {
# x.split("-")[0] + "_role": y for x, y in metric_dict.items() if "overall" in x
# }
metric_dict = {
"f1_role": role_metric_dict["fscore"],
"f1_frame": frame_metric_dict["fscore"],
}
return metric_dict
def _get_label_tokens(self, namespace: str = "labels"):
return self.vocab.get_token_to_index_vocabulary(namespace).keys()
def _get_label_ids(self, namespace: str = "labels"):
return self.vocab.get_index_to_token_vocabulary(namespace).keys()
default_predictor = "transformer_srl"
class AlbertClassifierF1(Model):
"""
文本分类模型。
"""
def __init__(
self,
vocab: Vocabulary,
vocab_path: str = None,
config_path: str = None,
model_path: str = None,
dropout: float = None,
label_namespace: str = "labels",
num_labels: int = None,
loss: str = None, # focal_loss
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None,
) -> None:
super().__init__(vocab, regularizer)
config, tokenizer, model = get_albert_total(config_path, vocab_path,
model_path)
self._bert = model
if dropout:
self._dropout = torch.nn.Dropout(dropout)
else:
self._dropout = None
self._label_namespace = label_namespace
if num_labels:
self._num_labels = num_labels
else:
self._num_labels = vocab.get_vocab_size(
namespace=self._label_namespace)
self._classification_layer = torch.nn.Linear(config.hidden_size,
config.num_labels)
self._accuracy = CategoricalAccuracy()
if loss is None:
self._loss = torch.nn.CrossEntropyLoss()
elif loss == 'focal_loss':
self._loss = FocalLoss(alpha=0.25,
num_classes=self._num_labels) # focal loss
elif loss == 'cross_entropy_loss':
self._loss = torch.nn.CrossEntropyLoss()
else:
raise ValueError('wrong loss type')
self._f1_measure = FBetaMeasure()
initializer(self)
def forward(
self, # type: ignore
tokens: Dict[str, torch.LongTensor],
label: torch.IntTensor = None) -> Dict[str, torch.Tensor]:
# print(tokens)
outputs = self._bert(tokens['bert'],
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None)
if self._dropout:
embedded_text = self._dropout(outputs[1])
logits = self._classification_layer(embedded_text)
probs = torch.nn.functional.softmax(logits, dim=-1)
output_dict = {"logits": logits, "probs": probs}
if label is not None:
loss = self._loss(logits, label.long().view(-1))
output_dict["loss"] = loss
self._accuracy(logits, label)
self._f1_measure(logits, label)
return output_dict
@overrides
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does a simple argmax over the probabilities, converts index to string label, and
add ``"label"`` key to the dictionary with the result.
"""
predictions = output_dict["probs"]
if predictions.dim() == 2:
predictions_list = [
predictions[i] for i in range(predictions.shape[0])
]
else:
predictions_list = [predictions]
classes = []
for prediction in predictions_list:
label_idx = prediction.argmax(dim=-1).item()
label_str = self.vocab.get_index_to_token_vocabulary(
self._label_namespace).get(label_idx, str(label_idx))
classes.append(label_str)
output_dict["label"] = classes
return output_dict
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
f1_dict = self._f1_measure.get_metric(reset)
output = {}
output['accuracy'] = self._accuracy.get_metric(reset=reset)
counter = 0
for precision, recall, fscore in zip(f1_dict['precision'],
f1_dict['recall'],
f1_dict['fscore']):
output[str(counter) + '_precision'] = precision
output[str(counter) + '_recall'] = recall
output[str(counter) + '_fscore'] = fscore
counter += 1
return output
class LieDetector(Model):
def __init__(self,
vocab: Vocabulary,
embedder: TextFieldEmbedder,
encoder: Seq2VecEncoder,
posclass_weight: Optional[float] = 1,
use_power: Optional[bool] = False,
dropout: Optional[float] = 0) -> None:
super().__init__(vocab)
self.embedder = embedder
self.encoder = encoder
if use_power:
self.classifier = torch.nn.Linear(
in_features=encoder.get_output_dim() + 1,
out_features=vocab.get_vocab_size('labels')
)
else:
self.classifier = torch.nn.Linear(
in_features=encoder.get_output_dim(),
out_features=vocab.get_vocab_size('labels')
)
self.use_power = use_power
self.f1_lie = F1Measure(vocab.get_token_index('False', 'labels'))
self.f1_truth = F1Measure(vocab.get_token_index('True', 'labels'))
self.micro_f1 = FBetaMeasure(average='micro')
self.macro_f1 = FBetaMeasure(average='macro')
weights = [1,1]
weights[vocab.get_token_index('False', 'labels')] = posclass_weight
self.loss = torch.nn.CrossEntropyLoss(weight = torch.Tensor(weights))
self.dropout = torch.nn.Dropout(dropout)
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
lie_precision, lie_recall, lie_fscore = self.f1_lie.get_metric(reset)
truth_precision, truth_recall, truth_fscore = self.f1_truth.get_metric(reset)
micro_metrics = self.micro_f1.get_metric(reset)
macro_metrics = self.macro_f1.get_metric(reset)
return {
'truth_precision': truth_precision,
'truth_recall': truth_recall,
'truth_fscore': truth_fscore,
'lie_precision': lie_precision,
'lie_recall': lie_recall,
'lie_fscore': lie_fscore,
'macro_fscore': macro_metrics['fscore'],
'micro_precision':micro_metrics['precision'],
'micro_recall':micro_metrics['recall'],
'micro_fscore':micro_metrics['fscore']
}
def forward(self,
message: Dict[str, torch.Tensor],
score_delta: torch.Tensor,
label: torch.Tensor = None) -> Dict[str, torch.Tensor]:
mask = get_text_field_mask(message)
embedded = self.embedder(message)
#embedded = self._dropout(embedded)
encoded = self.encoder(embedded, mask)
if self.use_power:
encoded = torch.cat((score_delta.view(-1,1),encoded),1)
encoded = self.dropout(encoded)
classified = self.classifier(encoded)
output = {}
output["logits"] = classified
if label is not None:
self.f1_lie(classified, label)
self.f1_truth(classified, label)
self.micro_f1(classified, label)
self.macro_f1(classified, label)
output["loss"] = self.loss(classified, label)
return output