def calculate_class_metrics(y_true: List[List[int]], y_pred: List[List[int]],
labels: Dictionary) -> List[Metric]:
"""
Calculates the metrics for the individual classes for the given predictions.
The labels should be converted into a one-hot-list.
:param y_true: list of true labels
:param y_pred: list of predicted labels
:param labels: the label dictionary
:return: the metrics for every class
"""
metrics = []
for label in labels.get_items():
metric = Metric(label)
label_idx = labels.get_idx_for_item(label)
for true, pred in zip(y_true, y_pred):
if true[label_idx] == 1 and pred[label_idx] == 1:
metric.tp()
elif true[label_idx] == 1 and pred[label_idx] == 0:
metric.fn()
elif true[label_idx] == 0 and pred[label_idx] == 1:
metric.fp()
elif true[label_idx] == 0 and pred[label_idx] == 0:
metric.tn()
metrics.append(metric)
return metrics
def _determine_if_span_prediction_problem(self,
dictionary: Dictionary) -> bool:
for item in dictionary.get_items():
if item.startswith("B-") or item.startswith(
"S-") or item.startswith("I-"):
return True
return False
def convert_labels_to_one_hot(label_list: List[List[str]], label_dict: Dictionary) -> List[List[int]]:
"""
Convert list of labels (strings) to a one hot list.
:param label_list: list of labels
:param label_dict: label dictionary
:return: converted label list
"""
return [[1 if l in labels else 0 for l in label_dict.get_items()] for labels in label_list]
def __init__(
self,
word_embeddings: flair.embeddings.TokenEmbeddings,
label_dictionary: Dictionary,
pooling_operation: str = "first&last",
label_type: str = "nel",
dropout: float = 0.5,
skip_unk_probability: Optional[float] = None,
**classifierargs,
):
"""
Initializes an EntityLinker
:param word_embeddings: embeddings used to embed the words/sentences
:param label_dictionary: dictionary that gives ids to all classes. Should contain <unk>
:param pooling_operation: either 'average', 'first', 'last' or 'first&last'. Specifies the way of how text representations of entity mentions (with more than one word) are handled.
E.g. 'average' means that as text representation we take the average of the embeddings of the words in the mention. 'first&last' concatenates
the embedding of the first and the embedding of the last word.
:param label_type: name of the label you use.
"""
super(EntityLinker, self).__init__(
label_dictionary=label_dictionary,
final_embedding_size=word_embeddings.embedding_length *
2 if pooling_operation == "first&last" else
word_embeddings.embedding_length,
**classifierargs,
)
self.word_embeddings = word_embeddings
self.pooling_operation = pooling_operation
self._label_type = label_type
self.skip_unk_probability = skip_unk_probability
if self.skip_unk_probability:
self.known_entities = label_dictionary.get_items()
# ----- Dropout parameters -----
# dropouts
self.use_dropout: float = dropout
if dropout > 0.0:
self.dropout = torch.nn.Dropout(dropout)
cases = {
"average": self.emb_mean,
"first": self.emb_first,
"last": self.emb_last,
"first&last": self.emb_firstAndLast,
}
if pooling_operation not in cases:
raise KeyError(
'pooling_operation has to be one of "average", "first", "last" or "first&last"'
)
self.aggregated_embedding = cases[pooling_operation]
self.to(flair.device)
def test_dictionary_get_items_without_unk():
dictionary = Dictionary(add_unk=False)
dictionary.add_item('class_1')
dictionary.add_item('class_2')
dictionary.add_item('class_3')
items = dictionary.get_items()
assert (3 == len(items))
assert ('class_1' == items[0])
assert ('class_2' == items[1])
assert ('class_3' == items[2])
def test_dictionary_save_and_load():
dictionary = Dictionary(add_unk=False)
dictionary.add_item('class_1')
dictionary.add_item('class_2')
dictionary.add_item('class_3')
file_path = 'dictionary.txt'
dictionary.save(file_path)
loaded_dictionary = dictionary.load_from_file(file_path)
assert (len(dictionary) == len(loaded_dictionary))
assert (len(dictionary.get_items()) == len(loaded_dictionary.get_items()))
os.remove(file_path)
def test_dictionary_get_items_with_unk():
dictionary = Dictionary()
dictionary.add_item('class_1')
dictionary.add_item('class_2')
dictionary.add_item('class_3')
items = dictionary.get_items()
assert (4 == len(items))
assert ('<unk>' == items[0])
assert ('class_1' == items[1])
assert ('class_2' == items[2])
assert ('class_3' == items[3])
def predict_zero_shot(self,
sentences: Union[List[Sentence], Sentence],
candidate_label_set: Union[List[str], Set[str], str],
multi_label: bool = True):
"""
Method to make zero shot predictions from the TARS model
:param sentences: input sentence objects to classify
:param candidate_label_set: set of candidate labels
:param multi_label: indicates whether multi-label or single class prediction. Defaults to True.
"""
# check if candidate_label_set is empty
if candidate_label_set is None or len(candidate_label_set) == 0:
log.warning("Provided candidate_label_set is empty")
return
label_dictionary = Dictionary(add_unk=False)
label_dictionary.multi_label = multi_label
# make list if only one candidate label is passed
if isinstance(candidate_label_set, str):
candidate_label_set = {candidate_label_set}
# if list is passed, convert to set
if not isinstance(candidate_label_set, set):
candidate_label_set = set(candidate_label_set)
for label in candidate_label_set:
label_dictionary.add_item(label)
# note current task
existing_current_task = self._current_task
# create a temporary task
self.add_and_switch_to_new_task("ZeroShot",
label_dictionary,
'-'.join(label_dictionary.get_items()))
try:
# make zero shot predictions
self.predict(sentences)
finally:
# switch to the pre-existing task
self.switch_to_task(existing_current_task)
self._drop_task("ZeroShot")
return
def __init__(
self,
embeddings: TokenEmbeddings,
tag_dictionary: Dictionary,
tag_type: str,
use_rnn: bool = True,
rnn: Optional[torch.nn.RNN] = None,
rnn_type: str = "LSTM",
tag_format: str = "BIOES",
hidden_size: int = 256,
rnn_layers: int = 1,
bidirectional: bool = True,
use_crf: bool = True,
reproject_embeddings: bool = True,
dropout: float = 0.0,
word_dropout: float = 0.05,
locked_dropout: float = 0.5,
train_initial_hidden_state: bool = False,
loss_weights: Dict[str, float] = None,
init_from_state_dict: bool = False,
allow_unk_predictions: bool = False,
):
"""
Sequence Tagger class for predicting labels for single tokens. Can be parameterized by several attributes.
In case of multitask learning, pass shared embeddings or shared rnn into respective attributes.
:param embeddings: Embeddings to use during training and prediction
:param tag_dictionary: Dictionary containing all tags from corpus which can be predicted
:param tag_type: type of tag which is going to be predicted in case a corpus has multiple annotations
:param use_rnn: If true, use a RNN, else Linear layer.
:param rnn: (Optional) Takes a torch.nn.Module as parameter by which you can pass a shared RNN between
different tasks.
:param rnn_type: Specifies the RNN type to use, default is 'LSTM', can choose between 'GRU' and 'RNN' as well.
:param hidden_size: Hidden size of RNN layer
:param rnn_layers: number of RNN layers
:param bidirectional: If True, RNN becomes bidirectional
:param use_crf: If True, use a Conditional Random Field for prediction, else linear map to tag space.
:param reproject_embeddings: If True, add a linear layer on top of embeddings, if you want to imitate
fine tune non-trainable embeddings.
:param dropout: If > 0, then use dropout.
:param word_dropout: If > 0, then use word dropout.
:param locked_dropout: If > 0, then use locked dropout.
:param train_initial_hidden_state: if True, trains initial hidden state of RNN
:param loss_weights: Dictionary of weights for labels for the loss function
(if any label's weight is unspecified it will default to 1.0)
:param init_from_state_dict: Indicator whether we are loading a model from state dict
since we need to transform previous models' weights into CRF instance weights
"""
super(SequenceTagger, self).__init__()
# ----- Create the internal tag dictionary -----
self.tag_type = tag_type
self.tag_format = tag_format.upper()
if init_from_state_dict:
self.label_dictionary = tag_dictionary
else:
# span-labels need special encoding (BIO or BIOES)
if tag_dictionary.span_labels:
# the big question is whether the label dictionary should contain an UNK or not
# without UNK, we cannot evaluate on data that contains labels not seen in test
# with UNK, the model learns less well if there are no UNK examples
self.label_dictionary = Dictionary(
add_unk=allow_unk_predictions)
for label in tag_dictionary.get_items():
if label == "<unk>":
continue
self.label_dictionary.add_item("O")
if tag_format == "BIOES":
self.label_dictionary.add_item("S-" + label)
self.label_dictionary.add_item("B-" + label)
self.label_dictionary.add_item("E-" + label)
self.label_dictionary.add_item("I-" + label)
if tag_format == "BIO":
self.label_dictionary.add_item("B-" + label)
self.label_dictionary.add_item("I-" + label)
else:
self.label_dictionary = tag_dictionary
# is this a span prediction problem?
self.predict_spans = self._determine_if_span_prediction_problem(
self.label_dictionary)
self.tagset_size = len(self.label_dictionary)
log.info(f"SequenceTagger predicts: {self.label_dictionary}")
# ----- Embeddings -----
self.embeddings = embeddings
embedding_dim: int = embeddings.embedding_length
# ----- Initial loss weights parameters -----
self.weight_dict = loss_weights
self.loss_weights = self._init_loss_weights(
loss_weights) if loss_weights else None
# ----- RNN specific parameters -----
self.use_rnn = use_rnn
self.rnn_type = rnn_type if not rnn else rnn._get_name()
self.hidden_size = hidden_size if not rnn else rnn.hidden_size
self.rnn_layers = rnn_layers if not rnn else rnn.num_layers
self.bidirectional = bidirectional if not rnn else rnn.bidirectional
# ----- Conditional Random Field parameters -----
self.use_crf = use_crf
# Previously trained models have been trained without an explicit CRF, thus it is required to check
# whether we are loading a model from state dict in order to skip or add START and STOP token
if use_crf and not init_from_state_dict and not self.label_dictionary.start_stop_tags_are_set(
):
self.label_dictionary.set_start_stop_tags()
self.tagset_size += 2
# ----- Dropout parameters -----
# dropouts
self.use_dropout: float = dropout
self.use_word_dropout: float = word_dropout
self.use_locked_dropout: float = locked_dropout
if dropout > 0.0:
self.dropout = torch.nn.Dropout(dropout)
if word_dropout > 0.0:
self.word_dropout = flair.nn.WordDropout(word_dropout)
if locked_dropout > 0.0:
self.locked_dropout = flair.nn.LockedDropout(locked_dropout)
# ----- Model layers -----
self.reproject_embeddings = reproject_embeddings
if self.reproject_embeddings:
self.embedding2nn = torch.nn.Linear(embedding_dim, embedding_dim)
# ----- RNN layer -----
if use_rnn:
# If shared RNN provided, else create one for model
self.rnn: torch.nn.RNN = (rnn if rnn else self.RNN(
rnn_type,
rnn_layers,
hidden_size,
bidirectional,
rnn_input_dim=embedding_dim,
))
num_directions = 2 if self.bidirectional else 1
hidden_output_dim = self.rnn.hidden_size * num_directions
# Whether to train initial hidden state
self.train_initial_hidden_state = train_initial_hidden_state
if self.train_initial_hidden_state:
(
self.hs_initializer,
self.lstm_init_h,
self.lstm_init_c,
) = self._init_initial_hidden_state(num_directions)
# final linear map to tag space
self.linear = torch.nn.Linear(hidden_output_dim,
len(self.label_dictionary))
else:
self.linear = torch.nn.Linear(embedding_dim,
len(self.label_dictionary))
# the loss function is Viterbi if using CRF, else regular Cross Entropy Loss
self.loss_function = (ViterbiLoss(self.label_dictionary)
if use_crf else torch.nn.CrossEntropyLoss(
weight=self.loss_weights, reduction="sum"))
# if using CRF, we also require a CRF and a Viterbi decoder
if use_crf:
self.crf = CRF(self.label_dictionary, self.tagset_size,
init_from_state_dict)
self.viterbi_decoder = ViterbiDecoder(self.label_dictionary)
self.to(flair.device)
class SequenceTagger(flair.nn.Classifier[Sentence]):
def __init__(
self,
embeddings: TokenEmbeddings,
tag_dictionary: Dictionary,
tag_type: str,
use_rnn: bool = True,
rnn: Optional[torch.nn.RNN] = None,
rnn_type: str = "LSTM",
tag_format: str = "BIOES",
hidden_size: int = 256,
rnn_layers: int = 1,
bidirectional: bool = True,
use_crf: bool = True,
reproject_embeddings: bool = True,
dropout: float = 0.0,
word_dropout: float = 0.05,
locked_dropout: float = 0.5,
train_initial_hidden_state: bool = False,
loss_weights: Dict[str, float] = None,
init_from_state_dict: bool = False,
allow_unk_predictions: bool = False,
):
"""
Sequence Tagger class for predicting labels for single tokens. Can be parameterized by several attributes.
In case of multitask learning, pass shared embeddings or shared rnn into respective attributes.
:param embeddings: Embeddings to use during training and prediction
:param tag_dictionary: Dictionary containing all tags from corpus which can be predicted
:param tag_type: type of tag which is going to be predicted in case a corpus has multiple annotations
:param use_rnn: If true, use a RNN, else Linear layer.
:param rnn: (Optional) Takes a torch.nn.Module as parameter by which you can pass a shared RNN between
different tasks.
:param rnn_type: Specifies the RNN type to use, default is 'LSTM', can choose between 'GRU' and 'RNN' as well.
:param hidden_size: Hidden size of RNN layer
:param rnn_layers: number of RNN layers
:param bidirectional: If True, RNN becomes bidirectional
:param use_crf: If True, use a Conditional Random Field for prediction, else linear map to tag space.
:param reproject_embeddings: If True, add a linear layer on top of embeddings, if you want to imitate
fine tune non-trainable embeddings.
:param dropout: If > 0, then use dropout.
:param word_dropout: If > 0, then use word dropout.
:param locked_dropout: If > 0, then use locked dropout.
:param train_initial_hidden_state: if True, trains initial hidden state of RNN
:param loss_weights: Dictionary of weights for labels for the loss function
(if any label's weight is unspecified it will default to 1.0)
:param init_from_state_dict: Indicator whether we are loading a model from state dict
since we need to transform previous models' weights into CRF instance weights
"""
super(SequenceTagger, self).__init__()
# ----- Create the internal tag dictionary -----
self.tag_type = tag_type
self.tag_format = tag_format.upper()
if init_from_state_dict:
self.label_dictionary = tag_dictionary
else:
# span-labels need special encoding (BIO or BIOES)
if tag_dictionary.span_labels:
# the big question is whether the label dictionary should contain an UNK or not
# without UNK, we cannot evaluate on data that contains labels not seen in test
# with UNK, the model learns less well if there are no UNK examples
self.label_dictionary = Dictionary(
add_unk=allow_unk_predictions)
for label in tag_dictionary.get_items():
if label == "<unk>":
continue
self.label_dictionary.add_item("O")
if tag_format == "BIOES":
self.label_dictionary.add_item("S-" + label)
self.label_dictionary.add_item("B-" + label)
self.label_dictionary.add_item("E-" + label)
self.label_dictionary.add_item("I-" + label)
if tag_format == "BIO":
self.label_dictionary.add_item("B-" + label)
self.label_dictionary.add_item("I-" + label)
else:
self.label_dictionary = tag_dictionary
# is this a span prediction problem?
self.predict_spans = self._determine_if_span_prediction_problem(
self.label_dictionary)
self.tagset_size = len(self.label_dictionary)
log.info(f"SequenceTagger predicts: {self.label_dictionary}")
# ----- Embeddings -----
self.embeddings = embeddings
embedding_dim: int = embeddings.embedding_length
# ----- Initial loss weights parameters -----
self.weight_dict = loss_weights
self.loss_weights = self._init_loss_weights(
loss_weights) if loss_weights else None
# ----- RNN specific parameters -----
self.use_rnn = use_rnn
self.rnn_type = rnn_type if not rnn else rnn._get_name()
self.hidden_size = hidden_size if not rnn else rnn.hidden_size
self.rnn_layers = rnn_layers if not rnn else rnn.num_layers
self.bidirectional = bidirectional if not rnn else rnn.bidirectional
# ----- Conditional Random Field parameters -----
self.use_crf = use_crf
# Previously trained models have been trained without an explicit CRF, thus it is required to check
# whether we are loading a model from state dict in order to skip or add START and STOP token
if use_crf and not init_from_state_dict and not self.label_dictionary.start_stop_tags_are_set(
):
self.label_dictionary.set_start_stop_tags()
self.tagset_size += 2
# ----- Dropout parameters -----
# dropouts
self.use_dropout: float = dropout
self.use_word_dropout: float = word_dropout
self.use_locked_dropout: float = locked_dropout
if dropout > 0.0:
self.dropout = torch.nn.Dropout(dropout)
if word_dropout > 0.0:
self.word_dropout = flair.nn.WordDropout(word_dropout)
if locked_dropout > 0.0:
self.locked_dropout = flair.nn.LockedDropout(locked_dropout)
# ----- Model layers -----
self.reproject_embeddings = reproject_embeddings
if self.reproject_embeddings:
self.embedding2nn = torch.nn.Linear(embedding_dim, embedding_dim)
# ----- RNN layer -----
if use_rnn:
# If shared RNN provided, else create one for model
self.rnn: torch.nn.RNN = (rnn if rnn else self.RNN(
rnn_type,
rnn_layers,
hidden_size,
bidirectional,
rnn_input_dim=embedding_dim,
))
num_directions = 2 if self.bidirectional else 1
hidden_output_dim = self.rnn.hidden_size * num_directions
# Whether to train initial hidden state
self.train_initial_hidden_state = train_initial_hidden_state
if self.train_initial_hidden_state:
(
self.hs_initializer,
self.lstm_init_h,
self.lstm_init_c,
) = self._init_initial_hidden_state(num_directions)
# final linear map to tag space
self.linear = torch.nn.Linear(hidden_output_dim,
len(self.label_dictionary))
else:
self.linear = torch.nn.Linear(embedding_dim,
len(self.label_dictionary))
# the loss function is Viterbi if using CRF, else regular Cross Entropy Loss
self.loss_function = (ViterbiLoss(self.label_dictionary)
if use_crf else torch.nn.CrossEntropyLoss(
weight=self.loss_weights, reduction="sum"))
# if using CRF, we also require a CRF and a Viterbi decoder
if use_crf:
self.crf = CRF(self.label_dictionary, self.tagset_size,
init_from_state_dict)
self.viterbi_decoder = ViterbiDecoder(self.label_dictionary)
self.to(flair.device)
@property
def label_type(self):
return self.tag_type
def _init_loss_weights(self, loss_weights: Dict[str,
float]) -> torch.Tensor:
"""
Intializes the loss weights based on given dictionary:
:param loss_weights: dictionary - contains loss weights
"""
n_classes = len(self.label_dictionary)
weight_list = [1.0 for _ in range(n_classes)]
for i, tag in enumerate(self.label_dictionary.get_items()):
if tag in loss_weights.keys():
weight_list[i] = loss_weights[tag]
return torch.tensor(weight_list).to(flair.device)
def _init_initial_hidden_state(self, num_directions: int):
"""
Intializes hidden states given the number of directions in RNN.
:param num_directions: Number of directions in RNN.
"""
hs_initializer = torch.nn.init.xavier_normal_
lstm_init_h = torch.nn.Parameter(
torch.randn(self.rnn.num_layers * num_directions,
self.hidden_size),
requires_grad=True,
)
lstm_init_c = torch.nn.Parameter(
torch.randn(self.rnn.num_layers * num_directions,
self.hidden_size),
requires_grad=True,
)
return hs_initializer, lstm_init_h, lstm_init_c
@staticmethod
def RNN(
rnn_type: str,
rnn_layers: int,
hidden_size: int,
bidirectional: bool,
rnn_input_dim: int,
) -> torch.nn.RNN:
"""
Static wrapper function returning an RNN instance from PyTorch
:param rnn_type: Type of RNN from torch.nn
:param rnn_layers: number of layers to include
:param hidden_size: hidden size of RNN cell
:param bidirectional: If True, RNN cell is bidirectional
:param rnn_input_dim: Input dimension to RNN cell
"""
if rnn_type in ["LSTM", "GRU", "RNN"]:
RNN = getattr(torch.nn, rnn_type)(
rnn_input_dim,
hidden_size,
num_layers=rnn_layers,
dropout=0.0 if rnn_layers == 1 else 0.5,
bidirectional=bidirectional,
batch_first=True,
)
else:
raise Exception(
f"Unknown RNN type: {rnn_type}. Please use either LSTM, GRU or RNN."
)
return RNN
def forward_loss(
self, sentences: Union[List[Sentence],
Sentence]) -> Tuple[torch.Tensor, int]:
# if there are no sentences, there is no loss
if len(sentences) == 0:
return torch.tensor(0.0,
dtype=torch.float,
device=flair.device,
requires_grad=True), 0
# forward pass to get scores
scores, gold_labels = self.forward(sentences) # type: ignore
# calculate loss given scores and labels
return self._calculate_loss(scores, gold_labels)
def forward(self, sentences: Union[List[Sentence], Sentence]):
"""
Forward propagation through network. Returns gold labels of batch in addition.
:param sentences: Batch of current sentences
"""
if not isinstance(sentences, list):
sentences = [sentences]
self.embeddings.embed(sentences)
# make a zero-padded tensor for the whole sentence
lengths, sentence_tensor = self._make_padded_tensor_for_batch(
sentences)
# sort tensor in decreasing order based on lengths of sentences in batch
sorted_lengths, length_indices = lengths.sort(dim=0, descending=True)
sentences = [sentences[i] for i in length_indices]
sentence_tensor = sentence_tensor[length_indices]
# ----- Forward Propagation -----
if self.use_dropout:
sentence_tensor = self.dropout(sentence_tensor)
if self.use_word_dropout:
sentence_tensor = self.word_dropout(sentence_tensor)
if self.use_locked_dropout:
sentence_tensor = self.locked_dropout(sentence_tensor)
if self.reproject_embeddings:
sentence_tensor = self.embedding2nn(sentence_tensor)
if self.use_rnn:
packed = pack_padded_sequence(sentence_tensor,
sorted_lengths,
batch_first=True,
enforce_sorted=False)
rnn_output, hidden = self.rnn(packed)
sentence_tensor, output_lengths = pad_packed_sequence(
rnn_output, batch_first=True)
if self.use_dropout:
sentence_tensor = self.dropout(sentence_tensor)
if self.use_locked_dropout:
sentence_tensor = self.locked_dropout(sentence_tensor)
# linear map to tag space
features = self.linear(sentence_tensor)
# Depending on whether we are using CRF or a linear layer, scores is either:
# -- A tensor of shape (batch size, sequence length, tagset size, tagset size) for CRF
# -- A tensor of shape (aggregated sequence length for all sentences in batch, tagset size) for linear layer
if self.use_crf:
features = self.crf(features)
scores = (features, sorted_lengths, self.crf.transitions)
else:
scores = self._get_scores_from_features(features, sorted_lengths)
# get the gold labels
gold_labels = self._get_gold_labels(sentences)
return scores, gold_labels
def _calculate_loss(self, scores, labels) -> Tuple[torch.Tensor, int]:
if not any(labels):
return torch.tensor(0.0, requires_grad=True,
device=flair.device), 1
labels = torch.tensor(
[
self.label_dictionary.get_idx_for_item(label[0]) if
len(label) > 0 else self.label_dictionary.get_idx_for_item("O")
for label in labels
],
dtype=torch.long,
device=flair.device,
)
return self.loss_function(scores, labels), len(labels)
def _make_padded_tensor_for_batch(
self,
sentences: List[Sentence]) -> Tuple[torch.Tensor, torch.Tensor]:
names = self.embeddings.get_names()
lengths: List[int] = [len(sentence.tokens) for sentence in sentences]
longest_token_sequence_in_batch: int = max(lengths)
pre_allocated_zero_tensor = torch.zeros(
self.embeddings.embedding_length * longest_token_sequence_in_batch,
dtype=torch.float,
device=flair.device,
)
all_embs = list()
for sentence in sentences:
all_embs += [
emb for token in sentence
for emb in token.get_each_embedding(names)
]
nb_padding_tokens = longest_token_sequence_in_batch - len(sentence)
if nb_padding_tokens > 0:
t = pre_allocated_zero_tensor[:self.embeddings.
embedding_length *
nb_padding_tokens]
all_embs.append(t)
sentence_tensor = torch.cat(all_embs).view([
len(sentences),
longest_token_sequence_in_batch,
self.embeddings.embedding_length,
])
return torch.tensor(lengths, dtype=torch.long), sentence_tensor
@staticmethod
def _get_scores_from_features(features: torch.Tensor,
lengths: torch.Tensor):
"""
Trims current batch tensor in shape (batch size, sequence length, tagset size) in such a way that all
pads are going to be removed.
:param features: torch.tensor containing all features from forward propagation
:param lengths: length from each sentence in batch in order to trim padding tokens
"""
features_formatted = []
for feat, length in zip(features, lengths):
features_formatted.append(feat[:length])
scores = torch.cat(features_formatted)
return scores
def _get_gold_labels(self, sentences: Union[List[Sentence], Sentence]):
"""
Extracts gold labels from each sentence.
:param sentences: List of sentences in batch
"""
# spans need to be encoded as token-level predictions
if self.predict_spans:
all_sentence_labels = []
for sentence in sentences:
sentence_labels = ["O"] * len(sentence)
for label in sentence.get_labels(self.label_type):
span: Span = label.data_point
if len(span) == 1:
sentence_labels[span[0].idx - 1] = "S-" + label.value
else:
sentence_labels[span[0].idx - 1] = "B-" + label.value
sentence_labels[span[-1].idx - 1] = "E-" + label.value
for i in range(span[0].idx, span[-1].idx - 1):
sentence_labels[i] = "I-" + label.value
all_sentence_labels.extend(sentence_labels)
labels = [[label] for label in all_sentence_labels]
# all others are regular labels for each token
else:
labels = [[token.get_label(self.label_type, "O").value]
for sentence in sentences for token in sentence]
return labels
def predict(
self,
sentences: Union[List[Sentence], Sentence],
mini_batch_size: int = 32,
return_probabilities_for_all_classes: bool = False,
verbose: bool = False,
label_name: Optional[str] = None,
return_loss=False,
embedding_storage_mode="none",
):
"""
Predicts labels for current batch with CRF or Softmax.
:param sentences: List of sentences in batch
:param mini_batch_size: batch size for test data
:param return_probabilities_for_all_classes: Whether to return probabilites for all classes
:param verbose: whether to use progress bar
:param label_name: which label to predict
:param return_loss: whether to return loss value
:param embedding_storage_mode: determines where to store embeddings - can be "gpu", "cpu" or None.
"""
if label_name is None:
label_name = self.tag_type
with torch.no_grad():
if not sentences:
return sentences
# make sure its a list
if not isinstance(sentences, list) and not isinstance(
sentences, flair.data.Dataset):
sentences = [sentences]
# filter empty sentences
sentences = [
sentence for sentence in sentences if len(sentence) > 0
]
# reverse sort all sequences by their length
reordered_sentences = sorted(sentences,
key=lambda s: len(s),
reverse=True)
if len(reordered_sentences) == 0:
return sentences
dataloader = DataLoader(
dataset=FlairDatapointDataset(reordered_sentences),
batch_size=mini_batch_size,
)
# progress bar for verbosity
if verbose:
dataloader = tqdm(dataloader, desc="Batch inference")
overall_loss = torch.zeros(1, device=flair.device)
batch_no = 0
label_count = 0
for batch in dataloader:
batch_no += 1
# stop if all sentences are empty
if not batch:
continue
# get features from forward propagation
features, gold_labels = self.forward(batch)
# remove previously predicted labels of this type
for sentence in batch:
sentence.remove_labels(label_name)
# if return_loss, get loss value
if return_loss:
loss = self._calculate_loss(features, gold_labels)
overall_loss += loss[0]
label_count += loss[1]
# Sort batch in same way as forward propagation
lengths = torch.LongTensor(
[len(sentence) for sentence in batch])
_, sort_indices = lengths.sort(dim=0, descending=True)
batch = [batch[i] for i in sort_indices]
# make predictions
if self.use_crf:
predictions, all_tags = self.viterbi_decoder.decode(
features, return_probabilities_for_all_classes)
else:
predictions, all_tags = self._standard_inference(
features, batch, return_probabilities_for_all_classes)
# add predictions to Sentence
for sentence, sentence_predictions in zip(batch, predictions):
# BIOES-labels need to be converted to spans
if self.predict_spans:
sentence_tags = [
label[0] for label in sentence_predictions
]
sentence_scores = [
label[1] for label in sentence_predictions
]
predicted_spans = get_spans_from_bio(
sentence_tags, sentence_scores)
for predicted_span in predicted_spans:
span: Span = sentence[
predicted_span[0][0]:predicted_span[0][-1] + 1]
span.add_label(label_name,
value=predicted_span[2],
score=predicted_span[1])
# token-labels can be added directly
else:
for token, label in zip(sentence.tokens,
sentence_predictions):
token.add_label(typename=label_name,
value=label[0],
score=label[1])
# all_tags will be empty if all_tag_prob is set to False, so the for loop will be avoided
for (sentence, sent_all_tags) in zip(batch, all_tags):
for (token, token_all_tags) in zip(sentence.tokens,
sent_all_tags):
token.add_tags_proba_dist(label_name, token_all_tags)
store_embeddings(sentences,
storage_mode=embedding_storage_mode)
if return_loss:
return overall_loss, label_count
def _standard_inference(self, features: torch.Tensor,
batch: List[Sentence],
probabilities_for_all_classes: bool):
"""
Softmax over emission scores from forward propagation.
:param features: sentence tensor from forward propagation
:param batch: list of sentence
:param probabilities_for_all_classes: whether to return score for each tag in tag dictionary
"""
softmax_batch = F.softmax(features, dim=1).cpu()
scores_batch, prediction_batch = torch.max(softmax_batch, dim=1)
predictions = []
all_tags = []
for sentence in batch:
scores = scores_batch[:len(sentence)]
predictions_for_sentence = prediction_batch[:len(sentence)]
predictions.append([
(self.label_dictionary.get_item_for_index(prediction),
score.item()) for token, score, prediction in zip(
sentence, scores, predictions_for_sentence)
])
scores_batch = scores_batch[len(sentence):]
prediction_batch = prediction_batch[len(sentence):]
if probabilities_for_all_classes:
lengths = [len(sentence) for sentence in batch]
all_tags = self._all_scores_for_token(softmax_batch, lengths)
return predictions, all_tags
def _all_scores_for_token(self, scores: torch.Tensor, lengths: List[int]):
"""
Returns all scores for each tag in tag dictionary.
:param scores: Scores for current sentence.
"""
scores = scores.numpy()
prob_all_tags = [[
Label(self.label_dictionary.get_item_for_index(score_id), score)
for score_id, score in enumerate(score_dist)
] for score_dist in scores]
prob_tags_per_sentence = []
previous = 0
for length in lengths:
prob_tags_per_sentence.append(prob_all_tags[previous:previous +
length])
previous = length
return prob_tags_per_sentence
def _get_state_dict(self):
"""Returns the state dictionary for this model."""
model_state = {
**super()._get_state_dict(),
"embeddings": self.embeddings,
"hidden_size": self.hidden_size,
"tag_dictionary": self.label_dictionary,
"tag_type": self.tag_type,
"use_crf": self.use_crf,
"use_rnn": self.use_rnn,
"rnn_layers": self.rnn_layers,
"use_dropout": self.use_dropout,
"use_word_dropout": self.use_word_dropout,
"use_locked_dropout": self.use_locked_dropout,
"rnn_type": self.rnn_type,
"reproject_embeddings": self.reproject_embeddings,
"weight_dict": self.weight_dict,
}
return model_state
@classmethod
def _init_model_with_state_dict(cls, state, **kwargs):
"""Initialize the model from a state dictionary."""
rnn_type = "LSTM" if "rnn_type" not in state.keys(
) else state["rnn_type"]
use_dropout = 0.0 if "use_dropout" not in state.keys(
) else state["use_dropout"]
use_word_dropout = 0.0 if "use_word_dropout" not in state.keys(
) else state["use_word_dropout"]
use_locked_dropout = 0.0 if "use_locked_dropout" not in state.keys(
) else state["use_locked_dropout"]
reproject_embeddings = True if "reproject_embeddings" not in state.keys(
) else state["reproject_embeddings"]
weights = None if "weight_dict" not in state.keys(
) else state["weight_dict"]
if state["use_crf"]:
if "transitions" in state["state_dict"]:
state["state_dict"]["crf.transitions"] = state["state_dict"][
"transitions"]
del state["state_dict"]["transitions"]
return super()._init_model_with_state_dict(
state,
embeddings=state["embeddings"],
tag_dictionary=state["tag_dictionary"],
tag_type=state["tag_type"],
use_crf=state["use_crf"],
use_rnn=state["use_rnn"],
rnn_layers=state["rnn_layers"],
hidden_size=state["hidden_size"],
dropout=use_dropout,
word_dropout=use_word_dropout,
locked_dropout=use_locked_dropout,
rnn_type=rnn_type,
reproject_embeddings=reproject_embeddings,
loss_weights=weights,
init_from_state_dict=True,
**kwargs,
)
@staticmethod
def _fetch_model(model_name) -> str:
# core Flair models on Huggingface ModelHub
huggingface_model_map = {
"ner": "flair/ner-english",
"ner-fast": "flair/ner-english-fast",
"ner-ontonotes": "flair/ner-english-ontonotes",
"ner-ontonotes-fast": "flair/ner-english-ontonotes-fast",
# Large NER models,
"ner-large": "flair/ner-english-large",
"ner-ontonotes-large": "flair/ner-english-ontonotes-large",
"de-ner-large": "flair/ner-german-large",
"nl-ner-large": "flair/ner-dutch-large",
"es-ner-large": "flair/ner-spanish-large",
# Multilingual NER models
"ner-multi": "flair/ner-multi",
"multi-ner": "flair/ner-multi",
"ner-multi-fast": "flair/ner-multi-fast",
# English POS models
"upos": "flair/upos-english",
"upos-fast": "flair/upos-english-fast",
"pos": "flair/pos-english",
"pos-fast": "flair/pos-english-fast",
# Multilingual POS models
"pos-multi": "flair/upos-multi",
"multi-pos": "flair/upos-multi",
"pos-multi-fast": "flair/upos-multi-fast",
"multi-pos-fast": "flair/upos-multi-fast",
# English SRL models
"frame": "flair/frame-english",
"frame-fast": "flair/frame-english-fast",
# English chunking models
"chunk": "flair/chunk-english",
"chunk-fast": "flair/chunk-english-fast",
# Language-specific NER models
"da-ner": "flair/ner-danish",
"de-ner": "flair/ner-german",
"de-ler": "flair/ner-german-legal",
"de-ner-legal": "flair/ner-german-legal",
"fr-ner": "flair/ner-french",
"nl-ner": "flair/ner-dutch",
}
hu_path: str = "https://nlp.informatik.hu-berlin.de/resources/models"
hu_model_map = {
# English NER models
"ner":
"/".join([hu_path, "ner", "en-ner-conll03-v0.4.pt"]),
"ner-pooled":
"/".join([hu_path, "ner-pooled", "en-ner-conll03-pooled-v0.5.pt"]),
"ner-fast":
"/".join([hu_path, "ner-fast", "en-ner-fast-conll03-v0.4.pt"]),
"ner-ontonotes":
"/".join([hu_path, "ner-ontonotes", "en-ner-ontonotes-v0.4.pt"]),
"ner-ontonotes-fast":
"/".join([
hu_path, "ner-ontonotes-fast", "en-ner-ontonotes-fast-v0.4.pt"
]),
# Multilingual NER models
"ner-multi":
"/".join([hu_path, "multi-ner", "quadner-large.pt"]),
"multi-ner":
"/".join([hu_path, "multi-ner", "quadner-large.pt"]),
"ner-multi-fast":
"/".join([hu_path, "multi-ner-fast", "ner-multi-fast.pt"]),
# English POS models
"upos":
"/".join([hu_path, "upos", "en-pos-ontonotes-v0.4.pt"]),
"upos-fast":
"/".join([hu_path, "upos-fast", "en-upos-ontonotes-fast-v0.4.pt"]),
"pos":
"/".join([hu_path, "pos", "en-pos-ontonotes-v0.5.pt"]),
"pos-fast":
"/".join([hu_path, "pos-fast", "en-pos-ontonotes-fast-v0.5.pt"]),
# Multilingual POS models
"pos-multi":
"/".join([hu_path, "multi-pos", "pos-multi-v0.1.pt"]),
"multi-pos":
"/".join([hu_path, "multi-pos", "pos-multi-v0.1.pt"]),
"pos-multi-fast":
"/".join([hu_path, "multi-pos-fast", "pos-multi-fast.pt"]),
"multi-pos-fast":
"/".join([hu_path, "multi-pos-fast", "pos-multi-fast.pt"]),
# English SRL models
"frame":
"/".join([hu_path, "frame", "en-frame-ontonotes-v0.4.pt"]),
"frame-fast":
"/".join(
[hu_path, "frame-fast", "en-frame-ontonotes-fast-v0.4.pt"]),
# English chunking models
"chunk":
"/".join([hu_path, "chunk", "en-chunk-conll2000-v0.4.pt"]),
"chunk-fast":
"/".join(
[hu_path, "chunk-fast", "en-chunk-conll2000-fast-v0.4.pt"]),
# Danish models
"da-pos":
"/".join([hu_path, "da-pos", "da-pos-v0.1.pt"]),
"da-ner":
"/".join([hu_path, "NER-danish", "da-ner-v0.1.pt"]),
# German models
"de-pos":
"/".join([hu_path, "de-pos", "de-pos-ud-hdt-v0.5.pt"]),
"de-pos-tweets":
"/".join([hu_path, "de-pos-tweets", "de-pos-twitter-v0.1.pt"]),
"de-ner":
"/".join([hu_path, "de-ner", "de-ner-conll03-v0.4.pt"]),
"de-ner-germeval":
"/".join([hu_path, "de-ner-germeval", "de-ner-germeval-0.4.1.pt"]),
"de-ler":
"/".join([hu_path, "de-ner-legal", "de-ner-legal.pt"]),
"de-ner-legal":
"/".join([hu_path, "de-ner-legal", "de-ner-legal.pt"]),
# French models
"fr-ner":
"/".join([hu_path, "fr-ner", "fr-ner-wikiner-0.4.pt"]),
# Dutch models
"nl-ner":
"/".join([hu_path, "nl-ner", "nl-ner-bert-conll02-v0.8.pt"]),
"nl-ner-rnn":
"/".join([hu_path, "nl-ner-rnn", "nl-ner-conll02-v0.5.pt"]),
# Malayalam models
"ml-pos":
"https://raw.githubusercontent.com/qburst/models-repository/master/FlairMalayalamModels/malayalam-xpos-model.pt",
"ml-upos":
"https://raw.githubusercontent.com/qburst/models-repository/master/FlairMalayalamModels/malayalam-upos-model.pt",
# Portuguese models
"pt-pos-clinical":
"/".join([
hu_path,
"pt-pos-clinical",
"pucpr-flair-clinical-pos-tagging-best-model.pt",
]),
# Keyphase models
"keyphrase":
"/".join([hu_path, "keyphrase", "keyphrase-en-scibert.pt"]),
"negation-speculation":
"/".join([
hu_path, "negation-speculation",
"negation-speculation-model.pt"
]),
# Biomedical models
"hunflair-paper-cellline":
"/".join([
hu_path,
"hunflair_smallish_models",
"cellline",
"hunflair-celline-v1.0.pt",
]),
"hunflair-paper-chemical":
"/".join([
hu_path,
"hunflair_smallish_models",
"chemical",
"hunflair-chemical-v1.0.pt",
]),
"hunflair-paper-disease":
"/".join([
hu_path,
"hunflair_smallish_models",
"disease",
"hunflair-disease-v1.0.pt",
]),
"hunflair-paper-gene":
"/".join([
hu_path, "hunflair_smallish_models", "gene",
"hunflair-gene-v1.0.pt"
]),
"hunflair-paper-species":
"/".join([
hu_path,
"hunflair_smallish_models",
"species",
"hunflair-species-v1.0.pt",
]),
"hunflair-cellline":
"/".join([
hu_path,
"hunflair_smallish_models",
"cellline",
"hunflair-celline-v1.0.pt",
]),
"hunflair-chemical":
"/".join([
hu_path,
"hunflair_allcorpus_models",
"huner-chemical",
"hunflair-chemical-full-v1.0.pt",
]),
"hunflair-disease":
"/".join([
hu_path,
"hunflair_allcorpus_models",
"huner-disease",
"hunflair-disease-full-v1.0.pt",
]),
"hunflair-gene":
"/".join([
hu_path,
"hunflair_allcorpus_models",
"huner-gene",
"hunflair-gene-full-v1.0.pt",
]),
"hunflair-species":
"/".join([
hu_path,
"hunflair_allcorpus_models",
"huner-species",
"hunflair-species-full-v1.1.pt",
]),
}
cache_dir = Path("models")
get_from_model_hub = False
# check if model name is a valid local file
if Path(model_name).exists():
model_path = model_name
# check if model key is remapped to HF key - if so, print out information
elif model_name in huggingface_model_map:
# get mapped name
hf_model_name = huggingface_model_map[model_name]
# use mapped name instead
model_name = hf_model_name
get_from_model_hub = True
# if not, check if model key is remapped to direct download location. If so, download model
elif model_name in hu_model_map:
model_path = cached_path(hu_model_map[model_name],
cache_dir=cache_dir)
# special handling for the taggers by the @redewiegergabe project (TODO: move to model hub)
elif model_name == "de-historic-indirect":
model_file = flair.cache_root / cache_dir / "indirect" / "final-model.pt"
if not model_file.exists():
cached_path(
"http://www.redewiedergabe.de/models/indirect.zip",
cache_dir=cache_dir,
)
unzip_file(
flair.cache_root / cache_dir / "indirect.zip",
flair.cache_root / cache_dir,
)
model_path = str(flair.cache_root / cache_dir / "indirect" /
"final-model.pt")
elif model_name == "de-historic-direct":
model_file = flair.cache_root / cache_dir / "direct" / "final-model.pt"
if not model_file.exists():
cached_path(
"http://www.redewiedergabe.de/models/direct.zip",
cache_dir=cache_dir,
)
unzip_file(
flair.cache_root / cache_dir / "direct.zip",
flair.cache_root / cache_dir,
)
model_path = str(flair.cache_root / cache_dir / "direct" /
"final-model.pt")
elif model_name == "de-historic-reported":
model_file = flair.cache_root / cache_dir / "reported" / "final-model.pt"
if not model_file.exists():
cached_path(
"http://www.redewiedergabe.de/models/reported.zip",
cache_dir=cache_dir,
)
unzip_file(
flair.cache_root / cache_dir / "reported.zip",
flair.cache_root / cache_dir,
)
model_path = str(flair.cache_root / cache_dir / "reported" /
"final-model.pt")
elif model_name == "de-historic-free-indirect":
model_file = flair.cache_root / cache_dir / "freeIndirect" / "final-model.pt"
if not model_file.exists():
cached_path(
"http://www.redewiedergabe.de/models/freeIndirect.zip",
cache_dir=cache_dir,
)
unzip_file(
flair.cache_root / cache_dir / "freeIndirect.zip",
flair.cache_root / cache_dir,
)
model_path = str(flair.cache_root / cache_dir / "freeIndirect" /
"final-model.pt")
# for all other cases (not local file or special download location), use HF model hub
else:
get_from_model_hub = True
# if not a local file, get from model hub
if get_from_model_hub:
hf_model_name = "pytorch_model.bin"
revision = "main"
if "@" in model_name:
model_name_split = model_name.split("@")
revision = model_name_split[-1]
model_name = model_name_split[0]
# use model name as subfolder
if "/" in model_name:
model_folder = model_name.split("/", maxsplit=1)[1]
else:
model_folder = model_name
# Lazy import
from huggingface_hub import cached_download, hf_hub_url
url = hf_hub_url(model_name,
revision=revision,
filename=hf_model_name)
try:
model_path = cached_download(
url=url,
library_name="flair",
library_version=flair.__version__,
cache_dir=flair.cache_root / "models" / model_folder,
)
except HTTPError:
# output information
log.error("-" * 80)
log.error(
f"ACHTUNG: The key '{model_name}' was neither found on the ModelHub nor is this a valid path to a file on your system!"
)
# log.error(f" - Error message: {e}")
log.error(
" -> Please check https://huggingface.co/models?filter=flair for all available models."
)
log.error(
" -> Alternatively, point to a model file on your local drive."
)
log.error("-" * 80)
Path(flair.cache_root / "models" /
model_folder).rmdir() # remove folder again if not valid
return model_path
@staticmethod
def _filter_empty_sentences(sentences: List[Sentence]) -> List[Sentence]:
filtered_sentences = [
sentence for sentence in sentences if sentence.tokens
]
if len(sentences) != len(filtered_sentences):
log.warning(
f"Ignore {len(sentences) - len(filtered_sentences)} sentence(s) with no tokens."
)
return filtered_sentences
def _determine_if_span_prediction_problem(self,
dictionary: Dictionary) -> bool:
for item in dictionary.get_items():
if item.startswith("B-") or item.startswith(
"S-") or item.startswith("I-"):
return True
return False
def _print_predictions(self, batch, gold_label_type):
lines = []
if self.predict_spans:
for datapoint in batch:
# all labels default to "O"
for token in datapoint:
token.set_label("gold_bio", "O")
token.set_label("predicted_bio", "O")
# set gold token-level
for gold_label in datapoint.get_labels(gold_label_type):
gold_span: Span = gold_label.data_point
prefix = "B-"
for token in gold_span:
token.set_label("gold_bio", prefix + gold_label.value)
prefix = "I-"
# set predicted token-level
for predicted_label in datapoint.get_labels("predicted"):
predicted_span: Span = predicted_label.data_point
prefix = "B-"
for token in predicted_span:
token.set_label("predicted_bio",
prefix + predicted_label.value)
prefix = "I-"
# now print labels in CoNLL format
for token in datapoint:
eval_line = (f"{token.text} "
f"{token.get_label('gold_bio').value} "
f"{token.get_label('predicted_bio').value}\n")
lines.append(eval_line)
lines.append("\n")
else:
for datapoint in batch:
# print labels in CoNLL format
for token in datapoint:
eval_line = (f"{token.text} "
f"{token.get_label(gold_label_type).value} "
f"{token.get_label('predicted').value}\n")
lines.append(eval_line)
lines.append("\n")
return lines
def convert_labels_to_one_hot(label_list: List[List[str]], label_dict: Dictionary) -> List[List[int]]:
'\n Convert list of labels (strings) to a one hot list.\n :param label_list: list of labels\n :param label_dict: label dictionary\n :return: converted label list\n '
return [[(1 if (l in labels) else 0) for l in label_dict.get_items()] for labels in label_list]
