def __init__(self,
text: str,
context: List[str],
tokenizers: Dict[str, BaseTokenizer] = None):
if tokenizers is None:
tokenizers = {"tokens": WordTokenizer()}
self.text = text
self.context = context
self.tokenizers = tokenizers
self.tokens: Dict[str, List[Any]] = defaultdict(list)
self.namespaces = list(tokenizers.keys())
for namespace in tokenizers.keys():
self.namespaces.append(f"contextual_{namespace}")
# add tokens for the word tokens
for namespace, tokenizer in self.tokenizers.items():
tokens = tokenizer.tokenize(text)
for token in tokens:
self.add_token(token=token, namespace=namespace)
# add tokens for the contextual lines
for namespace, tokenizer in self.tokenizers.items():
for contextual_line in self.context:
tokens = tokenizer.tokenize(contextual_line)
tokens = [Token(tok) for tok in tokens]
self.tokens[f"contextual_{namespace}"].append(tokens)
self.line = Line(text=text, tokenizers=self.tokenizers)
self.context_lines = []
for text in self.context:
context_line = Line(text=text, tokenizers=self.tokenizers)
self.context_lines.append(context_line)
def get_docs_labels_refs(
self) -> (List[List[Line]], List[SeqLabel], List[Line]):
docs: List[List[Line]] = []
labels: List[SeqLabel] = []
refs: List[Line] = []
with open(self.filename, "r", encoding="utf-8") as fp:
for line in fp:
line = line.strip()
if not bool(line):
continue
line_sents, line_labels, line_ref = line.strip().split("###")
sents: List[str] = [
sent.strip() for sent in line_sents.split("\t")
]
sents_labels: List[str] = [
sent_label.strip() for sent_label in line_labels.split(",")
]
sents_refs: str = line_ref
doc = [
Line(text=sent, tokenizers=self.tokenizers)
for sent in sents
]
label = SeqLabel(labels={"seq_label": sents_labels})
ref = Line(text=sents_refs, tokenizers=self.tokenizers)
docs.append(doc)
labels.append(label)
refs.append(ref)
return docs, labels, refs
def setup_scorer(abs_sum_dataset_manager):
dataset_manager = abs_sum_dataset_manager
scorer = SummarizationMetrics(dataset_manager)
lines = [
Line("word11_train word21_train"),
Line("word12_train word22_train word32_train"),
]
true_summary = [
Line("word11_label word21_label"),
Line("word11_label word22_label"),
]
true_summary_tokens = ["word11_label", "word22_label", "word33_label"]
pred_summary_tokens = [
"word11_label",
"word22_label",
"word23_label",
"word33_label",
]
predicted_tags = {"predicted_tags_tokens": [[0, 2], [1, 4, 5]]}
return (
scorer,
(lines, true_summary, predicted_tags),
(true_summary_tokens, pred_summary_tokens),
)
def setup_lstm2seqdecoder(request, ):
HIDDEN_DIM = 1024
NUM_LAYERS = request.param[0]
BIDIRECTIONAL = request.param[1]
TEACHER_FORCING_RATIO = request.param[3]
MAX_LENGTH = 5
lines = []
words = []
# texts = ["First", "second", "Third"]
texts = ["First sentence", "second sentence", "Third long sentence here"]
for text in texts:
line = Line(text=text)
word = Line(text=text.split()[0])
lines.append(line)
words.append(word)
flat_texts = [[word for sentence in texts for word in sentence]]
vocab = Vocab(flat_texts)
vocab.build_vocab()
num_direction = 2 if BIDIRECTIONAL else 1
h0 = torch.ones(NUM_LAYERS, len(texts), num_direction * HIDDEN_DIM) * 0.1
c0 = torch.ones(NUM_LAYERS, len(texts), num_direction * HIDDEN_DIM) * 0.2
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder_outputs = (torch.ones(len(texts), 5, num_direction * HIDDEN_DIM) *
0.5 if request.param[2] else None)
decoder = Lstm2SeqDecoder(
embedder=embedder,
vocab=vocab,
max_length=MAX_LENGTH,
attn_module=request.param[2],
dropout_value=0.0,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
rnn_bias=False,
num_layers=NUM_LAYERS,
)
return (
decoder,
{
"HIDDEN_DIM": HIDDEN_DIM,
"NUM_LAYERS": NUM_LAYERS,
"MAX_LENGTH": MAX_LENGTH,
"TEACHER_FORCING_RATIO": TEACHER_FORCING_RATIO,
"LINES": lines,
"WORDS": words,
"VOCAB_SIZE": vocab.get_vocab_len(),
"BIDIRECTIONAL": BIDIRECTIONAL,
},
encoder_outputs,
(h0, c0),
)
def setup_lstm2vecencoder(request):
hidden_dimension = 1024
combine_strategy = request.param[1]
bidirectional = request.param[0]
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder = LSTM2VecEncoder(
embedder=embedder,
dropout_value=0.0,
hidden_dim=hidden_dimension,
bidirectional=bidirectional,
combine_strategy=combine_strategy,
rnn_bias=False,
)
texts = ["First sentence", "second sentence"]
lines = []
for text in texts:
line = Line(text=text)
lines.append(line)
return (
encoder,
{
"hidden_dim": 2 * hidden_dimension
if bidirectional and combine_strategy == "concat"
else hidden_dimension,
"bidirectional": False,
"combine_strategy": combine_strategy,
"lines": lines,
},
)
def setup_lines():
texts = ["first line", "second line"]
lines = []
for text in texts:
line = Line(text=text)
lines.append(line)
return lines
def get_lines_labels(self) -> (List[Line], List[SeqLabel]):
lines: List[Line] = []
labels: List[SeqLabel] = []
with open(self.filename, "r", encoding="utf-8") as fp:
for line in fp:
line = line.strip()
if not bool(line):
continue
lines_and_labels = line.strip().split(" ")
words: List[str] = []
word_labels: List[str] = []
for word_line_labels in lines_and_labels:
word, word_label = word_line_labels.split("###")
word = word.strip()
word_label = word_label.strip()
words.append(word)
word_labels.append(word_label)
line = Line(text=" ".join(words), tokenizers=self.tokenizers)
label = SeqLabel(labels={"seq_label": word_labels})
lines.append(line)
labels.append(label)
return lines, labels
def setup_elmo_embedder():
elmo_embedder = ElmoEmbedder()
texts = ["I like to test elmo", "Elmo context embedder"]
lines = []
for text in texts:
line = Line(text=text)
lines.append(line)
return elmo_embedder, lines
def setup_lines():
texts = ["First sentence", "Second Sentence"]
lines = []
for text in texts:
line = Line(
text=text,
tokenizers={"tokens": WordTokenizer(), "char_tokens": CharacterTokenizer()},
)
lines.append(line)
return lines
def lines():
texts = ["First line", "Second Line which is longer"]
lines = []
for text in texts:
line = Line(
text=text, tokenizers={"tokens": WordTokenizer(tokenizer="vanilla")}
)
lines.append(line)
return lines
def test_line_word_tokenizers(self):
text = "This is a single line"
line = Line(text=text, tokenizers={"tokens": WordTokenizer()})
tokens = line.tokens
assert [token.text for token in tokens["tokens"]] == [
"This",
"is",
"a",
"single",
"line",
]
def setup_bow_encoder(request):
aggregation_type = request.param
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder = BOW_Encoder(embedder=embedder, aggregation_type=aggregation_type)
texts = ["First sentence", "second sentence"]
lines = []
for text in texts:
line = Line(text=text)
lines.append(line)
return encoder, lines
def _form_line_label(self, text: str, labels: List[str]):
line = Line(text=text, tokenizers=self.tokenizers)
labels_ = zip(*labels)
labels_ = zip(self.column_names, labels_)
labels_ = dict(labels_)
if self.train_only:
column_index = 0
column_name = self.column_names[column_index]
labels_ = {column_name: labels_[column_name]}
label = SeqLabel(labels=labels_)
return line, label
def setup_bow_elmo_encoder(request):
layer_aggregation = request.param
strings = [
"I like to eat carrot", "I like to go out on long drives in a car"
]
lines = []
for string in strings:
line = Line(text=string)
lines.append(line)
bow_elmo_embedder = BowElmoEmbedder(layer_aggregation=layer_aggregation)
return bow_elmo_embedder, lines
def get_lines_labels(self,
start_token: str = "<SOS>",
end_token: str = "<EOS>") -> (List[Line], List[Line]):
lines: List[Line] = []
labels: List[Line] = []
with open(self.filename) as fp:
for line in fp:
line, label = line.split("###")
line = line.strip()
label = label.strip()
line_instance = Line(text=line, tokenizers=self.tokenizers)
label_instance = Line(text=label, tokenizers=self.tokenizers)
for namespace, tokenizer in self.tokenizers.items():
line_instance.tokens[namespace].insert(
0, Token(start_token))
line_instance.tokens[namespace].append(Token(end_token))
label_instance.tokens[namespace].insert(
0, Token(start_token))
label_instance.tokens[namespace].append(Token(end_token))
lines.append(line_instance)
labels.append(label_instance)
return lines, labels
def make_line(self, line: str):
""" Makes a line object from string, having some characteristics as the lines used
by the datasets
Parameters
----------
line : str
Returns
-------
Line
"""
line_ = Line(text=line, tokenizers=self.train_dataset.tokenizers)
return line_
def get_lines_labels(self) -> (List[Line], List[Label]):
lines: List[Line] = []
labels: List[Label] = []
with open(self.filename) as fp:
for line in fp:
line, label = line.split("###")
line = line.strip()
label = label.strip()
line_instance = Line(text=line, tokenizers=self.tokenizers)
label_instance = Label(text=label)
lines.append(line_instance)
labels.append(label_instance)
return lines, labels
def test_line_char_tokenizer(self):
text = "Word"
line = Line(
text=text,
tokenizers={
"tokens": WordTokenizer(),
"chars": CharacterTokenizer()
},
)
tokens = line.tokens
word_tokens = tokens["tokens"]
char_tokens = tokens["chars"]
word_tokens = [tok.text for tok in word_tokens]
char_tokens = [tok.text for tok in char_tokens]
assert word_tokens == ["Word"]
assert char_tokens == ["W", "o", "r", "d"]
def setup_char_embedder(request, clf_dataset_manager):
char_embedding_dim, hidden_dim = request.param
datset_manager = clf_dataset_manager
embedder = CharEmbedder(
char_embedding_dimension=char_embedding_dim,
hidden_dimension=hidden_dim,
datasets_manager=datset_manager,
)
texts = ["This is sentence", "This is another sentence"]
lines = []
for text in texts:
line = Line(
text=text,
tokenizers={"tokens": WordTokenizer(), "char_tokens": CharacterTokenizer()},
)
lines.append(line)
return embedder, lines
def setup_lstm2seqencoder(request):
HIDDEN_DIM = 1024
BIDIRECTIONAL = request.param[0]
COMBINE_STRATEGY = request.param[1]
NUM_LAYERS = request.param[2]
ADD_PROJECTION_LAYER = request.param[3]
embedder = WordEmbedder(embedding_type="glove_6B_50")
encoder = Lstm2SeqEncoder(
embedder=embedder,
dropout_value=0.0,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
rnn_bias=False,
num_layers=NUM_LAYERS,
add_projection_layer=ADD_PROJECTION_LAYER,
)
lines = []
texts = ["First sentence", "second sentence"]
for text in texts:
line = Line(text=text)
lines.append(line)
return (
encoder,
{
"HIDDEN_DIM":
HIDDEN_DIM,
"COMBINE_STRATEGY":
COMBINE_STRATEGY,
"BIDIRECTIONAL":
BIDIRECTIONAL,
"EXPECTED_HIDDEN_DIM":
2 * HIDDEN_DIM if COMBINE_STRATEGY == "concat" and BIDIRECTIONAL
and not ADD_PROJECTION_LAYER else HIDDEN_DIM,
"NUM_LAYERS":
NUM_LAYERS,
"LINES":
lines,
"TIME_STEPS":
2,
},
)
def _form_line_label(self, text: str, labels: List[str]):
line = Line(text=text, tokenizers=self.tokenizers)
labels_ = zip(*labels)
labels_ = zip(self.column_names, labels_)
labels_ = dict(labels_)
if self.train_only:
if self.train_only == "pos":
column_index = 0
elif self.train_only == "dep":
column_index = 1
elif self.train_only == "ner":
column_index = 2
else:
raise ValueError(
f"train_only parameter can be one of [pos, dep, ner]")
column_name = self.column_names[column_index]
labels_ = {column_name: labels_[column_name]}
label = SeqLabel(labels=labels_)
return line, label
def setup_bert_embedder(request):
dropout_value = 0.0
bert_type, aggregation_type = request.param
bert_embedder = BertEmbedder(
dropout_value=dropout_value,
aggregation_type=aggregation_type,
bert_type=bert_type,
)
strings = [
"Lets start by talking politics",
"there are radical ways to test your code",
]
lines = []
for string in strings:
line = Line(text=string)
lines.append(line)
return bert_embedder, lines
def test_line_namespaces(self):
text = "Single line"
line = Line(text=text, tokenizers={"tokens": WordTokenizer()})
assert line.namespaces == ["tokens"]
def _generate_lines_with_start_token(self):
line = Line("")
line.add_token(self.start_token, "tokens")
return line
def forward(
self,
lines: List[Line],
c0: torch.FloatTensor,
h0: torch.FloatTensor,
encoder_outputs: torch.FloatTensor = None,
teacher_forcing_ratio: float = 0,
) -> torch.Tensor:
"""
Parameters
----------
lines : list of Line objects
Batched tokenized source sentence of shape [batch size].
h0, c0 : 3d torch.FloatTensor
Hidden and cell state of the LSTM layer. Each state's shape
[n layers * n directions, batch size, hidden dim]
Returns
-------
prediction : 2d torch.LongTensor
For each token in the batch, the predicted target vobulary.
Shape [batch size, output dim]
hn, cn : 3d torch.FloatTensor
Hidden and cell state of the LSTM layer. Each state's shape
[n layers * n directions, batch size, hidden dim]
"""
use_teacher_forcing = (True if
(random.random() < teacher_forcing_ratio) else
False)
if use_teacher_forcing:
max_length = max(len(line.tokens["tokens"]) for line in lines)
else:
max_length = self.max_length
batch_size = len(lines)
# tensor to store decoder's output
outputs = torch.zeros(max_length, batch_size,
self.vocab_size).to(self.device)
# last hidden & cell state of the encoder is used as the decoder's initial hidden state
if use_teacher_forcing:
prediction, _, _ = self.forward_step(
lines=lines, h0=h0, c0=c0, encoder_outputs=encoder_outputs)
outputs[1:] = prediction.permute(1, 0, 2)[:-1]
else:
lines = [self._generate_lines_with_start_token()] * batch_size
for i in range(1, max_length):
prediction, hn, cn = self.forward_step(
lines=lines, h0=h0, c0=c0, encoder_outputs=encoder_outputs)
prediction = prediction.squeeze(1)
outputs[i] = prediction
line_token_indexes = prediction.argmax(1)
line_tokens = [
self.vocab.idx2token[line_token_index]
for line_token_index in line_token_indexes.cpu().numpy()
]
lines = []
for token in line_tokens:
line = Line("")
line.add_token(token, "tokens")
lines.append(line)
h0, c0 = hn, cn
outputs = outputs.permute(1, 0, 2)
return outputs
