def _dict_to_samples(self, dictionary: dict, **kwargs) -> [Sample]:
# this tokenization also stores offsets and a start_of_word mask
tokenized = tokenize_with_metadata(dictionary["text"], self.tokenizer)
# truncate tokens, offsets and start_of_word to max_seq_len that can be handled by the model
for seq_name in tokenized.keys():
tokenized[seq_name], _, _ = truncate_sequences(
seq_a=tokenized[seq_name],
seq_b=None,
tokenizer=self.tokenizer,
max_seq_len=self.max_seq_len)
return [Sample(id=None, clear_text=dictionary, tokenized=tokenized)]
def _dict_to_samples(self, dictionary, all_dicts=None):
assert len(all_dicts) > 1, "Need at least 2 documents to sample random sentences from"
doc = dictionary["doc"]
samples = []
# create one sample for each sentence in the doc (except for the very last -> "nextSentence" is impossible)
for idx in range(len(doc) - 1):
tokenized = {}
if self.next_sent_pred:
text_a, text_b, is_next_label = get_sentence_pair(doc, all_dicts, idx)
sample_in_clear_text = {
"text_a": text_a,
"text_b": text_b,
"nextsentence_label": is_next_label,
}
# tokenize
tokenized["text_a"] = tokenize_with_metadata(
text_a, self.tokenizer
)
tokenized["text_b"] = tokenize_with_metadata(
text_b, self.tokenizer
)
# truncate to max_seq_len
for seq_name in ["tokens", "offsets", "start_of_word"]:
tokenized["text_a"][seq_name], tokenized["text_b"][seq_name], _ = truncate_sequences(
seq_a=tokenized["text_a"][seq_name],
seq_b=tokenized["text_b"][seq_name],
tokenizer=self.tokenizer,
max_seq_len=self.max_seq_len)
samples.append(Sample(id=None, clear_text=sample_in_clear_text, tokenized=tokenized))
# if we don't do next sentence prediction, we should feed in a single sentence
else:
text_a = doc[idx]
sample_in_clear_text = {
"text_a": text_a,
"text_b": None,
"nextsentence_label": None,
}
# tokenize
tokenized["text_a"] = tokenize_with_metadata(
text_a, self.tokenizer
)
# truncate to max_seq_len
for seq_name in ["tokens", "offsets", "start_of_word"]:
tokenized["text_a"][seq_name], _, _ = truncate_sequences(
seq_a=tokenized["text_a"][seq_name],
seq_b=None,
tokenizer=self.tokenizer,
max_seq_len=self.max_seq_len)
samples.append(Sample(id=None, clear_text=sample_in_clear_text, tokenized=tokenized))
return samples
def _dict_to_samples(self, dictionary: dict, **kwargs) -> [Sample]:
# this tokenization also stores offsets
tokenized = tokenize_with_metadata(dictionary["text"], self.tokenizer)
# truncate tokens, offsets and start_of_word to max_seq_len that can be handled by the model
for seq_name in tokenized.keys():
tokenized[seq_name], _, _ = truncate_sequences(seq_a=tokenized[seq_name], seq_b=None,
tokenizer=self.tokenizer,
max_seq_len=self.max_seq_len)
# Samples don't have labels during Inference mode
if "label" in dictionary:
label = float(dictionary["label"])
scaled_label = (label - self.tasks["regression"]["label_list"][0]) / self.tasks["regression"]["label_list"][1]
dictionary["label"] = scaled_label
return [Sample(id=None, clear_text=dictionary, tokenized=tokenized)]
def test_truncate_sequences(caplog):
caplog.set_level(logging.CRITICAL)
lang_names = ["bert-base-cased", "roberta-base", "xlnet-base-cased"]
tokenizers = []
for lang_name in lang_names:
t = Tokenizer.load(lang_name, lower_case=False)
tokenizers.append(t)
# artificial sequences (could be tokens, offsets, or anything else)
seq_a = list(range(10))
seq_b = list(range(15))
max_seq_len = 20
for tokenizer in tokenizers:
for strategy in ["longest_first", "only_first","only_second"]:
trunc_a, trunc_b, overflow = truncate_sequences(seq_a=seq_a,seq_b=seq_b,tokenizer=tokenizer,
max_seq_len=max_seq_len, truncation_strategy=strategy)
assert len(trunc_a) + len(trunc_b) + tokenizer.num_added_tokens(pair=True) == max_seq_len
def parts_to_sample(self, admission_part, discharge_part, label) -> Sample:
tokenized = {"text_a": admission_part, "text_b": discharge_part}
sample_in_clear_text = {
"text_a": admission_part["clear_text"],
"text_b": discharge_part["clear_text"],
"nextsentence_label": label,
}
# truncate to max_seq_len
for seq_name in ["tokens", "offsets", "start_of_word"]:
tokenized["text_a"][seq_name], tokenized["text_b"][
seq_name], _ = truncate_sequences(
seq_a=tokenized["text_a"][seq_name],
seq_b=tokenized["text_b"][seq_name],
tokenizer=self.tokenizer,
max_seq_len=self.max_seq_len)
return Sample(id=None,
clear_text=sample_in_clear_text,
tokenized=tokenized)
