def samples_to_features_ner(
sample,
tasks,
max_seq_len,
tokenizer,
non_initial_token="X",
**kwargs
):
"""
Generates a dictionary of features for a given input sample that is to be consumed by an NER model.
:param sample: Sample object that contains human readable text and label fields from a single NER data sample
:type sample: Sample
:param tasks: A dictionary where the keys are the names of the tasks and the values are the details of the task (e.g. label_list, metric, tensor name)
:type tasks: dict
:param max_seq_len: Sequences are truncated after this many tokens
:type max_seq_len: int
:param tokenizer: A tokenizer object that can turn string sentences into a list of tokens
:param non_initial_token: Token that is inserted into the label sequence in positions where there is a
non-word-initial token. This is done since the default NER performs prediction
only on word initial tokens
:return: A list with one dictionary containing the keys "input_ids", "padding_mask", "segment_ids", "initial_mask"
(also "label_ids" if not in inference mode). The values are lists containing those features.
:rtype: list
"""
tokens = sample.tokenized["tokens"]
if tokenizer.is_fast:
text = sample.clear_text["text"]
# Here, we tokenize the sample for the second time to get all relevant ids
# This should change once we git rid of FARM's tokenize_with_metadata()
inputs = tokenizer(text,
return_token_type_ids=True,
truncation=True,
truncation_strategy="longest_first",
max_length=max_seq_len,
return_special_tokens_mask=True)
if (len(inputs["input_ids"]) - inputs["special_tokens_mask"].count(1)) != len(sample.tokenized["tokens"]):
logger.error(f"FastTokenizer encoded sample {sample.clear_text['text']} to "
f"{len(inputs['input_ids']) - inputs['special_tokens_mask'].count(1)} tokens, which differs "
f"from number of tokens produced in tokenize_with_metadata().\n"
f"Further processing is likely to be wrong!")
else:
inputs = tokenizer.encode_plus(text=tokens,
text_pair=None,
add_special_tokens=True,
truncation=False,
return_special_tokens_mask=True,
return_token_type_ids=True,
is_pretokenized=False
)
input_ids, segment_ids, special_tokens_mask = inputs["input_ids"], inputs["token_type_ids"], inputs["special_tokens_mask"]
# We construct a mask to identify the first token of a word. We will later only use them for predicting entities.
# Special tokens don't count as initial tokens => we add 0 at the positions of special tokens
# For BERT we add a 0 in the start and end (for CLS and SEP)
initial_mask = [int(x) for x in sample.tokenized["start_of_word"]]
initial_mask = insert_at_special_tokens_pos(initial_mask, special_tokens_mask, insert_element=0)
assert len(initial_mask) == len(input_ids)
for task_name, task in tasks.items():
try:
label_list = task["label_list"]
label_name = task["label_name"]
label_tensor_name = task["label_tensor_name"]
labels_word = sample.clear_text[label_name]
labels_token = expand_labels(labels_word, initial_mask, non_initial_token)
# labels_token = add_cls_sep(labels_token, cls_token, sep_token)
label_ids = [label_list.index(lt) for lt in labels_token]
except ValueError:
label_ids = None
problematic_labels = set(labels_token).difference(set(label_list))
logger.warning(f"[Task: {task_name}] Could not convert labels to ids via label_list!"
f"\nWe found a problem with labels {str(problematic_labels)}")
except KeyError:
# For inference mode we don't expect labels
label_ids = None
logger.warning(f"[Task: {task_name}] Could not convert labels to ids via label_list!"
"\nIf your are running in *inference* mode: Don't worry!"
"\nIf you are running in *training* mode: Verify you are supplying a proper label list to your processor and check that labels in input data are correct.")
# This mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
padding_mask = [1] * len(input_ids)
# Padding up to the sequence length.
# Normal case: adding multiple 0 to the right
# Special cases:
# a) xlnet pads on the left and uses "4" for padding token_type_ids
if tokenizer.__class__.__name__ == "XLNetTokenizer":
pad_on_left = True
segment_ids = pad(segment_ids, max_seq_len, 4, pad_on_left=pad_on_left)
else:
pad_on_left = False
segment_ids = pad(segment_ids, max_seq_len, 0, pad_on_left=pad_on_left)
input_ids = pad(input_ids, max_seq_len, tokenizer.pad_token_id, pad_on_left=pad_on_left)
padding_mask = pad(padding_mask, max_seq_len, 0, pad_on_left=pad_on_left)
initial_mask = pad(initial_mask, max_seq_len, 0, pad_on_left=pad_on_left)
if label_ids:
label_ids = pad(label_ids, max_seq_len, 0, pad_on_left=pad_on_left)
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"initial_mask": initial_mask,
}
if label_ids:
feature_dict[label_tensor_name] = label_ids
return [feature_dict]
def samples_to_features_ner(sample,
label_list,
max_seq_len,
tokenizer,
cls_token="[CLS]",
pad_token="[PAD]",
sep_token="[SEP]",
non_initial_token="X",
**kwargs):
"""
Generates a dictionary of features for a given input sample that is to be consumed by an NER model.
:param sample: Sample object that contains human readable text and label fields from a single NER data sample
:type sample: Sample
:param label_list: A list of all unique labels
:type label_list: list
:param max_seq_len: Sequences are truncated after this many tokens
:type max_seq_len: int
:param tokenizer: A tokenizer object that can turn string sentences into a list of tokens
:param cls_token: Token used to represent the beginning of the sequence
:type cls_token: str
:param pad_token: Token used to represent sequence padding
:type pad_token: str
:param sep_token: Token used to represent the border between two sequences
:type sep_token: str
:param non_initial_token: Token that is inserted into the label sequence in positions where there is a
non-word-initial token. This is done since the default NER performs prediction
only on word initial tokens
:return: A dictionary containing the keys "input_ids", "padding_mask", "segment_ids", "initial_mask"
(also "label_ids" if not in inference mode). The values are lists containing those features.
:rtype: dict
"""
# Tokenize words and extend the labels so they are aligned with the tokens
# words = sample.clear_text["text"].split(" ")
# tokens, initial_mask = words_to_tokens(words, tokenizer, max_seq_len)
tokens = sample.tokenized["tokens"]
initial_mask = [int(x) for x in sample.tokenized["start_of_word"]]
# initial_mask =
# Add CLS and SEP tokens
tokens = add_cls_sep(tokens, cls_token, sep_token)
initial_mask = [0] + initial_mask + [
0
] # CLS and SEP don't count as initial tokens
padding_mask = [1] * len(tokens)
# Convert to input and labels to ids, generate masks
input_ids = tokenizer.convert_tokens_to_ids(tokens)
if "label" in sample.clear_text:
labels_word = sample.clear_text["label"]
labels_token = expand_labels(labels_word, initial_mask,
non_initial_token)
# labels_token = add_cls_sep(labels_token, cls_token, sep_token)
label_ids = [label_list.index(lt) for lt in labels_token]
# Inference mode
else:
label_ids = None
segment_ids = [0] * max_seq_len
# Pad
input_ids = pad(input_ids, max_seq_len, 0)
if label_ids:
label_ids = pad(label_ids, max_seq_len, 0)
initial_mask = pad(initial_mask, max_seq_len, 0)
padding_mask = pad(padding_mask, max_seq_len, 0)
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"initial_mask": initial_mask,
}
if label_ids:
feature_dict["label_ids"] = label_ids
return [feature_dict]
def samples_to_features_ner(sample,
tasks,
max_seq_len,
tokenizer,
cls_token="[CLS]",
sep_token="[SEP]",
non_initial_token="X",
**kwargs):
"""
Generates a dictionary of features for a given input sample that is to be consumed by an NER model.
:param sample: Sample object that contains human readable text and label fields from a single NER data sample
:type sample: Sample
:param tasks: A dictionary where the keys are the names of the tasks and the values are the details of the task (e.g. label_list, metric, tensor name)
:type tasks: dict
:param max_seq_len: Sequences are truncated after this many tokens
:type max_seq_len: int
:param tokenizer: A tokenizer object that can turn string sentences into a list of tokens
:param cls_token: Token used to represent the beginning of the sequence
:type cls_token: str
:param sep_token: Token used to represent the border between two sequences
:type sep_token: str
:param non_initial_token: Token that is inserted into the label sequence in positions where there is a
non-word-initial token. This is done since the default NER performs prediction
only on word initial tokens
:return: A dictionary containing the keys "input_ids", "padding_mask", "segment_ids", "initial_mask"
(also "label_ids" if not in inference mode). The values are lists containing those features.
:rtype: dict
"""
# Tokenize words and extend the labels so they are aligned with the tokens
# words = sample.clear_text["text"].split(" ")
# tokens, initial_mask = words_to_tokens(words, tokenizer, max_seq_len)
tokens = sample.tokenized["tokens"]
custom_data = sample.tokenized["custom_data"]
initial_mask = [int(x) for x in sample.tokenized["start_of_word"]]
# initial_mask =
# Add CLS and SEP tokens
tokens = add_cls_sep(tokens, cls_token, sep_token)
custom_data = [5] + custom_data + [4]
initial_mask = [0] + initial_mask + [
0
] # CLS and SEP don't count as initial tokens
padding_mask = [1] * len(tokens)
# Convert to input and labels to ids, generate masks
input_ids = tokenizer.convert_tokens_to_ids(tokens)
for task_name, task in tasks.items():
try:
label_list = task["label_list"]
label_name = task["label_name"]
label_tensor_name = task["label_tensor_name"]
labels_word = sample.clear_text[label_name]
labels_token = expand_labels(labels_word, initial_mask,
non_initial_token)
# labels_token = add_cls_sep(labels_token, cls_token, sep_token)
#label_ids = [label_list.index(lt) for lt in labels_token]
label_ids = [
label_list.index(lt) for lt in sample.tokenized['ner_label']
]
except ValueError:
label_ids = None
problematic_labels = set(labels_token).difference(set(label_list))
logger.warning(
f"[Task: {task_name}] Could not convert labels to ids via label_list!"
f"\nWe found a problem with labels {str(problematic_labels)}")
except KeyError:
# For inference mode we don't expect labels
label_ids = None
logger.warning(
f"[Task: {task_name}] Could not convert labels to ids via label_list!"
"\nIf your are running in *inference* mode: Don't worry!"
"\nIf you are running in *training* mode: Verify you are supplying a proper label list to your processor and check that labels in input data are corre"
)
label_ids = [5] + label_ids + [4]
segment_ids = []
next_sent = False
for x in input_ids:
if x == 102:
segment_ids.append(0)
next_sent = True
elif next_sent == True:
segment_ids.append(1)
else:
segment_ids.append(0)
segment_ids[len(segment_ids) - 1] = 1
# Pad
input_ids = pad(input_ids, max_seq_len, 0)
if label_ids:
label_ids = pad(label_ids, max_seq_len, 0)
initial_mask = pad(initial_mask, max_seq_len, 0)
padding_mask = pad(padding_mask, max_seq_len, 0)
custom_data = pad(custom_data, max_seq_len, 0)
segment_ids = pad(segment_ids, max_seq_len, 0)
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"initial_mask": initial_mask,
"custom_data": custom_data,
}
if label_ids:
feature_dict[label_tensor_name] = label_ids
return [feature_dict]
def dataset_from_dicts(self, dicts, indices=None, return_baskets=False, non_initial_token="X"):
self.baskets = []
self.pre_tokenizer = WhitespaceSplit()
texts = [x["text"] for x in dicts]
words_and_spans = [self.pre_tokenizer.pre_tokenize_str(x) for x in texts]
words = [[x[0] for x in y] for y in words_and_spans]
word_spans_batch = [[x[1] for x in y] for y in words_and_spans]
tokenized_batch = self.tokenizer.batch_encode_plus(
words,
return_offsets_mapping=True,
return_special_tokens_mask=True,
return_token_type_ids=True,
return_attention_mask=True,
truncation=True,
max_length=self.max_seq_len,
padding="max_length",
is_split_into_words=True,
)
for i in range(len(dicts)):
tokenized = tokenized_batch[i]
d = dicts[i]
id_external = self._id_from_dict(d)
if indices:
id_internal = indices[i]
else:
id_internal = i
input_ids = tokenized.ids
segment_ids = tokenized.type_ids
initial_mask = self._get_start_of_word(tokenized.words)
assert len(initial_mask) == len(input_ids)
padding_mask = tokenized.attention_mask
if return_baskets:
token_to_word_map = tokenized.words
word_spans = word_spans_batch[i]
tokenized_dict = {
"tokens": tokenized.tokens,
"word_spans": word_spans,
"token_to_word_map": token_to_word_map,
"start_of_word": initial_mask
}
else:
tokenized_dict = {}
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"initial_mask": initial_mask,
}
for task_name, task in self.tasks.items():
try:
label_name = task["label_name"]
labels_word = d[label_name]
label_list = task["label_list"]
label_tensor_name = task["label_tensor_name"]
if task["task_type"] == "classification":
label_ids = [label_list.index(labels_word)]
elif task["task_type"] == "ner":
labels_token = expand_labels(labels_word, initial_mask, non_initial_token)
label_ids = [label_list.index(lt) for lt in labels_token]
except ValueError:
label_ids = None
problematic_labels = set(labels_token).difference(set(label_list))
print(f"[Task: {task_name}] Could not convert labels to ids via label_list!"
f"\nWe found a problem with labels {str(problematic_labels)}")
except KeyError:
label_ids = None
# print(f"[Task: {task_name}] Could not convert labels to ids via label_list!"
# "\nIf your are running in *inference* mode: Don't worry!"
# "\nIf you are running in *training* mode: Verify you are supplying a proper label list to your processor and check that labels in input data are correct.")
if label_ids:
feature_dict[label_tensor_name] = label_ids
curr_sample = Sample(id=None,
clear_text=d,
tokenized=tokenized_dict,
features=[feature_dict])
curr_basket = SampleBasket(id_internal=id_internal,
raw=d,
id_external=id_external,
samples=[curr_sample])
self.baskets.append(curr_basket)
if indices and 0 not in indices:
pass
else:
self._log_samples(1)
dataset, tensor_names = self._create_dataset()
ret = [dataset, tensor_names, self.problematic_sample_ids]
if return_baskets:
ret.append(self.baskets)
return tuple(ret)
