text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)
self.examples = batch_encoding["input_ids"]
self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]
def __len__(self):
return len(self.examples)
def __ge... | 10,618 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
class LineByLineWithRefDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
""" | 10,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, ref_path: str):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm_wwm.py"
),
FutureWa... | 10,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
data = f.readlines() # use this method to avoid delimiter '\u2029' to split a line
data = [line.strip() for line in data if len(line) > 0 and not line.isspace()]
# Get ref inf from file
with open(ref_path, encoding="utf-8") as f:
ref = [json.loads(line) for line in f.read().splitlin... | 10,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
batch_encoding = tokenizer(data, add_special_tokens=True, truncation=True, max_length=block_size)
self.examples = batch_encoding["input_ids"]
self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]
n = len(self.examples)
for i in range(n):
s... | 10,619 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
class LineByLineWithSOPTextDataset(Dataset):
"""
Dataset for sentence order prediction task, prepare sentence pairs for SOP task
""" | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
def __init__(self, tokenizer: PreTrainedTokenizer, file_dir: str, block_size: int):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py"
),
FutureWarning,
)
... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
original_lines = f.readlines()
article_lines = []
for line in original_lines:
if "<doc id=" in line:
article_open = True
elif "</doc>" in line:
article_open = False
document = ... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
examples = self.create_examples_from_document(document, block_size, tokenizer)
self.examples.extend(examples)
article_lines = []
else:
if article_open:
article_lines.append(line)
logger.info(... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
# We *usually* want to fill up the entire sequence since we are padding
# to `block_size` anyways, so short sequences are generally wasted
# computation. However, we *sometimes*
# (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
# sequences to minimize the mismatch be... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
# We DON'T just concatenate all of the tokens from a document into a long
# sequence and choose an arbitrary split point because this would make the
# next sentence prediction task too easy. Instead, we split the input into
# segments "A" and "B" based on the actual "sentences" provided by the u... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
# `a_end` is how many segments from `current_chunk` go into the `A` (first) sentence.
a_end = 1
# if current chunk has more than 2 sentences, pick part of it `A` (first) sentence
if len(current_chunk) >= 2:
a_end = random.randint(1, len... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
# token b
tokens_b = []
for j in range(a_end, len(current_chunk)):
tokens_b.extend(current_chunk[j])
if len(tokens_a) == 0 or len(tokens_b) == 0:
continue
# switch tokens_a and tokens_b rand... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens):
"""Truncates a pair of sequences to a maximum sequence length."""
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_num_tokens:... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
truncate_seq_pair(tokens_a, tokens_b, max_num_tokens)
if not (len(tokens_a) >= 1):
raise ValueError(f"Length of sequence a is {len(tokens_a)} which must be no less than 1")
if not (len(tokens_b) >= 1):
raise ValueError(f"Length of s... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
example = {
"input_ids": torch.tensor(input_ids, dtype=torch.long),
"token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
"sentence_order_label": torch.tensor(0 if is_next else 1, dtype=torch.long),
}
... | 10,620 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
class TextDatasetForNextSentencePrediction(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(
self,
tokenizer: PreTrainedTokenizer,
file_path: str,
block_size: int,
overwrite_cache=False,
short_seq_probability=0... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
directory, filename = os.path.split(file_path)
cached_features_file = os.path.join(
directory,
f"cached_nsp_{tokenizer.__class__.__name__}_{block_size}_{filename}",
)
self.tokenizer = tokenizer
# Make sure only the first process in distributed training processes... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not overwrite_cache:
start = time.time()
with open(cached_features_file, "rb") as handle:
self.examples = pickle.load(handle)
logger.info(
f"Loading f... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
# Empty lines are used as document delimiters
if not line and len(self.documents[-1]) != 0:
self.documents.append([])
tokens = tokenizer.tokenize(line)
tokens = tokenizer.convert_tokens_to_ids(tokens)
... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
start = time.time()
with open(cached_features_file, "wb") as handle:
pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
# We *usually* want to fill up the entire sequence since we are padding
# to `block_size` anyways, so short sequences are generally wasted
# computation. However, we *sometimes*
# (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
# sequences to minimize the mismatch be... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
while i < len(document):
segment = document[i]
current_chunk.append(segment)
current_length += len(segment)
if i == len(document) - 1 or current_length >= target_seq_length:
if current_chunk:
# `a_end` is how many segments from `current... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
# This should rarely go for more than one iteration for large
# corpora. However, just to be careful, we try to make sure that
# the random document is not the same as the document
# we're processing.
for _ in range(10):
... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
random_document = self.documents[random_document_index]
random_start = random.randint(0, len(random_document) - 1)
for j in range(random_start, len(random_document)):
tokens_b.extend(random_document[j])
if len(tokens... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
if not (len(tokens_a) >= 1):
raise ValueError(f"Length of sequence a is {len(tokens_a)} which must be no less than 1")
if not (len(tokens_b) >= 1):
raise ValueError(f"Length of sequence b is {len(tokens_b)} which must be no less than 1")
... | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
self.examples.append(example)
current_chunk = []
current_length = 0
i += 1
def __len__(self):
return len(self.examples)
def __getitem__(self, i):
return self.examples[i] | 10,621 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
class SquadDataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
""" | 10,622 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
model_type: str = field(
default=None, metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_TYPES)}
)
data_dir: str = field(
default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
)
max_seq_length: int = field(
... | 10,622 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
},
)
max_answer_length: int = field(
default=30,
metadata={
"help": (
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
)
},
)
... | 10,622 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
"help": (
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
)
},
)
threads: int = field(default=1, metadata={"help": "multiple threads for converting example to features"}) | 10,622 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
class Split(Enum):
train = "train"
dev = "dev" | 10,623 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
class SquadDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
args: SquadDataTrainingArguments
features: List[SquadFeatures]
mode: Split
is_language_sensitive: bool | 10,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
def __init__(
self,
args: SquadDataTrainingArguments,
tokenizer: PreTrainedTokenizer,
limit_length: Optional[int] = None,
mode: Union[str, Split] = Split.train,
is_language_sensitive: Optional[bool] = False,
cache_dir: Optional[str] = None,
dataset_format:... | 10,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}",
) | 10,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not args.overwrite_cache:
start = ti... | 10,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
if self.dataset is None or self.examples is None:
logger.warning(
f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
" future run"
)
else:
if mode == Split.dev:... | 10,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
start = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples},
cached_features_file,
)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
... | 10,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
input_ids = torch.tensor(feature.input_ids, dtype=torch.long)
attention_mask = torch.tensor(feature.attention_mask, dtype=torch.long)
token_type_ids = torch.tensor(feature.token_type_ids, dtype=torch.long)
cls_index = torch.tensor(feature.cls_index, dtype=torch.long)
p_mask = torch.tenso... | 10,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask})
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible})
if self.is_language_sensitive:
inputs.update({"langs": (torch.o... | 10,624 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py |
class GlueDataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
line.
"""
task_name: str = field(metadata={"help": "Th... | 10,625 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
def __post_init__(self):
self.task_name = self.task_name.lower() | 10,625 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
class Split(Enum):
train = "train"
dev = "dev"
test = "test" | 10,626 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
class GlueDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
args: GlueDataTrainingArguments
output_mode: str
features: List[InputFeatures] | 10,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
def __init__(
self,
args: GlueDataTrainingArguments,
tokenizer: PreTrainedTokenizerBase,
limit_length: Optional[int] = None,
mode: Union[str, Split] = Split.train,
cache_dir: Optional[str] = None,
):
warnings.warn(
"This dataset will be removed fro... | 10,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
cached_features_file = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,
f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}",
)
label_list = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] an... | 10,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not args.overwrite_cache:
start = ti... | 10,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
if mode == Split.dev:
examples = self.processor.get_dev_examples(args.data_dir)
elif mode == Split.test:
examples = self.processor.get_test_examples(args.data_dir)
else:
examples = self.processor.get_train_examples(args.data_dir... | 10,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
) | 10,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
def get_labels(self):
return self.label_list | 10,627 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py |
class XnliProcessor(DataProcessor):
"""
Processor for the XNLI dataset. Adapted from
https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_classifier.py#L207
"""
def __init__(self, language, train_language=None):
self.language = language
self.trai... | 10,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/xnli.py |
def get_train_examples(self, data_dir):
"""See base class."""
lg = self.language if self.train_language is None else self.train_language
lines = self._read_tsv(os.path.join(data_dir, f"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"))
examples = []
for i, line in enumerate(lines):
... | 10,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/xnli.py |
def get_test_examples(self, data_dir):
"""See base class."""
lines = self._read_tsv(os.path.join(data_dir, "XNLI-1.0/xnli.test.tsv"))
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
language = line[0]
if language != s... | 10,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/xnli.py |
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"] | 10,628 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/xnli.py |
class SquadProcessor(DataProcessor):
"""
Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
version 2.0 of SQuAD, respectively.
"""
train_file = None
dev_file = None
def _get_example_from_tensor_dict(self, tensor_dict, evaluat... | 10,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
return SquadExample(
qas_id=tensor_dict["id"].numpy().decode("utf-8"),
question_text=tensor_dict["question"].numpy().decode("utf-8"),
context_text=tensor_dict["context"].numpy().decode("utf-8"),
answer_text=answer,
start_position_character=answer_start,
... | 10,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
>>> training_examples = get_examples_from_dataset(dataset, evaluate=False)
>>> evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)
```"""
if evaluate:
dataset = dataset["validation"]
else:
dataset = dataset["train"]
examples = []
... | 10,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
if self.train_file is None:
raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
with open(
os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding="utf-8"
) as reader:
input_data = json.l... | 10,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
if self.dev_file is None:
raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
with open(
os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding="utf-8"
) as reader:
input_data = json.load(... | 10,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
is_impossible = qa.get("is_impossible", False)
if not is_impossible:
if is_training:
answer = qa["answers"][0]
answer_text = answer["text"]
start_position_character = answer["answer_start"]
... | 10,629 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
class SquadV1Processor(SquadProcessor):
train_file = "train-v1.1.json"
dev_file = "dev-v1.1.json" | 10,630 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
class SquadV2Processor(SquadProcessor):
train_file = "train-v2.0.json"
dev_file = "dev-v2.0.json" | 10,631 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
class SquadExample:
"""
A single training/test example for the Squad dataset, as loaded from disk.
Args:
qas_id: The example's unique identifier
question_text: The question string
context_text: The context string
answer_text: The answer string
start_position_characte... | 10,632 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
def __init__(
self,
qas_id,
question_text,
context_text,
answer_text,
start_position_character,
title,
answers=[],
is_impossible=False,
):
self.qas_id = qas_id
self.question_text = question_text
self.context_text = conte... | 10,632 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
# Split on whitespace so that different tokens may be attributed to their original position.
for c in self.context_text:
if _is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
e... | 10,632 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
class SquadFeatures:
"""
Single squad example features to be fed to a model. Those features are model-specific and can be crafted from
[`~data.processors.squad.SquadExample`] using the
:method:*~transformers.data.processors.squad.squad_convert_examples_to_features* method. | 10,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
token_type_ids: Segment token indices to indicate first and second portions of the inputs.
cls_index: the index of the CLS token.
p_ma... | 10,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
tokens: list of tokens corresponding to the input ids
token_to_orig_map: mapping between the tokens and the original text, needed in order to identify the answer.
start_position: start of the answer token index
end_position: end of the answer token index
encoding: optionally store the Ba... | 10,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
def __init__(
self,
input_ids,
attention_mask,
token_type_ids,
cls_index,
p_mask,
example_index,
unique_id,
paragraph_len,
token_is_max_context,
tokens,
token_to_orig_map,
start_position,
end_position,
... | 10,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
self.encoding = encoding | 10,633 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
class SquadResult:
"""
Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
Args:
unique_id: The unique identifier corresponding to that example.
start_logits: The logits corresponding to the start of the answer
end_logits: The logits corresp... | 10,634 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py |
class OutputMode(Enum):
classification = "classification"
regression = "regression" | 10,635 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class MrpcProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,636 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates ex... | 10,636 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class MnliProcessor(DataProcessor):
"""Processor for the MultiNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test_matched")
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
def _create_exampl... | 10,637 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class MnliMismatchedProcessor(MnliProcessor):
"""Processor for the MultiNLI Mismatched data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_dev_examples(self, data_di... | 10,638 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class ColaProcessor(DataProcessor):
"""Processor for the CoLA data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates ex... | 10,639 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class Sst2Processor(DataProcessor):
"""Processor for the SST-2 data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,640 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates ex... | 10,640 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class StsbProcessor(DataProcessor):
"""Processor for the STS-B data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,641 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return [None]
def _create_examples(self, lines, set_type):
"""Creates exampl... | 10,641 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class QqpProcessor(DataProcessor):
"""Processor for the QQP data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates ex... | 10,642 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class QnliProcessor(DataProcessor):
"""Processor for the QNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
def _create_examples(self, lines, set_type):... | 10,643 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class RteProcessor(DataProcessor):
"""Processor for the RTE data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
def _create_examples(self, lines, set_type):... | 10,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class WnliProcessor(DataProcessor):
"""Processor for the WNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | 10,645 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates ex... | 10,645 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py |
class InputExample:
"""
A single training/test example for simple sequence classification.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optiona... | 10,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
class InputFeatures:
"""
A single set of features of data. Property names are the same names as the corresponding inputs to a model.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
... | 10,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(dataclasses.asdict(self)) + "\n" | 10,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
class DataProcessor:
"""Base class for data converters for sequence classification data sets."""
def get_example_from_tensor_dict(self, tensor_dict):
"""
Gets an example from a dict with tensorflow tensors.
Args:
tensor_dict: Keys and values should match the corresponding G... | 10,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
def tfds_map(self, example):
"""
Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.
"""
if len(self.get_labels()) > 1:
example.label = self.get_labels()[int(example.label)]
... | 10,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
class SingleSentenceClassificationProcessor(DataProcessor):
"""Generic processor for a single sentence classification data set."""
def __init__(self, labels=None, examples=None, mode="classification", verbose=False):
self.labels = [] if labels is None else labels
self.examples = [] if examples ... | 10,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
@classmethod
def create_from_csv(
cls, file_name, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, **kwargs
):
processor = cls(**kwargs)
processor.add_examples_from_csv(
file_name,
split_name=split_name,
column_label=... | 10,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
def add_examples_from_csv(
self,
file_name,
split_name="",
column_label=0,
column_text=1,
column_id=None,
skip_first_row=False,
overwrite_labels=False,
overwrite_examples=False,
):
lines = self._read_tsv(file_name)
if skip_first... | 10,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
def add_examples(
self, texts_or_text_and_labels, labels=None, ids=None, overwrite_labels=False, overwrite_examples=False
):
if labels is not None and len(texts_or_text_and_labels) != len(labels):
raise ValueError(
f"Text and labels have mismatched lengths {len(texts_or_t... | 10,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py |
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