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Merge pull request #1724 from huggingface/fix_encode_plus
Fix encode_plus
This commit is contained in:
Thomas Wolf
GitHub
commit
5afca00b47
@ -273,7 +273,11 @@ class CommonTestCases:
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sequence = tokenizer.encode(seq_0, add_special_tokens=False)
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num_added_tokens = tokenizer.num_added_tokens()
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total_length = len(sequence) + num_added_tokens
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information = tokenizer.encode_plus(seq_0, max_length=total_length - 2, add_special_tokens=True, stride=stride)
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information = tokenizer.encode_plus(seq_0,
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max_length=total_length - 2,
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add_special_tokens=True,
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stride=stride,
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return_overflowing_tokens=True)
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truncated_sequence = information["input_ids"]
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overflowing_tokens = information["overflowing_tokens"]
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@ -300,10 +304,12 @@ class CommonTestCases:
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)
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information = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2, add_special_tokens=True,
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stride=stride, truncation_strategy='only_second')
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stride=stride, truncation_strategy='only_second',
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return_overflowing_tokens=True)
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information_first_truncated = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2,
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add_special_tokens=True, stride=stride,
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truncation_strategy='only_first')
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truncation_strategy='only_first',
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return_overflowing_tokens=True)
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truncated_sequence = information["input_ids"]
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overflowing_tokens = information["overflowing_tokens"]
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@ -335,7 +341,7 @@ class CommonTestCases:
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# Testing single inputs
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encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
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encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True)
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encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True, return_special_tokens_mask=True)
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encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
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special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
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self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
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@ -347,7 +353,8 @@ class CommonTestCases:
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# Testing inputs pairs
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encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) + tokenizer.encode(sequence_1,
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add_special_tokens=False)
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encoded_sequence_dict = tokenizer.encode_plus(sequence_0, sequence_1, add_special_tokens=True)
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encoded_sequence_dict = tokenizer.encode_plus(sequence_0, sequence_1, add_special_tokens=True,
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return_special_tokens_mask=True)
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encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
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special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
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self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
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@ -359,7 +366,9 @@ class CommonTestCases:
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# Testing with already existing special tokens
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if tokenizer.cls_token_id == tokenizer.unk_token_id and tokenizer.cls_token_id == tokenizer.unk_token_id:
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tokenizer.add_special_tokens({'cls_token': '</s>', 'sep_token': '<s>'})
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encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True)
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encoded_sequence_dict = tokenizer.encode_plus(sequence_0,
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add_special_tokens=True,
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return_special_tokens_mask=True)
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encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
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special_tokens_mask_orig = encoded_sequence_dict["special_tokens_mask"]
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special_tokens_mask = tokenizer.get_special_tokens_mask(encoded_sequence_w_special, already_has_special_tokens=True)
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@ -750,6 +750,9 @@ class PreTrainedTokenizer(object):
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stride=0,
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truncation_strategy='longest_first',
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return_tensors=None,
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return_token_type_ids=True,
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return_overflowing_tokens=False,
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return_special_tokens_mask=False,
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**kwargs):
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"""
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Returns a dictionary containing the encoded sequence or sequence pair and additional informations:
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@ -776,7 +779,30 @@ class PreTrainedTokenizer(object):
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- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
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return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
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or PyTorch torch.Tensor instead of a list of python integers.
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return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default True).
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return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False).
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return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False).
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**kwargs: passed to the `self.tokenize()` method
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Return:
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A Dictionary of shape::
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{
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input_ids: list[int],
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token_type_ids: list[int] if return_token_type_ids is True (default)
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overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True
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num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True
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special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
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}
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With the fields:
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``input_ids``: list of token ids to be fed to a model
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``token_type_ids``: list of token type ids to be fed to a model
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``overflowing_tokens``: list of overflowing tokens if a max length is specified.
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``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
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``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
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tokens and 1 specifying sequence tokens.
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"""
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def get_input_ids(text):
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@ -798,10 +824,17 @@ class PreTrainedTokenizer(object):
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add_special_tokens=add_special_tokens,
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stride=stride,
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truncation_strategy=truncation_strategy,
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return_tensors=return_tensors)
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return_tensors=return_tensors,
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return_token_type_ids=return_token_type_ids,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask)
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def prepare_for_model(self, ids, pair_ids=None, max_length=None, add_special_tokens=True, stride=0,
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truncation_strategy='longest_first', return_tensors=None):
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truncation_strategy='longest_first',
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return_tensors=None,
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return_token_type_ids=True,
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return_overflowing_tokens=False,
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return_special_tokens_mask=False):
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"""
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Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
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It adds special tokens, truncates
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@ -826,21 +859,27 @@ class PreTrainedTokenizer(object):
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- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
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return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
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or PyTorch torch.Tensor instead of a list of python integers.
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return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default True).
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return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False).
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return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False).
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Return:
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A Dictionary of shape::
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{
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input_ids: list[int],
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overflowing_tokens: list[int] if a ``max_length`` is specified, else None
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special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True``
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token_type_ids: list[int] if return_token_type_ids is True (default)
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overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True
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num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True
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special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
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}
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With the fields:
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``input_ids``: list of tokens to be fed to a model
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``input_ids``: list of token ids to be fed to a model
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``token_type_ids``: list of token type ids to be fed to a model
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``overflowing_tokens``: list of overflowing tokens if a max length is specified.
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``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
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``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
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tokens and 1 specifying sequence tokens.
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"""
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@ -849,23 +888,31 @@ class PreTrainedTokenizer(object):
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len_pair_ids = len(pair_ids) if pair else 0
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encoded_inputs = {}
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# Handle max sequence length
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total_len = len_ids + len_pair_ids + (self.num_added_tokens(pair=pair) if add_special_tokens else 0)
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if max_length and total_len > max_length:
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ids, pair_ids, overflowing_tokens = self.truncate_sequences(ids, pair_ids=pair_ids,
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num_tokens_to_remove=total_len-max_length,
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truncation_strategy=truncation_strategy,
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stride=stride)
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encoded_inputs["overflowing_tokens"] = overflowing_tokens
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encoded_inputs["num_truncated_tokens"] = total_len - max_length
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if return_overflowing_tokens:
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encoded_inputs["overflowing_tokens"] = overflowing_tokens
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encoded_inputs["num_truncated_tokens"] = total_len - max_length
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# Handle special_tokens
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if add_special_tokens:
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sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
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token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
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encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
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special_tokens_mask = self.get_special_tokens_mask(ids, pair_ids)
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else:
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sequence = ids + pair_ids if pair else ids
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token_type_ids = [0] * len(ids) + ([1] * len(pair_ids) if pair else [])
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special_tokens_mask = [0] * (len(ids) + (len(pair_ids) if pair else 0))
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if return_special_tokens_mask:
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encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
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# Prepare inputs as tensors if asked
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if return_tensors == 'tf' and is_tf_available():
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sequence = tf.constant([sequence])
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token_type_ids = tf.constant([token_type_ids])
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@ -876,12 +923,15 @@ class PreTrainedTokenizer(object):
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logger.warning("Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(return_tensors))
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encoded_inputs["input_ids"] = sequence
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encoded_inputs["token_type_ids"] = token_type_ids
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if return_token_type_ids:
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encoded_inputs["token_type_ids"] = token_type_ids
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if max_length and len(encoded_inputs["input_ids"]) > max_length:
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encoded_inputs["input_ids"] = encoded_inputs["input_ids"][:max_length]
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encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length]
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encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"][:max_length]
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if return_token_type_ids:
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encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length]
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if return_special_tokens_mask:
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encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"][:max_length]
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if max_length is None and len(encoded_inputs["input_ids"]) > self.max_len:
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logger.warning("Token indices sequence length is longer than the specified maximum sequence length "
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