Source code for transformers.models.blenderbot.tokenization_blenderbot

# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Tokenization class for Blenderbot."""

from typing import TYPE_CHECKING, List

from ...utils import logging
from ..roberta.tokenization_roberta import RobertaTokenizer

    from transformers.pipelines.conversational import Conversation

logger = logging.get_logger(__name__)

    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",

    "vocab_file": {"facebook/blenderbot-3B": ""},
    "merges_file": {"facebook/blenderbot-3B": ""},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": ""

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot-3B": 128}

[docs]class BlenderbotTokenizer(RobertaTokenizer): r""" Construct a Blenderbot tokenizer. :class:`~transformers.Blenderbot` is nearly identical to :class:`~transformers.RobertaTokenizer` and runs end-to-end tokenization: punctuation splitting and wordpiece. The only difference is that it doesn't add BOS token to the beginning of sequences. Refer to superclass :class:`~transformers.RobertaTokenizer` for usage examples and documentation concerning parameters. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
[docs] def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: List[int] = None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Blenderbot sequence has the following format: - single sequence: `` X </s>`` Args: token_ids_0 (:obj:`List[int]`): List of IDs to which the special tokens will be added token_ids_1 (:obj:`List[int]`, `optional`): Will be ignored Returns: :obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. """ return token_ids_0 + [self.eos_token_id]
def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]: inputs = [] for is_user, text in conversation.iter_texts(): if is_user: # We need to space prefix as it's being done within blenderbot inputs.append(" " + text) else: # Generated responses should contain them already. inputs.append(text) full_string = " ".join(inputs) input_ids = self.encode(full_string) if len(input_ids) > self.model_max_length: input_ids = input_ids[-self.model_max_length :] logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.") return input_ids
def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char pairs = set(pairs) return pairs