Akshara-2B-Hindi / akshara_tokenizer.py
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Create akshara_tokenizer.py
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import os
import json
import regex as re
from typing import Dict, List, Optional, Tuple, Union
from transformers import PreTrainedTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
class AksharaTokenizer(PreTrainedTokenizer):
"""
Akshara tokenizer for processing Indic language text.
This tokenizer handles characters at the akshara (syllable) level.
"""
vocab_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
merges_file=None,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
**kwargs
):
super().__init__(
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
**kwargs,
)
# Load vocabulary
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
# Load merges if available
self.merges = {}
if merges_file is not None and os.path.isfile(merges_file):
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")
self.merges = {tuple(merge.split()): i for i, merge in enumerate(merges) if merge}
# Special token handling
self.add_prefix_space = add_prefix_space
# Pre-compile regex patterns for tokenization
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def _tokenize(self, text):
"""Tokenize text into akshara units."""
if self.add_prefix_space and not text.startswith(" "):
text = " " + text
tokens = re.findall(self.pat, text)
return tokens
def _convert_token_to_id(self, token):
"""Convert a token to its ID in the vocabulary."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Convert an ID to its token in the vocabulary."""
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Convert a sequence of tokens to a single string."""
text = "".join(tokens)
text = text.replace(" ", "").replace("▁", " ").strip()
return text
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""Build model inputs from a sequence by appending eos_token_id."""
if token_ids_1 is None:
return [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
return [self.bos_token_id] + token_ids_0 + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""Get list where entries are [1] if a token is special and [0] otherwise."""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
"""Create a mask from the two sequences for sequence classification tasks."""
eos = [self.eos_token_id]
bos = [self.bos_token_id]
if token_ids_1 is None:
return len(bos + token_ids_0 + eos) * [0]
return len(bos + token_ids_0 + eos) * [0] + len(token_ids_1 + eos) * [1]
def save_vocabulary(self, save_directory, filename_prefix=None):
"""Save the vocabulary and merges files to a directory."""
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
return (vocab_file,)
# Register the tokenizer with the AutoTokenizer class
from transformers import AutoTokenizer
AutoTokenizer.register("akshara", AksharaTokenizer)
# Register the model configuration if needed
from transformers.models.auto.configuration_auto import CONFIG_MAPPING
if "akshara" not in CONFIG_MAPPING:
from transformers import PretrainedConfig
class AksharaConfig(PretrainedConfig):
model_type = "akshara"
CONFIG_MAPPING.register("akshara", AksharaConfig)