metadata
language:
- multilingual
tags:
- ethiobbpe
- bpe
- tokenizer
- byte-level
license: apache-2.0
datasets:
- user-provided
EthioBBPE Tokenizer
This is a production-ready Byte-Level BPE tokenizer with advanced features for deployment.
Features
- Byte-Level: Handles any Unicode character without .
- Multi-format Compression: Supports gzip, bz2, and lzma compression.
- Checkpointing: Built-in safety checkpoints with metadata tracking.
- Quantization: Optional 8-bit/4-bit quantization for efficient deployment.
- Training Metrics: Comprehensive metrics tracking and logging.
- Automatic Backup: Checkpoint rotation to manage disk space.
Usage
Transformers
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EthioBBPE_AmharicBible")
Tokenizers Library
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_file("tokenizer.json")
Loading Compressed Vocab
import gzip
import json
# Load compressed vocabulary
with gzip.open("vocab.json.gz", 'rt', encoding='utf-8') as f:
vocab = json.load(f)
Training Configuration
{
"vocab_size": 16000,
"min_frequency": 2,
"show_progress": true,
"special_tokens": [
"<pad>",
"<unk>",
"<s>",
"</s>",
"<mask>"
],
"lowercase": false,
"dropout": null,
"data_dir": "./data",
"model_save_dir": "models",
"model_name": "EthioBBPE_AmharicBible",
"use_checkpoint": true,
"checkpoint_dir": "./models/checkpoints",
"save_compressed": true,
"compression_format": "gzip",
"compression_level": 9,
"checkpoint_steps": null,
"num_threads": -1,
"enable_backup": true,
"max_checkpoints": 5,
"enable_quantization": true,
"quantization_bits": 8
}
Model Files
tokenizer.json: Standard tokenizer file (required)vocab.json.gz: Compressed vocabulary (optional, smaller size)config.json: Training configurationtraining_metrics.json: Training statisticsspecial_tokens_map.json: Special tokens mappingREADME.md: This file
Checkpoints
Checkpoints are saved in the models/checkpoints directory with metadata including:
- Checkpoint ID and timestamp
- Vocabulary size
- SHA256 checksum for integrity verification
- Training metrics at checkpoint time