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ksopyla/
minipile-unigram-32k-100k

Name: minipile-unigram-32k-100k
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Custom Unigram Tokenizer for Minipile (32k Vocab)

This is a Unigram tokenizer (SentencePiece-style) trained on the JeanKaddour/minipile dataset.

Language: English (en). Note: While the Unigram algorithm handles unicode characters, the vocabulary is optimized for English text, code, and common technical terms found in Minipile.

It was developed for the MrCogito project, which explores novel transformer architectures like the Concept Encoder.

Training Details

Dataset: JeanKaddour/minipile
Sample Size: 100000 documents
Preprocessing: Documents were truncated to a maximum length of 4096 characters to ensure training stability while preserving local context (code blocks, latex, paragraphs).
Algorithm: Unigram (SentencePiece)
Vocab Size: 32768
Normalization: NFKC (Cased)
Pre-tokenization: Metaspace

Motivation for Concept Encoders

This tokenizer is specifically optimized for Concept Encoder and Concept Decoder architectures (e.g., Perceiver IO, Latent Transformers).

Why Unigram for Concept Encoding?

Concept Encoders work by compressing a sequence of tokens $T$ into a smaller set of abstract latent vectors (concepts) $C$. The efficiency of this compression depends heavily on the input quality:

The Problem with BPE (BERT/GPT): BPE is a greedy compression algorithm. It often splits words into arbitrary frequent chunks (e.g., unbe + liev + able). A Concept Encoder must waste model capacity "repairing" these arbitrary splits to understand the word before it can even begin extracting the higher-level concept.
The Unigram Advantage: The Unigram algorithm is probabilistic and tends to preserve linguistically meaningful morphological units (e.g., un + believ + able). This acts as a "soft pre-compression", feeding the encoder units that already carry semantic weight. This allows the Concept Encoder to focus its limited latent capacity on semantic aggregation rather than morphological repair.

Why Minipile?

While XLNet uses the Unigram algorithm, its vocabulary (circa 2019) lacks modern terms. By training a fresh Unigram model on Minipile (2023), we combine the superior morphological segmentation of Unigram with the vocabulary coverage of modern LLMs (code, Python, ChatML, technical terms).

Features

Algorithm: Unigram (SentencePiece).
Vocab Size: 32768 tokens.
Normalization: NFKC (Cased).
Pre-tokenization: Metaspace (reversible).
Chat Support: Includes standard ChatML special tokens (<|im_start|>, <|im_end|>) and a pre-configured chat template.

Usage

python

from transformers import AutoTokenizer

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("ksopyla/minipile-unigram-32k-100k")

# Basic encoding
text = "Hello, world! This is a test."
tokens = tokenizer.tokenize(text)
print(tokens) 
# Output: [' Hello', ',', ' world', '!', ' This', ' is', ' a', ' test', '.']

# Chat Template
messages = [
    {"role": "user", "content": "What is the Concept Encoder?"},
    {"role": "assistant", "content": "It is a novel transformer architecture..."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(prompt)
# Output: <|im_start|>user\nWhat is the Concept Encoder?<|im_end|>\n<|im_start|>assistant\n...

Special Tokens

PAD: <pad>
UNK: <unk>
CLS: <cls>
SEP: <sep>
MASK: <mask>
Chat: <|im_start|>, <|im_end|>, <|user|>, <|assistant|>, <|system|>, <|endoftext|>
Unused: <|unused0|> ... <|unused99|> (100 reserved tokens)