---
license: apache-2.0
library_name: transformers
pipeline_tag: feature-extraction
tags:
  - tokenizer
  - embeddings
  - unicode
  - feature-extraction
---

# bvv241-2-3: Unicode - based Tokenizer with Precomputed Frozen Embeddings

This model is a tokenizer and associated precomputed frozen embeddings presented in the paper [Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations](https://huggingface.co/papers/2507.04886).

Code: https://github.com/AVBochkov/Embeddings

## Tokenizer Description

This tokenizer is based on a hybrid vocabulary:  

This tokenizer uses a strictly structured Unicode mapping scheme:

- Plane 0 (0–65535): All single Unicode code points (monograms) are mapped 1:1 to token codes, directly matching standard Unicode BMP.
- Private and unused code ranges (Plane 0, e.g., 0xE000–0xF8FF):
    - All multi-character tokens (bigrams, trigrams) are placed exclusively in these ranges.
- This design achieves total, lossless Unicode text coverage, with all multi-symbol tokens isolated above the core Unicode range.
- Data-driven bigrams and trigrams from Wikipedia (token co-occurrence),  
- Vocabulary size: 65,536 tokens,  
- Embedding dimension: 1024.

The associated `normalized_embeddings_weights.pt` file contains a [vocab_size x embed_dim] matrix of precomputed, L2-normalized, frozen embeddings.    
No semantic information is encoded; embeddings remain fixed throughout LM pretraining.

This tokenizer and embedding set is ideal for exploring semantic emergence and modular/fusion LM training over frozen, 
surface-level representations, enabling reproducible experiments and research.

## How to Get Started with the Tokenizer

```python

from transformers import AutoTokenizer

from huggingface_hub import hf_hub_download

import torch

tokenizer = AutoTokenizer.from_pretrained('Bochkov/bvv241-2-3')


emb_path = hf_hub_download(
    repo_id="Bochkov/bvv241-2-3",
    filename="normalized_embeddings_weights.pt"
)

embeddings = torch.load(emb_path)
```

## 🧑‍🔬 Citation & Concept

If you use this model or the underlying concepts in your research, please cite our work:

```
@article{
      bochkov2025emergent,
      title={Emergent Semantics Beyond Token Embeddings: Transformer {LM}s with Frozen Visual Unicode Representations},
      author={Andrey Bochkov},
      journal={Transactions on Machine Learning Research},
      issn={2835-8856},
      year={2025},
      url={https://openreview.net/forum?id=Odh8IynO1o},
      note={}
}

@misc{bochkov2025growingtransformersmodularcomposition,
      title={Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate}, 
      author={A. Bochkov},
      year={2025},
      eprint={2507.07129},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2507.07129}, 
}
```

This work demonstrates that transformer blocks, not token embeddings, carry the semantic burden in LLMs — a step toward modular, fusable, multilingual LMs.