semvec/pypi-packages huggingface dataset:
greedtok 0.14.1
Partition Cover Approach to Tokenization
License: MIT

# A partition cover approach to tokenization
In this work, we formulate tokenization as an optimization objective, show that it is NP-hard via a simple reduction from vertex cover, and propose a polynomial-time greedy algorithm **GreedTok**.
Our formulation naturally relaxes to the well-studied weighted maximum coverage problem which has a simple $(1 - 1/e)$-approximation greedy algorithm.

### Compute Performance Update
Compared to the base implementation, beta6 has a considerable speedup! The key difference is the implementation of greedy (lazy) updates on top of greedy choice. 

<center>

|Dataset| $k$ | #uniq words | #candidates | Current (0.14.x) | Gain from lazy updates |
| ------- | --- | -------: | -------: | -------:  | :----: |
| UN           |5K |105,505  |884,630  |  ~6 seconds | x23 |
| arXiv        |5K |881,233  |7,625,530| ~63 seconds | x26 |
| Wiki         |10K|8,769,943|93,243,449| ~11 minutes | x68 |
| PubMed       |10K|6,527,614|97,870,366| ~11 minutes | x133|
| Wiki-chinese |10K|7,035,544|69,728,860| ~8.5 minutes| x90 |
| Wiki-japanese|10K|2,737,555|60,410,961| ~8.5 minutes| x74 |
| Wiki-korean  |10K|5,459,833|130,927,124 | ~18 minutes | x86 |

</center>

Table results shows time to solve (obtain a $k$-sized token set) from word counts. Since most of the compute is front-heavy, solving for larger $k$ size is trivial.
For detailed logs, compare cpp_logs/{$data}/{$data}.log versus cpp_logs/{$data}/{$data}_fast.log. 


###  Huggingface AutoTokenizer interface

Install the v0.14.x version (for transformers >= 4):
```
wget "https://github.com/PreferredAI/pcatt/archive/refs/tags/v0.14.1.zip"
unzip v0.14.1.zip -d pcatt
cd pcatt
pip install -r requirements.txt
pip install .
```

For "training" either:
```
from pcatt.hf.greedtok import GreedTok
greedtok = GreedTok().train_new_from_iterator(word_iterator, 100, max_token_length=5, min_word_count=1)
```
or
```
from pcatt.hf.greedtok import GreedTok
greedtok = GreedTok().train_new_from_counts(word_count_dict, 100, max_token_length=5, min_word_count=1)
```
To use either:
```
from pcatt.hf.greedtok import GreedTok
greedtok = GreedTok.from_pretrained(greedtok_file_directory)
```
or
```
import pcatt.hf
greedtok = AutoTokenizer.from_pretrained("greedtok_file_directory")
```
Refer to [eval_hf.ipynb](https://github.com/PreferredAI/aoatt/blob/main/eval_hf.ipynb) for examples and tips. Note that the code in pcatt.hf is Apache 2.0 (following Huggingface Tokenizers).

Evaluations in [eval_notebook.ipynb](https://github.com/PreferredAI/aoatt/blob/main/eval_notebook.ipynb).

### Citation
Withheld for repo anonymity