The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
morphbpe_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
morphbpe_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
morphbpe_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
vs
bpe_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
morphbpe_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
unigram_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 764, in write_table
self.write_rows_on_file() # in case there are buffered rows to write first
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 662, in write_rows_on_file
table = pa.concat_tables(self.current_rows)
File "pyarrow/table.pxi", line 6320, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
morphbpe_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
morphbpe_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
morphbpe_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
vs
bpe_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
morphbpe_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
unigram_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 662, in write_rows_on_file
table = pa.concat_tables(self.current_rows)
File "pyarrow/table.pxi", line 6320, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
morphbpe_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed42: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
bpe_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
morphbpe_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
morphbpe_seed1: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
unigram_seed2: struct<test_loss: double, test_f1: double, test_precision: double, test_recall: double, test_accuracy: double, test_runtime: double, test_samples_per_second: double, test_steps_per_second: double, epoch: double, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string>
vs
bpe_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
morphbpe_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
unigram_seed42: struct<test_em: double, test_f1: double, n_test: int64, finetune_minutes: double, pretrained_model: string, dataset: string, seed: int64, epochs: int64, tag: string, max_len: int64>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
tokenizer string | vocab_size int64 | fertility float64 | chars_per_token float64 | bytes_per_token float64 | total_tokens int64 | total_words int64 | seconds float64 |
|---|---|---|---|---|---|---|---|
kk-bpe-32k | 32,000 | 1.6793 | 4.6724 | 8.4599 | 4,981,192 | 2,966,207 | 12.6 |
kk-unigram-32k | 4 | 2 | 3.9232 | 7.1034 | 5,932,414 | 2,966,207 | 10.3 |
XLM-R | 250,002 | 2.1811 | 3.5974 | 6.5136 | 6,469,672 | 2,966,207 | 16.4 |
GPT-4o (o200k) | 200,019 | 2.5986 | 3.0196 | 5.4672 | 7,707,849 | 2,966,207 | 4.8 |
mBERT | 119,547 | 2.9292 | 2.6787 | 4.8502 | 8,688,494 | 2,966,207 | 13 |
Qwen2.5 | 151,643 | 4.7862 | 1.6394 | 2.9683 | 14,196,803 | 2,966,207 | 17.3 |
Llama-3 | 128,000 | 4.8033 | 1.6336 | 2.9577 | 14,247,604 | 2,966,207 | 16 |
GPT-4 (cl100k) | 100,277 | 5.8952 | 1.331 | 2.4099 | 17,486,314 | 2,966,207 | 3.5 |
null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null |
Qwen2.5 | null | null | null | null | null | null | null |
XLM-R | null | null | null | null | null | null | null |
mBERT | null | null | null | null | null | null | null |
kk-morph-hf-unigram-32k | null | null | null | null | null | null | null |
kk-morph-sp-unigram-32k | null | null | null | null | null | null | null |
kk-hf-unigram-32k | null | null | null | null | null | null | null |
kk-bpe-32k | null | null | null | null | null | null | null |
kk-morph-hf-bpe-32k | null | null | null | null | null | null | null |
kk-sp-unigram-32k | null | null | null | null | null | null | null |
kk-sp-bpe-32k | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null |
kk-bpe-32k | 32,000 | 1.6793 | 4.6724 | 8.4599 | 4,981,192 | 2,966,207 | 12.9 |
kk-sp-bpe-32k | 32,000 | 1.758 | 4.4632 | 8.0812 | 5,214,647 | 2,966,207 | 11.2 |
kk-sp-unigram-32k | 32,000 | 1.7617 | 4.454 | 8.0645 | 5,225,471 | 2,966,207 | 4.1 |
kk-hf-unigram-32k | 32,000 | 2.0264 | 3.8722 | 7.011 | 6,010,620 | 2,966,207 | 23.8 |
XLM-R | 250,002 | 2.1811 | 3.5974 | 6.5136 | 6,469,672 | 2,966,207 | 16.5 |
GPT-4o (o200k) | 200,019 | 2.5986 | 3.0196 | 5.4672 | 7,707,849 | 2,966,207 | 4.9 |
mBERT | 119,547 | 2.9292 | 2.6787 | 4.8502 | 8,688,494 | 2,966,207 | 13.4 |
Qwen2.5 | 151,643 | 4.7862 | 1.6394 | 2.9683 | 14,196,803 | 2,966,207 | 17.4 |
Llama-3 | 128,000 | 4.8033 | 1.6336 | 2.9577 | 14,247,604 | 2,966,207 | 16 |
GPT-4 (cl100k) | 100,277 | 5.8952 | 1.331 | 2.4099 | 17,486,314 | 2,966,207 | 3.6 |
kk-bpe-32k | 32,000 | 1.6793 | 4.6724 | 8.4599 | 4,981,192 | 2,966,207 | 12.9 |
kk-sp-bpe-32k | 32,000 | 1.758 | 4.4632 | 8.0812 | 5,214,647 | 2,966,207 | 11.2 |
kk-morph-hf-bpe-32k | 32,000 | 1.7608 | 4.4561 | 8.0682 | 5,223,030 | 2,966,207 | 62.7 |
kk-sp-unigram-32k | 32,000 | 1.7617 | 4.454 | 8.0645 | 5,225,471 | 2,966,207 | 4.1 |
kk-morph-sp-unigram-32k | 32,000 | 1.9449 | 4.0344 | 7.3048 | 5,768,923 | 2,966,207 | 8.5 |
kk-hf-unigram-32k | 32,000 | 2.0264 | 3.8722 | 7.011 | 6,010,620 | 2,966,207 | 23.8 |
XLM-R | 250,002 | 2.1811 | 3.5974 | 6.5136 | 6,469,672 | 2,966,207 | 16.5 |
kk-morph-hf-unigram-32k | 32,000 | 2.3461 | 3.3445 | 6.0556 | 6,958,922 | 2,966,207 | 27 |
GPT-4o (o200k) | 200,019 | 2.5986 | 3.0196 | 5.4672 | 7,707,849 | 2,966,207 | 4.9 |
mBERT | 119,547 | 2.9292 | 2.6787 | 4.8502 | 8,688,494 | 2,966,207 | 13.4 |
Qwen2.5 | 151,643 | 4.7862 | 1.6394 | 2.9683 | 14,196,803 | 2,966,207 | 17.4 |
Llama-3 | 128,000 | 4.8033 | 1.6336 | 2.9577 | 14,247,604 | 2,966,207 | 16 |
GPT-4 (cl100k) | 100,277 | 5.8952 | 1.331 | 2.4099 | 17,486,314 | 2,966,207 | 3.6 |
Kazakh Tokenizer Fertility Baseline
Reproducible fertility benchmark of subword tokenizers on the Kazakh language. Companion artifact for the paper "Tokenizer Optimization for Kazakh Small Language Models" (in preparation, target: ACM TALLIP).
Headline numbers
| Tokenizer | Fertility | |
|---|---|---|
| π₯ Best overall | kk-bpe-32k |
1.679 |
| π¨ Worst | GPT-4 (cl100k) |
5.895 |
| GPT-4 penalty | GPT-4 (cl100k) is 3.51Γ worse than the best Kazakh-trained tokenizer |
β The custom Kazakh BPE tokenizer is 3.51Γ more efficient than GPT-4 for Kazakh text, which translates to ~72% lower API cost and ~3.5Γ longer effective context window.
Full ranking (13 tokenizers)
Ours β Kazakh-trained, no morphology pre-segmentation
| Tokenizer | Vocab | Fertility β | Chars/Token β |
|---|---|---|---|
kk-bpe-32k |
32,000 | 1.679 | 4.672 |
kk-sp-bpe-32k |
32,000 | 1.758 | 4.463 |
kk-sp-unigram-32k |
32,000 | 1.762 | 4.454 |
kk-hf-unigram-32k |
32,000 | 2.026 | 3.872 |
Ours β Kazakh-trained, morphology-aware (Morfessor β tokenizer)
| Tokenizer | Vocab | Fertility β | Chars/Token β |
|---|---|---|---|
kk-morph-hf-bpe-32k |
32,000 | 1.761 | 4.456 |
kk-morph-sp-unigram-32k |
32,000 | 1.945 | 4.034 |
kk-morph-hf-unigram-32k |
32,000 | 2.346 | 3.345 |
Reference β multilingual / industry tokenizers
| Tokenizer | Vocab | Fertility β | Chars/Token β |
|---|---|---|---|
XLM-R |
250,002 | 2.181 | 3.597 |
GPT-4o (o200k) |
200,019 | 2.599 | 3.020 |
mBERT |
119,547 | 2.929 | 2.679 |
Qwen2.5 |
151,643 | 4.786 | 1.639 |
Llama-3 |
128,000 | 4.803 | 1.634 |
GPT-4 (cl100k) |
100,277 | 5.895 | 1.331 |
Methodology
Held-out evaluation set: 2,966,207 whitespace-words (β15K documents)
from the validation split of Abzalbek89/corpus_clean.
Metrics:
- Fertility = total tokens / total whitespace-words (lower is better).
- Compression (chars/token) = total characters / total tokens (higher is better).
- Compression (bytes/token) = total UTF-8 bytes / total tokens (higher is better).
Trained tokenizers (vocab = 32,000, all share the same training corpus):
kk-bpe-32kβ HF tokenizers ByteLevel BPEkk-sp-bpe-32kβ SentencePiece BPE (character_coverage=1.0, NFKC)kk-sp-unigram-32kβ SentencePiece Unigramkk-hf-unigram-32kβ HF tokenizers Unigram with ByteLevel pre-tokkk-morph-hf-bpe-32kβ HF BPE on Morfessor-segmented corpuskk-morph-hf-unigram-32kβ HF Unigram on Morfessor-segmented corpuskk-morph-sp-unigram-32kβ SentencePiece Unigram on Morfessor-segmented corpus
Reference tokenizers: mBERT, XLM-R, Llama-3, Qwen 2.5, GPT-4 (cl100k_base), GPT-4o (o200k_base).
Files
| Path | Description |
|---|---|
experiment.py |
v1 β single Unigram baseline |
experiment_v2.py |
v2 β extended (BPE/Unigram across libraries) |
experiment_v3_fix.py |
v2 fix β re-measure SentencePiece tokenizers via raw SentencePieceProcessor |
experiment_morph.py |
Experiment 2 β morphology-aware variants |
morfessor.bin |
trained Morfessor segmentation model (top-1M Kazakh words) |
v2/fertility_v2.csv, .json, .png |
fixed v2 numbers |
v3/fertility_v3.csv, .json, .png |
extended v3 numbers (with morph-aware) |
v3/RESULTS_V3.md |
human-readable v3 report |
Reproducing
On a vast.ai / RunPod / Colab instance with β₯30GB disk and β₯16GB RAM:
pip install 'datasets>=2.14' 'transformers>=4.45' 'tokenizers>=0.20' \
'sentencepiece>=0.2.0' 'huggingface_hub>=0.25' \
tiktoken matplotlib morfessor
export HF_TOKEN=hf_... # write access
export HF_USER=Abzalbek89
curl -fsSL -o experiment_morph.py \
https://huggingface.co/datasets/Abzalbek89/kk-tokenizer-fertility-baseline/resolve/main/experiment_morph.py
python experiment_morph.py
Total runtime: ~30 min on cached Morfessor model, ~70 min cold start.
Companion tokenizer repos
Abzalbek89/kk-tokenizer-bpe-32kAbzalbek89/kk-tokenizer-sp-bpe-32kAbzalbek89/kk-tokenizer-sp-unigram-32kAbzalbek89/kk-tokenizer-hf-unigram-32kAbzalbek89/kk-tokenizer-morph-hf-bpe-32kΒΉAbzalbek89/kk-tokenizer-morph-hf-unigram-32kΒΉAbzalbek89/kk-tokenizer-morph-sp-unigram-32kΒΉ
ΒΉ Morphology-aware variants β apply Morfessor (morfessor.bin in this repo) before encoding.
Citation
@misc{kk_tokenizer_fertility_2026,
title = {Kazakh Tokenizer Fertility Baseline},
author = {Abzalbek Ulasbek},
year = {2026},
howpublished = {\url{https://huggingface.co/datasets/Abzalbek89/kk-tokenizer-fertility-baseline}},
}
License
Apache 2.0
Last updated: fertility_v3.csv (13 tokenizers)
- Downloads last month
- 354