Datasets:

alibayram
/

cosmos-corpus-encoded

language:
  - tr
license: mit
task_categories:
  - feature-extraction
size_categories:
  - 100K<n<1M
tags:
  - embedding-distillation
  - student-teacher
pretty_name: Cosmos Corpus Encoded
dataset_info:
  features:
    - name: text
      dtype: string
    - name: teacher_embedding_final
      list: float64
    - name: teacher_embedding_pre_dense
      list: float64
    - name: tabi_input_ids
      list: int64
    - name: cosmos_input_ids
      list: int64
    - name: mursit_input_ids
      list: int64
    - name: mft_input_ids
      list: int64
  splits:
    - name: train
      num_bytes: 6603761113
      num_examples: 224779
  download_size: 1576033082
  dataset_size: 6603761113
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Cosmos Corpus Encoded for Embedding Distillation

This dataset is a pre-tokenized version of alibayram/cosmos-corpus-0-05-with-embeddings, designed for efficient embedding distillation training of MFT and TabiBERT models.

Dataset Description

Source: alibayram/cosmos-corpus-0-05-with-embeddings
Language: Turkish
Task: Embedding Distillation (Teacher-Student Training)
Total Examples: 224,807 (Filtered from 300,000)
Max Sequence Length: 2048 tokens

Pre-processing & Filtering

The dataset was processed using two different tokenizers to support multiple student architectures:

MFT Tokenizer: A custom morphologically informed tokenizer.
TabiBERT Tokenizer: A BERT-based tokenizer with 32k vocabulary.

Filtering:

Original size: 300,000 examples.
Filtered size: 224,807 examples (~75%).
Criterion: Both mft_input_ids and tabi_input_ids must be <= 2048 tokens.
Sequences longer than 2048 tokens were dropped to ensure efficient training within context limits.

Dataset Structure

The dataset contains the following columns:

Column	Type	Description
`text`	`string`	The original raw text content.
`mft_input_ids`	`list[int]`	Token IDs encoded using the MFT tokenizer.
`tabi_input_ids`	`list[int]`	Token IDs encoded using the TabiBERT tokenizer.
`teacher_embedding_final`	`list[float]`	Final layer embeddings from the teacher model (Gemma-2-9b-it).

Data Instances

{
  'text': 'Makine öğrenmesi, verilerden öğrenen algoritmaların çalışılmasıdır.',
  'mft_input_ids': [124, 5921, ...],
  'tabi_input_ids': [101, 2341, ...],
  'teacher_embedding_final': [0.021, -0.054, ...]  # 3584-dimensional vectors
}

Usage

This dataset is optimized for the EmbeddingDistillationTrainer. You can load it directly without needing to re-tokenize during training.

from datasets import load_dataset

dataset = load_dataset("alibayram/cosmos-corpus-encoded")

Training Example

To train a model using the mft_input_ids column:

from embedding_trainer import EmbeddingDistillationTrainer, EmbeddingTrainerConfig

config = EmbeddingTrainerConfig(
    student_model="alibayram/mft-downstream-task-embeddinggemma",
    input_ids_column="mft_input_ids",  # or "tabi_input_ids"
    embedding_column="teacher_embedding_final",
    loss_type="cosine",
    batch_size=256
)

trainer = EmbeddingDistillationTrainer(config)
trainer.train("alibayram/cosmos-corpus-encoded")

Creation Details

Created by: Ali Bayram
Date: 2026-01-25
Teacher Model: google/gemma-2-9b-it (Embeddings extracted via sartify-llm/Gemma-2-9b-it-v2-embedding)
Processing Script: prepare_dataset.py

License

MIT