embedding-training
embedding-training-data
Training Data for Text Embedding Models
[!NOTE]
This repository contains raw datasets, all of which have also been formatted for easy training in the Embedding Model Datasets collection. We recommend looking there first.
This repository contains training files to train text embedding models, e.g. using sentence-transformers.
Data Format
All files are in a jsonl.gz format: Each line contains a JSON-object that represent one training example.
The JSON objects can… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/embedding-training-data.KaLM-reranker-training-data
Lychee-KaLM-Reranker Training Data
A large-scale, ready-to-use multilingual dataset for fine-tuning reranking models.
This repository contains 3,885,265 training samples collected from 54 datasets, covering English, Chinese, and multilingual retrieval tasks. Each sample includes task instructions, positive passages, at least 16 hard negatives, and teacher scores annotated by Qwen3-Reranker-8B.
When expanded into point-wise query–passage pairs, the dataset provides at least 66… See the full description on the dataset page: https://huggingface.co/datasets/KaLM-Embedding/KaLM-reranker-training-data.turkish_embedding_model_training_datacleaned_turkish_embedding_model_training_data_colabcleaned_turkish_embedding_model_training_data_colab
Citation
If you use this dataset in your research, please cite the following paper:
@inproceedings{baysan-gungor-2025-tr,
title = "{TR}-{MTEB}: A Comprehensive Benchmark and Embedding Model Suite for {T}urkish Sentence Representations",
author = "Baysan, Mehmet Selman and
Gungor, Tunga",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher =… See the full description on the dataset page: https://huggingface.co/datasets/trmteb/cleaned_turkish_embedding_model_training_data_colab.eti-embedding-training-data-2048-v3
Version note (v3): third generation of the ETI dense training data. Reuses the questions of NorskHelsenett/eti-embedding-training-data-v2 (which trained eti-embeddinggemma-v2), but re-maps every question to its article URL, re-cuts positives at a 2048-token window, re-mines hard negatives with granite + reranker validation (pos_score/neg_score), adds the keyword half, and filters junk anchors. Trained NorskHelsenett/eti-embeddinggemma-v3.
eti-embedding-training-data-2048… See the full description on the dataset page: https://huggingface.co/datasets/NorskHelsenett/eti-embedding-training-data-2048-v3.