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Datasets:
BUT-FIT
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FLiP-data

Tasks:
Sentence Similarity
Feature Extraction
Languages:
English
ArXiv:
Tags:
sonar
speech-embeddings
text-embeddings
common-voice
interpretability
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metadata 
language:
  - en
license: cc-by-4.0
multilinguality: monolingual
task_categories:
  - sentence-similarity
  - feature-extraction
tags:
  - sonar
  - speech-embeddings
  - text-embeddings
  - common-voice
  - interpretability
arxiv: 2604.18109
pretty_name: FLiP-data

FLiP-data

Preprocessed data for the FLiP project — Factorized Linear Projection for Interpreting Multimodal Multilingual Sentence Embeddings.

FLiP trains a factorized log-linear model to recover lexical content (keywords) from pretrained sentence embeddings via a single linear projection, with no fine-tuning of the encoder.

Contents

SONAR embeddings and transcripts for Mozilla Common Voice v15 English (train / dev / test):

File Description
*_speech_embs.npy SONAR speech embeddings (float32, shape [N, 1024])
*_text_embs.npy SONAR text embeddings (float32, shape [N, 1024])
*_sim_scores.npy Cosine similarity between paired speech and text embeddings
*_transcript.txt Reference transcripts (one utterance per line)
*_entities_gemini2.5_flash_lite.jsonl Named entities extracted with Gemini 2.5 Flash Lite

Splits: train (1M utterances), dev (16k), test (~16k).

Source data

Embeddings were computed from Mozilla Common Voice v15 English using the SONAR encoder. Audio and transcripts from Common Voice are licensed under CC BY 4.0.

Trained checkpoints

HF repo Training data Embedding Rank Size
BUT-FIT/FLiP-en-sonar → mcv15/rank-512/ MCV v15 EN SONAR 512 207 MB
BUT-FIT/FLiP-en-sonar → mcv15/rank-1024/ MCV v15 EN SONAR 1024 414 MB

Usage

See the FLiP GitHub repo for full installation instructions and training/evaluation scripts.

Quick start after downloading:

import numpy as np

train_speech = np.load("cv_15/en/sonar_embeddings/train_speech_embs.npy")
train_text   = np.load("cv_15/en/sonar_embeddings/train_text_embs.npy")

Citation 

@misc{kesiraju2026flip,
  title         = {{FLiP}: Towards understanding and interpreting multimodal multilingual sentence embeddings},
  author        = {Kesiraju, Santosh and Yusuf, Bolaji and Sedl{\'a}{\v{c}}ek, Simon and Plchot, Old{\v{r}}ich and Schwarz, Petr},
  year          = {2026},
  eprint        = {2604.18109},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2604.18109},
}