datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
two-towers
Load
with open("thefile.pkl", "rb") as f:
query_vectors, passage_vectors = pickle.load(f)
mlx7-two-tower-data
mlx7-two-tower-data
This repository contains datasets used for training Two-Tower (Dual Encoder) models for document retrieval.
Dataset Description
The datasets provided here are structured for training dual encoder models with various sampling strategies:
classic_triplets: 48.2 MB
intra_query_neg: 47.6 MB
multi_pos_multi_neg: 126.5 MB
Dataset Details
classic_triplets.parquet: Standard triplet format with (query, positive_document, negative_document)… See the full description on the dataset page: https://huggingface.co/datasets/Azuremis/mlx7-two-tower-data.lastfm-1k-twotower
Last.fm 1K — Two-Tower Ready
A processed, train-ready version of the Last.fm 1K users dataset, prepared for retrieval/recommender models (two-tower, ALS, BPR, etc.).
Built as part of loopback — an open-source two-tower neural recommender for music.
What's inside
Split
Rows
Size
train
15,320,694
144 MB
val
1,915,086
18 MB
test
1,915,087
18 MB
Each row: (user_idx, track_idx, artist_idx, ts) — integer-encoded IDs ready to plug into an embedding layer… See the full description on the dataset page: https://huggingface.co/datasets/DanielRegaladoCardoso/lastfm-1k-twotower.triples_two_towersml6-two-towers