rerank
Datasets
All datasets matching “rerank”scidocs-rerankingaskubuntudupquestions-rerankingettin-reranker-v1-data
Ettin Reranker v1 Training Data
This is the training dataset used to produce the cross-encoder/ettin-reranker-{17m,32m,68m,150m,400m,1b}-v1 family of CrossEncoder rerankers. It's a mix of broad-domain text-pair data and retrieval pairs rescored with a strong teacher reranker, with every label produced by an automated scoring system rather than a human annotator.
Structure
Every config has the same three columns:
column
type
description
query
string
The… See the full description on the dataset page: https://huggingface.co/datasets/cross-encoder/ettin-reranker-v1-data.CMedQAv1-reranking
CMedQAv1-reranking
An MTEB dataset
Massive Text Embedding Benchmark
Chinese community medical question answering
Task category
t2t
Domains
Medical, Written
Reference
https://github.com/zhangsheng93/cMedQA
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["CMedQAv1-reranking"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/CMedQAv1-reranking.INQUIRE_Rerank
Dataset Card for INQUIRE-ReRank
This is a FiftyOne dataset with 16000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/INQUIRE_Rerank")
# Launch the App
session = fo.launch_app(dataset)
Important Notes:
Although… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/INQUIRE_Rerank.CMedQAv2-reranking
CMedQAv2-reranking
An MTEB dataset
Massive Text Embedding Benchmark
Chinese community medical question answering
Task category
t2t
Domains
Medical, Written
Reference
https://github.com/zhangsheng93/cMedQA2
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["CMedQAv2-reranking"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/CMedQAv2-reranking.