datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
massive_serve_pes2o_v3_e5_base_v2wikipedia-embeddings-cs-e5-baseThis dataset contains the Czech subset of the wikimedia/wikipedia dataset. Each page is divided into paragraphs, stored as a list in the chunks column. For every paragraph, embeddings are created using the intfloat/multilingual-e5-base model.
Usage
Load the dataset:
from datasets import load_dataset
ds = load_dataset("karmiq/wikipedia-embeddings-cs-e5-base", split="train")
ds[1]
{
'id': '1',
'url': 'https://cs.wikipedia.org/wiki/Astronomie',
'title': 'Astronomie'… See the full description on the dataset page: https://huggingface.co/datasets/karmiq/wikipedia-embeddings-cs-e5-base.massive_serve_dpr_wiki_e5_base_v2wikipedia-longest-stride-chunked-500-embed-intfloat-multilingual-e5-base
Wikipedia-Longest-Stride-Chunked-500-Embed-intfloat-multilingual-e5-base
This is the embed processed version of Wikipedia-Longest-Stride-Chunked-500 HF Dataset.
Computational Resources
Used Computational Resources : Colab Pro+ H100 Instance
Total Consumed Time : 24h
Dataset processing
train : 974720 sequences, (1 ~ 512) range length of intfloat/multilingual-e5-base embeddings.
test, val : each 1000 sequences, (1 ~ 512) range length of intfloat/multilingual-e5-base embeddings.… See the full description on the dataset page: https://huggingface.co/datasets/enzoescipy/wikipedia-longest-stride-chunked-500-embed-intfloat-multilingual-e5-base.wikipedia-longest-salami-chunked-500-embed-intfloat-multilingual-e5-basemsmarco_passage_trec_dl_2019_judged_intfloat_e5_base_v2.flex
msmarco_passage_trec_dl_2019_judged_intfloat_e5_base_v2.flex
Description
TODO: What is the artifact?
Usage
# Load the artifact
import pyterrier as pt
artifact = pt.Artifact.from_hf('JackMcKechnie/msmarco_passage_trec_dl_2019_judged_intfloat_e5_base_v2.flex')
# TODO: Show how you use the artifact
Benchmarks
TODO: Provide benchmarks for the artifact.
Reproduction
# TODO: Show how you constructed the artifact.
Metadata
{… See the full description on the dataset page: https://huggingface.co/datasets/JackMcKechnie/msmarco_passage_trec_dl_2019_judged_intfloat_e5_base_v2.flex.massive_serve_dpr_wiki_e5_base_v2_ivfpqsara_intfloat_e5_base_v2.flex
sara_intfloat_e5_base_v2.flex
Description
TODO: What is the artifact?
Usage
# Load the artifact
import pyterrier as pt
artifact = pt.Artifact.from_hf('JackMcKechnie/sara_intfloat_e5_base_v2.flex')
# TODO: Show how you use the artifact
Benchmarks
TODO: Provide benchmarks for the artifact.
Reproduction
# TODO: Show how you constructed the artifact.
Metadata
{
"type": "dense_index",
"format": "flex",
"vec_size": 768… See the full description on the dataset page: https://huggingface.co/datasets/JackMcKechnie/sara_intfloat_e5_base_v2.flex.python-py_base1_sft_20k_e5-emptysara_large_intfloat_e5_base_v2.flex
sara_large_intfloat_e5_base_v2.flex
Description
TODO: What is the artifact?
Usage
# Load the artifact
import pyterrier as pt
artifact = pt.Artifact.from_hf('JackMcKechnie/sara_large_intfloat_e5_base_v2.flex')
# TODO: Show how you use the artifact
Benchmarks
TODO: Provide benchmarks for the artifact.
Reproduction
# TODO: Show how you constructed the artifact.
Metadata
{
"type": "dense_index",
"format": "flex",
"vec_size":… See the full description on the dataset page: https://huggingface.co/datasets/JackMcKechnie/sara_large_intfloat_e5_base_v2.flex.retrieved_results_cosqa_qe_baseline_dres_intfloat_e5-base-v2E5-base_1000diss_512chunksizeretrieved_results_cosqa_upd_deepseek1_dres_intfloat_e5-base-v2minerva-rag-e5-baseretrieved_results_cosqa_deepseek1_dres_intfloat_e5-base-v2retrieved_results_cosqa_baseline_dres_intfloat_e5-base-v2retrieved_results_cosqa_granite1_dres_intfloat_e5-base-v2retrieved_results_cosqa_upd_baseline_dres_intfloat_e5-base-v2bengali-qa-faiss-index-e5-baseE5-base_100diss_512chunksizeintfloat-e5-base-arguana-text-30multilingual-e5-base