ONNX-Demo / pyserini /encoded_corpus_info.py
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#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
CORPUS_INFO = {
"scipy-sparse-vectors.msmarco-v1-passage-slimr": {
"description": "MS MARCO passages-v1 corpus encoded by SLIM trained with BM25 negatives. (Scipy)",
"filename": "scipy-sparse-vectors.msmarco-v1-passage-slimr.20230220.tar.gz",
"urls": [
"https://vault.cs.uwaterloo.ca/s/4MRXSmiDqNH4mgF/download",
],
"md5": "7ec96c74dced272712fcbb091bb671a8",
"size (bytes)": 16533697862,
"documents": 8841823,
"downloaded": False
},
"scipy-sparse-vectors.msmarco-v1-passage-slimr-pp": {
"description": "MS MARCO passages-v1 corpus encoded by SLIM trained with cross-encoder distillation and hardnegative mining (Scipy)",
"filename": "scipy-sparse-vectors.msmarco-v1-passage-slimr-pp.20230220.tar.gz",
"urls": [
"https://vault.cs.uwaterloo.ca/s/gDJnrYGKsq6ir4w/download",
],
"md5": "05ce2ce5f64b668a487909ab538ef2a5",
"size (bytes)": 15785241481,
"documents": 8841823,
"downloaded": False
},
}