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Xianbao QIAN
commited on
Commit
•
9279ca3
1
Parent(s):
836ccb6
query tables and create example
Browse files- .gitattributes +10 -0
- content/data.json +1 -0
- python/0_download_files.py +103 -0
- python/1_ancestors.py +1 -0
- requirements.txt +3 -0
- tables/datasets.example.yaml +250 -0
- tables/datasets.parquet +3 -0
- tables/models.example.yaml +219 -0
- tables/models.parquet +3 -0
- tables/spaces.example.yaml +171 -0
- tables/spaces.parquet +3 -0
.gitattributes
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@@ -0,0 +1,10 @@
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public/ancestor_children.parquet filter=lfs diff=lfs merge=lfs -text
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public/datasets.parquet filter=lfs diff=lfs merge=lfs -text
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public/models.parquet filter=lfs diff=lfs merge=lfs -text
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public/parents.parquet filter=lfs diff=lfs merge=lfs -text
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public/spaces.parquet filter=lfs diff=lfs merge=lfs -text
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tables/ancestor_children.parquet filter=lfs diff=lfs merge=lfs -text
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tables/datasets.parquet filter=lfs diff=lfs merge=lfs -text
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tables/models.parquet filter=lfs diff=lfs merge=lfs -text
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tables/spaces.parquet filter=lfs diff=lfs merge=lfs -text
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tables/parents.parquet filter=lfs diff=lfs merge=lfs -text
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content/data.json
CHANGED
@@ -19,3 +19,4 @@
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}
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]
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}
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}
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]
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}
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python/0_download_files.py
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import os
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import requests
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from concurrent.futures import ThreadPoolExecutor
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from tqdm import tqdm
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import duckdb
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import random
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import argparse
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import yaml
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# Create the "tables" folders if they don't exist
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os.makedirs("tables", exist_ok=True)
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# URLs of the files to download
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urls = [
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"https://huggingface.co/datasets/cfahlgren1/hub-stats/resolve/main/models.parquet?download=true",
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"https://huggingface.co/datasets/cfahlgren1/hub-stats/resolve/main/datasets.parquet?download=true",
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"https://huggingface.co/datasets/cfahlgren1/hub-stats/resolve/main/spaces.parquet?download=true"
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]
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def download_file(url, overwrite=True):
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filename = os.path.join("tables", url.split("/")[-1].split("?")[0])
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if not overwrite and os.path.exists(filename):
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print(f"File already exists: {filename}. Skipping download.")
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return
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response = requests.get(url, stream=True)
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total_size = int(response.headers.get("Content-Length", 0))
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block_size = 1024 # 1 KB
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with open(filename, "wb") as file, tqdm(
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desc=filename,
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total=total_size,
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unit="iB",
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unit_scale=True,
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unit_divisor=1024,
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bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]"
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) as progress_bar:
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for data in response.iter_content(block_size):
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size = file.write(data)
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progress_bar.update(size)
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print(f"Downloaded: {filename}")
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def main(overwrite):
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# Create a ThreadPoolExecutor with max_workers set to 3 (number of files to download)
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with ThreadPoolExecutor(max_workers=3) as executor:
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# Submit download tasks to the executor
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futures = [executor.submit(download_file, url, overwrite) for url in urls]
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# Wait for all tasks to complete
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for future in futures:
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future.result()
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print("All files downloaded successfully.")
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# Process each downloaded Parquet file
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for url in urls:
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filename = os.path.join("tables", url.split("/")[-1].split("?")[0])
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table_name = os.path.splitext(os.path.basename(filename))[0]
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# Connect to the Parquet file using DuckDB
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con = duckdb.connect(database=':memory:')
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con.execute(f"CREATE VIEW {table_name} AS SELECT * FROM parquet_scan('{filename}')")
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# Retrieve the table structure
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table_structure = con.execute(f"DESCRIBE {table_name}").fetchall()
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# Generate the YAML content
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yaml_content = f"{table_name}:\n"
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yaml_content += " table_structure:\n"
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for row in table_structure:
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column, dtype = row[:2] # Unpack only the first two values
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yaml_content += f" - column: {column}\n"
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yaml_content += f" type: {dtype}\n"
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# Retrieve 10 random items from the table
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con.execute(f"CREATE VIEW {table_name}_random AS SELECT * FROM {table_name} ORDER BY RANDOM() LIMIT 10")
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random_items = con.execute(f"SELECT * FROM {table_name}_random").fetchall()
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yaml_content += " random_items:\n"
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for item in random_items:
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yaml_content += " - "
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for column, value in zip([row[0] for row in table_structure], item):
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yaml_content += f"{column}: {value}\n "
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yaml_content = yaml_content.rstrip() # Remove trailing spaces
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yaml_content += "\n"
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# Save the YAML content to a file in the "tables" folder
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yaml_file = os.path.join("tables", f"{table_name}.example.yaml")
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with open(yaml_file, "w") as file:
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file.write(yaml_content)
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print(f"Generated: {yaml_file}")
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print("Example files generated successfully.")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Download and process Parquet files.")
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parser.add_argument("--no-overwrite", action="store_true", help="Skip downloading files that already exist.")
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args = parser.parse_args()
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main(overwrite=not args.no_overwrite)
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python/1_ancestors.py
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``
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requirements.txt
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requests
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tqdm
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duckdb
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tables/datasets.example.yaml
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datasets:
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table_structure:
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- column: _id
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type: VARCHAR
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- column: id
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type: VARCHAR
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- column: author
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type: VARCHAR
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- column: cardData
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type: VARCHAR
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+
- column: disabled
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+
type: BOOLEAN
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- column: gated
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type: VARCHAR
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- column: lastModified
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type: VARCHAR
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- column: likes
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type: BIGINT
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- column: trendingScore
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type: DOUBLE
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- column: private
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type: BOOLEAN
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- column: sha
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type: VARCHAR
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- column: description
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type: VARCHAR
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- column: downloads
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type: BIGINT
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- column: tags
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type: VARCHAR[]
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- column: createdAt
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type: VARCHAR
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- column: key
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type: VARCHAR
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- column: paperswithcode_id
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type: VARCHAR
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- column: citation
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type: VARCHAR
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+
random_items:
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+
- _id: 64c38c73a8de22f7a1a6c7e1
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id: C-MTEB/EcomRetrieval-qrels
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+
author: C-MTEB
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cardData: {"configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "qid", "dtype": "string"}, {"name": "pid", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 27890, "num_examples": 1000}], "download_size": 14540, "dataset_size": 27890}}
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disabled: False
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gated: False
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lastModified: 2023-07-28T09:37:58.000Z
|
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likes: 0
|
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+
trendingScore: 0.0
|
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+
private: False
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sha: 39c90699b034ec22ac45b3abf5b0bbb5ffd421f9
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description:
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|
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|
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Dataset Card for "EcomRetrieval-qrels"
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57 |
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|
58 |
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|
59 |
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More Information needed
|
60 |
+
|
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downloads: 2893
|
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+
tags: ['size_categories:1K<n<10K', 'format:parquet', 'modality:text', 'library:datasets', 'library:pandas', 'library:mlcroissant', 'library:polars', 'region:us']
|
63 |
+
createdAt: 2023-07-28T09:37:55.000Z
|
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+
key:
|
65 |
+
paperswithcode_id: None
|
66 |
+
citation: None
|
67 |
+
- _id: 6571a64493ea01156d9c5f2b
|
68 |
+
id: topeomole/fin
|
69 |
+
author: topeomole
|
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+
cardData: None
|
71 |
+
disabled: False
|
72 |
+
gated: False
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+
lastModified: 2023-12-29T08:49:31.000Z
|
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+
likes: 0
|
75 |
+
trendingScore: 0.0
|
76 |
+
private: False
|
77 |
+
sha: 6e68c3a7cce23e0e650997d672fbef009b14af32
|
78 |
+
description: None
|
79 |
+
downloads: 0
|
80 |
+
tags: ['size_categories:10K<n<100K', 'format:csv', 'modality:text', 'library:datasets', 'library:dask', 'library:mlcroissant', 'library:polars', 'region:us']
|
81 |
+
createdAt: 2023-12-07T11:02:28.000Z
|
82 |
+
key:
|
83 |
+
paperswithcode_id: None
|
84 |
+
citation: None
|
85 |
+
- _id: 659193db35c41262d6a53f71
|
86 |
+
id: greathero/evenmorex11-newthreeclass-newercontrailsvalidationdataset
|
87 |
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author: greathero
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88 |
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cardData: {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 162552, "num_examples": 9}], "download_size": 38146, "dataset_size": 162552}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
89 |
+
disabled: False
|
90 |
+
gated: False
|
91 |
+
lastModified: 2023-12-31T19:12:42.000Z
|
92 |
+
likes: 0
|
93 |
+
trendingScore: 0.0
|
94 |
+
private: False
|
95 |
+
sha: b7b5c1282518329689aba655335e218e79831e97
|
96 |
+
description: None
|
97 |
+
downloads: 0
|
98 |
+
tags: ['size_categories:n<1K', 'format:parquet', 'modality:image', 'library:datasets', 'library:pandas', 'library:mlcroissant', 'library:polars', 'region:us']
|
99 |
+
createdAt: 2023-12-31T16:16:27.000Z
|
100 |
+
key:
|
101 |
+
paperswithcode_id: None
|
102 |
+
citation: None
|
103 |
+
- _id: 62d3dd1ac85b0fcf7fd98c9d
|
104 |
+
id: Maxmioti/GDRP-fines
|
105 |
+
author: Maxmioti
|
106 |
+
cardData: {"license": "other"}
|
107 |
+
disabled: False
|
108 |
+
gated: False
|
109 |
+
lastModified: 2022-07-17T10:03:34.000Z
|
110 |
+
likes: 0
|
111 |
+
trendingScore: 0.0
|
112 |
+
private: False
|
113 |
+
sha: 85b9612b440ac0158d5722d0d45b849a012468ec
|
114 |
+
description: Opensource DataSet form a Kaggle competition https://www.kaggle.com/datasets/andreibuliga1/gdpr-fines-20182020-updated-23012021
|
115 |
+
GDPR-fines is a dataset with summary of GDPR cases from companies that were find between 2018 and 2021. You will find the summary plus the Articles violated in the cases (3 most importants + "Others" regrouping the rest of articles).
|
116 |
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Raw text and lemmatized text available plus multi-labels.
|
117 |
+
|
118 |
+
downloads: 0
|
119 |
+
tags: ['license:other', 'size_categories:n<1K', 'format:csv', 'modality:tabular', 'modality:text', 'library:datasets', 'library:pandas', 'library:mlcroissant', 'library:polars', 'region:us']
|
120 |
+
createdAt: 2022-07-17T09:57:46.000Z
|
121 |
+
key:
|
122 |
+
paperswithcode_id: None
|
123 |
+
citation: None
|
124 |
+
- _id: 65f4980b0a4480a3d52dbbf3
|
125 |
+
id: reach-vb/mls-eng-10k-repunct-test-v4
|
126 |
+
author: reach-vb
|
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cardData: {"dataset_info": {"features": [{"name": "original_path", "dtype": "string"}, {"name": "begin_time", "dtype": "float64"}, {"name": "end_time", "dtype": "float64"}, {"name": "transcript", "dtype": "string"}, {"name": "audio_duration", "dtype": "float64"}, {"name": "speaker_id", "dtype": "string"}, {"name": "book_id", "dtype": "string"}, {"name": "repunct_text", "dtype": "string"}], "splits": [{"name": "dev", "num_bytes": 2182587, "num_examples": 3807}], "download_size": 1221776, "dataset_size": 2182587}, "configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "data/dev-*"}]}]}
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tags: ['size_categories:1K<n<10K', 'format:parquet', 'modality:tabular', 'modality:text', 'library:datasets', 'library:pandas', 'library:mlcroissant', 'library:polars', 'region:us']
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createdAt: 2024-03-15T18:48:43.000Z
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key:
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paperswithcode_id: None
|
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citation: None
|
142 |
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- _id: 661fa0bcb0e9b7e049a9202b
|
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id: atluzz/train_doc_en_jsonl
|
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author: atluzz
|
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cardData: {"license": "apache-2.0"}
|
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|
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|
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|
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key:
|
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paperswithcode_id: None
|
159 |
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citation: None
|
160 |
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- _id: 662ed2502e1ed663bc3570c9
|
161 |
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id: leharris3/airline_reviews_servq
|
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author: leharris3
|
163 |
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cardData: {"license": "mit"}
|
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|
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description: None
|
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downloads: 0
|
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tags: ['license:mit', 'size_categories:10K<n<100K', 'format:csv', 'modality:text', 'library:datasets', 'library:pandas', 'library:mlcroissant', 'library:polars', 'region:us']
|
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createdAt: 2024-04-28T22:48:48.000Z
|
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key:
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paperswithcode_id: None
|
177 |
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citation: None
|
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- _id: 6670d2f2b0cc541843371fb4
|
179 |
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id: rahulsikder223/SentEval-CR
|
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author: rahulsikder223
|
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sha: b092bc7763a0514f2c454d001abdfbf138a6be74
|
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description: This is the SentEval Customer Reviews dataset which has been divided into train and test splits. Here, the labels are binary where 1 corresponds to 'Positive Reviews' and 0 corresponds to 'Negative Reviews'.
|
190 |
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|
191 |
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downloads: 18
|
192 |
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tags: ['size_categories:1K<n<10K', 'format:parquet', 'modality:text', 'library:datasets', 'library:pandas', 'library:mlcroissant', 'library:polars', 'region:us']
|
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createdAt: 2024-06-18T00:21:06.000Z
|
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key:
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paperswithcode_id: None
|
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citation: None
|
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- _id: 6412397cbf081e031e9391a7
|
198 |
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id: fathyshalab/reklamation24_wasser-strom-gas-intent
|
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author: fathyshalab
|
200 |
+
cardData: {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 203230, "num_examples": 383}, {"name": "test", "num_bytes": 52516, "num_examples": 96}], "download_size": 142247, "dataset_size": 255746}}
|
201 |
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disabled: False
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lastModified: 2023-03-15T22:09:40.000Z
|
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likes: 0
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sha: c8d8322e9e75b8c81eb9b31eb4f4ad9a420f6dbb
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208 |
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description:
|
209 |
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|
210 |
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|
211 |
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|
212 |
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|
213 |
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Dataset Card for "reklamation24_wasser-strom-gas-intent"
|
214 |
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|
215 |
+
|
216 |
+
More Information needed
|
217 |
+
|
218 |
+
downloads: 0
|
219 |
+
tags: ['size_categories:n<1K', 'format:parquet', 'modality:tabular', 'modality:text', 'library:datasets', 'library:pandas', 'library:mlcroissant', 'library:polars', 'region:us']
|
220 |
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createdAt: 2023-03-15T21:32:44.000Z
|
221 |
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key:
|
222 |
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paperswithcode_id: None
|
223 |
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citation: None
|
224 |
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- _id: 66071d3e8ea886bc670da278
|
225 |
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id: BangumiBase/saijakutamerwagomihiroinotabiwohajimemashita
|
226 |
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author: BangumiBase
|
227 |
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cardData: {"license": "mit", "tags": ["art"], "size_categories": ["1K<n<10K"]}
|
228 |
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disabled: False
|
229 |
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gated: False
|
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lastModified: 2024-03-30T01:28:15.000Z
|
231 |
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likes: 0
|
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trendingScore: 0.0
|
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private: False
|
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sha: ecf08621c8d5dc8d1051abdad6568f99e20b9bd0
|
235 |
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description:
|
236 |
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|
237 |
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|
238 |
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|
239 |
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|
240 |
+
Bangumi Image Base of Saijaku Tamer Wa Gomi Hiroi No Tabi Wo Hajimemashita
|
241 |
+
|
242 |
+
|
243 |
+
This is the image base of bangumi Saijaku Tamer wa Gomi Hiroi no Tabi wo Hajimemashita, we detected 81 characters, 6058 images in total. The full dataset is here.
|
244 |
+
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/saijakutamerwagomihiroinotabiwohajimemashita.
|
245 |
+
downloads: 0
|
246 |
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tags: ['license:mit', 'size_categories:1K<n<10K', 'modality:image', 'region:us', 'art']
|
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createdAt: 2024-03-29T19:57:50.000Z
|
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key:
|
249 |
+
paperswithcode_id: None
|
250 |
+
citation: None
|
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version https://git-lfs.github.com/spec/v1
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size 123482092
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tables/models.example.yaml
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author: miittnnss
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siblings: [{'rfilename': '.gitattributes'}]
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|
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|
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inference: library-not-detected
|
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|
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createdAt: 2024-08-11T15:37:33.000Z
|
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modelId: shayekh/aya8b-distillkit-logits
|
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siblings: [{'rfilename': '.gitattributes'}, {'rfilename': 'README.md'}, {'rfilename': 'checkpoint-1000/added_tokens.json'}, {'rfilename': 'checkpoint-1000/config.json'}, {'rfilename': 'checkpoint-1000/generation_config.json'}, {'rfilename': 'checkpoint-1000/merges.txt'}, {'rfilename': 'checkpoint-1000/model.safetensors'}, {'rfilename': 'checkpoint-1000/optimizer.pt'}, {'rfilename': 'checkpoint-1000/rng_state.pth'}, {'rfilename': 'checkpoint-1000/scheduler.pt'}, {'rfilename': 'checkpoint-1000/special_tokens_map.json'}, {'rfilename': 'checkpoint-1000/tokenizer.json'}, {'rfilename': 'checkpoint-1000/tokenizer_config.json'}, {'rfilename': 'checkpoint-1000/trainer_state.json'}, {'rfilename': 'checkpoint-1000/training_args.bin'}, {'rfilename': 'checkpoint-1000/vocab.json'}, {'rfilename': 'checkpoint-2000/added_tokens.json'}, {'rfilename': 'checkpoint-2000/config.json'}, {'rfilename': 'checkpoint-2000/generation_config.json'}, {'rfilename': 'checkpoint-2000/merges.txt'}, {'rfilename': 'checkpoint-2000/model.safetensors'}, {'rfilename': 'checkpoint-2000/optimizer.pt'}, {'rfilename': 'checkpoint-2000/rng_state.pth'}, {'rfilename': 'checkpoint-2000/scheduler.pt'}, {'rfilename': 'checkpoint-2000/special_tokens_map.json'}, {'rfilename': 'checkpoint-2000/tokenizer.json'}, {'rfilename': 'checkpoint-2000/tokenizer_config.json'}, {'rfilename': 'checkpoint-2000/trainer_state.json'}, {'rfilename': 'checkpoint-2000/training_args.bin'}, {'rfilename': 'checkpoint-2000/vocab.json'}, {'rfilename': 'checkpoint-3000/added_tokens.json'}, {'rfilename': 'checkpoint-3000/config.json'}, {'rfilename': 'checkpoint-3000/generation_config.json'}, {'rfilename': 'checkpoint-3000/merges.txt'}, {'rfilename': 'checkpoint-3000/model.safetensors'}, {'rfilename': 'checkpoint-3000/optimizer.pt'}, {'rfilename': 'checkpoint-3000/rng_state.pth'}, {'rfilename': 'checkpoint-3000/scheduler.pt'}, {'rfilename': 'checkpoint-3000/special_tokens_map.json'}, {'rfilename': 'checkpoint-3000/tokenizer.json'}, {'rfilename': 'checkpoint-3000/tokenizer_config.json'}, {'rfilename': 'checkpoint-3000/trainer_state.json'}, {'rfilename': 'checkpoint-3000/training_args.bin'}, {'rfilename': 'checkpoint-3000/vocab.json'}, {'rfilename': 'checkpoint-4000/added_tokens.json'}, {'rfilename': 'checkpoint-4000/config.json'}, {'rfilename': 'checkpoint-4000/generation_config.json'}, {'rfilename': 'checkpoint-4000/merges.txt'}, {'rfilename': 'checkpoint-4000/model.safetensors'}, {'rfilename': 'checkpoint-4000/optimizer.pt'}, {'rfilename': 'checkpoint-4000/rng_state.pth'}, {'rfilename': 'checkpoint-4000/scheduler.pt'}, {'rfilename': 'checkpoint-4000/special_tokens_map.json'}, {'rfilename': 'checkpoint-4000/tokenizer.json'}, {'rfilename': 'checkpoint-4000/tokenizer_config.json'}, {'rfilename': 'checkpoint-4000/trainer_state.json'}, {'rfilename': 'checkpoint-4000/training_args.bin'}, {'rfilename': 'checkpoint-4000/vocab.json'}, {'rfilename': 'checkpoint-5000/added_tokens.json'}, {'rfilename': 'checkpoint-5000/config.json'}, {'rfilename': 'checkpoint-5000/generation_config.json'}, {'rfilename': 'checkpoint-5000/merges.txt'}, {'rfilename': 'checkpoint-5000/model.safetensors'}, {'rfilename': 'checkpoint-5000/optimizer.pt'}, {'rfilename': 'checkpoint-5000/rng_state.pth'}, {'rfilename': 'checkpoint-5000/scheduler.pt'}, {'rfilename': 'checkpoint-5000/special_tokens_map.json'}, {'rfilename': 'checkpoint-5000/tokenizer.json'}, {'rfilename': 'checkpoint-5000/tokenizer_config.json'}, {'rfilename': 'checkpoint-5000/trainer_state.json'}, {'rfilename': 'checkpoint-5000/training_args.bin'}, {'rfilename': 'checkpoint-5000/vocab.json'}, {'rfilename': 'checkpoint-6000/added_tokens.json'}, {'rfilename': 'checkpoint-6000/config.json'}, {'rfilename': 'checkpoint-6000/generation_config.json'}, {'rfilename': 'checkpoint-6000/merges.txt'}, {'rfilename': 'checkpoint-6000/model.safetensors'}, {'rfilename': 'checkpoint-6000/optimizer.pt'}, {'rfilename': 'checkpoint-6000/rng_state.pth'}, {'rfilename': 'checkpoint-6000/scheduler.pt'}, {'rfilename': 'checkpoint-6000/special_tokens_map.json'}, {'rfilename': 'checkpoint-6000/tokenizer.json'}, {'rfilename': 'checkpoint-6000/tokenizer_config.json'}, {'rfilename': 'checkpoint-6000/trainer_state.json'}, {'rfilename': 'checkpoint-6000/training_args.bin'}, {'rfilename': 'checkpoint-6000/vocab.json'}]
|
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- _id: 66d6d98a673a350b18b7309b
|
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id: athirdpath/Aeonis-20b
|
78 |
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author: athirdpath
|
79 |
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gated: False
|
80 |
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inference: library-not-detected
|
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lastModified: 2024-09-08T03:28:52.000Z
|
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likes: 2
|
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trendingScore: 0.2
|
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private: False
|
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sha: 5e421f2b5781055bbc5eb00d9d7fba8250348c45
|
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