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rosete-romeo

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license: apache-2.0 language:

  • en tags:
  • chemistry
  • biology
  • finance
  • legal
  • music
  • art
  • code
  • climate
  • medical
  • synthetic pretty_name: https://romeo-rosete.org/owner size_categories:
  • 100B<n<1T task_categories:
  • token-classification
  • summarization

Dataset Card for Dataset Name

$ pip install autotrain-advanced https://huggingface.co/docs/datasets/loading

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

Dataset Details

image/png

Dataset Description

SELECT sign, count(*), AVG(LENGTH(text)) AS avg_blog_length

FROM url(hf('tasksource/blog_authorship_corpus')) GROUP BY sign ORDER BY avg_blog_length DESC LIMIT(5)

┌───────────┬────────┬────────────────────┐ │ sign │ count │ avg_blog_length │ ├───────────┼────────┼────────────────────┤ │ Aquarius │ 49687 │ 1193.9523819107615 │ │ Leo │ 53811 │ 1186.0665291483153 │ │ Cancer │ 65048 │ 1160.8010392325666 │ │ Gemini │ 51985 │ 1158.4132922958545 │ │ Vurgi │ 60399 │ 1142.9977648636566 │ └───────────┴────────┴────────────────────┘

  • Curated by: [More Information Needed] import cudf

df = ( cudf.read_parquet("https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet") .groupby('sign')['text'] .apply(lambda x: x.str.len().mean()) .sort_values(ascending=False) .head(5) )

  • Funded by [optional]: [More Information Needed] import dask import dask.dataframe as dd

dask.config.set({"dataframe.backend": "cudf"})

df = ( dd.read_parquet("https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/*.parquet") )

  • Shared by [optional]: [More Information Needed] import dask import dask.dataframe as dd

dask.config.set({"dataframe.backend": "cudf"})

df = ( dd.read_parquet("https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/*.parquet") )

  • Language(s) (NLP): [More Information Needed] import duckdb

url = "https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet"

con = duckdb.connect() con.execute("INSTALL httpfs;") con.execute("LOAD httpfs;")

  • License: [More Information Needed]

Dataset Sources [optional]

con.sql(f"SELECT sign, count(*), AVG(LENGTH(text)) AS avg_blog_length FROM '{url}' GROUP BY sign ORDER BY avg_blog_length DESC LIMIT(5)") ┌───────────┬──────────────┬────────────────────┐ │ sign │ count_star() │ avg_blog_length │ │ varchar │ int64 │ double │ ├───────────┼──────────────┼────────────────────┤ │ Cancer │ 38956 │ 1206.5212034089743 │ │ Leo │ 35487 │ 1180.0673767858652 │ │ Aquarius │ 32723 │ 1152.1136815084192 │ │ Virgo │ 36189 │ 1117.1982094006466 │ │ Capricorn │ 31825 │ 1102.397360565593 │ └───────────┴──────────────┴────────────────────┘

con.sql(f"SELECT sign, count(*), AVG(LENGTH(text)) AS avg_blog_length FROM read_parquet({urls}) GROUP BY sign ORDER BY avg_blog_length DESC LIMIT(5)") ┌──────────┬──────────────┬────────────────────┐ │ sign │ count_star() │ avg_blog_length │ │ varchar │ int64 │ double │ ├──────────┼──────────────┼────────────────────┤ │ Aquarius │ 49687 │ 1191.417211745527 │ │ Leo │ 53811 │ 1183.8782219248853 │ │ Cancer │ 65048 │ 1158.9691612347804 │ │ Gemini │ 51985 │ 1156.0693084543618 │ │ Virgo │ 60399 │ 1140.9584430205798 │ └──────────┴──────────────┴────────────────────┘

  • Paper [optional]: [More Information Needed] import pandas as pd

df = ( pd.read_parquet("https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet") .groupby('sign')['text'] .apply(lambda x: x.str.len().mean()) .sort_values(ascending=False) .head(5) )

df = ( pd.concat([pd.read_parquet(url) for url in urls]) .groupby('sign')['text'] .apply(lambda x: x.str.len().mean()) .sort_values(ascending=False) .head(5) )

Uses

import requests

r = requests.get("https://datasets-server.huggingface.co/parquet?dataset=tasksource/blog_authorship_corpus") j = r.json() urls = [f['url'] for f in j['parquet_files'] if f['split'] == 'train'] urls ['https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet', 'https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/0001.parquet']

Direct Use

import polars as pl

df = ( pl.read_parquet("https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet") .group_by("sign") .agg( [ pl.count(), pl.col("text").str.len_chars().mean().alias("avg_blog_length") ] ) .sort("avg_blog_length", descending=True) .limit(5) ) print(df) shape: (5, 3) ┌───────────┬───────┬─────────────────┐ │ sign ┆ count ┆ avg_blog_length │ │ --- ┆ --- ┆ --- │ │ str ┆ u32 ┆ f64 │ ╞═══════════╪═══════╪═════════════════╡ │ Cancer ┆ 38956 ┆ 1206.521203 │ │ Leo ┆ 35487 ┆ 1180.067377 │ │ Aquarius ┆ 32723 ┆ 1152.113682 │ │ Virgo ┆ 36189 ┆ 1117.198209 │ │ Capricorn ┆ 31825 ┆ 1102.397361 │ └───────────┴───────┴─────────────────┘

[More Information Needed] import polars as pl

df = ( pl.concat([pl.read_parquet(url) for url in urls]) .group_by("sign") .agg( [ pl.count(), pl.col("text").str.len_chars().mean().alias("avg_blog_length") ] ) .sort("avg_blog_length", descending=True) .limit(5) ) print(df) shape: (5, 3) ┌──────────┬───────┬─────────────────┐ │ sign ┆ count ┆ avg_blog_length │ │ --- ┆ --- ┆ --- │ │ str ┆ u32 ┆ f64 │ ╞══════════╪═══════╪═════════════════╡ │ Aquarius ┆ 49687 ┆ 1191.417212 │ │ Leo ┆ 53811 ┆ 1183.878222 │ │ Cancer ┆ 65048 ┆ 1158.969161 │ │ Gemini ┆ 51985 ┆ 1156.069308 │ │ Virgo ┆ 60399 ┆ 1140.958443 │ └──────────┴───────┴─────────────────┘

Out-of-Scope Use

[More Information Needed] import polars as pl

q = ( pl.scan_parquet("https://huggingface.co/datasets/tasksource/blog_authorship_corpus/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet") .group_by("sign") .agg( [ pl.count(), pl.col("text").str.len_chars().mean().alias("avg_blog_length") ] ) .sort("avg_blog_length", descending=True) .limit(5) ) df = q.collect()

Dataset Structure

[More Information Needed] docker run -d --name pgai -p 5432:5432
-v pg-data:/home/postgres/pgdata/data
-e POSTGRES_PASSWORD=password timescale/timescaledb-ha:pg17

Dataset Creation

Curation Rationale

[More Information Needed] docker exec -it pgai psql -c "CREATE EXTENSION ai CASCADE;"

Source Data

docker exec -it pgai psql

Data Collection and Processing

[More Information Needed] select ai.load_dataset('rajpurkar/squad', table_name => 'squad');

Who are the source data producers?

[More Information Needed] select * from squad limit 10;

Annotations [optional]

SELECT ai.load_dataset('rajpurkar/squad', table_name => 'squad', batch_size => 100, max_batches => 1);

Annotation process

[More Information Needed] select ai.load_dataset('rajpurkar/squad', table_name => 'squad', if_table_exists => 'append');

Who are the annotators?

[More Information Needed] from mlcroissant import Dataset ds = Dataset(jsonld="https://huggingface.co/api/datasets/tasksource/blog_authorship_corpus/croissant")

Personal and Sensitive Information

[More Information Needed] records = ds.records("default")

Bias, Risks, and Limitations

[More Information Needed] import itertools

import pandas as pd

df = ( pd.DataFrame(list(itertools.islice(records, 100))) .groupby("default/sign")["default/text"] .apply(lambda x: x.str.len().mean()) .sort_values(ascending=False) .head(5) ) print(df) default/sign b'Leo' 6463.500000 b'Capricorn' 2374.500000 b'Aquarius' 2303.757143 b'Gemini' 1420.333333 b'Aries' 918.666667 Name: default/text, dtype: float64

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

BibTeX:

[More Information Needed] @misc{romeo_rosete_2025, author = { Romeo Rosete }, title = { romeo-rosete (Revision f0f3e58) }, year = 2025, url = { https://huggingface.co/bombastictranz/romeo-rosete }, doi = { 10.57967/hf/5106 }, publisher = { Hugging Face } }

Initialize a Spark session

spark = SparkSession.builder.appName("WineReviews").getOrCreate()

Add the Parquet file to the Spark context

spark.sparkContext.addFile("https://huggingface.co/api/datasets/james-burton/wine_reviews/parquet/default/train/0.parquet")

Read the Parquet file into a DataFrame

df = spark.read.parquet(SparkFiles.get("0.parquet"))

APA:

[More Information Needed] import requests

image/png

Fetch the URLs of the Parquet files for the train split

r = requests.get('https://huggingface.co/api/datasets/james-burton/wine_reviews/parquet') train_parquet_files = r.json()['default']['train']

Add each Parquet file to the Spark context

for url in train_parquet_files: spark.sparkContext.addFile(url)

Read all Parquet files into a single DataFrame

df = spark.read.parquet(SparkFiles.getRootDirectory() + "/*.parquet")

Glossary [optional]

[More Information Needed] print(f"Shape of the dataset: {df.count()}, {len(df.columns)}")

Display first 10 rows

df.show(n=10)

Get a statistical summary of the data

df.describe().show()

Print the schema of the DataFrame

df.printSchema()

More Information [optional]

[More Information Needed] {"dataset": "cornell-movie-review-data/rotten_tomatoes", "config": "default", "split": "train", "features": [{"feature_idx": 0, "name": "text", "type": {"dtype": "string", "id": null, "_type": "Value"}}, {"feature_idx": 1, "name": "label", "type": {"num_classes": 2, "names": ["neg", "pos"], "id": null, "_type": "ClassLabel"}}], ... }

image/png

Dataset Card Authors [optional]

[More Information Needed] data: path: sentence-transformers/all-nli train_split: pair-class:train valid_split: pair-class:test column_mapping: sentence1_column: premise sentence2_column: hypothesis target_column: label

Dataset Card Contact

[More Information Needed] import os

image/png

from autotrain.params import LLMTrainingParams from autotrain.project import AutoTrainProject

params = LLMTrainingParams( model="meta-llama/Llama-3.2-1B-Instruct", data_path="HuggingFaceH4/no_robots", chat_template="tokenizer", text_column="messages", train_split="train", trainer="sft", epochs=3, batch_size=1, lr=1e-5, peft=True, quantization="int4", target_modules="all-linear", padding="right", optimizer="paged_adamw_8bit", scheduler="cosine", gradient_accumulation=8, mixed_precision="bf16", merge_adapter=True, project_name="autotrain-llama32-1b-finetune", log="tensorboard", push_to_hub=True, username=os.environ.get("HF_USERNAME"), token=os.environ.get("HF_TOKEN"), ) view-source:https://huggingface.co/docs/hub/repositories-licenses @misc{romeo_rosete_2025, author = { Romeo Rosete }, title = { rosete-romeo (Revision 381bef3) }, year = 2025, url = { https://huggingface.co/datasets/roseteromeo56/rosete-romeo }, doi = { 10.57967/hf/5099 }, publisher = { Hugging Face } } backend = "local" project = AutoTrainProject(params=params, backend=backend, process=True) project.create()