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ljnlonoljpiljm/coyo-tags-xxs | ljnlonoljpiljm | "2024-12-15T19:44:35Z" | 32 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T07:50:11Z" | ---
dataset_info:
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configs:
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data_files:
- split: train
path: data/train-*
---
|
amuvarma/luna-snacced-ds | amuvarma | "2024-12-15T08:44:31Z" | 32 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T08:44:23Z" | ---
dataset_info:
features:
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- name: emotion
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- config_name: default
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path: data/train-*
---
|
Yusufabddd/data_pengangguran_gen_z | Yusufabddd | "2024-12-15T10:17:36Z" | 32 | 0 | [
"task_categories:table-question-answering",
"license:other",
"size_categories:1K<n<10K",
"region:us",
"code"
] | [
"table-question-answering"
] | "2024-12-15T09:50:05Z" | ---
license: other
license_name: project
license_link: LICENSE
task_categories:
- table-question-answering
tags:
- code
pretty_name: dataset prngangguran gen z
size_categories:
- 1K<n<10K
---
---
license: other
license_name: project
license_link: LICE
# Data-Pengangguran-Gen-Z
#Sebuah coding tentang data pengangguran Gen Z
import pandas as pd
# Membuat DataFrame dengan data pengangguran Gen Z
data = {
'Usia': [15, 16, 17, 18, 19, 20, 21, 22, 23, 24],
'Jumlah_Pengangguran': [3600000, 400000, 500000, 600000, 700000, 800000, 900000, 1000000, 1100000, 1200000],
'Tingkat_Pengangguran': [9.37, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0]
}
df = pd.DataFrame(data)
# Menampilkan DataFrame
print("Data Pengangguran Gen Z:")
print(df)
# Analisis data
total_pengangguran = df['Jumlah_Pengangguran'].sum()
rata_tingkat_pengangguran = df['Tingkat_Pengangguran'].mean()
print("\nTotal Jumlah Pengangguran Gen Z:", total_pengangguran)
print("Rata-rata Tingkat Pengangguran Gen Z:", rata_tingkat_pengangguran) |
prosodyntax/vxp-perspeak-withprom-final-v2 | prosodyntax | "2024-12-16T07:43:15Z" | 32 | 0 | [
"size_categories:1M<n<10M",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T09:52:00Z" | ---
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data_files:
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path: de/train-*
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data_files:
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path: es/train-*
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data_files:
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path: fr/train-*
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data_files:
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path: hr/train-*
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data_files:
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path: hu/train-*
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data_files:
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path: it/train-*
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path: pl/train-*
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data_files:
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path: ro/train-*
---
|
RyanYr/reflect_llama8b-t0_llama33-t12_om2-42 | RyanYr | "2024-12-15T16:16:15Z" | 32 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T16:16:13Z" | ---
dataset_info:
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dtype: string
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sequence: string
splits:
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num_examples: 10000
download_size: 36522638
dataset_size: 89357607
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
RyanYr/reflect_llama8b-t0_llama33-t12_om2-130k | RyanYr | "2024-12-15T17:32:55Z" | 32 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T17:32:42Z" | ---
dataset_info:
features:
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dtype: string
- name: generated_solution
dtype: string
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dtype: string
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dtype: string
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sequence: string
splits:
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num_examples: 130000
download_size: 448036414
dataset_size: 1083925223
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
RyanYr/reflect_llama8b-t0_llama33-t12_om2-130k_llama_reflection | RyanYr | "2024-12-15T17:51:39Z" | 32 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T17:51:32Z" | ---
dataset_info:
features:
- name: prompt
dtype: string
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dtype: string
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dtype: bool
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dtype: bool
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download_size: 105967729
dataset_size: 295758280
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CatWithIcecream/eval_act_so100_test | CatWithIcecream | "2024-12-16T00:40:14Z" | 32 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot",
"so100",
"tutorial",
"eval"
] | [
"robotics"
] | "2024-12-16T00:39:12Z" | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
- eval
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.0",
"robot_type": "so100",
"total_episodes": 10,
"total_frames": 11941,
"total_tasks": 1,
"total_videos": 10,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"main_shoulder_pan",
"main_shoulder_lift",
"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
]
},
"observation.images.laptop": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
dgambettaphd/D_gen1_run0_llama2-7b_sciabs_doc1000_real64_synt64_vuw | dgambettaphd | "2024-12-16T01:58:43Z" | 32 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-16T01:58:40Z" | ---
dataset_info:
features:
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dtype: int64
- name: doc
dtype: string
splits:
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num_examples: 1000
download_size: 351173
dataset_size: 688969
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
anonymousforemotion/merged_qwen_audio_dataset_subset | anonymousforemotion | "2024-12-16T03:28:16Z" | 32 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-16T03:28:11Z" | ---
dataset_info:
features:
- name: text
dtype: string
- name: chosen
dtype: audio
- name: rejected
dtype: audio
- name: qwen_input_text
dtype: string
splits:
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num_bytes: 613352668.0
num_examples: 2000
download_size: 142141187
dataset_size: 613352668.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cybersmurf2/llama3data_hkcode | cybersmurf2 | "2024-12-16T04:52:25Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-16T04:52:23Z" | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 7274
num_examples: 32
download_size: 4618
dataset_size: 7274
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
arthrod/ex-10-material-contracts-2023 | arthrod | "2024-12-17T02:27:34Z" | 32 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-16T05:54:39Z" | ---
dataset_info:
features:
- name: submission
struct:
- name: cik
dtype: string
- name: company_name
dtype: string
- name: form_type
dtype: string
- name: date_filed
dtype: timestamp[s]
- name: master_file
dtype: string
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dtype: string
- name: filing_url
dtype: string
- name: accession_number
dtype: string
- name: header
struct:
- name: sec_document
dtype: string
- name: acceptance_datetime
dtype: string
- name: description
dtype: string
- name: filing_form_type
dtype: string
- name: submission_type
dtype: string
- name: conformed_submission_type
dtype: string
- name: period_of_report
dtype: string
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dtype: string
- name: standard_industrial_classification
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: document_from_text
dtype: string
- name: document_metadata
struct:
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dtype: string
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dtype: string
- name: document_filename
dtype: string
- name: description
dtype: string
- name: title
dtype: string
- name: _id
dtype: string
- name: timestamp_collection
dtype: string
- name: doc_url
dtype: string
- name: raw_document_content
dtype: string
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---
# Dataset Card for SEC Exhibit 10 Material Contracts Dataset
## Dataset Details
### Dataset Description
This training dataset contains SEC filing documents specifically focused on Exhibit 10 (Material Contracts) from a specified quarter. Exhibit 10 includes material contracts and similar agreements that are required to be filed with the SEC. This collection provides structured access to full contract texts and associated metadata.
- **Curated by:** Arthur (arthur@cicero.chat)
- **Language(s):** English
- **License:** Likely public domain as US government data
- **Scope:** Material contracts filed as Exhibit 10 in SEC filings
## Dataset Structure
The dataset has a nested structure with the following fields:
### Top Level Fields
1. **submission**: Dictionary containing the full submission data
- Type: dict
- Contains nested document and header information
2. **header**: Dictionary containing filing header information
- Type: dict
- Contains SEC header metadata
3. **document_from_text**: String containing contract text
- Type: string
- Length: Variable
- Contains the full text of material contracts
4. **document_metadata**: Dictionary containing document-specific metadata
- Type: dict
- Contains filing metadata like CIK, form type, dates, etc.
5. **_id**: Unique identifier
- Type: string
- Present in all records
6. **timestamp_collection**: Timestamp of when record was collected
- Type: string
- Present in all records
7. **doc_url**: URL to the document on SEC website
- Type: string
- Present in all records
8. **raw_document_content**: Raw document content
- Type: string
- Contains unprocessed contract text and markup
### Document Metadata Fields
The document_metadata dictionary contains:
- cik: Company identifier
- company_name: Name of filing company
- form_type: Type of SEC form (all Exhibit 10 variants)
- date_filed: Filing date
- master_file: Reference to master index file
- submission_filename: Path to filing in EDGAR system
- submission_url: Direct link to filing
- accession_number: SEC accession number
### Header Fields
The header dictionary includes:
- sec_document: Document identifier and date
- acceptance_datetime: Filing acceptance timestamp
- filing_form_type: Type of SEC form
- submission_type: Type of submission
- period_of_report: Report period
- standard_industrial_classification: Industry classification
- classification_number: Industry code
- public_document_count: Number of documents in submission
- company_name: Filing company name
## Uses
### Direct Use
- Analysis of material business contracts
- Corporate relationship mapping
- Contract term analysis
- Legal document processing
- Identification of business arrangements and terms
- Research on contract structures and patterns
- Corporate governance analysis
### Out-of-Scope Use
- Analysis of other SEC filing types
- Real-time contract monitoring
- Legal advice or compliance determinations
- Analysis of non-material contracts
- Trading signals without proper analysis
## Limitations and Considerations
- Limited to material contracts (Exhibit 10)
- Focused on a specific quarter
- Large variance in document sizes
- Contains HTML/XML markup requiring processing
- May not include all exhibits referenced in contracts
- Historical data only
## Recommendations
- Implement proper HTML/XML parsing for clean text extraction
- Consider contract structure when processing documents
- Cross-reference with master index for complete context
- Consider industry classification when analyzing contracts
- Validate document completeness
- Process tables and formatted content appropriately
- Consider legal expertise for proper interpretation
## Dataset Card Contact
Arthur (arthur@cicero.chat)
## Code
https://github.com/arthrod/sec-edgar-bulker
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---
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HE-Baek/olympic-ragas-eval-dataset | HE-Baek | "2024-12-17T06:46:39Z" | 32 | 0 | [
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data_files:
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---
|
sungko/olympic-ragas-eval-dataset | sungko | "2024-12-17T06:46:54Z" | 32 | 0 | [
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] | null | "2024-12-17T06:46:50Z" | ---
dataset_info:
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---
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arthrod/new3_2excluded_exhibits_part0_8652.93mb | arthrod | "2024-12-17T07:06:58Z" | 32 | 0 | [
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"library:mlcroissant",
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] | null | "2024-12-17T07:05:41Z" | ---
dataset_info:
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configs:
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data_files:
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---
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arthrod/new3_excluded_exhibits_part3_11360.9mb | arthrod | "2024-12-17T07:08:48Z" | 32 | 0 | [
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] | null | "2024-12-17T07:08:17Z" | ---
dataset_info:
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---
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arthrod/new3_5excluded_exhibits_part3_6137.29mb | arthrod | "2024-12-17T07:49:22Z" | 32 | 0 | [
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] | null | "2024-12-17T07:48:59Z" | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
dgambettaphd/D_gen1_run0_llama2-7b_sciabs_doc1000_real32_synt96_vuw | dgambettaphd | "2024-12-17T08:03:34Z" | 32 | 0 | [
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] | null | "2024-12-17T08:03:30Z" | ---
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---
|
dgambettaphd/D_gen3_run0_llama2-7b_sciabs_doc1000_real32_synt96_vuw | dgambettaphd | "2024-12-17T09:29:20Z" | 32 | 0 | [
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] | null | "2024-12-17T09:29:15Z" | ---
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---
|
dgambettaphd/D_gen1_run2_llama2-7b_wiki_doc1000_real32_synt96_vuw | dgambettaphd | "2024-12-17T10:01:08Z" | 32 | 0 | [
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"library:pandas",
"library:mlcroissant",
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] | null | "2024-12-17T10:01:05Z" | ---
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|
dgambettaphd/D_gen4_run0_llama2-7b_sciabs_doc1000_real32_synt96_vuw | dgambettaphd | "2024-12-17T10:06:11Z" | 32 | 0 | [
"size_categories:1K<n<10K",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
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] | null | "2024-12-17T10:06:07Z" | ---
dataset_info:
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configs:
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---
|
PattyKerry/my-distiset-8dba07e0 | PattyKerry | "2024-12-17T10:28:40Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
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"synthetic",
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] | null | "2024-12-17T10:16:03Z" | ---
size_categories: n<1K
dataset_info:
features:
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dtype: string
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dtype:
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'0': synthesis
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configs:
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data_files:
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path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for my-distiset-8dba07e0
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/PattyKerry/my-distiset-8dba07e0/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/PattyKerry/my-distiset-8dba07e0/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"label": 1,
"text": "How does the author\u0027s use of symbolism in the novel \u0027The Giver\u0027 contribute to the theme of conformity versus individuality, and what message do you think the author is trying to convey through this symbolism?"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("PattyKerry/my-distiset-8dba07e0", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("PattyKerry/my-distiset-8dba07e0")
```
</details>
|
dgambettaphd/D_gen5_run0_llama2-7b_sciabs_doc1000_real32_synt96_vuw | dgambettaphd | "2024-12-17T10:41:59Z" | 32 | 0 | [
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"library:mlcroissant",
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] | null | "2024-12-17T10:41:55Z" | ---
dataset_info:
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download_size: 188559
dataset_size: 366955
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
chenchen1112111/ruozhiba111-qwen-tt | chenchen1112111 | "2024-12-17T11:13:07Z" | 32 | 0 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-17T11:11:54Z" | ---
license: apache-2.0
---
|
dgambettaphd/D_gen9_run1_llama2-7b_xlsum_doc1000_real96_synt32_vuw | dgambettaphd | "2024-12-17T11:18:45Z" | 32 | 0 | [
"size_categories:1K<n<10K",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-17T11:18:38Z" | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
ameen2/my-distiset-8dba07e0 | ameen2 | "2024-12-17T11:25:13Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"library:distilabel",
"region:us",
"synthetic",
"distilabel",
"rlaif",
"datacraft"
] | null | "2024-12-17T11:25:11Z" | ---
size_categories: n<1K
dataset_info:
features:
- name: text
dtype: string
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sequence:
class_label:
names:
'0': entertainment
'1': sports
'2': global
'3': politics
'4': economics
splits:
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num_bytes: 41028
num_examples: 100
download_size: 23221
dataset_size: 41028
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for my-distiset-8dba07e0
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/ameen2/my-distiset-8dba07e0/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/ameen2/my-distiset-8dba07e0/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"labels": [
4,
3
],
"text": "The recent surge in the popularity of non-fungible tokens (NFTs) has led to an increase in the demand for digital art, with artists leveraging blockchain technology to create unique and verifiable digital collectibles. This phenomenon has sparked a debate among art critics and economists regarding the value and legitimacy of digital art in the contemporary art market."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("ameen2/my-distiset-8dba07e0", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("ameen2/my-distiset-8dba07e0")
```
</details>
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_d392bd7a-5d37-43b1-ad50-de6ca5c9b2c9 | argilla-internal-testing | "2024-12-17T12:11:35Z" | 32 | 0 | [
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] | null | "2024-12-17T12:11:34Z" | ---
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configs:
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data_files:
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---
|
dgambettaphd/D_gen9_run0_llama2-7b_sciabs_doc1000_real32_synt96_vuw | dgambettaphd | "2024-12-17T13:05:55Z" | 32 | 0 | [
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"region:us"
] | null | "2024-12-17T13:05:51Z" | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
mohanj/whisper-dataset | mohanj | "2024-12-17T18:26:13Z" | 32 | 0 | [
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] | null | "2024-12-17T13:18:05Z" | ---
dataset_info:
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num_examples: 1
download_size: 493704
dataset_size: 479490.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_8d058f75-401b-4315-bff9-5518fd1d98b9 | argilla-internal-testing | "2024-12-17T13:28:10Z" | 32 | 0 | [
"size_categories:n<1K",
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"library:pandas",
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] | null | "2024-12-17T13:28:10Z" | ---
dataset_info:
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configs:
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data_files:
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---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_219b1629-e5a7-4e79-b197-c742eda23972 | argilla-internal-testing | "2024-12-17T13:28:32Z" | 32 | 0 | [
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] | null | "2024-12-17T13:28:30Z" | ---
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---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_10c46093-83bb-4477-a4b4-828fe6b48839 | argilla-internal-testing | "2024-12-17T13:31:57Z" | 32 | 0 | [
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path: data/train-*
---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_f47cf34d-2dcb-4e42-a76a-5839a05a6200 | argilla-internal-testing | "2024-12-17T13:31:59Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
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"library:pandas",
"library:mlcroissant",
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] | null | "2024-12-17T13:31:58Z" | ---
dataset_info:
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configs:
- config_name: default
data_files:
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path: data/train-*
---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_7ae9cf54-97d7-4277-a441-1ff8bb5507a7 | argilla-internal-testing | "2024-12-17T14:44:22Z" | 32 | 0 | [
"size_categories:n<1K",
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"library:pandas",
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] | null | "2024-12-17T14:44:20Z" | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_8d4dc6f6-8534-4c49-8702-e0847aaa5d0f | argilla-internal-testing | "2024-12-17T14:45:52Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-17T14:45:45Z" | ---
dataset_info:
features:
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configs:
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data_files:
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path: data/train-*
---
|
dgambettaphd/D_gen7_run2_llama2-7b_wiki_doc1000_real32_synt96_vuw | dgambettaphd | "2024-12-17T14:49:31Z" | 32 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
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DT4LM/debertav3base_mr_clare_advtraining | DT4LM | "2024-12-18T07:09:27Z" | 32 | 0 | [
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dgambettavuw/P_wiki_doc10000_real64 | dgambettavuw | "2024-12-18T07:25:59Z" | 32 | 0 | [
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dgambettavuw/P_wiki_doc1000_real32 | dgambettavuw | "2024-12-18T07:40:39Z" | 32 | 0 | [
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AsahiRokkaLOCK/test_data | AsahiRokkaLOCK | "2024-12-18T07:48:58Z" | 32 | 0 | [
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|
crtvai/arabic_speech_to_text_20241218_144853_7mfvp5 | crtvai | "2024-12-18T08:49:06Z" | 32 | 0 | [
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|
0x00raghu/eval_candy_to_hand | 0x00raghu | "2024-12-18T16:41:30Z" | 32 | 0 | [
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"LeRobot"
] | [
"robotics"
] | "2024-12-18T10:13:38Z" | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
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data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
## Dataset Description
- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0
## Dataset Structure
[meta/info.json](meta/info.json):
```json
{
"codebase_version": "v2.0",
"robot_type": "so100",
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"main_elbow_flex",
"main_wrist_flex",
"main_wrist_roll",
"main_gripper"
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"names": [
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]
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"names": [
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"info": {
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"video.channels": 3,
"video.codec": "av1",
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}
},
"observation.images.phone": {
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480,
640,
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"names": [
"height",
"width",
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],
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"video.codec": "av1",
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},
"timestamp": {
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"shape": [
1
],
"names": null
},
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1
],
"names": null
},
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"index": {
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1
],
"names": null
},
"task_index": {
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1
],
"names": null
}
}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
zenless-lab/wikicorpus | zenless-lab | "2024-12-18T10:13:57Z" | 32 | 0 | [
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|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_0bdf4d11-c2d7-4c0a-8e37-cdf09dac796f | argilla-internal-testing | "2024-12-18T10:18:52Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_ad0bfc78-43a9-4593-86ab-08553d938565 | argilla-internal-testing | "2024-12-18T10:23:25Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_52054f60-b2f2-4bd1-a977-74c3a77b16b7 | argilla-internal-testing | "2024-12-18T10:28:36Z" | 32 | 0 | [
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chiyuanhsiao/TTS_finetune_interleaf_3-1-8B_rank64_ljspeech_v8 | chiyuanhsiao | "2024-12-18T11:05:14Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_791d8660-506a-4997-a080-3d62b5488270 | argilla-internal-testing | "2024-12-18T11:06:44Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_2f2e7e91-f74e-45ca-b8a5-afa16b57800b | argilla-internal-testing | "2024-12-18T12:28:55Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_47fa288b-c535-4474-a8cd-9d6e6dcbd1ba | argilla-internal-testing | "2024-12-18T13:59:33Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_d7913780-be54-41e9-95ba-7bd056563ce6 | argilla-internal-testing | "2024-12-18T13:59:59Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_4740af7a-6d11-4df4-9761-fd501000cc84 | argilla-internal-testing | "2024-12-18T14:38:46Z" | 32 | 0 | [
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|
Subsets and Splits