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justus27/qwq_synthetic_sft_data_math | justus27 | "2024-12-11T21:31:55Z" | 32 | 0 | [
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"region:us"
] | null | "2024-12-11T21:29:13Z" | ---
dataset_info:
features:
- name: messages
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dtype: string
- name: role
dtype: string
- name: id
dtype: string
- name: source
dtype: string
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---
|
julietz/korean_paimon22050_2 | julietz | "2024-12-11T22:28:42Z" | 32 | 0 | [
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] | null | "2024-12-11T22:28:40Z" | ---
dataset_info:
features:
- name: Audio Path
dtype: string
- name: Sampling Rate
dtype: int64
- name: Waveform Data
dtype: string
- name: Language
dtype: string
- name: Transcription
dtype: string
- name: duration
dtype: float64
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---
|
mlfoundations-dev/stackexchange_movies | mlfoundations-dev | "2024-12-23T18:09:03Z" | 32 | 0 | [
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dataset_info:
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---
|
dgambettaphd/D_gen0_run0_llama2-7b_xlsum_doc1000_real64_synt64 | dgambettaphd | "2024-12-12T01:32:39Z" | 32 | 0 | [
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|
saketupadhyay/Function_BasicBlock_Features_NIST_Juliet1_3_C_CPP | saketupadhyay | "2024-12-12T03:25:32Z" | 32 | 0 | [
"task_categories:tabular-classification",
"license:mit",
"size_categories:10K<n<100K",
"arxiv:2412.08100",
"region:us",
"software"
] | [
"tabular-classification"
] | "2024-12-12T02:54:13Z" | ---
license: mit
task_categories:
- tabular-classification
tags:
- software
size_categories:
- 10K<n<100K
paper:
- arxiv.org/abs/2412.08100
---
# Function and BasicBlock Binary Classification Features based on NIST Juliet1.3 C/C++
The final Basic Block and Function features are extracted in Semicolon-Separated Values (SSV) format.
To load in pandas, use -
```python
import pandas as pd
data = pd.read_csv("FNFeatures.csv", sep=";")
```
Assuming `FNFeatures.csv` is the target feature file.
## Function Features
The generated dataset is list of functions with various characteristics and a label indicating whether each function is
vulnerable or not. The data is structured into 14 columns, which are described below:
1. **Function ID**: A unique identifier for each function.
2. **Function Name**: The name of the function.
3. **Instructions**: The number of instructions (e.g.,Intermediate Instructions) in the function.
4. **BBs** (Basic Blocks): The number of basic blocks in the function. A basic block is a sequence of instructions that
are executed together without any control flow changes.
5. **In-degree**: The number of incoming edges to the function in the call graph, indicating how many other functions
call this one.
6. **Out-degree**: The number of outgoing edges from the function in the call graph, indicating how many other functions
are called by this one.
7. **Num Loops**: The number of loops (e.g., for, while, do-while) present in the function.
8. **Static Allocations**: The number of static memory allocations made by the function.
9. **Dynamic Allocations**: The number of dynamic memory allocations made by the function (e.g., using `malloc`,
`realloc`).
10. **MemOps** (Memory Operations): The number of memory-related operations performed by the function (e.g., reads,
writes).
11. **CondBranches** (Conditional Branches): The number of conditional branches (e.g., if-else statements) in the
function.
12. **UnCondBranches** (Unconditional Branches): The number of unconditional branches (e.g., jumps, returns) in the
function.
13. **DirectCalls**: The number of direct function calls made by the function.
14. **InDirectCalls** (Indirect Calls): The number of indirect function calls made by the function (e.g., through a
pointer or a table).
15. **VULNERABLE**: A binary label indicating whether the function is vulnerable (1) or not (0).
## Basic Block Features
Generated Basic Block dataset a collection of basic blocks (BBs) from functions with various characteristics and a label
indicating whether each block is vulnerable or not. The data is structured into 13 columns, which are described below:
1. **Block ID**: A unique identifier for each basic block.
2. **Block Name**: Name of the block with following structure - `BB_<block #>_<demangled parent function>`
3. **Instructions**: The number of instructions (e.g., assembly code operations) in the basic block.
4. **In-degree**: The number of incoming edges to the basic block in the control flow graph, indicating how many other
blocks lead to this one.
5. **Out-degree**: The number of outgoing edges from the basic block in the control flow graph, indicating how many
other blocks are reachable from this one.
6. **Static Allocations**: The number of static memory allocations made by the basic block.
7. **Dynamic Allocations**: The number of dynamic memory allocations made by the basic block (e.g., using `new`,
`malloc`).
8. **MemOps** (Memory Operations): The number of memory-related operations performed by the basic block (e.g., reads,
writes).
9. **CondBranches** (Conditional Branches): The number of conditional branches (e.g., if-else statements) in the basic
block.
10. **UnCondBranches** (Unconditional Branches): The number of unconditional branches (e.g., jumps, returns) in the
basic block.
11. **DirectCalls**: The number of direct function calls made by the basic block.
12. **InDirectCalls** (Indirect Calls): The number of indirect function calls made by the basic block (e.g., through a
pointer or a table).
13. **VULNERABLE**: A binary label indicating whether the basic block is vulnerable (1) or not (0).
---
### A Note on Branches in Basic Blocks
Conditional branches (CondBranches) and unconditional branches (UnCondBranches) primarily serve as sanity checks and do
not significantly impact the categorization of Basic Blocks (it might actually harm the accuracy). Let’s analyze the
possible values of \( N \) (number of conditional branches) and \( M \) (number of unconditional branches).
A basic block can contain at most one conditional branch. A conditional branch is typically used to terminate the block
and transfer control to another location within the code. If there were multiple conditional branches, they would need
to be combined into a single decision point using logical operators, which would not increase the count of separate
conditional branches.
$$
\therefore N \in \{0, 1\}
$$
where \( N \) is either \( 0 \) (no conditional branch) or \( 1 \) (one conditional branch).
Similarly, a basic block can have at most one unconditional branch. An unconditional branch is typically used to exit
the block and jump to another location in the code. If there were multiple unconditional branches, they would be
redundant, as only one of them would be executed.
$$
\therefore M \in \{0, 1\}
$$
where \( M \) is either \( 0 \) (no unconditional branch) or \( 1 \) (one unconditional branch).
If a basic block contains a conditional branch (\( N = 1 \)), it is not possible to have an unconditional branch (\( M =
0 \)), as the control flow would be determined solely by the conditional branch. Conversely, if a basic block includes
an unconditional branch (\( M = 1 \)), it is not feasible to have a conditional branch (\( N = 0 \)), as the
unconditional branch would override any conditional decision.
Logically -
$$
N \times M = 0
$$
$$
(N = 1) \Rightarrow (M = 0)
$$
$$
(M = 1) \Rightarrow (N = 0)
$$
That means that only one of \( N \) or \( M \) can have the value of \( 1 \) at any given time.
If \( N \) is set to \( 1 \), \( M \) must be set to \( 0 \), and vice versa.
We can use this relationship to check the functionality of our BB compiler pass and sanity of our training dataset.
---
## Cite
If you utilize this project or any portion thereof, please ensure proper citation of the following work:
```text
@misc{upadhyay2024fuzzdistillintelligentfuzzingtarget,
title={FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning},
author={Saket Upadhyay},
year={2024},
eprint={2412.08100},
archivePrefix={arXiv},
primaryClass={cs.SE},
url={https://arxiv.org/abs/2412.08100},
}
```
|
xlzoiolx/Modelo_01 | xlzoiolx | "2024-12-12T04:25:38Z" | 32 | 0 | [
"license:cc",
"region:us"
] | null | "2024-12-12T04:24:35Z" | ---
license: cc
---
|
DT4LM/gp_mr_faster-alzantot_differential_original | DT4LM | "2024-12-12T04:34:48Z" | 32 | 0 | [
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historyHulk/MoDeTrans | historyHulk | "2024-12-15T09:28:27Z" | 32 | 0 | [
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--- |
SeppeV/results_joke_gen_of_mistral_curry_dpo_iter4_1000_jo | SeppeV | "2024-12-12T08:19:20Z" | 32 | 0 | [
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ShahadMAlshalawi/VQAv2-Encoder-Violet-Captions | ShahadMAlshalawi | "2024-12-12T09:29:53Z" | 32 | 0 | [
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---
|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_7da325d6-c13b-48de-b42d-9a43d06126a8 | argilla-internal-testing | "2024-12-12T09:58:09Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_1b6e623c-e080-42a6-94b6-404ef25b1a71 | argilla-internal-testing | "2024-12-12T10:06:18Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_b7e24593-07a1-4eca-9c47-009394a7d199 | argilla-internal-testing | "2024-12-12T10:34:54Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_51ea3487-50d6-4732-a5f2-1ae181099158 | argilla-internal-testing | "2024-12-12T10:35:07Z" | 32 | 0 | [
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vantral/selkup_me_12.12.2024 | vantral | "2024-12-12T11:06:17Z" | 32 | 0 | [
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khadimhadi/my-hand-captioning-dataset_2 | khadimhadi | "2024-12-12T14:40:25Z" | 32 | 0 | [
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yin30lei/wildlife_underexposed_clahe | yin30lei | "2024-12-12T19:19:22Z" | 32 | 0 | [
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yin30lei/wildlife_verydark_afifi | yin30lei | "2024-12-12T20:20:52Z" | 32 | 0 | [
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---
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mesjenet/kitten_cleaned_dataset | mesjenet | "2024-12-12T20:28:58Z" | 32 | 0 | [
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Shannnh/baseline-dataset-t5-base-1 | Shannnh | "2024-12-12T21:06:58Z" | 32 | 0 | [
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|
amuvarma/va-10k-310k-snac | amuvarma | "2024-12-12T23:17:11Z" | 32 | 0 | [
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yin30lei/wildlife_less_saturated_clahe | yin30lei | "2024-12-12T22:58:29Z" | 32 | 0 | [
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Asap7772/math_llamagen-flat | Asap7772 | "2024-12-12T23:15:50Z" | 32 | 0 | [
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amuvarma/va-310k-320k-snac | amuvarma | "2024-12-12T23:19:24Z" | 32 | 0 | [
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RyanYr/reflect_llama8b-t0_mistlarge-t12_om2-300k | RyanYr | "2024-12-13T01:21:22Z" | 32 | 0 | [
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1231czx/math_train | 1231czx | "2024-12-13T01:36:14Z" | 32 | 0 | [
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dgambettaphd/D_gen10_run0_llama2-7b_xlsum_doc1000_real64_synt64 | dgambettaphd | "2024-12-13T01:41:49Z" | 32 | 0 | [
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sangkm/pash | sangkm | "2024-12-13T04:28:14Z" | 32 | 0 | [
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sangkm/merged | sangkm | "2024-12-13T04:29:02Z" | 32 | 0 | [
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|
zuozhuan/so100_anything | zuozhuan | "2024-12-13T09:30:49Z" | 32 | 0 | [
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task_categories:
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---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
|
procit009/female_facebook_data | procit009 | "2024-12-13T06:59:11Z" | 32 | 0 | [
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ferrazzipietro/IK_llama3.1-8b_diann_16_16_0.01 | ferrazzipietro | "2024-12-13T08:11:18Z" | 32 | 0 | [
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ferrazzipietro/IK_llama3.1-8b_diann_16_64_0.05 | ferrazzipietro | "2024-12-13T08:11:36Z" | 32 | 0 | [
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ferrazzipietro/IK_llama3.1-8b_diann_32_16_0.05 | ferrazzipietro | "2024-12-13T08:11:42Z" | 32 | 0 | [
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Asap7772/elix_generations_gpt4o_pref_train | Asap7772 | "2024-12-13T08:52:15Z" | 32 | 0 | [
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Asap7772/elix_generations_gpt4o_pref_test | Asap7772 | "2024-12-13T09:06:01Z" | 32 | 0 | [
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|
DT4LM/gpt2_rte_pair_faster-alzantot | DT4LM | "2024-12-13T09:59:22Z" | 32 | 0 | [
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|
Bright8192/astralprojection | Bright8192 | "2024-12-13T10:17:06Z" | 32 | 1 | [
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license: mit
---
|
mlfoundations-dev/stackexchange_pets | mlfoundations-dev | "2024-12-23T17:33:48Z" | 32 | 0 | [
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|
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_744c6044-11fc-43ca-94d1-9b6d6ac4e921 | argilla-internal-testing | "2024-12-13T16:20:13Z" | 32 | 0 | [
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|
heegarthur/dutch-words-to-dutch | heegarthur | "2024-12-13T16:37:12Z" | 32 | 0 | [
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license: c-uda
---
in this dataset:
---dutch word=dutch explaining
_this does'nt contain that much info, only a few thousand words_ |
paulrichmond/hep_th_test1_temp07 | paulrichmond | "2024-12-13T17:04:55Z" | 32 | 0 | [
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argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_12498dd6-181e-4da8-8867-4ca00866ee9a | argilla-internal-testing | "2024-12-13T17:11:07Z" | 32 | 0 | [
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mlfoundations-dev/stackexchange_webapps | mlfoundations-dev | "2024-12-23T17:57:46Z" | 32 | 0 | [
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download_size: 124485517
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path: data/train-*
---
|
DT4LM/gpt2_sst2_faster-alzantot_advtraining | DT4LM | "2024-12-19T21:35:07Z" | 32 | 0 | [
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---
|
WendyHoang/corpus_pl_sop | WendyHoang | "2024-12-13T20:41:36Z" | 32 | 0 | [
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---
|
JesseLiu/Trouble_Makers | JesseLiu | "2024-12-13T23:26:53Z" | 32 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2024-12-13T23:25:10Z" | ---
license: apache-2.0
---
|
bustamiyusoef/deeplearning_lmm | bustamiyusoef | "2024-12-16T07:10:41Z" | 32 | 0 | [
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] | null | "2024-12-13T23:41:09Z" | ---
dataset_info:
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---
|
Asap7772/elix_gen_eval_4shot_infipo_beta0.05-pair_winrate_gpt4o_pref_train | Asap7772 | "2024-12-13T23:56:12Z" | 32 | 0 | [
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|
Asap7772/elix_gen_persona_eval_4shot_infsft-pair_winrate_gpt4o_pref_train | Asap7772 | "2024-12-13T23:57:19Z" | 32 | 0 | [
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|
myselfrew/baseline_math_70b_correct_llama3_8b_filtered_sft | myselfrew | "2024-12-14T03:41:32Z" | 32 | 0 | [
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dataset_info:
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|
dogtooth/uf_Meta-Llama-3.1-8B-Instruct_3 | dogtooth | "2024-12-14T06:18:38Z" | 32 | 0 | [
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---
|
jxtse/DSGram | jxtse | "2024-12-22T06:49:30Z" | 32 | 0 | [
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"modality:text",
"arxiv:2412.12832",
"region:us",
"Grammatical Error Correction"
] | null | "2024-12-14T07:43:35Z" | ---
license: apache-2.0
language:
- en
tags:
- Grammatical Error Correction
---
### Dataset Card for DSGram Datasets
#### Dataset Summary
The DSGram datasets are designed for the evaluation and development of Grammatical Error Correction (GEC) models in the era of large language models (LLMs). These datasets address key evaluation challenges by incorporating human annotations and LLM-generated scores. Two subsets are provided:
1. **DSGram-LLMs**: A simulated dataset containing GPT-4-annotated sentence pairs, enabling fine-tuning and cost-effective evaluation of GEC models.
2. **DSGram-Eval**: A manually annotated dataset providing high-quality, human-scored examples to benchmark the DSGram framework.
The datasets facilitate the evaluation of corrections based on three sub-metrics:
- **Semantic Coherence**: Preservation of original meaning.
- **Edit Level**: Appropriateness of modifications.
- **Fluency**: Grammatical correctness and natural flow.
#### Dataset Structure
##### DSGram-LLMs
- **Input**: Original and corrected sentences from CoNLL-2014 and BEA-2019 test sets.
- **Annotations**: Scores generated by GPT-4 using prompt engineering techniques (Chain-of-Thought, few-shot prompting).
- **Size**: ~2,500 entries.
##### DSGram-Eval
- **Input**: Sentences from CoNLL-2014.
- **Annotations**: Human-scored sentence pairs evaluated based on the three sub-metrics.
- **Size**: ~200 entries with multiple annotators for consistency.
#### Dataset Usage
##### Intended Use
- Fine-tuning open-source LLMs for GEC evaluation.
- Benchmarking GEC models with robust and context-sensitive metrics.
- Research on evaluation frameworks for text correction tasks.
#### Citation
If you use these datasets, please cite our paper.
```
@misc{xie2024dsgramdynamicweightingsubmetrics,
title={DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models},
author={Jinxiang Xie and Yilin Li and Xunjian Yin and Xiaojun Wan},
year={2024},
eprint={2412.12832},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.12832},
}
``` |
0x00raghu/so100_bimanual_test_3 | 0x00raghu | "2024-12-14T08:02:31Z" | 32 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | [
"robotics"
] | "2024-12-14T08:02:13Z" | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
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_bimanual",
"total_episodes": 2,
"total_frames": 2386,
"total_tasks": 1,
"total_videos": 4,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:2"
},
"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": [
12
],
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"left_elbow_flex",
"left_wrist_flex",
"left_wrist_roll",
"left_gripper",
"right_shoulder_pan",
"right_shoulder_lift",
"right_elbow_flex",
"right_wrist_flex",
"right_wrist_roll",
"right_gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
12
],
"names": [
"left_shoulder_pan",
"left_shoulder_lift",
"left_elbow_flex",
"left_wrist_flex",
"left_wrist_roll",
"left_gripper",
"right_shoulder_pan",
"right_shoulder_lift",
"right_elbow_flex",
"right_wrist_flex",
"right_wrist_roll",
"right_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
}
},
"observation.images.phone": {
"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]
``` |
ferrazzipietro/IK_llama3.1-8b_diann_64_64_0.01 | ferrazzipietro | "2024-12-14T08:26:27Z" | 32 | 0 | [
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-14T08:26:25Z" | ---
dataset_info:
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dataset_size: 1112142
configs:
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data_files:
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path: data/validation-*
- split: test
path: data/test-*
---
|
0x01/lerobot-so100-1 | 0x01 | "2024-12-14T16:47:07Z" | 32 | 0 | [
"license:cdla-permissive-2.0",
"region:us"
] | null | "2024-12-14T16:47:07Z" | ---
license: cdla-permissive-2.0
---
|
myselfrew/math_70b_correct_llama3_8b_filtered_sft_chat | myselfrew | "2024-12-14T19:35:30Z" | 32 | 0 | [
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] | null | "2024-12-14T19:35:12Z" | ---
dataset_info:
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dataset_size: 1428054570
configs:
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path: data/train-*
---
|
amuvarma/zuck-3-snacced | amuvarma | "2024-12-15T03:07:00Z" | 32 | 0 | [
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"modality:text",
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"library:pandas",
"library:mlcroissant",
"library:polars",
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] | null | "2024-12-15T03:06:58Z" | ---
dataset_info:
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dtype: string
- name: codes_list
sequence: int64
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---
|
dgambettaphd/D_gen1_run3_llama2-7b_wiki_doc1000_real96_synt32 | dgambettaphd | "2024-12-15T03:32:56Z" | 32 | 0 | [
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"library:pandas",
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] | null | "2024-12-15T03:32:54Z" | ---
dataset_info:
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dtype: int64
- name: doc
dtype: string
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---
|
Billtrancend/story_outline | Billtrancend | "2024-12-15T04:49:06Z" | 32 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T04:44:44Z" | ---
license: mit
---
This dataset includes 1722 Chinese story outlines that contain multiple tags and characters.
The prompt contain the following message:
必须包含的关键词：
${mandatory_words}
可选的关键词：
${optional_words}
你需要根据这些词语，创作一个故事大纲。对于可选的词语，你可以挑选至少6个相关的词语，然后再联想出6个其他的词语作为故事元素。
用发散的思维，拓展各种故事元素。
要求至少有 ${n_characters} 个角色
至少有 ${n_scenes} 个场景
至少有 ${n_events} 个事件
严格遵守以下格式
## 必须关键词
1. ...
2. ...
3. ...
## 可选关键词
1. ...
## 自创关键词
1. ...
## 故事氛围
故事的氛围是什么样的？可以用好几个词语，来分别形容故事不同阶段的气氛
## 故事背景
是什么时代的？这个时代有什么特点？
人们是怎么生活的？
是否有什么影响深远的事件？
## 人设
### 1. 人名，基本信息，身份
- 外貌，经常穿着的服装
- 有什么样的性格
- 有什么重大的人生经历
- 有什么动机和追求
## 场景
- 用数字列表的方式呈现。
- 场景中包含会发生的事情，同时包括场景中有什么可用的物品，景色，氛围。
## 目的
- 人物有什么样的目的？
- 人物各自有什么样的动机？需求？痛苦？追求？
- 有什么必须被解决的问题？
- 这个目标为什么重要？
## 高潮和结局
- 故事的高潮是什么？
- 最终结局是悲剧还是喜剧？或者是无疾而终引人深思？
## 事件大纲
- 挑选可用的场景和人物。
- 创造具有悬念的情节。
- 制造出期待感和剧情张力。期待感是说把人物放进一个可能会产生有趣的事件的情景，比如扮猪吃虎。
- 尽量创作有起伏的故事，同时角色要有所改变，不论是处境还是人格。
- 用数字列表列出每个大致的事件。
<EOF>
# 详细大纲
## 1. 这里是对于这个事件的概括
给大纲填充更多的细节，比如逻辑上的关联，让事件更加合理。
用多个段落，详细描述各个事件。使用想象力补充。
同时，详细大纲的数量应该和事件大纲中的事件数量吻合。
## 2. ...
|
amuvarma/zuck-l-snacced | amuvarma | "2024-12-15T05:42:59Z" | 32 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T05:42:55Z" | ---
dataset_info:
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dtype: string
- name: codes_list
sequence: int64
splits:
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num_examples: 326
download_size: 926079
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---
|
RyanYr/reflect_llama8b-t0_mistlarge-t12_om2-300k_correction_150k | RyanYr | "2024-12-15T06:22:54Z" | 32 | 0 | [
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"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T06:20:38Z" | ---
dataset_info:
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- name: prompt
dtype: string
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dtype: string
- name: response@0_correctness
dtype: bool
- name: response@2_correctness
dtype: bool
splits:
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num_bytes: 837079308.2287472
num_examples: 152866
download_size: 306448410
dataset_size: 837079308.2287472
configs:
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data_files:
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path: data/train-*
---
|
hasav/llama3 | hasav | "2024-12-15T09:57:35Z" | 32 | 0 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T07:45:58Z" | ---
license: apache-2.0
---
|
myselfrew/llama3_8b_math_new_prompt2 | myselfrew | "2024-12-15T08:27:35Z" | 32 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T08:27:04Z" | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: gt
dtype: string
- name: prompt
dtype: string
- name: level
dtype: string
- name: type
dtype: string
- name: solution
dtype: string
- name: my_solu
sequence: string
- name: pred
sequence: string
splits:
- name: train
num_bytes: 2535049324
num_examples: 555000
download_size: 840803816
dataset_size: 2535049324
configs:
- config_name: default
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:
- name: text
dtype: string
- name: emotion
dtype: string
- name: codes_list
sequence: int64
splits:
- name: train
num_bytes: 22599766
num_examples: 5674
download_size: 6048975
dataset_size: 22599766
configs:
- config_name: default
data_files:
- split: train
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",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T09:52:00Z" | ---
dataset_info:
- config_name: cs
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- name: unit_duration
sequence: float64
- name: phones
sequence: string
- name: phones_duration
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sequence: float64
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- name: joint_pronunciation
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- name: pos
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- name: head
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- name: deprel
sequence: string
- name: ud_id
sequence: string
- name: sentence_id
dtype: string
- name: speaker
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- name: prominence_strength
dtype: float64
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- name: chunk_lab
sequence: string
splits:
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download_size: 20236220
dataset_size: 106346383
- config_name: de
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- name: speaker
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- name: prominence_strength
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splits:
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splits:
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sequence: string
splits:
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dataset_size: 189412686
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features:
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sequence: float64
- name: phones
sequence: string
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dtype: float64
- name: chunk_lab
sequence: string
splits:
- name: train
num_bytes: 20297520
num_examples: 78958
download_size: 3760029
dataset_size: 20297520
configs:
- config_name: cs
data_files:
- split: train
path: cs/train-*
- config_name: de
data_files:
- split: train
path: de/train-*
- config_name: es
data_files:
- split: train
path: es/train-*
- config_name: fr
data_files:
- split: train
path: fr/train-*
- config_name: hr
data_files:
- split: train
path: hr/train-*
- config_name: hu
data_files:
- split: train
path: hu/train-*
- config_name: it
data_files:
- split: train
path: it/train-*
- config_name: nl
data_files:
- split: train
path: nl/train-*
- config_name: pl
data_files:
- split: train
path: pl/train-*
- config_name: ro
data_files:
- split: train
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:
features:
- name: problem
dtype: string
- name: generated_solution
dtype: string
- name: answer
dtype: string
- name: problem_source
dtype: string
- name: response@0
sequence: string
- name: response@1
sequence: string
- name: response@2
sequence: string
splits:
- name: train
num_bytes: 89357607
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:
- name: problem
dtype: string
- name: generated_solution
dtype: string
- name: answer
dtype: string
- name: problem_source
dtype: string
- name: response@0
sequence: string
- name: response@1
sequence: string
- name: response@2
sequence: string
splits:
- name: train
num_bytes: 1083925223
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
- name: response
dtype: string
- name: response@0_correctness
dtype: bool
- name: response@2_correctness
dtype: bool
splits:
- name: train
num_bytes: 295758280
num_examples: 67534
download_size: 105967729
dataset_size: 295758280
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
equiron-ai/translator_dpo | equiron-ai | "2024-12-15T19:47:37Z" | 32 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-12-15T19:44:36Z" | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 756129
num_examples: 185
download_size: 88819
dataset_size: 756129
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jclinton1/wedgit_stack | jclinton1 | "2024-12-16T02:52:57Z" | 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"
] | [
"robotics"
] | "2024-12-15T23:53:41Z" | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
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": "unknown",
"total_episodes": 50,
"total_frames": 29612,
"total_tasks": 1,
"total_videos": 50,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:50"
},
"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": {
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": {
"motors": [
"motor_0",
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5"
]
}
},
"observation.images.webcam": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channel"
],
"video_info": {
"video.fps": 30.0,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"action": {
"dtype": "float32",
"shape": [
6
],
"names": {
"motors": [
"motor_0",
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5"
]
}
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"next.done": {
"dtype": "bool",
"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]
``` |
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:
- name: id
dtype: int64
- name: doc
dtype: string
splits:
- name: train
num_bytes: 688969
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 | [
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arthrod/ex-10-material-contracts-2023 | arthrod | "2024-12-17T02:27:34Z" | 32 | 0 | [
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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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maxfortremblay/HealthCAN-M-decine-et-sant-Medicine-and-Health-NZ | maxfortremblay | "2024-12-16T23:38:53Z" | 32 | 0 | [
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NZBM/Nnewresume_k_v2 | NZBM | "2024-12-17T04:09:10Z" | 32 | 0 | [
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---
|
DT4LM/t5v1-1base_rte_clare_original_old | DT4LM | "2024-12-17T05:01:49Z" | 32 | 0 | [
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---
|
dgambettaphd/D_gen3_run1_llama2-7b_wiki_doc1000_real96_synt32_vuw | dgambettaphd | "2024-12-17T05:05:14Z" | 32 | 0 | [
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|
dgambettaphd/D_gen8_run1_llama2-7b_xlsum_doc1000_real64_synt64_vuw | dgambettaphd | "2024-12-17T05:17:35Z" | 32 | 0 | [
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] | null | "2024-12-17T05:17:28Z" | ---
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|
dgambettaphd/D_gen5_run1_llama2-7b_wiki_doc1000_real96_synt32_vuw | dgambettaphd | "2024-12-17T05:53:43Z" | 32 | 0 | [
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] | null | "2024-12-17T05:53:40Z" | ---
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dgambettaphd/D_gen7_run1_llama2-7b_wiki_doc1000_real96_synt32_vuw | dgambettaphd | "2024-12-17T06:43:58Z" | 32 | 0 | [
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] | null | "2024-12-17T06:43:54Z" | ---
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---
|
HE-Baek/olympic-ragas-eval-dataset | HE-Baek | "2024-12-17T06:46:39Z" | 32 | 0 | [
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] | null | "2024-12-17T06:46:35Z" | ---
dataset_info:
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---
|
Subsets and Splits