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3a22db985dd101a1f14030575159e7c52d944fed | ArchieMeng/20240217 | [
"region:us"
] | 2024-02-17T06:58:55+00:00 | {} | 2024-02-17T07:02:31+00:00 |
|
be7611934c4988fea6f5004b35315d3ac0641d76 | miittnnss/test-dataset | [
"region:us"
] | 2024-02-17T07:05:51+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18445.0, "num_examples": 2}], "download_size": 20023, "dataset_size": 18445.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T07:05:53+00:00 |
|
92e559aebea12ba1b83e8b30ab07241d6870052a | Mrfine/Mrfine | [
"region:us"
] | 2024-02-17T07:19:00+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4186564, "num_examples": 1000}], "download_size": 2245921, "dataset_size": 4186564}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T07:19:03+00:00 |
|
49e25f7cbc5a82e81dc7f43abe4cbf474f8d08df | Ataf/mini-platypus | [
"region:us"
] | 2024-02-17T07:19:21+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4186564, "num_examples": 1000}], "download_size": 2245921, "dataset_size": 4186564}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T07:19:23+00:00 |
|
b2be1c38d4361062fb2ad3421d553cc747169568 | Rahulenamala/mini-platypus | [
"region:us"
] | 2024-02-17T07:19:29+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4186564, "num_examples": 1000}], "download_size": 2245921, "dataset_size": 4186564}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T07:19:31+00:00 |
|
62dc34d0908f46f5837df12c3d51da0488cac2a9 | Yuga0530/mini-platypus | [
"region:us"
] | 2024-02-17T07:22:46+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4153564, "num_examples": 1000}], "download_size": 2240878, "dataset_size": 4153564}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T07:22:47+00:00 |
|
67fdf7b49f7c6d3e7a35286ee6c810668bc1bf94 |
# Dataset Card for Evaluation run of MaziyarPanahi/WizardLM-Math-70B-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [MaziyarPanahi/WizardLM-Math-70B-v0.1](https://huggingface.co/MaziyarPanahi/WizardLM-Math-70B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_MaziyarPanahi__WizardLM-Math-70B-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-18T02:24:17.988962](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__WizardLM-Math-70B-v0.1/blob/main/results_2024-02-18T02-24-17.988962.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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},
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},
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},
"harness|hendrycksTest-abstract_algebra|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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},
"harness|gsm8k|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
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[More Information Needed] | open-llm-leaderboard/details_MaziyarPanahi__WizardLM-Math-70B-v0.1 | [
"region:us"
] | 2024-02-17T07:29:49+00:00 | {"pretty_name": "Evaluation run of MaziyarPanahi/WizardLM-Math-70B-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [MaziyarPanahi/WizardLM-Math-70B-v0.1](https://huggingface.co/MaziyarPanahi/WizardLM-Math-70B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MaziyarPanahi__WizardLM-Math-70B-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-18T02:24:17.988962](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__WizardLM-Math-70B-v0.1/blob/main/results_2024-02-18T02-24-17.988962.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6914116069568377,\n \"acc_stderr\": 0.03063431437342948,\n \"acc_norm\": 0.6938613221179539,\n \"acc_norm_stderr\": 0.031238741076549784,\n \"mc1\": 0.40269277845777235,\n \"mc1_stderr\": 0.01716883093518722,\n \"mc2\": 0.5707095526544473,\n \"mc2_stderr\": 0.01525040450448649\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6322525597269625,\n \"acc_stderr\": 0.014090995618168482,\n \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635474\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6746664011153157,\n \"acc_stderr\": 0.0046754187743142306,\n \"acc_norm\": 0.8600876319458275,\n \"acc_norm_stderr\": 0.0034618713240671846\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03317672787533157,\n \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03317672787533157\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7283018867924528,\n \"acc_stderr\": 0.027377706624670713,\n \"acc_norm\": 0.7283018867924528,\n \"acc_norm_stderr\": 0.027377706624670713\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n \"acc_stderr\": 0.032166008088022675,\n \"acc_norm\": 0.8194444444444444,\n \"acc_norm_stderr\": 0.032166008088022675\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.035331333893236574,\n \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.035331333893236574\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.047551296160629475,\n \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.047551296160629475\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.676595744680851,\n \"acc_stderr\": 0.030579442773610337,\n \"acc_norm\": 0.676595744680851,\n \"acc_norm_stderr\": 0.030579442773610337\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.04113914981189261,\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.04113914981189261\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4497354497354497,\n \"acc_stderr\": 0.02562085704293665,\n \"acc_norm\": 0.4497354497354497,\n \"acc_norm_stderr\": 0.02562085704293665\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8129032258064516,\n \"acc_stderr\": 0.022185710092252252,\n \"acc_norm\": 0.8129032258064516,\n \"acc_norm_stderr\": 0.022185710092252252\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5369458128078818,\n \"acc_stderr\": 0.035083705204426656,\n \"acc_norm\": 0.5369458128078818,\n \"acc_norm_stderr\": 0.035083705204426656\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8363636363636363,\n \"acc_stderr\": 0.02888787239548795,\n \"acc_norm\": 0.8363636363636363,\n \"acc_norm_stderr\": 0.02888787239548795\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822502,\n \"acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822502\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678178,\n \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678178\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.7025641025641025,\n \"acc_stderr\": 0.023177408131465953,\n \"acc_norm\": 0.7025641025641025,\n \"acc_norm_stderr\": 0.023177408131465953\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.02904560029061626,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.02904560029061626\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.02626502460827588,\n \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.02626502460827588\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.4503311258278146,\n \"acc_stderr\": 0.04062290018683776,\n \"acc_norm\": 0.4503311258278146,\n \"acc_norm_stderr\": 0.04062290018683776\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8954128440366973,\n \"acc_stderr\": 0.013120530245265593,\n \"acc_norm\": 0.8954128440366973,\n \"acc_norm_stderr\": 0.013120530245265593\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813905,\n \"acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813905\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8860759493670886,\n \"acc_stderr\": 0.020681745135884565,\n \"acc_norm\": 0.8860759493670886,\n \"acc_norm_stderr\": 0.020681745135884565\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.757847533632287,\n \"acc_stderr\": 0.028751392398694755,\n \"acc_norm\": 0.757847533632287,\n \"acc_norm_stderr\": 0.028751392398694755\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097655,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097655\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n \"acc_stderr\": 0.019119892798924974,\n \"acc_norm\": 0.905982905982906,\n \"acc_norm_stderr\": 0.019119892798924974\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8697318007662835,\n \"acc_stderr\": 0.012036729568216054,\n \"acc_norm\": 0.8697318007662835,\n \"acc_norm_stderr\": 0.012036729568216054\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7774566473988439,\n \"acc_stderr\": 0.02239421566194282,\n \"acc_norm\": 0.7774566473988439,\n \"acc_norm_stderr\": 0.02239421566194282\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5553072625698324,\n \"acc_stderr\": 0.016619881988177012,\n \"acc_norm\": 0.5553072625698324,\n \"acc_norm_stderr\": 0.016619881988177012\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.024739981355113592,\n \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.024739981355113592\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.77491961414791,\n \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7962962962962963,\n \"acc_stderr\": 0.02240967454730417,\n \"acc_norm\": 0.7962962962962963,\n \"acc_norm_stderr\": 0.02240967454730417\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5390070921985816,\n \"acc_stderr\": 0.029736592526424445,\n \"acc_norm\": 0.5390070921985816,\n \"acc_norm_stderr\": 0.029736592526424445\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5586701434159062,\n \"acc_stderr\": 0.012682016335646683,\n \"acc_norm\": 0.5586701434159062,\n \"acc_norm_stderr\": 0.012682016335646683\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7242647058823529,\n \"acc_stderr\": 0.027146271936625162,\n \"acc_norm\": 0.7242647058823529,\n \"acc_norm_stderr\": 0.027146271936625162\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.017242385828779627,\n \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.017242385828779627\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866767,\n \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866767\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8596491228070176,\n \"acc_stderr\": 0.0266405825391332,\n \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.0266405825391332\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40269277845777235,\n \"mc1_stderr\": 0.01716883093518722,\n \"mc2\": 0.5707095526544473,\n \"mc2_stderr\": 0.01525040450448649\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8176795580110497,\n \"acc_stderr\": 0.010851565594267207\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6444275966641395,\n \"acc_stderr\": 0.013185402252713852\n }\n}\n```", "repo_url": "https://huggingface.co/MaziyarPanahi/WizardLM-Math-70B-v0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "clementine@hf.co", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_17T07_27_21.521286", "path": ["**/details_harness|arc:challenge|25_2024-02-17T07-27-21.521286.parquet"]}, {"split": "2024_02_17T08_53_12.931134", "path": ["**/details_harness|arc:challenge|25_2024-02-17T08-53-12.931134.parquet"]}, {"split": "2024_02_18T02_24_17.988962", "path": ["**/details_harness|arc:challenge|25_2024-02-18T02-24-17.988962.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-18T02-24-17.988962.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_17T07_27_21.521286", "path": ["**/details_harness|gsm8k|5_2024-02-17T07-27-21.521286.parquet"]}, {"split": "2024_02_17T08_53_12.931134", "path": ["**/details_harness|gsm8k|5_2024-02-17T08-53-12.931134.parquet"]}, {"split": "2024_02_18T02_24_17.988962", "path": ["**/details_harness|gsm8k|5_2024-02-18T02-24-17.988962.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-18T02-24-17.988962.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_17T07_27_21.521286", "path": ["**/details_harness|hellaswag|10_2024-02-17T07-27-21.521286.parquet"]}, {"split": "2024_02_17T08_53_12.931134", "path": ["**/details_harness|hellaswag|10_2024-02-17T08-53-12.931134.parquet"]}, {"split": "2024_02_18T02_24_17.988962", "path": ["**/details_harness|hellaswag|10_2024-02-18T02-24-17.988962.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-18T02-24-17.988962.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_17T07_27_21.521286", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T07-27-21.521286.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-17T07-27-21.521286.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-17T07-27-21.521286.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T07-27-21.521286.parquet", 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60afdcd6b7232f991f7f8ffa0be01e6736666863 | PulsarAI/test | [
"region:us"
] | 2024-02-17T07:38:26+00:00 | {} | 2024-02-17T08:44:33+00:00 |
|
e6b3e2b9d716273d20009c77f8c4364d0916702b | Harikrishnan46624/AI_QA_Data | [
"license:apache-2.0",
"region:us"
] | 2024-02-17T07:47:15+00:00 | {"license": "apache-2.0"} | 2024-02-17T14:28:49+00:00 |
|
18420199d1ef41b2391b93d531d54cd3cf9df9e7 | 63days/splat | [
"region:us"
] | 2024-02-17T07:49:32+00:00 | {} | 2024-02-17T08:32:36+00:00 |
|
b1dfb1b7a73f4ad4a1f2d4f36c2152d214d0b072 | Quangnguyen711/Fashion_Shop_Consultant | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"finance",
"region:us"
] | 2024-02-17T07:49:33+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "tags": ["finance"]} | 2024-02-17T07:51:02+00:00 |
|
e36b50d154bd69e7a484f0c1b2c858dc16276897 | benayas/massive_augmented_20pct_v0 | [
"region:us"
] | 2024-02-17T08:00:08+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": "float64"}, {"name": "intent", "dtype": "float64"}, {"name": "utt", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "struct": [{"name": "method", "sequence": "null"}, {"name": "slot", "sequence": "null"}]}, {"name": "judgments", "struct": [{"name": "grammar_score", "sequence": "int8"}, {"name": "intent_score", "sequence": "int8"}, {"name": "language_identification", "sequence": "null"}, {"name": "slots_score", "sequence": "int8"}, {"name": "spelling_score", "sequence": "int8"}, {"name": "worker_id", "sequence": "null"}]}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1763554, "num_examples": 11514}], "download_size": 475194, "dataset_size": 1763554}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T08:00:10+00:00 |
|
ffcb31158f9bd9f72abcc5fd2add91e4f2eaa11a | alisson40889/global | [
"license:openrail",
"region:us"
] | 2024-02-17T08:15:12+00:00 | {"license": "openrail"} | 2024-02-17T08:16:17+00:00 |
|
2258470247657e714d29ce5e3c5bec82ed0c911b | DaviG117/test | [
"region:us"
] | 2024-02-17T08:16:03+00:00 | {} | 2024-02-17T08:29:01+00:00 |
|
8b926400b0641bba3feae66deb5fbee37de80343 | Atipico1/nq-test-adv-replace-v3 | [
"region:us"
] | 2024-02-17T08:19:38+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "entity", "dtype": "string"}, {"name": "similar_entity", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "ctxs", "list": [{"name": "hasanswer", "dtype": "bool"}, {"name": "score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "masked_query", "dtype": "string"}, {"name": "original_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "unans_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "conflict_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "conflict_context", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "context", "dtype": "string"}, {"name": "context_vague", "dtype": "string"}, {"name": "entities", "dtype": "string"}, {"name": "entities_count", "dtype": "int64"}, {"name": "adv_sent", "dtype": "string"}, {"name": "adv_passage", "dtype": "string"}, {"name": "hasanswer", "dtype": "bool"}, {"name": "is_adversarial", "dtype": "bool"}], "splits": [{"name": "test", "num_bytes": 57386242, "num_examples": 3610}], "download_size": 32792526, "dataset_size": 57386242}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2024-02-17T08:19:46+00:00 |
|
7975f71b5187817dc0a32ce9af9b7df564c8ecc2 | azlan8289/Book_Genre | [
"license:apache-2.0",
"region:us"
] | 2024-02-17T08:36:42+00:00 | {"license": "apache-2.0"} | 2024-02-17T08:37:58+00:00 |
|
8f2c6956b433a407e60ca10e6998723ebf3d527f | Atipico1/nq-test-valid_adv_passage | [
"region:us"
] | 2024-02-17T08:37:41+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "entity", "dtype": "string"}, {"name": "similar_entity", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "ctxs", "list": [{"name": "hasanswer", "dtype": "bool"}, {"name": "score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "masked_query", "dtype": "string"}, {"name": "original_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "unans_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "conflict_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "conflict_context", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "context", "dtype": "string"}, {"name": "context_vague", "dtype": "string"}, {"name": "entities", "dtype": "string"}, {"name": "entities_count", "dtype": "int64"}, {"name": "adv_sent", "dtype": "string"}, {"name": "adv_passage", "dtype": "string"}, {"name": "cos_sim", "dtype": "float64"}, {"name": "answer_match", "dtype": "bool"}, {"name": "is_valid_adversary", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 58428413, "num_examples": 3610}], "download_size": 33883766, "dataset_size": 58428413}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T08:37:47+00:00 |
|
4b4d224eeb0d298a520faadaf4253d9f42f75635 | fattahharith/sejarah-stpm | [
"region:us"
] | 2024-02-17T08:39:41+00:00 | {} | 2024-02-17T08:40:43+00:00 |
|
4cc176b64dfb92a5b388dfe177d90f7f8ad5bf9e | JachinL/data2 | [
"region:us"
] | 2024-02-17T08:42:01+00:00 | {} | 2024-02-17T08:43:00+00:00 |
|
2c8c6a2c1ebf6af865f2a9cf77543a1257e783d5 | GGital/Signal_Test07 | [
"region:us"
] | 2024-02-17T08:42:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6"}}}}], "splits": [{"name": "train", "num_bytes": 264257993.609, "num_examples": 3507}], "download_size": 260755282, "dataset_size": 264257993.609}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T08:42:51+00:00 |
|
49d28a3c8829e47a84e666cb114f0e9085438b29 | MesutUnutur/guanaco-llama2-1k | [
"region:us"
] | 2024-02-17T08:44:46+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966692, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T08:44:47+00:00 |
|
30db011f3f68834ded0d35c6fc0fda03d7a714d2 | Bhagya17/Summarization_5.0 | [
"region:us"
] | 2024-02-17T08:46:07+00:00 | {} | 2024-02-17T08:46:28+00:00 |
|
2b56ed8cddbe24cdce776115e6a366b7a224f877 | nizamovtimur/wikiutmn-study-gigachat | [
"license:mit",
"region:us"
] | 2024-02-17T08:51:43+00:00 | {"license": "mit", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "document", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 473636, "num_examples": 251}], "download_size": 84353, "dataset_size": 473636}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T09:36:31+00:00 |
|
bd51a77387e0f9637a2d97d211004c9e32f2cff8 | Anonymous0591/C3PSKT | [
"license:mit",
"region:us"
] | 2024-02-17T09:02:26+00:00 | {"license": "mit"} | 2024-02-17T09:02:26+00:00 |
|
4548bcf07b77c84a36fbbeb136ffb4dd4978a2c7 |
# Dataset Card for Evaluation run of Kquant03/NeuralTrix-7B-dpo-relaser
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Kquant03/NeuralTrix-7B-dpo-relaser](https://huggingface.co/Kquant03/NeuralTrix-7B-dpo-relaser) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Kquant03__NeuralTrix-7B-dpo-relaser",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T09:02:39.812409](https://huggingface.co/datasets/open-llm-leaderboard/details_Kquant03__NeuralTrix-7B-dpo-relaser/blob/main/results_2024-02-17T09-02-39.812409.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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}
```
## Dataset Details
### Dataset Description
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## Dataset Structure
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## Dataset Creation
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Kquant03__NeuralTrix-7B-dpo-relaser | [
"region:us"
] | 2024-02-17T09:05:00+00:00 | {"pretty_name": "Evaluation run of Kquant03/NeuralTrix-7B-dpo-relaser", "dataset_summary": "Dataset automatically created during the evaluation run of model [Kquant03/NeuralTrix-7B-dpo-relaser](https://huggingface.co/Kquant03/NeuralTrix-7B-dpo-relaser) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Kquant03__NeuralTrix-7B-dpo-relaser\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T09:02:39.812409](https://huggingface.co/datasets/open-llm-leaderboard/details_Kquant03__NeuralTrix-7B-dpo-relaser/blob/main/results_2024-02-17T09-02-39.812409.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6457696459415244,\n \"acc_stderr\": 0.032218510960558784,\n \"acc_norm\": 0.6454366964555287,\n \"acc_norm_stderr\": 0.032889882572498676,\n \"mc1\": 0.627906976744186,\n \"mc1_stderr\": 0.01692109011881403,\n \"mc2\": 0.7797569119907752,\n \"mc2_stderr\": 0.01376117718949703\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6885665529010239,\n \"acc_stderr\": 0.013532472099850947,\n \"acc_norm\": 0.7133105802047781,\n \"acc_norm_stderr\": 0.013214986329274772\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6978689504082852,\n \"acc_stderr\": 0.004582433109636472,\n \"acc_norm\": 0.8840868352917746,\n \"acc_norm_stderr\": 0.0031946652660786025\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5446808510638298,\n \"acc_stderr\": 0.03255525359340355,\n \"acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.03255525359340355\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546954,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546954\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7612903225806451,\n \"acc_stderr\": 0.02425107126220884,\n \"acc_norm\": 0.7612903225806451,\n \"acc_norm_stderr\": 0.02425107126220884\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.02247325333276877,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.02247325333276877\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.028406533090608452,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.028406533090608452\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121626,\n \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121626\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3973509933774834,\n \"acc_stderr\": 0.039955240076816806,\n \"acc_norm\": 0.3973509933774834,\n \"acc_norm_stderr\": 0.039955240076816806\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8422018348623853,\n \"acc_stderr\": 0.015630022970092427,\n \"acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.015630022970092427\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.708092485549133,\n \"acc_stderr\": 0.02447699407624734,\n \"acc_norm\": 0.708092485549133,\n \"acc_norm_stderr\": 0.02447699407624734\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43798882681564244,\n \"acc_stderr\": 0.016593394227564843,\n \"acc_norm\": 0.43798882681564244,\n \"acc_norm_stderr\": 0.016593394227564843\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600713,\n \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600713\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46936114732724904,\n \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.627906976744186,\n \"mc1_stderr\": 0.01692109011881403,\n \"mc2\": 0.7797569119907752,\n \"mc2_stderr\": 0.01376117718949703\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.840568271507498,\n \"acc_stderr\": 0.010288617479454764\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6815769522365428,\n \"acc_stderr\": 0.012832225723075408\n }\n}\n```", "repo_url": "https://huggingface.co/Kquant03/NeuralTrix-7B-dpo-relaser", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "clementine@hf.co", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|arc:challenge|25_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|gsm8k|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hellaswag|10_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T09-02-39.812409.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T09-02-39.812409.parquet", 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["**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["**/details_harness|winogrande|5_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-17T09-02-39.812409.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_17T09_02_39.812409", "path": ["results_2024-02-17T09-02-39.812409.parquet"]}, {"split": "latest", "path": ["results_2024-02-17T09-02-39.812409.parquet"]}]}]} | 2024-02-17T09:05:22+00:00 |
2790f707bb56e84073bd13d330f001a06859ad8d | # Dataset Card for "RefGPT-Fact-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Mutonix/RefGPT-Fact-v2 | [
"region:us"
] | 2024-02-17T09:05:53+00:00 | {"dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}, {"name": "reference", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "zh", "num_bytes": 605149267, "num_examples": 143154}, {"name": "en", "num_bytes": 4303277735, "num_examples": 522354}], "download_size": 599049240, "dataset_size": 4908427002}} | 2024-02-17T13:04:19+00:00 |
6e005eaca403a715bf0f1561a72a9ebcac3483dc | andersonbcdefg/misc_sts_pairs_v2 | [
"region:us"
] | 2024-02-17T09:08:18+00:00 | {"dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "jaccard", "dtype": "float64"}, {"name": "sim", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1859391440.7831883, "num_examples": 13184276}], "download_size": 1277788105, "dataset_size": 1859391440.7831883}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T09:25:43+00:00 |
|
975a5a58fa42f9fa11249f888d823b156a34b74e | from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt") | dsplz/dasdasd | [
"region:us"
] | 2024-02-17T09:22:56+00:00 | {} | 2024-02-17T09:23:05+00:00 |
31ba481e65d4d7262f10beeab13ea7a405e5241d | PreciousT/mphs | [
"region:us"
] | 2024-02-17T09:27:59+00:00 | {"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "Question Text", "dtype": "string"}, {"name": "Question Answer", "dtype": "string"}, {"name": "Reference Document", "dtype": "string"}, {"name": "Paragraph(s) Number", "dtype": "string"}, {"name": "Keywords", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 437265, "num_examples": 748}], "download_size": 210581, "dataset_size": 437265}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T09:28:00+00:00 |
|
033894b457da975034134d223716d6300ce43468 | grishi118/evm_question | [
"region:us"
] | 2024-02-17T09:37:59+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 743381, "num_examples": 2167}], "download_size": 300157, "dataset_size": 743381}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T09:38:00+00:00 |
|
4646a7d3b5383437f10eb5f2ea590d15b66f72b9 | # Degarbayan-SC: A Colloquial Paraphrase Farsi using pre-trained mT5
This is the Dataset of [Degarbayan-SC paper](https://ieeexplore.ieee.org/abstract/document/9959983).
You can Finetune with this dataset on the transformers models using [Github](https://https://github.com/m0javad/Degarbayan-SC).
```python
from datasets import load_dataset
dataset = load_dataset("m0javad/Degarbayan-SC-dataset")
```
### Statistic
![Lenght of sentences](https://i.ibb.co/C1RJhTZ/lenght.jpg")
our sentence length distribution is between 3 and 19 words and sentences are an average of 8 words. This makes sense because in the movie subtitles, sentences are shown in a range of time and we matched them with timespans. Humans can say a certain number of words in a certain period of time. Our collected sentences have 128,699 unique words.
as you see in the table above, our dataset contains a large number of paraphrasing sentences in various forms such syntactic, semantic and conceptual paraphrases.
### contact
contact me for contribution and future possible works at: mjaghajani.ai@gmail.com | m0javad/Degarbayan-SC-dataset | [
"task_categories:text-generation",
"task_categories:conversational",
"task_categories:text2text-generation",
"size_categories:100M<n<1B",
"language:fa",
"region:us"
] | 2024-02-17T09:49:03+00:00 | {"language": ["fa"], "size_categories": ["100M<n<1B"], "task_categories": ["text-generation", "conversational", "text2text-generation"]} | 2024-02-17T10:00:27+00:00 |
81aca92e98b367fc6c9ffa9dca38fe453373c942 | lvdthieu/compilable_rate-v2 | [
"region:us"
] | 2024-02-17T09:50:26+00:00 | {} | 2024-02-17T09:56:04+00:00 |
|
4cd2ff54497a83d2d1f24cce672e7d38ba20b850 | # Strix
134k question-answer pairs based on [AiresPucrs'](https://huggingface.co/datasets/AiresPucrs/stanford-encyclopedia-philosophy) [stanford-encyclopedia-philosophy](https://huggingface.co/datasets/AiresPucrs/stanford-encyclopedia-philosophy) dataset. | sayhan/strix-philosophy-qa | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"philosophy",
"region:us"
] | 2024-02-17T09:50:32+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["question-answering"], "tags": ["philosophy"]} | 2024-02-17T10:34:28+00:00 |
b7608b1f149519dcfb3d84706bcbf09f69878b8c |
This is a dataset that was created from [HuggingFaceH4/OpenHermes-2.5-1k-longest](https://huggingface.co/datasets/HuggingFaceH4/OpenHermes-2.5-1k-longest).
The purpose is to be able to use in [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) config by adding:
```yaml
datasets:
- path: Mihaiii/OpenHermes-2.5-1k-longest-curated
type: alpaca
```
I elimininated rows that:
1) Had sys prompt (only 3 rows eliminated)
2) Contained on output a character that is repeated 10 times in a row (478 rows eliminated)
So from a 1000 rows dataset, I ended up with a 519 rows dataset.
See the [OpenHermes-2.5-1k-longest-curated.ipynb](https://huggingface.co/datasets/Mihaiii/OpenHermes-2.5-1k-longest-curated/blob/main/OpenHermes-2.5-1k-longest-curated.ipynb) notebook for details on how the dataset was constructed.
**Later edit**: after a more in depth analysis on the dataset, I noticed that:
1) The imported subset is `test_sft`, but this is the 2nd chunk of top 1k records. The first one is in `train_sft` subset.
2) Valid code records that contained 10 repeated spaces for indentation were also eliminated. | Mihaiii/OpenHermes-2.5-1k-longest-curated | [
"region:us"
] | 2024-02-17T09:52:59+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4176433, "num_examples": 519}], "download_size": 1835764, "dataset_size": 4176433}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T12:36:56+00:00 |
089cacdf1b0d94fff165f54d02c0c188f75abd8d | maywell/kiqu_finaltune | [
"region:us"
] | 2024-02-17T09:58:30+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2006869, "num_examples": 997}], "download_size": 1135101, "dataset_size": 2006869}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T09:58:32+00:00 |
|
dab2692ca42a3ec59762114f7719d046296edbb9 |
# Dataset Card for Evaluation run of freeCS-dot-org/OpenAGI-testing-truthyDPO-1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [freeCS-dot-org/OpenAGI-testing-truthyDPO-1](https://huggingface.co/freeCS-dot-org/OpenAGI-testing-truthyDPO-1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T10:15:19.081933](https://huggingface.co/datasets/open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1/blob/main/results_2024-02-17T10-15-19.081933.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6304857303424044,
"acc_stderr": 0.03249871750345685,
"acc_norm": 0.635809758335016,
"acc_norm_stderr": 0.033161164304669005,
"mc1": 0.5361077111383109,
"mc1_stderr": 0.017457800422268625,
"mc2": 0.7112471524136447,
"mc2_stderr": 0.014852535681165156
},
"harness|arc:challenge|25": {
"acc": 0.6322525597269625,
"acc_stderr": 0.014090995618168478,
"acc_norm": 0.6732081911262798,
"acc_norm_stderr": 0.01370666597558733
},
"harness|hellaswag|10": {
"acc": 0.6648078072097192,
"acc_stderr": 0.004710928569985755,
"acc_norm": 0.8598884684325832,
"acc_norm_stderr": 0.003463933286063884
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
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}
}
```
## Dataset Details
### Dataset Description
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1 | [
"region:us"
] | 2024-02-17T10:17:35+00:00 | {"pretty_name": "Evaluation run of freeCS-dot-org/OpenAGI-testing-truthyDPO-1", "dataset_summary": "Dataset automatically created during the evaluation run of model [freeCS-dot-org/OpenAGI-testing-truthyDPO-1](https://huggingface.co/freeCS-dot-org/OpenAGI-testing-truthyDPO-1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T10:15:19.081933](https://huggingface.co/datasets/open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-truthyDPO-1/blob/main/results_2024-02-17T10-15-19.081933.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6304857303424044,\n \"acc_stderr\": 0.03249871750345685,\n \"acc_norm\": 0.635809758335016,\n \"acc_norm_stderr\": 0.033161164304669005,\n \"mc1\": 0.5361077111383109,\n \"mc1_stderr\": 0.017457800422268625,\n \"mc2\": 0.7112471524136447,\n \"mc2_stderr\": 0.014852535681165156\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6322525597269625,\n \"acc_stderr\": 0.014090995618168478,\n \"acc_norm\": 0.6732081911262798,\n \"acc_norm_stderr\": 0.01370666597558733\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6648078072097192,\n \"acc_stderr\": 0.004710928569985755,\n \"acc_norm\": 0.8598884684325832,\n \"acc_norm_stderr\": 0.003463933286063884\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["**/details_harness|winogrande|5_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-17T10-15-19.081933.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_17T10_15_19.081933", "path": ["results_2024-02-17T10-15-19.081933.parquet"]}, {"split": "latest", "path": ["results_2024-02-17T10-15-19.081933.parquet"]}]}]} | 2024-02-17T10:17:55+00:00 |
e620e4336aff109feb588cc3b3c2a41d8617d196 | # What Is This
これは [RVC-Models](https://huggingface.co/kuwacom/RVC-Models) のモデルの学習に利用したデータセットの生データです。
利用する際は学習環境にあったデータに変換して利用してください。
# About Dataset
## yukkuri
ゆっくりムービーメーカー4でChatGPTを利用して生成した日本語の文章を約100分作成した動画です。 | kuwacom/Character-Dataset | [
"license:mit",
"region:us"
] | 2024-02-17T10:25:03+00:00 | {"license": "mit", "pretty_name": "d"} | 2024-02-17T13:14:04+00:00 |
977c295328baa4ac59b4409a3c43771d65b219e2 |
# Dataset Card for Evaluation run of freeCS-dot-org/OpenAGI-testing-intelDPO-2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [freeCS-dot-org/OpenAGI-testing-intelDPO-2](https://huggingface.co/freeCS-dot-org/OpenAGI-testing-intelDPO-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-intelDPO-2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T10:25:04.402687](https://huggingface.co/datasets/open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-intelDPO-2/blob/main/results_2024-02-17T10-25-04.402687.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6269237223761134,
"acc_stderr": 0.03285871940715557,
"acc_norm": 0.6302370765791581,
"acc_norm_stderr": 0.03352050314315757,
"mc1": 0.40636474908200737,
"mc1_stderr": 0.0171938358120939,
"mc2": 0.5828319150839333,
"mc2_stderr": 0.015305820815113032
},
"harness|arc:challenge|25": {
"acc": 0.590443686006826,
"acc_stderr": 0.01437035863247244,
"acc_norm": 0.6279863481228669,
"acc_norm_stderr": 0.014124597881844463
},
"harness|hellaswag|10": {
"acc": 0.6442939653455487,
"acc_stderr": 0.004777483159634023,
"acc_norm": 0.8463453495319657,
"acc_norm_stderr": 0.00359880385546063
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6644736842105263,
"acc_stderr": 0.03842498559395268,
"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.03842498559395268
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7430555555555556,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6127167630057804,
"acc_stderr": 0.037143259063020656,
"acc_norm": 0.6127167630057804,
"acc_norm_stderr": 0.037143259063020656
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5574468085106383,
"acc_stderr": 0.032469569197899575,
"acc_norm": 0.5574468085106383,
"acc_norm_stderr": 0.032469569197899575
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.046854730419077895,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.046854730419077895
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.40476190476190477,
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"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.025279850397404904
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04444444444444449,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04444444444444449
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7096774193548387,
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"acc_norm": 0.7096774193548387,
"acc_norm_stderr": 0.02582210611941589
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939098,
"acc_norm": 0.63,
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},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
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"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.03374402644139403
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.844559585492228,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6051282051282051,
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"acc_norm": 0.6051282051282051,
"acc_norm_stderr": 0.024784316942156395
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
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"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6764705882352942,
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"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.030388353551886786
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
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"acc_norm_stderr": 0.038796870240733264
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8110091743119267,
"acc_stderr": 0.016785481159203624,
"acc_norm": 0.8110091743119267,
"acc_norm_stderr": 0.016785481159203624
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5370370370370371,
"acc_stderr": 0.03400603625538272,
"acc_norm": 0.5370370370370371,
"acc_norm_stderr": 0.03400603625538272
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7843137254901961,
"acc_stderr": 0.02886743144984932,
"acc_norm": 0.7843137254901961,
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},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7932489451476793,
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"acc_norm": 0.7932489451476793,
"acc_norm_stderr": 0.02636165166838909
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6547085201793722,
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"acc_norm": 0.6547085201793722,
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},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7557251908396947,
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},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8181818181818182,
"acc_stderr": 0.03520893951097652,
"acc_norm": 0.8181818181818182,
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},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7037037037037037,
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},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
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},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
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"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.047268355537191
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
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"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
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"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092375
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
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"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8109833971902938,
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"acc_norm": 0.8109833971902938,
"acc_norm_stderr": 0.014000791294407
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"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.684971098265896,
"acc_stderr": 0.025009313790069723,
"acc_norm": 0.684971098265896,
"acc_norm_stderr": 0.025009313790069723
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.40782122905027934,
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"acc_norm": 0.40782122905027934,
"acc_norm_stderr": 0.016435865260914742
},
"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.7124183006535948,
"acc_norm_stderr": 0.02591780611714716
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"harness|hendrycksTest-philosophy|5": {
"acc": 0.6720257234726688,
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"acc_norm": 0.6720257234726688,
"acc_norm_stderr": 0.02666441088693762
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.7067901234567902,
"acc_norm_stderr": 0.025329888171900926
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"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
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"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.029752389657427047
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"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.44328552803129073,
"acc_norm_stderr": 0.012687818419599924
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"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.6433823529411765,
"acc_norm_stderr": 0.02909720956841195
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_stderr": 0.01943177567703731,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.01943177567703731
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7,
"acc_stderr": 0.04389311454644287,
"acc_norm": 0.7,
"acc_norm_stderr": 0.04389311454644287
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6979591836734694,
"acc_stderr": 0.0293936093198798,
"acc_norm": 0.6979591836734694,
"acc_norm_stderr": 0.0293936093198798
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7014925373134329,
"acc_stderr": 0.03235743789355042,
"acc_norm": 0.7014925373134329,
"acc_norm_stderr": 0.03235743789355042
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653693,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653693
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.40636474908200737,
"mc1_stderr": 0.0171938358120939,
"mc2": 0.5828319150839333,
"mc2_stderr": 0.015305820815113032
},
"harness|winogrande|5": {
"acc": 0.7884767166535123,
"acc_stderr": 0.011477747684223178
},
"harness|gsm8k|5": {
"acc": 0.5094768764215315,
"acc_stderr": 0.01377001065116882
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Dataset Structure
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#### Personal and Sensitive Information
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[More Information Needed] | open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-intelDPO-2 | [
"region:us"
] | 2024-02-17T10:27:23+00:00 | {"pretty_name": "Evaluation run of freeCS-dot-org/OpenAGI-testing-intelDPO-2", "dataset_summary": "Dataset automatically created during the evaluation run of model [freeCS-dot-org/OpenAGI-testing-intelDPO-2](https://huggingface.co/freeCS-dot-org/OpenAGI-testing-intelDPO-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-intelDPO-2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T10:25:04.402687](https://huggingface.co/datasets/open-llm-leaderboard/details_freeCS-dot-org__OpenAGI-testing-intelDPO-2/blob/main/results_2024-02-17T10-25-04.402687.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6269237223761134,\n \"acc_stderr\": 0.03285871940715557,\n \"acc_norm\": 0.6302370765791581,\n \"acc_norm_stderr\": 0.03352050314315757,\n \"mc1\": 0.40636474908200737,\n \"mc1_stderr\": 0.0171938358120939,\n \"mc2\": 0.5828319150839333,\n \"mc2_stderr\": 0.015305820815113032\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.590443686006826,\n \"acc_stderr\": 0.01437035863247244,\n \"acc_norm\": 0.6279863481228669,\n \"acc_norm_stderr\": 0.014124597881844463\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6442939653455487,\n \"acc_stderr\": 0.004777483159634023,\n \"acc_norm\": 0.8463453495319657,\n \"acc_norm_stderr\": 0.00359880385546063\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.037143259063020656,\n \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.037143259063020656\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7096774193548387,\n \"acc_stderr\": 0.02582210611941589,\n \"acc_norm\": 0.7096774193548387,\n \"acc_norm_stderr\": 0.02582210611941589\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939098,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939098\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153314,\n \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153314\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6051282051282051,\n \"acc_stderr\": 0.024784316942156395,\n \"acc_norm\": 0.6051282051282051,\n \"acc_norm_stderr\": 0.024784316942156395\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886786,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886786\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8110091743119267,\n \"acc_stderr\": 0.016785481159203624,\n \"acc_norm\": 0.8110091743119267,\n \"acc_norm_stderr\": 0.016785481159203624\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7843137254901961,\n \"acc_stderr\": 0.02886743144984932,\n \"acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.02886743144984932\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7932489451476793,\n \"acc_stderr\": 0.02636165166838909,\n \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.02636165166838909\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097652,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097652\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 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eb2c7438edbabac6a9b7a3ab9464de4f30b66d79 | Antonio88/Talistran-Dataset | [
"license:apache-2.0",
"region:us"
] | 2024-02-17T10:37:35+00:00 | {"license": "apache-2.0"} | 2024-02-17T12:52:16+00:00 |
|
e1b378589c85e0ef8cb85f21bc7f120e92dce08f | benayas/massive_augmented_5pct_v1 | [
"region:us"
] | 2024-02-17T10:38:00+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": "float64"}, {"name": "intent", "dtype": "float64"}, {"name": "utt", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "struct": [{"name": "method", "sequence": "null"}, {"name": "slot", "sequence": "null"}]}, {"name": "judgments", "struct": [{"name": "grammar_score", "sequence": "int8"}, {"name": "intent_score", "sequence": "int8"}, {"name": "language_identification", "sequence": "null"}, {"name": "slots_score", "sequence": "int8"}, {"name": "spelling_score", "sequence": "int8"}, {"name": "worker_id", "sequence": "null"}]}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1660855, "num_examples": 11514}], "download_size": 398336, "dataset_size": 1660855}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T10:38:02+00:00 |
|
d5388dfbdeb29953d5cd10e735755e28d39cad94 | Dev2410/CR2 | [
"license:apache-2.0",
"region:us"
] | 2024-02-17T10:48:01+00:00 | {"license": "apache-2.0"} | 2024-02-17T11:06:06+00:00 |
|
b55c5525bb0b7b0634af996e0137a3b76ef69550 | Atipico1/nq-test-valid-adversary-replace | [
"region:us"
] | 2024-02-17T10:49:19+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "entity", "dtype": "string"}, {"name": "similar_entity", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "ctxs", "list": [{"name": "hasanswer", "dtype": "bool"}, {"name": "score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "masked_query", "dtype": "string"}, {"name": "original_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "unans_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "conflict_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "conflict_context", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "context", "dtype": "string"}, {"name": "context_vague", "dtype": "string"}, {"name": "entities", "dtype": "string"}, {"name": "entities_count", "dtype": "int64"}, {"name": "adv_sent", "dtype": "string"}, {"name": "adv_passage", "dtype": "string"}, {"name": "cos_sim", "dtype": "float64"}, {"name": "answer_match", "dtype": "bool"}, {"name": "is_valid_adversary", "dtype": "bool"}, {"name": "hasanswer", "dtype": "bool"}, {"name": "is_adversarial", "dtype": "bool"}], "splits": [{"name": "test", "num_bytes": 58345319, "num_examples": 3610}], "download_size": 34093569, "dataset_size": 58345319}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2024-02-17T10:49:26+00:00 |
|
3ea4553075f4ce21a0cc0baab3ef0d0cabad2e93 | maghwa/OpenHermes-2-AR-10K-28-700k-710k | [
"region:us"
] | 2024-02-17T10:51:22+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "hash", "dtype": "null"}, {"name": "category", "dtype": "null"}, {"name": "system_prompt", "dtype": "null"}, {"name": "model_name", "dtype": "null"}, {"name": "language", "dtype": "null"}, {"name": "views", "dtype": "float64"}, {"name": "conversations", "dtype": "string"}, {"name": "topic", "dtype": "null"}, {"name": "id", "dtype": "null"}, {"name": "avatarUrl", "dtype": "null"}, {"name": "custom_instruction", "dtype": "null"}, {"name": "skip_prompt_formatting", "dtype": "null"}, {"name": "idx", "dtype": "null"}, {"name": "title", "dtype": "null"}, {"name": "model", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25266234, "num_examples": 10001}], "download_size": 11486043, "dataset_size": 25266234}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T10:51:24+00:00 |
|
33b71c67632904c145c65c85ccf996d84a132507 | vaidehikale24/sample_data | [
"region:us"
] | 2024-02-17T10:54:36+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20697.1875, "num_examples": 33}, {"name": "test", "num_bytes": 9407.8125, "num_examples": 15}], "download_size": 41615, "dataset_size": 30105.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-17T11:09:33+00:00 |
|
dac2ef99ee0eba53a75e77c68d80eb1b6b8f1c91 | ptd150101/infore | [
"region:us"
] | 2024-02-17T10:58:45+00:00 | {} | 2024-02-17T11:02:43+00:00 |
|
ff3eba76d4a124c860e6c161ddbd9c5454ee7227 | Kyudan/MathAccess_GTNT311K | [
"region:us"
] | 2024-02-17T11:04:32+00:00 | {} | 2024-02-17T11:59:16+00:00 |
|
154a2f22f74a9b6023a6f4687218b3f39cb0e741 | CesarChaMal/my-personal-model | [
"region:us"
] | 2024-02-17T11:11:53+00:00 | {"dataset_info": {"features": [{"name": "train", "struct": [{"name": "text", "sequence": "string"}]}, {"name": "test", "struct": [{"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 45371, "num_examples": 1}], "download_size": 37247, "dataset_size": 45371}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T11:11:58+00:00 |
|
1d829104879065d0d0f2fd6521b827ca8767b063 | kalabedo/meum | [
"region:us"
] | 2024-02-17T11:15:57+00:00 | {} | 2024-02-17T11:30:14+00:00 |
|
d5e1a9b9d28c3df70bbd0ff3f5dfbd5c7e0b83d5 | chronopt-research/vggface2 | [
"region:us"
] | 2024-02-17T11:17:32+00:00 | {} | 2024-02-17T17:40:05+00:00 |
|
b7630b7df89149147dddbd0fa0b3775fb3622de2 | shajiu/ParallelCorpusSFT | [
"license:apache-2.0",
"region:us"
] | 2024-02-17T11:27:36+00:00 | {"license": "apache-2.0"} | 2024-02-17T11:37:10+00:00 |
|
2b8fd0d01eb9cb26d06ee4c3083c2707df9526ca | bertram-gilfoyle/CC-MAIN-2023-06 | [
"region:us"
] | 2024-02-17T11:44:26+00:00 | {} | 2024-02-17T15:33:13+00:00 |
|
1c9ea2a655daaa0894c5839114b61a8557c84e8d | cmcmaster/rheumatology-vignettes-prefs | [
"region:us"
] | 2024-02-17T11:46:36+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "generation_model", "sequence": "string"}, {"name": "generation_prompt", "list": {"list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}}, {"name": "raw_generation_responses", "sequence": "string"}, {"name": "generations", "sequence": "string"}, {"name": "labelling_model", "dtype": "string"}, {"name": "labelling_prompt", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_labelling_response", "dtype": "string"}, {"name": "rating", "sequence": "float64"}, {"name": "rationale", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3916648, "num_examples": 165}], "download_size": 1463137, "dataset_size": 3916648}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T11:46:42+00:00 |
|
9a25594de460d8e2daaaa950915ff55e1d654e2c | AsphyXIA/Baarat-Kan-QA | [
"region:us"
] | 2024-02-17T12:02:31+00:00 | {"dataset_info": {"features": [{"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 69642578, "num_examples": 99544}], "download_size": 26721665, "dataset_size": 69642578}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T12:02:33+00:00 |
|
f517da1233a70b6127468ede8f1d000adbd58206 |
# phrase-ticker Dataset
## Description
The Phrase Ticker Dataset enables the extraction of stock ticker symbols from natural language queries. The dataset pairs NL utterances commonly associated with S&P 500 companies with their corresponding ticker symbols, providing a simple resource for understanding how companies are referred to in various contexts.
## Structure
The dataset comprises two columns:
- `phrase`: This column contains natural language phrases that reference or describe companies in ways that are commonly used in financial news, reports, and discussions. These include not only formal company names and products but also informal and colloquial references.
- `ticker`: Each phrase is associated with a unique stock ticker symbol, identifying the company mentioned or described in the phrase.
## Primary Use Case
**Ticker Extraction from Natural Language Queries**: The main application of this dataset is to train models that can accurately identify and extract stock ticker symbols from text. This capability is crucial for automating the analysis of financial news, social media mentions, analyst reports, and any textual content where companies are discussed without directly mentioning their ticker symbols.
## Getting Started
To begin working with the phrase-ticker Dataset in your projects, you can load it using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset("rohanmahen/phrase-ticker")
```
## Contributions
Contributions to the phrase-ticker Dataset are welcomed, including the addition of new phrases, refinement of existing data, and suggestions for improvement. Please checkout the repository on [github](https://github.com/rohanmahen/phrase-ticker) for more info.
| rohanmahen/phrase-ticker | [
"license:mit",
"region:us"
] | 2024-02-17T12:04:15+00:00 | {"license": "mit"} | 2024-02-17T12:55:53+00:00 |
103a7209e484f5b8c31c4584fa48736a8f8e1eba | maghwa/OpenHermes-2-AR-10K-29-710k-720k | [
"region:us"
] | 2024-02-17T12:04:28+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "hash", "dtype": "null"}, {"name": "category", "dtype": "null"}, {"name": "system_prompt", "dtype": "null"}, {"name": "model_name", "dtype": "null"}, {"name": "language", "dtype": "null"}, {"name": "views", "dtype": "float64"}, {"name": "conversations", "dtype": "string"}, {"name": "topic", "dtype": "null"}, {"name": "id", "dtype": "null"}, {"name": "avatarUrl", "dtype": "null"}, {"name": "custom_instruction", "dtype": "null"}, {"name": "skip_prompt_formatting", "dtype": "null"}, {"name": "idx", "dtype": "null"}, {"name": "title", "dtype": "null"}, {"name": "model", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25440345, "num_examples": 10001}], "download_size": 11528460, "dataset_size": 25440345}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T12:04:29+00:00 |
|
95c7b548d60917dc408a8569558da290424165ac | kyujinpy/KoCommercial-Dataset | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2024-02-17T12:11:56+00:00 | {"license": "cc-by-nc-sa-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9585708419, "num_examples": 1826703}], "download_size": 4024941178, "dataset_size": 9585708419}} | 2024-02-17T17:11:11+00:00 |
|
2a3b3406e9ef2fe616c0cd1ccf1d47193f0fbeb4 |
 | PotatoOff/Milo | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"region:us"
] | 2024-02-17T12:15:22+00:00 | {"language": ["en"], "task_categories": ["text-generation", "question-answering"]} | 2024-02-17T12:15:56+00:00 |
c6fcab91636b451af464d9b46f7dfedd24224139 | 0x7o/RussianVibe-data | [
"region:us"
] | 2024-02-17T12:29:32+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3735780097.615, "num_examples": 3497}], "download_size": 4135366884, "dataset_size": 3735780097.615}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T12:32:59+00:00 |
|
75ca409bcce2e97ab67213b7c6143788fae071c0 | kkknikodem1/oskar | [
"region:us"
] | 2024-02-17T12:48:01+00:00 | {} | 2024-02-17T12:48:01+00:00 |
|
0d1c3b840c0ef8696f6f2fe7d4410279054fae0e | goodbee1/ttttttt | [
"region:us"
] | 2024-02-17T12:51:33+00:00 | {} | 2024-02-17T12:51:33+00:00 |
|
9586281859956244356969a0679230de7a3da68f | lab42/cov-json-vqa | [
"region:us"
] | 2024-02-17T12:55:58+00:00 | {"dataset_info": {"features": [{"name": "image_0", "dtype": "image"}, {"name": "image_1", "dtype": "image"}, {"name": "image_2", "dtype": "image"}, {"name": "images_rest", "sequence": "image"}, {"name": "mask_0", "dtype": "image"}, {"name": "mask_1", "dtype": "image"}, {"name": "mask_2", "dtype": "image"}, {"name": "masks_rest", "sequence": "image"}, {"name": "conversations", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "n_images", "dtype": "int32"}, {"name": "n_masks", "dtype": "int32"}, {"name": "n_conversations", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 45674133.5, "num_examples": 25210}, {"name": "validation", "num_bytes": 4944744.25, "num_examples": 2729}], "download_size": 9198559, "dataset_size": 50618877.75}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]} | 2024-02-17T12:56:07+00:00 |
|
16a487910a8f1e3380051b8010e6affec9b27420 | holikopii/holikopii | [
"region:us"
] | 2024-02-17T13:02:11+00:00 | {} | 2024-02-17T13:02:11+00:00 |
|
09e0ccbbdd7909573be28f8131fd037e9a3f11e4 |
# Dataset Card for Evaluation run of cloudyu/Mixtral_13B_Chat
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cloudyu/Mixtral_13B_Chat](https://huggingface.co/cloudyu/Mixtral_13B_Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_cloudyu__Mixtral_13B_Chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T13:01:58.551979](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Mixtral_13B_Chat/blob/main/results_2024-02-17T13-01-58.551979.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6471647618048562,
"acc_stderr": 0.03218980683733778,
"acc_norm": 0.6495327471727932,
"acc_norm_stderr": 0.032835191770398446,
"mc1": 0.42962056303549573,
"mc1_stderr": 0.0173292345804091,
"mc2": 0.5897994402086952,
"mc2_stderr": 0.015625316517181305
},
"harness|arc:challenge|25": {
"acc": 0.64419795221843,
"acc_stderr": 0.01399057113791876,
"acc_norm": 0.674061433447099,
"acc_norm_stderr": 0.013697432466693247
},
"harness|hellaswag|10": {
"acc": 0.6725751842262497,
"acc_stderr": 0.004683146373232271,
"acc_norm": 0.8586934873531169,
"acc_norm_stderr": 0.0034762555096445303
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7302631578947368,
"acc_stderr": 0.03611780560284898,
"acc_norm": 0.7302631578947368,
"acc_norm_stderr": 0.03611780560284898
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
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}
```
## Dataset Details
### Dataset Description
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_cloudyu__Mixtral_13B_Chat | [
"region:us"
] | 2024-02-17T13:04:13+00:00 | {"pretty_name": "Evaluation run of cloudyu/Mixtral_13B_Chat", "dataset_summary": "Dataset automatically created during the evaluation run of model [cloudyu/Mixtral_13B_Chat](https://huggingface.co/cloudyu/Mixtral_13B_Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cloudyu__Mixtral_13B_Chat\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T13:01:58.551979](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Mixtral_13B_Chat/blob/main/results_2024-02-17T13-01-58.551979.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6471647618048562,\n \"acc_stderr\": 0.03218980683733778,\n \"acc_norm\": 0.6495327471727932,\n \"acc_norm_stderr\": 0.032835191770398446,\n \"mc1\": 0.42962056303549573,\n \"mc1_stderr\": 0.0173292345804091,\n \"mc2\": 0.5897994402086952,\n \"mc2_stderr\": 0.015625316517181305\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.64419795221843,\n \"acc_stderr\": 0.01399057113791876,\n \"acc_norm\": 0.674061433447099,\n \"acc_norm_stderr\": 0.013697432466693247\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6725751842262497,\n \"acc_stderr\": 0.004683146373232271,\n \"acc_norm\": 0.8586934873531169,\n \"acc_norm_stderr\": 0.0034762555096445303\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.44,\n 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"path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["**/details_harness|winogrande|5_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-17T13-01-58.551979.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_17T13_01_58.551979", "path": ["results_2024-02-17T13-01-58.551979.parquet"]}, {"split": "latest", "path": ["results_2024-02-17T13-01-58.551979.parquet"]}]}]} | 2024-02-17T13:04:34+00:00 |
55ded5c57fb947052f9f5afa3327b57a59433b18 | benayas/massive_augmented_10pct_v1 | [
"region:us"
] | 2024-02-17T13:07:56+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": "float64"}, {"name": "intent", "dtype": "float64"}, {"name": "utt", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "struct": [{"name": "method", "sequence": "null"}, {"name": "slot", "sequence": "null"}]}, {"name": "judgments", "struct": [{"name": "grammar_score", "sequence": "int8"}, {"name": "intent_score", "sequence": "int8"}, {"name": "language_identification", "sequence": "null"}, {"name": "slots_score", "sequence": "int8"}, {"name": "spelling_score", "sequence": "int8"}, {"name": "worker_id", "sequence": "null"}]}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1691763, "num_examples": 11514}], "download_size": 428870, "dataset_size": 1691763}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T13:07:58+00:00 |
|
9ba7d4a4ee7294d33d16aba3387436bf617cb996 | itsvivek/imagenet-1k | [
"region:us"
] | 2024-02-17T13:09:13+00:00 | {} | 2024-02-17T13:09:13+00:00 |
|
e687705166667d226aee58fb783f7587b971e49c | maghwa/OpenHermes-2-AR-10K-30-720k-730k | [
"region:us"
] | 2024-02-17T13:10:13+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "hash", "dtype": "null"}, {"name": "category", "dtype": "null"}, {"name": "system_prompt", "dtype": "null"}, {"name": "model_name", "dtype": "null"}, {"name": "language", "dtype": "null"}, {"name": "views", "dtype": "float64"}, {"name": "conversations", "dtype": "string"}, {"name": "topic", "dtype": "null"}, {"name": "id", "dtype": "null"}, {"name": "avatarUrl", "dtype": "null"}, {"name": "custom_instruction", "dtype": "null"}, {"name": "skip_prompt_formatting", "dtype": "null"}, {"name": "idx", "dtype": "null"}, {"name": "title", "dtype": "null"}, {"name": "model", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25337285, "num_examples": 10001}], "download_size": 11474223, "dataset_size": 25337285}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T13:10:14+00:00 |
|
4673330aa463021368d2df5be17c29c8b4f2431d | ddahlmeier/sutd_instruct | [
"region:us"
] | 2024-02-17T13:24:51+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 99626, "num_examples": 30}], "download_size": 45451, "dataset_size": 99626}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T15:19:01+00:00 |
|
974623f6718e1fdbd75f7c83b2f858cf85b65d5e | # Dataset Card for "RefGPT-Reason"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Mutonix/RefGPT-Reason | [
"region:us"
] | 2024-02-17T13:31:17+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "best_answer", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 351127497, "num_examples": 288285}], "download_size": 210897840, "dataset_size": 351127497}} | 2024-02-17T13:32:56+00:00 |
bc390756b7c49e353bc1f4c12a75fb10a7261eeb | hbx/IN3 | [
"license:apache-2.0",
"region:us"
] | 2024-02-17T13:31:22+00:00 | {"license": "apache-2.0"} | 2024-02-17T13:47:55+00:00 |
|
139fa20f189383bb5978a736274cf17ea7cc864f | Kalfrin/camus | [
"license:openrail",
"region:us"
] | 2024-02-17T13:38:16+00:00 | {"license": "openrail"} | 2024-02-17T13:38:53+00:00 |
|
7c73af7e30499b0ff3eefbf8b88eca92fd1a2960 | # Dataset Card for "vikwiki-qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | boapps/vikwiki-qa | [
"region:us"
] | 2024-02-17T13:44:14+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "correct_answer", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1267180.5238698774, "num_examples": 5325}, {"name": "test", "num_bytes": 422631.4761301225, "num_examples": 1776}], "download_size": 983857, "dataset_size": 1689812.0}} | 2024-02-17T13:44:20+00:00 |
bfaf34347c4f74efe93807194962edc9ed4c5156 |
# Dataset of conte_di_cavour/コンテ·ディ·カブール/加富尔伯爵 (Azur Lane)
This is the dataset of conte_di_cavour/コンテ·ディ·カブール/加富尔伯爵 (Azur Lane), containing 9 images and their tags.
The core tags of this character are `grey_hair, yellow_eyes, short_hair, bangs, beret, hat, black_headwear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:---------|:--------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 9 | 6.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 9 | 5.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 13 | 7.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 9 | 6.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 13 | 8.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/conte_di_cavour_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/conte_di_cavour_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, solo, weapon, black_thighhighs, closed_mouth, holding, looking_at_viewer, white_gloves, cape, full_body, simple_background, water, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | weapon | black_thighhighs | closed_mouth | holding | looking_at_viewer | white_gloves | cape | full_body | simple_background | water | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------|:-------------------|:---------------|:----------|:--------------------|:---------------|:-------|:------------|:--------------------|:--------|:-------------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/conte_di_cavour_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-02-17T13:50:29+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-02-17T14:04:47+00:00 |
f9ea974443459643a40e834cb3d75c22ba3848f5 | Diiiann/ossetian_lang | [
"region:us"
] | 2024-02-17T14:00:13+00:00 | {} | 2024-02-17T14:00:13+00:00 |
|
76c5d7ce9a21002bf989a3a3a0802f395c361f4f | Diiiann/ossetian-russian | [
"region:us"
] | 2024-02-17T14:00:59+00:00 | {"dataset_info": {"features": [{"name": "oss", "dtype": "string"}, {"name": "ru", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 373189, "num_examples": 141}], "download_size": 189545, "dataset_size": 373189}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T14:01:00+00:00 |
|
4db4eedcf9ae6fc375c9199c93fe38a73b9481d2 | # Dataset Card for "ytrends5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | firstgradeai/ytrends5 | [
"region:us"
] | 2024-02-17T14:02:11+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13395866.378974993, "num_examples": 9069}, {"name": "test", "num_bytes": 5741507.621025008, "num_examples": 3887}], "download_size": 9624751, "dataset_size": 19137374.0}} | 2024-02-17T14:05:09+00:00 |
a3437118039be6e62a2605ce93900fb29ea6d4cd | lab42/cov-json-vqa-10-v3 | [
"region:us"
] | 2024-02-17T14:02:48+00:00 | {"dataset_info": {"features": [{"name": "image_0", "dtype": "image"}, {"name": "image_1", "dtype": "image"}, {"name": "image_2", "dtype": "image"}, {"name": "images_rest", "sequence": "image"}, {"name": "mask_0", "dtype": "image"}, {"name": "mask_1", "dtype": "image"}, {"name": "mask_2", "dtype": "image"}, {"name": "masks_rest", "sequence": "image"}, {"name": "conversations", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "n_images", "dtype": "int32"}, {"name": "n_masks", "dtype": "int32"}, {"name": "n_conversations", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 16780.0, "num_examples": 9}, {"name": "validation", "num_bytes": 1876.0, "num_examples": 1}], "download_size": 43039, "dataset_size": 18656.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]} | 2024-02-17T14:02:54+00:00 |
|
fd2fb64e5850c62a0a1ce26acc8fa0c263bee9df | looper525/mini-platypus | [
"region:us"
] | 2024-02-17T14:06:00+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "data_source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30776452, "num_examples": 24926}], "download_size": 15552844, "dataset_size": 30776452}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T14:06:02+00:00 |
|
e885bc2e0ef4d10274628f38850d8dc0f65952a8 | dakkulanthu/news | [
"region:us"
] | 2024-02-17T14:08:30+00:00 | {} | 2024-02-17T14:21:27+00:00 |
|
e012e15427ab8b4cc996bb99228a89a133c3e79a | lab42/cov-json-vqa-v3 | [
"region:us"
] | 2024-02-17T14:26:21+00:00 | {"dataset_info": {"features": [{"name": "image_0", "dtype": "image"}, {"name": "image_1", "dtype": "image"}, {"name": "image_2", "dtype": "image"}, {"name": "images_rest", "sequence": "image"}, {"name": "mask_0", "dtype": "image"}, {"name": "mask_1", "dtype": "image"}, {"name": "mask_2", "dtype": "image"}, {"name": "masks_rest", "sequence": "image"}, {"name": "conversations", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "n_images", "dtype": "int32"}, {"name": "n_masks", "dtype": "int32"}, {"name": "n_conversations", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 55013986.5, "num_examples": 25210}, {"name": "validation", "num_bytes": 5952357.25, "num_examples": 2729}], "download_size": 19882115, "dataset_size": 60966343.75}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]} | 2024-02-17T14:26:29+00:00 |
|
d1bfb74f3331f6fa46c48e519ee07b2e5be289f5 | maghwa/OpenHermes-2-AR-10K-31-730k-740k | [
"region:us"
] | 2024-02-17T14:28:08+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "hash", "dtype": "null"}, {"name": "category", "dtype": "null"}, {"name": "system_prompt", "dtype": "null"}, {"name": "model_name", "dtype": "null"}, {"name": "language", "dtype": "null"}, {"name": "views", "dtype": "float64"}, {"name": "conversations", "dtype": "string"}, {"name": "topic", "dtype": "null"}, {"name": "id", "dtype": "null"}, {"name": "avatarUrl", "dtype": "null"}, {"name": "custom_instruction", "dtype": "null"}, {"name": "skip_prompt_formatting", "dtype": "null"}, {"name": "idx", "dtype": "null"}, {"name": "title", "dtype": "null"}, {"name": "model", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25172981, "num_examples": 10001}], "download_size": 11410894, "dataset_size": 25172981}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T14:28:09+00:00 |
|
ce6e3ed267388f8725bff3b07a7bb18ec475276f |
# Dataset Card for Evaluation run of DatPySci/pythia-1b-sft-50k
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [DatPySci/pythia-1b-sft-50k](https://huggingface.co/DatPySci/pythia-1b-sft-50k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_DatPySci__pythia-1b-sft-50k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T14:41:59.887810](https://huggingface.co/datasets/open-llm-leaderboard/details_DatPySci__pythia-1b-sft-50k/blob/main/results_2024-02-17T14-41-59.887810.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"acc_stderr": 0.03024534477539282,
"acc_norm": 0.24555697815658845,
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"mc1_stderr": 0.01450904517148729,
"mc2": 0.37005968856579075,
"mc2_stderr": 0.014337009699291485
},
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"acc_stderr": 0.01311904089772592,
"acc_norm": 0.3003412969283277,
"acc_norm_stderr": 0.013395909309957009
},
"harness|hellaswag|10": {
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"acc_stderr": 0.00486901015228075,
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"acc_norm_stderr": 0.004988979750014438
},
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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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"harness|hendrycksTest-high_school_microeconomics|5": {
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},
"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_statistics|5": {
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},
"harness|hendrycksTest-high_school_us_history|5": {
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},
"harness|hendrycksTest-high_school_world_history|5": {
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},
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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},
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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"acc_norm_stderr": 0.03061111655743253
},
"harness|truthfulqa:mc|0": {
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},
"harness|winogrande|5": {
"acc": 0.5406471981057617,
"acc_stderr": 0.014005973823825136
},
"harness|gsm8k|5": {
"acc": 0.017437452615617893,
"acc_stderr": 0.003605486867998255
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_DatPySci__pythia-1b-sft-50k | [
"region:us"
] | 2024-02-17T14:29:57+00:00 | {"pretty_name": "Evaluation run of DatPySci/pythia-1b-sft-50k", "dataset_summary": "Dataset automatically created during the evaluation run of model [DatPySci/pythia-1b-sft-50k](https://huggingface.co/DatPySci/pythia-1b-sft-50k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_DatPySci__pythia-1b-sft-50k\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T14:41:59.887810](https://huggingface.co/datasets/open-llm-leaderboard/details_DatPySci__pythia-1b-sft-50k/blob/main/results_2024-02-17T14-41-59.887810.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.24467360878008643,\n \"acc_stderr\": 0.03024534477539282,\n \"acc_norm\": 0.24555697815658845,\n \"acc_norm_stderr\": 0.030978837434188194,\n \"mc1\": 0.22031823745410037,\n \"mc1_stderr\": 0.01450904517148729,\n \"mc2\": 0.37005968856579075,\n \"mc2_stderr\": 0.014337009699291485\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.27986348122866894,\n \"acc_stderr\": 0.01311904089772592,\n \"acc_norm\": 0.3003412969283277,\n \"acc_norm_stderr\": 0.013395909309957009\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3906592312288389,\n \"acc_stderr\": 0.00486901015228075,\n \"acc_norm\": 0.4910376419040032,\n \"acc_norm_stderr\": 0.004988979750014438\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322716,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322716\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123394,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123394\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.27547169811320754,\n \"acc_stderr\": 0.02749566368372407,\n \"acc_norm\": 0.27547169811320754,\n \"acc_norm_stderr\": 0.02749566368372407\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.04158307533083286,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.04158307533083286\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.23404255319148937,\n \"acc_stderr\": 0.02767845257821238,\n \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.02767845257821238\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n \"acc_stderr\": 0.038924311065187504,\n \"acc_norm\": 0.21929824561403508,\n \"acc_norm_stderr\": 0.038924311065187504\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.0333333333333333,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2619047619047619,\n \"acc_stderr\": 0.039325376803928704,\n \"acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.039325376803928704\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.25161290322580643,\n \"acc_stderr\": 0.024685979286239963,\n \"acc_norm\": 0.25161290322580643,\n \"acc_norm_stderr\": 0.024685979286239963\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.21674876847290642,\n \"acc_stderr\": 0.028990331252516235,\n \"acc_norm\": 0.21674876847290642,\n \"acc_norm_stderr\": 0.028990331252516235\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055952,\n \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055952\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.1717171717171717,\n \"acc_stderr\": 0.026869716187429917,\n \"acc_norm\": 0.1717171717171717,\n \"acc_norm_stderr\": 0.026869716187429917\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.2694300518134715,\n \"acc_stderr\": 0.03201867122877795,\n \"acc_norm\": 0.2694300518134715,\n \"acc_norm_stderr\": 0.03201867122877795\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2230769230769231,\n \"acc_stderr\": 0.021107730127244,\n \"acc_norm\": 0.2230769230769231,\n \"acc_norm_stderr\": 0.021107730127244\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.29259259259259257,\n \"acc_stderr\": 0.027738969632176088,\n \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.027738969632176088\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.22268907563025211,\n \"acc_stderr\": 0.027025433498882385,\n \"acc_norm\": 0.22268907563025211,\n \"acc_norm_stderr\": 0.027025433498882385\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2119205298013245,\n \"acc_stderr\": 0.03336767086567978,\n \"acc_norm\": 0.2119205298013245,\n \"acc_norm_stderr\": 0.03336767086567978\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.30275229357798167,\n \"acc_stderr\": 0.019698711434756343,\n \"acc_norm\": 0.30275229357798167,\n \"acc_norm_stderr\": 0.019698711434756343\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.24537037037037038,\n \"acc_stderr\": 0.02934666509437295,\n \"acc_norm\": 0.24537037037037038,\n \"acc_norm_stderr\": 0.02934666509437295\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.20098039215686275,\n \"acc_stderr\": 0.028125972265654355,\n \"acc_norm\": 0.20098039215686275,\n \"acc_norm_stderr\": 0.028125972265654355\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.25316455696202533,\n \"acc_stderr\": 0.028304657943035296,\n \"acc_norm\": 0.25316455696202533,\n \"acc_norm_stderr\": 0.028304657943035296\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.336322869955157,\n \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.336322869955157,\n \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.23140495867768596,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.23140495867768596,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.2085889570552147,\n \"acc_stderr\": 0.03192193448934724,\n \"acc_norm\": 0.2085889570552147,\n \"acc_norm_stderr\": 0.03192193448934724\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.20388349514563106,\n \"acc_stderr\": 0.039891398595317706,\n \"acc_norm\": 0.20388349514563106,\n \"acc_norm_stderr\": 0.039891398595317706\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2863247863247863,\n \"acc_stderr\": 0.029614323690456648,\n \"acc_norm\": 0.2863247863247863,\n \"acc_norm_stderr\": 0.029614323690456648\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26947637292464877,\n \"acc_stderr\": 0.015866243073215037,\n \"acc_norm\": 0.26947637292464877,\n \"acc_norm_stderr\": 0.015866243073215037\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.02344582627654555,\n \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.02344582627654555\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n \"acc_stderr\": 0.014355911964767857,\n \"acc_norm\": 0.2435754189944134,\n \"acc_norm_stderr\": 0.014355911964767857\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.023805186524888156,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.023805186524888156\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2572347266881029,\n \"acc_stderr\": 0.024826171289250888,\n \"acc_norm\": 0.2572347266881029,\n \"acc_norm_stderr\": 0.024826171289250888\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.2654320987654321,\n \"acc_stderr\": 0.024569223600460845,\n \"acc_norm\": 0.2654320987654321,\n \"acc_norm_stderr\": 0.024569223600460845\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432403,\n \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432403\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2392438070404172,\n \"acc_stderr\": 0.010896123652676651,\n \"acc_norm\": 0.2392438070404172,\n \"acc_norm_stderr\": 0.010896123652676651\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.34558823529411764,\n \"acc_stderr\": 0.028888193103988644,\n \"acc_norm\": 0.34558823529411764,\n \"acc_norm_stderr\": 0.028888193103988644\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.28104575163398693,\n \"acc_stderr\": 0.018185218954318086,\n \"acc_norm\": 0.28104575163398693,\n \"acc_norm_stderr\": 0.018185218954318086\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.19090909090909092,\n \"acc_stderr\": 0.03764425585984926,\n \"acc_norm\": 0.19090909090909092,\n \"acc_norm_stderr\": 0.03764425585984926\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.17959183673469387,\n \"acc_stderr\": 0.024573293589585637,\n \"acc_norm\": 0.17959183673469387,\n \"acc_norm_stderr\": 0.024573293589585637\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370519,\n \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370519\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.19883040935672514,\n \"acc_stderr\": 0.03061111655743253,\n \"acc_norm\": 0.19883040935672514,\n \"acc_norm_stderr\": 0.03061111655743253\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22031823745410037,\n \"mc1_stderr\": 0.01450904517148729,\n \"mc2\": 0.37005968856579075,\n \"mc2_stderr\": 0.014337009699291485\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5406471981057617,\n \"acc_stderr\": 0.014005973823825136\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.017437452615617893,\n \"acc_stderr\": 0.003605486867998255\n }\n}\n```", "repo_url": "https://huggingface.co/DatPySci/pythia-1b-sft-50k", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "clementine@hf.co", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|arc:challenge|25_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|arc:challenge|25_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|arc:challenge|25_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|gsm8k|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|gsm8k|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|gsm8k|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|hellaswag|10_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|hellaswag|10_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|hellaswag|10_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T14-28-13.386969.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-17T14-28-13.386969.parquet", 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["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["**/details_harness|winogrande|5_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["**/details_harness|winogrande|5_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["**/details_harness|winogrande|5_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-17T14-41-59.887810.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_17T14_28_13.386969", "path": ["results_2024-02-17T14-28-13.386969.parquet"]}, {"split": "2024_02_17T14_28_30.118401", "path": ["results_2024-02-17T14-28-30.118401.parquet"]}, {"split": "2024_02_17T14_41_59.887810", "path": ["results_2024-02-17T14-41-59.887810.parquet"]}, {"split": "latest", "path": ["results_2024-02-17T14-41-59.887810.parquet"]}]}]} | 2024-02-17T14:43:48+00:00 |
46bcf8001d07d4295361c788ee8754aa57254492 | Orenbac/amz-press-release_summarized_embedded | [
"region:us"
] | 2024-02-17T14:30:49+00:00 | {} | 2024-02-17T14:32:21+00:00 |
|
6711d2f2a9c7d585e61ecee5138c3252d3367fb1 |
## Sources of the corpus used
- [Opus](https://opus.nlpl.eu/results/en&eu/corpus-result-table)
- [Orai](https://www.orai.eus/en/resources)
| itzune/basque-parallel-corpus | [
"task_categories:translation",
"language:eu",
"language:en",
"language:es",
"language:fr",
"region:us"
] | 2024-02-17T14:36:43+00:00 | {"language": ["eu", "en", "es", "fr"], "task_categories": ["translation"], "pretty_name": "Parallel Basque Corpus"} | 2024-02-17T14:52:38+00:00 |
4878482e839a5a71bd03f49b755f33916d657a57 | Weni/zeroshot-validation-3.1.0 | [
"region:us"
] | 2024-02-17T14:40:23+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "all_classes", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 970707, "num_examples": 1000}], "download_size": 95553, "dataset_size": 970707}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T15:16:39+00:00 |
|
b4020ab3a4b832f42e8b226e9be64efb7f3b8f43 |
# Dataset Card for Evaluation run of athirdpath/Orca-2-13b-Alpaca-Uncensored
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [athirdpath/Orca-2-13b-Alpaca-Uncensored](https://huggingface.co/athirdpath/Orca-2-13b-Alpaca-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_athirdpath__Orca-2-13b-Alpaca-Uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T14:58:45.053086](https://huggingface.co/datasets/open-llm-leaderboard/details_athirdpath__Orca-2-13b-Alpaca-Uncensored/blob/main/results_2024-02-17T14-58-45.053086.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6002703838763287,
"acc_stderr": 0.032941926494755115,
"acc_norm": 0.6047089287996544,
"acc_norm_stderr": 0.03361522848210774,
"mc1": 0.37821297429620565,
"mc1_stderr": 0.01697633590754687,
"mc2": 0.5358987502340753,
"mc2_stderr": 0.01565060464007792
},
"harness|arc:challenge|25": {
"acc": 0.5750853242320819,
"acc_stderr": 0.014445698968520769,
"acc_norm": 0.6109215017064846,
"acc_norm_stderr": 0.014247309976045607
},
"harness|hellaswag|10": {
"acc": 0.6100378410675165,
"acc_stderr": 0.004867445945277156,
"acc_norm": 0.792670782712607,
"acc_norm_stderr": 0.004045648954769832
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7302631578947368,
"acc_stderr": 0.036117805602848975,
"acc_norm": 0.7302631578947368,
"acc_norm_stderr": 0.036117805602848975
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6150943396226415,
"acc_stderr": 0.02994649856769995,
"acc_norm": 0.6150943396226415,
"acc_norm_stderr": 0.02994649856769995
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.03942082639927213,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.03942082639927213
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.45,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5664739884393064,
"acc_stderr": 0.03778621079092056,
"acc_norm": 0.5664739884393064,
"acc_norm_stderr": 0.03778621079092056
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.04617034827006718,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.04617034827006718
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5361702127659574,
"acc_stderr": 0.03260038511835771,
"acc_norm": 0.5361702127659574,
"acc_norm_stderr": 0.03260038511835771
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2719298245614035,
"acc_stderr": 0.04185774424022056,
"acc_norm": 0.2719298245614035,
"acc_norm_stderr": 0.04185774424022056
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5379310344827586,
"acc_stderr": 0.04154659671707548,
"acc_norm": 0.5379310344827586,
"acc_norm_stderr": 0.04154659671707548
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.37566137566137564,
"acc_stderr": 0.024942368931159795,
"acc_norm": 0.37566137566137564,
"acc_norm_stderr": 0.024942368931159795
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.31746031746031744,
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"acc_norm": 0.31746031746031744,
"acc_norm_stderr": 0.04163453031302859
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
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"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7387096774193549,
"acc_stderr": 0.024993053397764805,
"acc_norm": 0.7387096774193549,
"acc_norm_stderr": 0.024993053397764805
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.46798029556650245,
"acc_stderr": 0.03510766597959217,
"acc_norm": 0.46798029556650245,
"acc_norm_stderr": 0.03510766597959217
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7151515151515152,
"acc_stderr": 0.03524390844511781,
"acc_norm": 0.7151515151515152,
"acc_norm_stderr": 0.03524390844511781
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
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"acc_norm_stderr": 0.030954055470365897
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8549222797927462,
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"acc_norm": 0.8549222797927462,
"acc_norm_stderr": 0.02541634309630642
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5794871794871795,
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"acc_norm": 0.5794871794871795,
"acc_norm_stderr": 0.025028610276710855
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
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"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.028661201116524586
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6260504201680672,
"acc_stderr": 0.031429466378837076,
"acc_norm": 0.6260504201680672,
"acc_norm_stderr": 0.031429466378837076
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
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"acc_norm_stderr": 0.038796870240733264
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8073394495412844,
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"acc_norm": 0.8073394495412844,
"acc_norm_stderr": 0.016909276884936073
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5046296296296297,
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"acc_norm": 0.5046296296296297,
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},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
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},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
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"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.025955020841621112
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6681614349775785,
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},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7175572519083969,
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},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
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},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
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"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7055214723926381,
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"acc_norm_stderr": 0.03581165790474082
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.35714285714285715,
"acc_stderr": 0.04547960999764377,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.04547960999764377
},
"harness|hendrycksTest-management|5": {
"acc": 0.7572815533980582,
"acc_stderr": 0.04245022486384493,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.04245022486384493
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8632478632478633,
"acc_stderr": 0.022509033937077802,
"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.022509033937077802
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.776500638569604,
"acc_stderr": 0.01489723522945071,
"acc_norm": 0.776500638569604,
"acc_norm_stderr": 0.01489723522945071
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.025070713719153186,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.025070713719153186
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3318435754189944,
"acc_stderr": 0.015748421208187306,
"acc_norm": 0.3318435754189944,
"acc_norm_stderr": 0.015748421208187306
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6601307189542484,
"acc_stderr": 0.027121956071388852,
"acc_norm": 0.6601307189542484,
"acc_norm_stderr": 0.027121956071388852
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6816720257234726,
"acc_stderr": 0.026457225067811025,
"acc_norm": 0.6816720257234726,
"acc_norm_stderr": 0.026457225067811025
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7067901234567902,
"acc_stderr": 0.025329888171900933,
"acc_norm": 0.7067901234567902,
"acc_norm_stderr": 0.025329888171900933
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4716312056737589,
"acc_stderr": 0.029779450957303062,
"acc_norm": 0.4716312056737589,
"acc_norm_stderr": 0.029779450957303062
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4380704041720991,
"acc_stderr": 0.012671902782567657,
"acc_norm": 0.4380704041720991,
"acc_norm_stderr": 0.012671902782567657
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5772058823529411,
"acc_stderr": 0.030008562845003476,
"acc_norm": 0.5772058823529411,
"acc_norm_stderr": 0.030008562845003476
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6143790849673203,
"acc_stderr": 0.01969145905235402,
"acc_norm": 0.6143790849673203,
"acc_norm_stderr": 0.01969145905235402
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.0289205832206756,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.0289205832206756
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7263681592039801,
"acc_stderr": 0.03152439186555402,
"acc_norm": 0.7263681592039801,
"acc_norm_stderr": 0.03152439186555402
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5060240963855421,
"acc_stderr": 0.03892212195333045,
"acc_norm": 0.5060240963855421,
"acc_norm_stderr": 0.03892212195333045
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7953216374269005,
"acc_stderr": 0.03094445977853321,
"acc_norm": 0.7953216374269005,
"acc_norm_stderr": 0.03094445977853321
},
"harness|truthfulqa:mc|0": {
"mc1": 0.37821297429620565,
"mc1_stderr": 0.01697633590754687,
"mc2": 0.5358987502340753,
"mc2_stderr": 0.01565060464007792
},
"harness|winogrande|5": {
"acc": 0.7742699289660616,
"acc_stderr": 0.011749626260902545
},
"harness|gsm8k|5": {
"acc": 0.38286580742987114,
"acc_stderr": 0.013389223491820465
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_athirdpath__Orca-2-13b-Alpaca-Uncensored | [
"region:us"
] | 2024-02-17T15:01:02+00:00 | {"pretty_name": "Evaluation run of athirdpath/Orca-2-13b-Alpaca-Uncensored", "dataset_summary": "Dataset automatically created during the evaluation run of model [athirdpath/Orca-2-13b-Alpaca-Uncensored](https://huggingface.co/athirdpath/Orca-2-13b-Alpaca-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_athirdpath__Orca-2-13b-Alpaca-Uncensored\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T14:58:45.053086](https://huggingface.co/datasets/open-llm-leaderboard/details_athirdpath__Orca-2-13b-Alpaca-Uncensored/blob/main/results_2024-02-17T14-58-45.053086.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6002703838763287,\n \"acc_stderr\": 0.032941926494755115,\n \"acc_norm\": 0.6047089287996544,\n \"acc_norm_stderr\": 0.03361522848210774,\n \"mc1\": 0.37821297429620565,\n \"mc1_stderr\": 0.01697633590754687,\n \"mc2\": 0.5358987502340753,\n \"mc2_stderr\": 0.01565060464007792\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520769,\n \"acc_norm\": 0.6109215017064846,\n \"acc_norm_stderr\": 0.014247309976045607\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6100378410675165,\n \"acc_stderr\": 0.004867445945277156,\n \"acc_norm\": 0.792670782712607,\n \"acc_norm_stderr\": 0.004045648954769832\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.036117805602848975,\n \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.036117805602848975\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6150943396226415,\n \"acc_stderr\": 0.02994649856769995,\n \"acc_norm\": 0.6150943396226415,\n \"acc_norm_stderr\": 0.02994649856769995\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5664739884393064,\n \"acc_stderr\": 0.03778621079092056,\n \"acc_norm\": 0.5664739884393064,\n \"acc_norm_stderr\": 0.03778621079092056\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006718,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006718\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.03260038511835771,\n \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.03260038511835771\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n \"acc_stderr\": 0.04185774424022056,\n \"acc_norm\": 0.2719298245614035,\n \"acc_norm_stderr\": 0.04185774424022056\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159795,\n \"acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159795\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.31746031746031744,\n \"acc_stderr\": 0.04163453031302859,\n \"acc_norm\": 0.31746031746031744,\n \"acc_norm_stderr\": 0.04163453031302859\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7387096774193549,\n \"acc_stderr\": 0.024993053397764805,\n \"acc_norm\": 0.7387096774193549,\n \"acc_norm_stderr\": 0.024993053397764805\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.03510766597959217,\n \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.03510766597959217\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7151515151515152,\n \"acc_stderr\": 0.03524390844511781,\n \"acc_norm\": 0.7151515151515152,\n \"acc_norm_stderr\": 0.03524390844511781\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365897,\n \"acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365897\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.02541634309630642,\n \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.02541634309630642\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5794871794871795,\n \"acc_stderr\": 0.025028610276710855,\n \"acc_norm\": 0.5794871794871795,\n \"acc_norm_stderr\": 0.025028610276710855\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524586,\n \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524586\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6260504201680672,\n \"acc_stderr\": 0.031429466378837076,\n \"acc_norm\": 0.6260504201680672,\n \"acc_norm_stderr\": 0.031429466378837076\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8073394495412844,\n \"acc_stderr\": 0.016909276884936073,\n \"acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.016909276884936073\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621112,\n \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621112\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n \"acc_stderr\": 0.04547960999764377,\n \"acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.04547960999764377\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384493,\n \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384493\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n \"acc_stderr\": 0.022509033937077802,\n \"acc_norm\": 0.8632478632478633,\n \"acc_norm_stderr\": 0.022509033937077802\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 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53dd169c863af510885a96925c6f95f0a02aba77 | Guilherme34/Pygmalion-dataset | [
"region:us"
] | 2024-02-17T15:08:21+00:00 | {} | 2024-02-17T15:09:42+00:00 |
|
62888c073f7a0b16e25e84852b46229af80e3f89 | Weni/zeroshot-3.1.0 | [
"region:us"
] | 2024-02-17T15:09:41+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "all_classes", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "language", "dtype": {"class_label": {"names": {"0": "pt", "1": "en", "2": "es"}}}}], "splits": [{"name": "train", "num_bytes": 18334765, "num_examples": 29448}], "download_size": 8387033, "dataset_size": 18334765}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T15:10:56+00:00 |
|
a27f09c7090519d25f10ce86aa7a9bb7a70edc22 | ANDRIZZBIRCH/arianagrandesweetner | [
"task_categories:text-classification",
"language:aa",
"license:apache-2.0",
"region:us"
] | 2024-02-17T15:09:46+00:00 | {"language": ["aa"], "license": "apache-2.0", "task_categories": ["text-classification"]} | 2024-02-17T15:14:44+00:00 |
|
a9197c003f4483b63083a474759a03a42d213f02 | alinet/balanced_qg | [
"region:us"
] | 2024-02-17T15:12:53+00:00 | {} | 2024-02-17T15:13:40+00:00 |
|
6c57624d94cdec5af451133a8430797d435542d8 | benayas/massive_augmented_20pct_v1 | [
"region:us"
] | 2024-02-17T15:17:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": "float64"}, {"name": "intent", "dtype": "float64"}, {"name": "utt", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "struct": [{"name": "method", "sequence": "null"}, {"name": "slot", "sequence": "null"}]}, {"name": "judgments", "struct": [{"name": "grammar_score", "sequence": "int8"}, {"name": "intent_score", "sequence": "int8"}, {"name": "language_identification", "sequence": "null"}, {"name": "slots_score", "sequence": "int8"}, {"name": "spelling_score", "sequence": "int8"}, {"name": "worker_id", "sequence": "null"}]}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1758967, "num_examples": 11514}], "download_size": 472580, "dataset_size": 1758967}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-02-17T15:17:06+00:00 |
|
59fb34ca7c3f3c613c5c24ebe5a80fbb53e06ae0 | mb7419/legal-advice-reddit | [
"region:us"
] | 2024-02-17T15:21:12+00:00 | {"dataset_info": {"features": [{"name": "ques_title", "dtype": "string"}, {"name": "ques_text", "dtype": "string"}, {"name": "ques_created", "dtype": "string"}, {"name": "ques_score", "dtype": "int64"}, {"name": "ans_text", "dtype": "string"}, {"name": "ans_created", "dtype": "string"}, {"name": "ans_score", "dtype": "float64"}, {"name": "dominant_topic_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 228451850, "num_examples": 115359}, {"name": "validation", "num_bytes": 49046245, "num_examples": 24720}, {"name": "test", "num_bytes": 49058903, "num_examples": 24720}], "download_size": 197856704, "dataset_size": 326556998}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-17T15:21:22+00:00 |
|
33a5335cb3c30d270cb984f980894c95bbe1fc73 | 1. edjudgement.zip consist of originally scrapped pdf files
2. pdf_to_text_raw.csv is a processed data (stage 1 - non refined) from the scraped pdf | izardy/malaysia-ejudgement | [
"region:us"
] | 2024-02-17T15:28:45+00:00 | {} | 2024-02-17T16:01:56+00:00 |
e68c1a09560c9f8359873f74fd5db534f08dc00b | hannademaria/ARVR | [
"region:us"
] | 2024-02-17T15:31:30+00:00 | {} | 2024-02-17T15:31:30+00:00 |
|
4570667d64bcd1741501c700899b11f321198570 | # Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)
## Project Overview
The Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage.
## Background and Rationale
Durham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come.
## Data Sources and Methodology
### Data Sources
We will leverage the following files from the Durham Trees & Planting Sites Dataset, as found on the Durham Open Data portal:
- `GS_TreeInventory.shp`
- `Trees_&_Planting_Sites.csv`
- `Trees_%26_Planting_Sites.geojson`
# Dataset Card for Urban Tree Inventory
## Dataset Description
This dataset provides comprehensive information about urban trees within a specified area, including their physical characteristics, environmental benefits, and the economic value they add in terms of ecosystem services.
### Spatial Data (GeoJSON)
**Format:** GeoJSON
**Content:**
- **Type:** `FeatureCollection` - A collection of feature objects.
- **Features:** Each feature object represents a tree and contains:
- **Type:** `Feature`
- **Geometry:** `Point` (includes longitude and latitude of the tree location).
- **Properties:** Detailed information about the tree (some fields may overlap with the CSV structure below).
### Tabular Data (CSV)
**Format:** CSV
**Columns:**
- **X, Y:** Coordinates of the tree location.
- **OBJECTID:** Unique identifier for the tree.
- **streetaddress:** Street address nearest to the tree.
- **city:** City where the tree is located.
- **zipcode:** Zip code for the location of the tree.
- **facilityid:** Identifier for the facility associated with the tree, if any.
- **present:** Indication of whether the tree is currently present.
- **genus, species, commonname:** Botanical and common names of the tree.
- **plantingdate:** Date when the tree was planted.
- **diameterin:** Diameter of the tree trunk in inches.
- **heightft:** Height of the tree in feet.
- **condition:** Health condition of the tree.
- **contractwork:** Indicates if the tree has had any contract work done.
- **neighborhood:** Neighborhood where the tree is located.
- **program:** The program under which the tree was planted.
- **plantingw:** Width of the planting site.
- **plantingcond:** Condition of the planting site.
- **underpwerlins:** Whether the tree is under power lines.
- **matureheight:** The mature height of the tree.
- **GlobalID:** A global unique identifier for the tree.
- **created_user:** The user who created the record.
- **created_date:** The date the record was created.
- **last_edited_user:** The user who last edited the record.
- **last_edited_date:** The date the record was last edited.
#### Environmental and Economic Data:
- **isoprene, monoterpene, vocs:** Emissions and absorption data for various compounds.
- **coremoved_ozperyr, o3removed_ozperyr, etc.:** Annual pollutant removal metrics.
- **o2production_lbperyr:** Annual oxygen production.
- **carbonstorage_lb, carbonstorage_dol:** Carbon storage metrics.
- **grosscarseq_lbperyr, grosscarseq_dolperyr:** Gross carbon sequestration.
- **avoidrunoff_ft2peryr, avoidrunoff_dol2peryr:** Metrics related to stormwater runoff avoidance.
- **totannbenefits_dolperyr:** Total annual dollar benefits from the tree.
- **leafarea_sqft, potevapotran_cuftperyr, etc.:** Metrics related to the water cycle.
- **heating_mbtuperyr, cooling_kwhperyr, etc.:** Energy savings related to the tree's impact on building energy use.
### Example Record
**GeoJSON Feature:**
```json
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [-78.90863, 36.00441]
},
"properties": {
"OBJECTID": 2840940,
"commonname": "Willow Oak",
// Additional properties...
}
}
```
The `GS_TreeInventory.shp` file encompasses a range of attributes for each record:
- **OBJECTID:** Unique identifier for each record.
- **streetaddr:** Street address where the tree or planting site is located.
- **city:** The city name, which is Durham.
- **zipcode:** Postal code for the location.
- **facilityid:** Identifier possibly linked to a facility or area associated with the tree.
- **present:** Type of feature present, such as a tree or a planting site.
- **genus:** Genus of the tree.
- **species:** Species of the tree.
- **commonname:** Common name of the tree.
- **plantingda:** Date or year range when the tree was planted or the planting site was established.
- ...
### Objectives
1. Combine Shapefile and CSV data into a comprehensive geospatial dataset using Python.
2. Apply Python libraries to uncover relationships between tree canopy data and urban development.
3. Provide practical insights and strategies for the expansion of Durham's urban tree canopy.
4. Produce analyses and visualizations with the GeoJSON file.
### Methodology
Our analytical process within Google Colab will encompass:
- **Data Preparation and Integration:** Using tools like Geopandas, Pandas, and PyShp to organize and combine spatial and tabular data.
- **Geospatial Analysis:** Applying Shapely and Rtree for spatial analysis, and using SciPy or Statsmodels for statistical correlations.
- **Visualization and Optimization:** Generating maps and graphs with Matplotlib, Seaborn, or Plotly, and utilizing optimization algorithms to suggest optimal planting locations.
## Deliverables
1. A collection of Google Colab Python notebooks that outline our analytical processes.
2. Interactive maps and visualizations that connect tree canopy coverage with urban development metrics.
3. An exhaustive report that contains our findings and recommendations for enhancing the urban canopy.
## Limitations
- **Computational Resources:** The limited computational offerings of Google Colab may pose a challenge to the size of the datasets or the complexity of models we can employ.
- **Data Quality:** The accuracy and currency of the data ultimately affect the precision of our recommendations.
- **Sociopolitical Considerations:** Implementation of our data-driven suggestions must be reviewed within the context of local policy and community input.
## Conclusion
DUCAEI aims to create a more verdant and livable urban landscape in Durham through this Python-based analytical project. By laying a strong foundation for data-informed decision-making, we hope to cultivate a thriving, green, and sustainable urban environment. | Ziyuan111/Urban_Tree_Canopy_in_Durham2 | [
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | 2024-02-17T15:32:47+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"]} | 2024-02-17T16:36:59+00:00 |
03605da446ad99e0d52506c72536900e270a768f |
<div align="center">
<h1> HALvest </h1>
<h3> Multilingual Research Papers Harvested from HAL </h3>
</div>
## Dataset Description
- **Repository:** [GitHub](https://github.com/Madjakul/HALvesting/tree/main)
- **Papers:** TBD
## Dataset Summary
### overview:
HALvest is dataset comprise of fulltext from open papers from [Hyper Articles en Ligne (HAL)](https://hal.science/). Our dump gather papers written in TBD languages across TBD domains from TBD/date to TBD/date.
You can download the dataset using Hugging Face datasets:
*You may need to follow these instructions to setup authentication before downloading the dataset: [https://huggingface.co/docs/huggingface_hub/quick-start#login](https://huggingface.co/docs/huggingface_hub/quick-start#login)*
```py
from datasets import load_dataset
ds = load_dataset(
"Madjakul/halvest",
"en",
trust_remote_code=True
)
```
### Details:
- We first request [HAL's API](https://api.archives-ouvertes.fr/docs) in order to gather open research papers and parse it -- effectively sorting papers by language, year of production and domain.
- We then download the PDF of the fetched papers.
- Using [GROBID](https://github.com/kermitt2/grobid), we convert each PDF to `xml-tei` format in order to have structured data.
- Using [TBD](https://github.com), we convert each `xml-tei` to LLM's compatible `txt` format and concatenate it with the papaper's metadata fetched earlier.
### Languages
The supported languages and statistics for our dataset can be found below:
TBD
### Domains
The supported domains and statistics for our dataset can be found below:
TBD
### Dataset Structure
```json
{
"halid": ...,
"lang": ...,
"domain": ...,
"timestamp": ...,
"year": ...,
"url": ...,
"text": ...
}
```
## Considerations for Using the Data
HALvest is a direct conversion of reasearch papers found on HAL. Hence, fullnames of individuals, as well as their research e-mail adress can be found within the dataset. This must be considered prior to using this dataset for any purpose, such as training deep learning models, etc.
## Dataset Copyright
The licence terms for HALvest strictly follows those of HAL. Please refer to the below license when using this dataset.
- [HAL license](https://doc.archives-ouvertes.fr/en/legal-aspects/)
## Citation
```bibtex
@proceedings{TBD
}
```
## Acknowledegment
This dataset is built upon the following works:
```
GROBID: A machine learning software for extracting information from scholarly documents
https://github.com/kermitt2/grobid
harvesting: Collection of data parser for harvested data in [...]
[...]
```
| Madjakul/halvest | [
"task_categories:text-generation",
"task_categories:fill-mask",
"academia",
"research",
"region:us"
] | 2024-02-17T15:42:06+00:00 | {"annotations_creators": ["no-annotation"], "multilinguality": ["multilingual"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "HALvest", "tags": ["academia", "research"]} | 2024-02-17T15:53:20+00:00 |
afe9f82a5b85b8bfc258d5348d9821a67ab7d45e | mesolitica/noisy-augmentation | [
"region:us"
] | 2024-02-17T15:45:07+00:00 | {} | 2024-02-17T15:47:31+00:00 |
|
b53c97aeaeb9e1b4459299f38b282bd76a3c5586 |
# Dataset Card for Evaluation run of bardsai/jaskier-7b-dpo-v5.6
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_bardsai__jaskier-7b-dpo-v5.6",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T15:44:22.548008](https://huggingface.co/datasets/open-llm-leaderboard/details_bardsai__jaskier-7b-dpo-v5.6/blob/main/results_2024-02-17T15-44-22.548008.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6501937924638518,
"acc_stderr": 0.032025249091365754,
"acc_norm": 0.6494469200952948,
"acc_norm_stderr": 0.03269529478578274,
"mc1": 0.6303549571603427,
"mc1_stderr": 0.01689818070697388,
"mc2": 0.7781424860062839,
"mc2_stderr": 0.013751565023330138
},
"harness|arc:challenge|25": {
"acc": 0.7090443686006825,
"acc_stderr": 0.01327307786590759,
"acc_norm": 0.7303754266211604,
"acc_norm_stderr": 0.012968040686869147
},
"harness|hellaswag|10": {
"acc": 0.7137024497112129,
"acc_stderr": 0.004511063351278702,
"acc_norm": 0.8899621589324835,
"acc_norm_stderr": 0.00312297363203947
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6943396226415094,
"acc_stderr": 0.028353298073322663,
"acc_norm": 0.6943396226415094,
"acc_norm_stderr": 0.028353298073322663
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542126,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542126
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146268,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146268
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.025379524910778394,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.025379524910778394
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542126,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542126
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
"acc_stderr": 0.023540799358723295,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.023540799358723295
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
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"acc_norm_stderr": 0.03317505930009182
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
"acc": 0.8453038674033149,
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},
"harness|gsm8k|5": {
"acc": 0.6967399545109931,
"acc_stderr": 0.012661502663418697
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Dataset Structure
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## Dataset Creation
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_bardsai__jaskier-7b-dpo-v5.6 | [
"region:us"
] | 2024-02-17T15:46:42+00:00 | {"pretty_name": "Evaluation run of bardsai/jaskier-7b-dpo-v5.6", "dataset_summary": "Dataset automatically created during the evaluation run of model [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bardsai__jaskier-7b-dpo-v5.6\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T15:44:22.548008](https://huggingface.co/datasets/open-llm-leaderboard/details_bardsai__jaskier-7b-dpo-v5.6/blob/main/results_2024-02-17T15-44-22.548008.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6501937924638518,\n \"acc_stderr\": 0.032025249091365754,\n \"acc_norm\": 0.6494469200952948,\n \"acc_norm_stderr\": 0.03269529478578274,\n \"mc1\": 0.6303549571603427,\n \"mc1_stderr\": 0.01689818070697388,\n \"mc2\": 0.7781424860062839,\n \"mc2_stderr\": 0.013751565023330138\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.7090443686006825,\n \"acc_stderr\": 0.01327307786590759,\n \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869147\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7137024497112129,\n \"acc_stderr\": 0.004511063351278702,\n \"acc_norm\": 0.8899621589324835,\n \"acc_norm_stderr\": 0.00312297363203947\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322663,\n \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322663\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542126,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542126\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778394,\n \"acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778394\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542126,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542126\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.02403548967633508,\n \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.02403548967633508\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066485,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066485\n },\n 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["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T15-44-22.548008.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_17T15_44_22.548008", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T15-44-22.548008.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_17T15_44_22.548008", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T15-44-22.548008.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_17T15_44_22.548008", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T15-44-22.548008.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_17T15_44_22.548008", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T15-44-22.548008.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_17T15_44_22.548008", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T15-44-22.548008.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_17T15_44_22.548008", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T15-44-22.548008.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_17T15_44_22.548008", "path": ["**/details_harness|winogrande|5_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-17T15-44-22.548008.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_17T15_44_22.548008", "path": ["results_2024-02-17T15-44-22.548008.parquet"]}, {"split": "latest", "path": ["results_2024-02-17T15-44-22.548008.parquet"]}]}]} | 2024-02-17T15:47:04+00:00 |
f425906b3e9e0c339730ce5cfd630c706b601b70 | # Dataset Card for "samantar_with_idx_merged_with_train_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mlsquare/samantar_with_idx_merged_with_train_val | [
"region:us"
] | 2024-02-17T15:52:01+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}]}], "dataset_info": {"features": [{"name": "idx", "dtype": "int64"}, {"name": "tgt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11263548923.940233, "num_examples": 79638793}, {"name": "valid", "num_bytes": 2815887337.0597663, "num_examples": 19909699}], "download_size": 9506498914, "dataset_size": 14079436261.0}} | 2024-02-17T16:17:45+00:00 |
2bc2ae985070fc69750b3a8c49f2aefb83dc3428 | saniket919/verbalyze_chat_multilingual | [
"region:us"
] | 2024-02-17T15:54:39+00:00 | {} | 2024-02-17T15:54:39+00:00 |
|
77c688e2b35e0e786a4b7a4bf264cbd514fcd027 | verbalyze/verbalyze_chat_multilingual | [
"region:us"
] | 2024-02-17T15:54:53+00:00 | {} | 2024-02-17T16:12:48+00:00 |