id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
CyberHarem/miyao_miya_theidolmstermillionlive | 2023-09-17T17:43:31.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of miyao_miya (THE iDOLM@STER: Million Live!)
This is the dataset of miyao_miya (THE iDOLM@STER: Million Live!), containing 192 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 192 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 512 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 192 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 192 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 192 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 192 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 192 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 512 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 512 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 512 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
Hatanaka744/kung12 | 2023-09-17T01:45:50.000Z | [
"region:us"
] | Hatanaka744 | null | null | null | 0 | 0 | Entry not found |
CyberHarem/emily_stewart_theidolmstermillionlive | 2023-09-17T17:43:33.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of emily_stewart (THE iDOLM@STER: Million Live!)
This is the dataset of emily_stewart (THE iDOLM@STER: Million Live!), containing 132 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 132 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 359 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 132 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 132 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 132 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 132 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 132 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 359 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 359 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 359 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
Taroemon63/Taroemon63 | 2023-09-17T02:02:41.000Z | [
"region:us"
] | Taroemon63 | null | null | null | 0 | 0 | Entry not found |
Hokama3/Hokama3 | 2023-09-17T02:03:53.000Z | [
"region:us"
] | Hokama3 | null | null | null | 0 | 0 | Entry not found |
Ippei34/Ippei34 | 2023-09-17T02:05:00.000Z | [
"region:us"
] | Ippei34 | null | null | null | 0 | 0 | Entry not found |
Amano28/Amano28 | 2023-09-17T02:05:57.000Z | [
"region:us"
] | Amano28 | null | null | null | 0 | 0 | Entry not found |
Kanong63/Kanong63 | 2023-09-17T02:06:57.000Z | [
"region:us"
] | Kanong63 | null | null | null | 0 | 0 | Entry not found |
Fukuhara32/Fukuhara32 | 2023-09-17T02:07:47.000Z | [
"region:us"
] | Fukuhara32 | null | null | null | 0 | 0 | Entry not found |
CyberHarem/handa_roco_theidolmstermillionlive | 2023-09-17T17:43:35.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of handa_roco (THE iDOLM@STER: Million Live!)
This is the dataset of handa_roco (THE iDOLM@STER: Million Live!), containing 122 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 122 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 335 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 122 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 122 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 122 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 122 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 122 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 335 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 335 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 335 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v10-7B | 2023-09-17T02:50:48.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of PeanutJar/LLaMa-2-PeanutButter_v10-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PeanutJar/LLaMa-2-PeanutButter_v10-7B](https://huggingface.co/PeanutJar/LLaMa-2-PeanutButter_v10-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v10-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T02:50:37.300317](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v10-7B/blob/main/results_2023-09-17T02-50-37.300317.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 \"em\": 0.006082214765100671,\n\
\ \"em_stderr\": 0.000796243239302896,\n \"f1\": 0.059260696308725026,\n\
\ \"f1_stderr\": 0.0014614581539411243,\n \"acc\": 0.3839482806388088,\n\
\ \"acc_stderr\": 0.009633147982899772\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.006082214765100671,\n \"em_stderr\": 0.000796243239302896,\n\
\ \"f1\": 0.059260696308725026,\n \"f1_stderr\": 0.0014614581539411243\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05913570887035633,\n \
\ \"acc_stderr\": 0.006497266660428848\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7087608524072613,\n \"acc_stderr\": 0.012769029305370695\n\
\ }\n}\n```"
repo_url: https://huggingface.co/PeanutJar/LLaMa-2-PeanutButter_v10-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T02_50_37.300317
path:
- '**/details_harness|drop|3_2023-09-17T02-50-37.300317.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T02-50-37.300317.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T02_50_37.300317
path:
- '**/details_harness|gsm8k|5_2023-09-17T02-50-37.300317.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T02-50-37.300317.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T02_50_37.300317
path:
- '**/details_harness|winogrande|5_2023-09-17T02-50-37.300317.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T02-50-37.300317.parquet'
- config_name: results
data_files:
- split: 2023_09_17T02_50_37.300317
path:
- results_2023-09-17T02-50-37.300317.parquet
- split: latest
path:
- results_2023-09-17T02-50-37.300317.parquet
---
# Dataset Card for Evaluation run of PeanutJar/LLaMa-2-PeanutButter_v10-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PeanutJar/LLaMa-2-PeanutButter_v10-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [PeanutJar/LLaMa-2-PeanutButter_v10-7B](https://huggingface.co/PeanutJar/LLaMa-2-PeanutButter_v10-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_PeanutJar__LLaMa-2-PeanutButter_v10-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T02:50:37.300317](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__LLaMa-2-PeanutButter_v10-7B/blob/main/results_2023-09-17T02-50-37.300317.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": {
"em": 0.006082214765100671,
"em_stderr": 0.000796243239302896,
"f1": 0.059260696308725026,
"f1_stderr": 0.0014614581539411243,
"acc": 0.3839482806388088,
"acc_stderr": 0.009633147982899772
},
"harness|drop|3": {
"em": 0.006082214765100671,
"em_stderr": 0.000796243239302896,
"f1": 0.059260696308725026,
"f1_stderr": 0.0014614581539411243
},
"harness|gsm8k|5": {
"acc": 0.05913570887035633,
"acc_stderr": 0.006497266660428848
},
"harness|winogrande|5": {
"acc": 0.7087608524072613,
"acc_stderr": 0.012769029305370695
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
CyberHarem/shinomiya_karen_theidolmstermillionlive | 2023-09-17T17:43:37.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shinomiya_karen (THE iDOLM@STER: Million Live!)
This is the dataset of shinomiya_karen (THE iDOLM@STER: Million Live!), containing 42 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 42 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 110 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 42 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 42 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 42 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 42 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 42 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 110 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 110 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 110 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
open-llm-leaderboard/details_lizhuang144__starcoder_mirror | 2023-09-17T02:55:48.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of lizhuang144/starcoder_mirror
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lizhuang144/starcoder_mirror](https://huggingface.co/lizhuang144/starcoder_mirror)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lizhuang144__starcoder_mirror\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T02:55:35.893698](https://huggingface.co/datasets/open-llm-leaderboard/details_lizhuang144__starcoder_mirror/blob/main/results_2023-09-17T02-55-35.893698.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 \"em\": 0.0018875838926174498,\n\
\ \"em_stderr\": 0.0004445109990558897,\n \"f1\": 0.04898594798657743,\n\
\ \"f1_stderr\": 0.001215831642948078,\n \"acc\": 0.3137813978564757,\n\
\ \"acc_stderr\": 0.010101677905009763\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0018875838926174498,\n \"em_stderr\": 0.0004445109990558897,\n\
\ \"f1\": 0.04898594798657743,\n \"f1_stderr\": 0.001215831642948078\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05534495830174375,\n \
\ \"acc_stderr\": 0.006298221796179574\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5722178374112076,\n \"acc_stderr\": 0.013905134013839953\n\
\ }\n}\n```"
repo_url: https://huggingface.co/lizhuang144/starcoder_mirror
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T02_55_35.893698
path:
- '**/details_harness|drop|3_2023-09-17T02-55-35.893698.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T02-55-35.893698.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T02_55_35.893698
path:
- '**/details_harness|gsm8k|5_2023-09-17T02-55-35.893698.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T02-55-35.893698.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T02_55_35.893698
path:
- '**/details_harness|winogrande|5_2023-09-17T02-55-35.893698.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T02-55-35.893698.parquet'
- config_name: results
data_files:
- split: 2023_09_17T02_55_35.893698
path:
- results_2023-09-17T02-55-35.893698.parquet
- split: latest
path:
- results_2023-09-17T02-55-35.893698.parquet
---
# Dataset Card for Evaluation run of lizhuang144/starcoder_mirror
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lizhuang144/starcoder_mirror
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [lizhuang144/starcoder_mirror](https://huggingface.co/lizhuang144/starcoder_mirror) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_lizhuang144__starcoder_mirror",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T02:55:35.893698](https://huggingface.co/datasets/open-llm-leaderboard/details_lizhuang144__starcoder_mirror/blob/main/results_2023-09-17T02-55-35.893698.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": {
"em": 0.0018875838926174498,
"em_stderr": 0.0004445109990558897,
"f1": 0.04898594798657743,
"f1_stderr": 0.001215831642948078,
"acc": 0.3137813978564757,
"acc_stderr": 0.010101677905009763
},
"harness|drop|3": {
"em": 0.0018875838926174498,
"em_stderr": 0.0004445109990558897,
"f1": 0.04898594798657743,
"f1_stderr": 0.001215831642948078
},
"harness|gsm8k|5": {
"acc": 0.05534495830174375,
"acc_stderr": 0.006298221796179574
},
"harness|winogrande|5": {
"acc": 0.5722178374112076,
"acc_stderr": 0.013905134013839953
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
Omnibus/blockchain-sim-2 | 2023-09-27T06:55:30.000Z | [
"region:us"
] | Omnibus | null | null | null | 0 | 0 | Entry not found |
linhqyy/data_aug_random | 2023-09-17T03:04:52.000Z | [
"region:us"
] | linhqyy | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence
dtype: string
- name: intent
dtype: string
- name: entities
list:
- name: type
dtype: string
- name: filler
dtype: string
- name: labels
dtype: string
splits:
- name: train
num_bytes: 3248023
num_examples: 15255
- name: test
num_bytes: 362513
num_examples: 1695
download_size: 758928
dataset_size: 3610536
---
# Dataset Card for "data_aug_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/matsuda_arisa_theidolmstermillionlive | 2023-09-17T17:43:39.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of matsuda_arisa (THE iDOLM@STER: Million Live!)
This is the dataset of matsuda_arisa (THE iDOLM@STER: Million Live!), containing 74 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 74 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 200 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 74 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 74 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 74 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 74 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 74 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 200 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 200 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 200 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k | 2023-09-23T08:31:09.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of conceptofmind/LLongMA-2-13b-16k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [conceptofmind/LLongMA-2-13b-16k](https://huggingface.co/conceptofmind/LLongMA-2-13b-16k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T08:30:56.994435](https://huggingface.co/datasets/open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k/blob/main/results_2023-09-23T08-30-56.994435.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 \"em\": 0.002202181208053691,\n\
\ \"em_stderr\": 0.0004800510816619487,\n \"f1\": 0.05451552013422845,\n\
\ \"f1_stderr\": 0.001321043219231616,\n \"acc\": 0.39035575610663886,\n\
\ \"acc_stderr\": 0.009395368385266412\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.0004800510816619487,\n\
\ \"f1\": 0.05451552013422845,\n \"f1_stderr\": 0.001321043219231616\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05458680818802123,\n \
\ \"acc_stderr\": 0.006257444037912527\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620296\n\
\ }\n}\n```"
repo_url: https://huggingface.co/conceptofmind/LLongMA-2-13b-16k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T03_21_57.837796
path:
- '**/details_harness|drop|3_2023-09-17T03-21-57.837796.parquet'
- split: 2023_09_23T08_30_56.994435
path:
- '**/details_harness|drop|3_2023-09-23T08-30-56.994435.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T08-30-56.994435.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T03_21_57.837796
path:
- '**/details_harness|gsm8k|5_2023-09-17T03-21-57.837796.parquet'
- split: 2023_09_23T08_30_56.994435
path:
- '**/details_harness|gsm8k|5_2023-09-23T08-30-56.994435.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T08-30-56.994435.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T03_21_57.837796
path:
- '**/details_harness|winogrande|5_2023-09-17T03-21-57.837796.parquet'
- split: 2023_09_23T08_30_56.994435
path:
- '**/details_harness|winogrande|5_2023-09-23T08-30-56.994435.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T08-30-56.994435.parquet'
- config_name: results
data_files:
- split: 2023_09_17T03_21_57.837796
path:
- results_2023-09-17T03-21-57.837796.parquet
- split: 2023_09_23T08_30_56.994435
path:
- results_2023-09-23T08-30-56.994435.parquet
- split: latest
path:
- results_2023-09-23T08-30-56.994435.parquet
---
# Dataset Card for Evaluation run of conceptofmind/LLongMA-2-13b-16k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/conceptofmind/LLongMA-2-13b-16k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [conceptofmind/LLongMA-2-13b-16k](https://huggingface.co/conceptofmind/LLongMA-2-13b-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 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 agregated 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_conceptofmind__LLongMA-2-13b-16k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T08:30:56.994435](https://huggingface.co/datasets/open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k/blob/main/results_2023-09-23T08-30-56.994435.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": {
"em": 0.002202181208053691,
"em_stderr": 0.0004800510816619487,
"f1": 0.05451552013422845,
"f1_stderr": 0.001321043219231616,
"acc": 0.39035575610663886,
"acc_stderr": 0.009395368385266412
},
"harness|drop|3": {
"em": 0.002202181208053691,
"em_stderr": 0.0004800510816619487,
"f1": 0.05451552013422845,
"f1_stderr": 0.001321043219231616
},
"harness|gsm8k|5": {
"acc": 0.05458680818802123,
"acc_stderr": 0.006257444037912527
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620296
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
CyberHarem/nikaidou_chizuru_theidolmstermillionlive | 2023-09-17T17:43:42.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of nikaidou_chizuru (THE iDOLM@STER: Million Live!)
This is the dataset of nikaidou_chizuru (THE iDOLM@STER: Million Live!), containing 67 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 67 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 177 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 67 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 67 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 67 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 67 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 67 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 177 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 177 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 177 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
Yoja15/RVCukr | 2023-09-20T12:31:44.000Z | [
"region:us"
] | Yoja15 | null | null | null | 0 | 0 | Entry not found |
MaxReynolds/cifar10_512x512px | 2023-09-17T04:10:15.000Z | [
"region:us"
] | MaxReynolds | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': airplane
'1': automobile
'2': bird
'3': cat
'4': deer
'5': dog
'6': frog
'7': horse
'8': ship
'9': truck
- name: pixel_values
dtype: image
splits:
- name: train
num_bytes: 6445891560.0
num_examples: 50000
download_size: 6446258731
dataset_size: 6445891560.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cifar10_512x512px"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HuggingFaceH4/surge_instruct_longest_llama2 | 2023-09-17T04:07:08.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | null | 1 | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: source
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 5641006
num_examples: 500
- name: test
num_bytes: 1530441
num_examples: 500
download_size: 4243981
dataset_size: 7171447
---
# Dataset Card for "surge_instruct_longest_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/nonohara_akane_theidolmstermillionlive | 2023-09-17T17:43:44.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of nonohara_akane (THE iDOLM@STER: Million Live!)
This is the dataset of nonohara_akane (THE iDOLM@STER: Million Live!), containing 66 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 66 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 171 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 66 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 66 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 66 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 66 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 66 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 171 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 171 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 171 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
sanali209/bf_sketch | 2023-09-17T04:36:13.000Z | [
"license:gpl-3.0",
"region:us"
] | sanali209 | null | null | null | 0 | 0 | ---
license: gpl-3.0
---
|
CyberHarem/fukuda_noriko_theidolmstermillionlive | 2023-09-17T17:43:46.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of fukuda_noriko (THE iDOLM@STER: Million Live!)
This is the dataset of fukuda_noriko (THE iDOLM@STER: Million Live!), containing 96 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 96 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 264 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 96 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 96 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 96 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 96 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 96 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 264 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 264 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 264 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
stealthwriter/newAIHumanGPT3.5 | 2023-09-17T04:52:21.000Z | [
"region:us"
] | stealthwriter | null | null | null | 0 | 0 | Entry not found |
CyberHarem/aoba_misaki_theidolmstermillionlive | 2023-09-17T17:43:48.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of aoba_misaki (THE iDOLM@STER: Million Live!)
This is the dataset of aoba_misaki (THE iDOLM@STER: Million Live!), containing 31 images and their tags.
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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 31 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 87 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 31 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 31 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 31 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 31 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 31 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 87 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 87 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 87 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
open-llm-leaderboard/details_MBZUAI__lamini-cerebras-590m | 2023-09-17T04:57:17.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of MBZUAI/lamini-cerebras-590m
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [MBZUAI/lamini-cerebras-590m](https://huggingface.co/MBZUAI/lamini-cerebras-590m)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MBZUAI__lamini-cerebras-590m\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T04:57:06.330423](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__lamini-cerebras-590m/blob/main/results_2023-09-17T04-57-06.330423.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 \"em\": 0.007445469798657718,\n\
\ \"em_stderr\": 0.0008803652515899861,\n \"f1\": 0.07449664429530209,\n\
\ \"f1_stderr\": 0.001794948262867366,\n \"acc\": 0.24030037584379355,\n\
\ \"acc_stderr\": 0.00755598242138111\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.007445469798657718,\n \"em_stderr\": 0.0008803652515899861,\n\
\ \"f1\": 0.07449664429530209,\n \"f1_stderr\": 0.001794948262867366\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \
\ \"acc_stderr\": 0.0010717793485492634\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.47908445146014206,\n \"acc_stderr\": 0.014040185494212955\n\
\ }\n}\n```"
repo_url: https://huggingface.co/MBZUAI/lamini-cerebras-590m
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T04_57_06.330423
path:
- '**/details_harness|drop|3_2023-09-17T04-57-06.330423.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T04-57-06.330423.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T04_57_06.330423
path:
- '**/details_harness|gsm8k|5_2023-09-17T04-57-06.330423.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T04-57-06.330423.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T04_57_06.330423
path:
- '**/details_harness|winogrande|5_2023-09-17T04-57-06.330423.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T04-57-06.330423.parquet'
- config_name: results
data_files:
- split: 2023_09_17T04_57_06.330423
path:
- results_2023-09-17T04-57-06.330423.parquet
- split: latest
path:
- results_2023-09-17T04-57-06.330423.parquet
---
# Dataset Card for Evaluation run of MBZUAI/lamini-cerebras-590m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/MBZUAI/lamini-cerebras-590m
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [MBZUAI/lamini-cerebras-590m](https://huggingface.co/MBZUAI/lamini-cerebras-590m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_MBZUAI__lamini-cerebras-590m",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T04:57:06.330423](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__lamini-cerebras-590m/blob/main/results_2023-09-17T04-57-06.330423.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": {
"em": 0.007445469798657718,
"em_stderr": 0.0008803652515899861,
"f1": 0.07449664429530209,
"f1_stderr": 0.001794948262867366,
"acc": 0.24030037584379355,
"acc_stderr": 0.00755598242138111
},
"harness|drop|3": {
"em": 0.007445469798657718,
"em_stderr": 0.0008803652515899861,
"f1": 0.07449664429530209,
"f1_stderr": 0.001794948262867366
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492634
},
"harness|winogrande|5": {
"acc": 0.47908445146014206,
"acc_stderr": 0.014040185494212955
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
joshclark756/arkham-knight-10-minutes | 2023-09-17T05:26:25.000Z | [
"region:us"
] | joshclark756 | null | null | null | 0 | 0 | Entry not found |
emmajoanne/sd-configs-4 | 2023-09-17T07:33:48.000Z | [
"region:us"
] | emmajoanne | null | null | null | 0 | 0 | Entry not found |
Reggie370/laionDemo | 2023-09-18T15:24:12.000Z | [
"region:us"
] | Reggie370 | null | null | null | 0 | 0 | Entry not found |
minlik/text-summarization | 2023-09-17T06:22:33.000Z | [
"region:us"
] | minlik | null | null | null | 0 | 0 | Entry not found |
linhqyy/data_aug_not_rand | 2023-09-17T06:42:08.000Z | [
"region:us"
] | linhqyy | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_augtoma__qCammel-70v1 | 2023-09-17T06:45:29.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of augtoma/qCammel-70v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [augtoma/qCammel-70v1](https://huggingface.co/augtoma/qCammel-70v1) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_augtoma__qCammel-70v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T06:45:18.044644](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel-70v1/blob/main/results_2023-09-17T06-45-18.044644.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 \"em\": 0.033766778523489936,\n\
\ \"em_stderr\": 0.001849802869119515,\n \"f1\": 0.10340918624161041,\n\
\ \"f1_stderr\": 0.0022106009828094797,\n \"acc\": 0.5700654570173166,\n\
\ \"acc_stderr\": 0.011407494958111332\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.033766778523489936,\n \"em_stderr\": 0.001849802869119515,\n\
\ \"f1\": 0.10340918624161041,\n \"f1_stderr\": 0.0022106009828094797\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2971948445792267,\n \
\ \"acc_stderr\": 0.012588685966624186\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8429360694554064,\n \"acc_stderr\": 0.010226303949598479\n\
\ }\n}\n```"
repo_url: https://huggingface.co/augtoma/qCammel-70v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T06_45_18.044644
path:
- '**/details_harness|drop|3_2023-09-17T06-45-18.044644.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T06-45-18.044644.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T06_45_18.044644
path:
- '**/details_harness|gsm8k|5_2023-09-17T06-45-18.044644.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T06-45-18.044644.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T06_45_18.044644
path:
- '**/details_harness|winogrande|5_2023-09-17T06-45-18.044644.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T06-45-18.044644.parquet'
- config_name: results
data_files:
- split: 2023_09_17T06_45_18.044644
path:
- results_2023-09-17T06-45-18.044644.parquet
- split: latest
path:
- results_2023-09-17T06-45-18.044644.parquet
---
# Dataset Card for Evaluation run of augtoma/qCammel-70v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/augtoma/qCammel-70v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [augtoma/qCammel-70v1](https://huggingface.co/augtoma/qCammel-70v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_augtoma__qCammel-70v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T06:45:18.044644](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel-70v1/blob/main/results_2023-09-17T06-45-18.044644.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": {
"em": 0.033766778523489936,
"em_stderr": 0.001849802869119515,
"f1": 0.10340918624161041,
"f1_stderr": 0.0022106009828094797,
"acc": 0.5700654570173166,
"acc_stderr": 0.011407494958111332
},
"harness|drop|3": {
"em": 0.033766778523489936,
"em_stderr": 0.001849802869119515,
"f1": 0.10340918624161041,
"f1_stderr": 0.0022106009828094797
},
"harness|gsm8k|5": {
"acc": 0.2971948445792267,
"acc_stderr": 0.012588685966624186
},
"harness|winogrande|5": {
"acc": 0.8429360694554064,
"acc_stderr": 0.010226303949598479
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
DialogueCharacter/chinese_alpaca_unfiltered | 2023-09-17T07:04:31.000Z | [
"region:us"
] | DialogueCharacter | null | null | null | 1 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 32887574
num_examples: 48818
download_size: 21230689
dataset_size: 32887574
---
# Dataset Card for "chinese_alpaca_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DialogueCharacter/chinese_firefly_unfiltered | 2023-09-17T07:16:14.000Z | [
"region:us"
] | DialogueCharacter | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1127002621
num_examples: 1649399
download_size: 793361458
dataset_size: 1127002621
---
# Dataset Card for "chinese_firefly_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DialogueCharacter/chinese_instinwild_unfiltered | 2023-09-17T07:16:54.000Z | [
"region:us"
] | DialogueCharacter | null | null | null | 2 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 30197794
num_examples: 51504
download_size: 17704859
dataset_size: 30197794
---
# Dataset Card for "chinese_instinwild_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DialogueCharacter/chinese_moss_unfiltered | 2023-09-17T07:19:09.000Z | [
"region:us"
] | DialogueCharacter | null | null | null | 1 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3264688861
num_examples: 550415
download_size: 1534910020
dataset_size: 3264688861
---
# Dataset Card for "chinese_moss_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DialogueCharacter/english_moss_unfiltered | 2023-09-17T07:22:37.000Z | [
"region:us"
] | DialogueCharacter | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 5150827100
num_examples: 523390
download_size: 2313907146
dataset_size: 5150827100
---
# Dataset Card for "english_moss_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_frank098__Wizard-Vicuna-13B-juniper | 2023-09-17T07:24:55.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of frank098/Wizard-Vicuna-13B-juniper
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [frank098/Wizard-Vicuna-13B-juniper](https://huggingface.co/frank098/Wizard-Vicuna-13B-juniper)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_frank098__Wizard-Vicuna-13B-juniper\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T07:24:44.144750](https://huggingface.co/datasets/open-llm-leaderboard/details_frank098__Wizard-Vicuna-13B-juniper/blob/main/results_2023-09-17T07-24-44.144750.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 \"em\": 0.0025167785234899327,\n\
\ \"em_stderr\": 0.0005131152834514818,\n \"f1\": 0.06578020134228216,\n\
\ \"f1_stderr\": 0.0014299327364359015,\n \"acc\": 0.39984819046262715,\n\
\ \"acc_stderr\": 0.009838812433518467\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0025167785234899327,\n \"em_stderr\": 0.0005131152834514818,\n\
\ \"f1\": 0.06578020134228216,\n \"f1_stderr\": 0.0014299327364359015\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07278241091736164,\n \
\ \"acc_stderr\": 0.007155604761167476\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7269139700078927,\n \"acc_stderr\": 0.012522020105869456\n\
\ }\n}\n```"
repo_url: https://huggingface.co/frank098/Wizard-Vicuna-13B-juniper
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T07_24_44.144750
path:
- '**/details_harness|drop|3_2023-09-17T07-24-44.144750.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T07-24-44.144750.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T07_24_44.144750
path:
- '**/details_harness|gsm8k|5_2023-09-17T07-24-44.144750.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T07-24-44.144750.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T07_24_44.144750
path:
- '**/details_harness|winogrande|5_2023-09-17T07-24-44.144750.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T07-24-44.144750.parquet'
- config_name: results
data_files:
- split: 2023_09_17T07_24_44.144750
path:
- results_2023-09-17T07-24-44.144750.parquet
- split: latest
path:
- results_2023-09-17T07-24-44.144750.parquet
---
# Dataset Card for Evaluation run of frank098/Wizard-Vicuna-13B-juniper
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/frank098/Wizard-Vicuna-13B-juniper
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [frank098/Wizard-Vicuna-13B-juniper](https://huggingface.co/frank098/Wizard-Vicuna-13B-juniper) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_frank098__Wizard-Vicuna-13B-juniper",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T07:24:44.144750](https://huggingface.co/datasets/open-llm-leaderboard/details_frank098__Wizard-Vicuna-13B-juniper/blob/main/results_2023-09-17T07-24-44.144750.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": {
"em": 0.0025167785234899327,
"em_stderr": 0.0005131152834514818,
"f1": 0.06578020134228216,
"f1_stderr": 0.0014299327364359015,
"acc": 0.39984819046262715,
"acc_stderr": 0.009838812433518467
},
"harness|drop|3": {
"em": 0.0025167785234899327,
"em_stderr": 0.0005131152834514818,
"f1": 0.06578020134228216,
"f1_stderr": 0.0014299327364359015
},
"harness|gsm8k|5": {
"acc": 0.07278241091736164,
"acc_stderr": 0.007155604761167476
},
"harness|winogrande|5": {
"acc": 0.7269139700078927,
"acc_stderr": 0.012522020105869456
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
Dinghan/Test | 2023-09-17T07:28:12.000Z | [
"task_categories:text-classification",
"license:apache-2.0",
"region:us"
] | Dinghan | null | null | null | 0 | 0 | ---
license: apache-2.0
task_categories:
- text-classification
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
DialogueCharacter/english_ultra_unfiltered | 2023-09-17T07:28:27.000Z | [
"region:us"
] | DialogueCharacter | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 4667203735
num_examples: 711984
download_size: 2206564571
dataset_size: 4667203735
---
# Dataset Card for "english_ultra_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DialogueCharacter/english_wizard_unfiltered | 2023-09-17T07:33:07.000Z | [
"region:us"
] | DialogueCharacter | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 278812623
num_examples: 121930
download_size: 144938153
dataset_size: 278812623
---
# Dataset Card for "english_wizard_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Rardilit__Panther_v1 | 2023-09-17T07:57:13.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of Rardilit/Panther_v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Rardilit/Panther_v1](https://huggingface.co/Rardilit/Panther_v1) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Rardilit__Panther_v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T07:57:01.737780](https://huggingface.co/datasets/open-llm-leaderboard/details_Rardilit__Panther_v1/blob/main/results_2023-09-17T07-57-01.737780.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 \"em\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\
acc\": 0.2478295185477506,\n \"acc_stderr\": 0.007025978032038456\n },\n\
\ \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\
\ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.4956590370955012,\n \"acc_stderr\": 0.014051956064076911\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Rardilit/Panther_v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T07_57_01.737780
path:
- '**/details_harness|drop|3_2023-09-17T07-57-01.737780.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T07-57-01.737780.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T07_57_01.737780
path:
- '**/details_harness|gsm8k|5_2023-09-17T07-57-01.737780.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T07-57-01.737780.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T07_57_01.737780
path:
- '**/details_harness|winogrande|5_2023-09-17T07-57-01.737780.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T07-57-01.737780.parquet'
- config_name: results
data_files:
- split: 2023_09_17T07_57_01.737780
path:
- results_2023-09-17T07-57-01.737780.parquet
- split: latest
path:
- results_2023-09-17T07-57-01.737780.parquet
---
# Dataset Card for Evaluation run of Rardilit/Panther_v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Rardilit/Panther_v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Rardilit/Panther_v1](https://huggingface.co/Rardilit/Panther_v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_Rardilit__Panther_v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T07:57:01.737780](https://huggingface.co/datasets/open-llm-leaderboard/details_Rardilit__Panther_v1/blob/main/results_2023-09-17T07-57-01.737780.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": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0,
"acc": 0.2478295185477506,
"acc_stderr": 0.007025978032038456
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.4956590370955012,
"acc_stderr": 0.014051956064076911
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
nevillemthw/sample-floor-plans | 2023-09-17T08:12:56.000Z | [
"license:openrail",
"region:us"
] | nevillemthw | null | null | null | 0 | 0 | ---
license: openrail
---
|
abbiepam/shorty | 2023-10-02T14:42:38.000Z | [
"region:us"
] | abbiepam | null | null | null | 0 | 0 | Entry not found |
ai202388/yd_yun | 2023-09-20T05:22:49.000Z | [
"region:us"
] | ai202388 | null | null | null | 0 | 0 | Entry not found |
hynky/elon_tweets_instruct | 2023-09-17T08:55:29.000Z | [
"region:us"
] | hynky | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 827106
num_examples: 4821
download_size: 558180
dataset_size: 827106
---
# Dataset Card for "elon_tweets_instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
akenzc/newRepo | 2023-09-17T09:25:35.000Z | [
"license:afl-3.0",
"region:us"
] | akenzc | null | null | null | 0 | 0 | ---
license: afl-3.0
---
|
peeyoushh/test_images_self | 2023-09-18T08:46:21.000Z | [
"region:us"
] | peeyoushh | null | null | null | 0 | 0 | |
KartonCreations/karton | 2023-09-17T09:30:54.000Z | [
"license:cc",
"region:us"
] | KartonCreations | null | null | null | 0 | 0 | ---
license: cc
---
|
bongo2112/harmonize-SDxl-openpose-Styled-Output-Images | 2023-09-18T13:47:16.000Z | [
"region:us"
] | bongo2112 | null | null | null | 0 | 0 | Entry not found |
crumb/refinedweb-2mil-128clusters | 2023-09-17T10:01:51.000Z | [
"region:us"
] | crumb | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_Panchovix__WizardLM-33B-V1.0-Uncensored-SuperHOT-8k | 2023-09-17T17:54:03.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of Panchovix/WizardLM-33B-V1.0-Uncensored-SuperHOT-8k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Panchovix/WizardLM-33B-V1.0-Uncensored-SuperHOT-8k](https://huggingface.co/Panchovix/WizardLM-33B-V1.0-Uncensored-SuperHOT-8k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Panchovix__WizardLM-33B-V1.0-Uncensored-SuperHOT-8k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T17:53:55.496275](https://huggingface.co/datasets/open-llm-leaderboard/details_Panchovix__WizardLM-33B-V1.0-Uncensored-SuperHOT-8k/blob/main/results_2023-09-17T17-53-55.496275.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 \"em\": 0.0005243288590604027,\n\
\ \"em_stderr\": 0.00023443780464835843,\n \"f1\": 0.0018907298657718122,\n\
\ \"f1_stderr\": 0.0003791471390866532,\n \"acc\": 0.255327545382794,\n\
\ \"acc_stderr\": 0.007024647268145198\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0005243288590604027,\n \"em_stderr\": 0.00023443780464835843,\n\
\ \"f1\": 0.0018907298657718122,\n \"f1_stderr\": 0.0003791471390866532\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.510655090765588,\n\
\ \"acc_stderr\": 0.014049294536290396\n }\n}\n```"
repo_url: https://huggingface.co/Panchovix/WizardLM-33B-V1.0-Uncensored-SuperHOT-8k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T09_46_06.674365
path:
- '**/details_harness|drop|3_2023-09-17T09-46-06.674365.parquet'
- split: 2023_09_17T17_53_55.496275
path:
- '**/details_harness|drop|3_2023-09-17T17-53-55.496275.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T17-53-55.496275.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T09_46_06.674365
path:
- '**/details_harness|gsm8k|5_2023-09-17T09-46-06.674365.parquet'
- split: 2023_09_17T17_53_55.496275
path:
- '**/details_harness|gsm8k|5_2023-09-17T17-53-55.496275.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T17-53-55.496275.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T09_46_06.674365
path:
- '**/details_harness|winogrande|5_2023-09-17T09-46-06.674365.parquet'
- split: 2023_09_17T17_53_55.496275
path:
- '**/details_harness|winogrande|5_2023-09-17T17-53-55.496275.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T17-53-55.496275.parquet'
- config_name: results
data_files:
- split: 2023_09_17T09_46_06.674365
path:
- results_2023-09-17T09-46-06.674365.parquet
- split: 2023_09_17T17_53_55.496275
path:
- results_2023-09-17T17-53-55.496275.parquet
- split: latest
path:
- results_2023-09-17T17-53-55.496275.parquet
---
# Dataset Card for Evaluation run of Panchovix/WizardLM-33B-V1.0-Uncensored-SuperHOT-8k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Panchovix/WizardLM-33B-V1.0-Uncensored-SuperHOT-8k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Panchovix/WizardLM-33B-V1.0-Uncensored-SuperHOT-8k](https://huggingface.co/Panchovix/WizardLM-33B-V1.0-Uncensored-SuperHOT-8k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 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 agregated 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_Panchovix__WizardLM-33B-V1.0-Uncensored-SuperHOT-8k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T17:53:55.496275](https://huggingface.co/datasets/open-llm-leaderboard/details_Panchovix__WizardLM-33B-V1.0-Uncensored-SuperHOT-8k/blob/main/results_2023-09-17T17-53-55.496275.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": {
"em": 0.0005243288590604027,
"em_stderr": 0.00023443780464835843,
"f1": 0.0018907298657718122,
"f1_stderr": 0.0003791471390866532,
"acc": 0.255327545382794,
"acc_stderr": 0.007024647268145198
},
"harness|drop|3": {
"em": 0.0005243288590604027,
"em_stderr": 0.00023443780464835843,
"f1": 0.0018907298657718122,
"f1_stderr": 0.0003791471390866532
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.510655090765588,
"acc_stderr": 0.014049294536290396
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
MohamedTahir/text_to_jason | 2023-09-17T09:51:27.000Z | [
"task_categories:translation",
"size_categories:n<1K",
"region:us"
] | MohamedTahir | null | null | null | 0 | 0 | ---
task_categories:
- translation
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
LovelynRose/copywrite | 2023-09-17T09:54:22.000Z | [
"region:us"
] | LovelynRose | null | null | null | 0 | 0 | Entry not found |
Ayansk11/question_answer | 2023-09-17T10:02:11.000Z | [
"region:us"
] | Ayansk11 | null | null | null | 0 | 0 | Entry not found |
kuronomiki/valorant | 2023-09-17T10:37:16.000Z | [
"license:other",
"region:us"
] | kuronomiki | null | null | null | 0 | 0 | ---
license: other
---
|
GunA-SD/wiki_cs | 2023-09-28T18:21:45.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"region:us"
] | GunA-SD | null | null | null | 0 | 0 | ---
task_categories:
- text-generation
language:
- en
size_categories:
- n<1K
--- |
minh21/COVID-QA-1-unique-context-test-10-percent-validation-10-percent | 2023-09-17T11:23:19.000Z | [
"region:us"
] | minh21 | null | null | null | 0 | 0 | Entry not found |
minh21/COVID-QA-2-unique-context-test-10-percent-validation-10-percent | 2023-09-17T11:35:07.000Z | [
"region:us"
] | minh21 | null | null | null | 0 | 0 | Entry not found |
badokorach/translatedSquad | 2023-09-17T13:37:02.000Z | [
"region:us"
] | badokorach | null | null | null | 0 | 0 | Entry not found |
aviroes/augmented_above_70yo_elderly_people_dataset | 2023-09-17T12:19:04.000Z | [
"region:us"
] | aviroes | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: input_length
dtype: float64
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 8097082928.0
num_examples: 8430
- name: test
num_bytes: 159444680
num_examples: 166
- name: validation
num_bytes: 96050136
num_examples: 100
download_size: 1755695943
dataset_size: 8352577744.0
---
# Dataset Card for "augmented_above_70yo_elderly_people_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DmitrMakeev/train_dreambooth_lora_sdxl | 2023-09-17T12:30:06.000Z | [
"license:openrail",
"region:us"
] | DmitrMakeev | null | null | null | 0 | 0 | ---
license: openrail
---
|
DialogueCharacter/english_preference_chatbot_arena_unfiltered | 2023-09-17T12:46:20.000Z | [
"region:us"
] | DialogueCharacter | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 53048541
num_examples: 23294
download_size: 26870764
dataset_size: 53048541
---
# Dataset Card for "english_preference_chatbot_arena_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kuiugh/newbingto | 2023-09-17T13:01:13.000Z | [
"license:mit",
"region:us"
] | kuiugh | null | null | null | 0 | 0 | ---
license: mit
---
|
xinyuzhou2000/Towards-Joint-Modeling-of-Dialogue-Response-and-Speech-Synthesis-based-on-Large-Language-Model | 2023-09-17T13:09:33.000Z | [
"region:us"
] | xinyuzhou2000 | null | null | null | 0 | 0 | Entry not found |
fbw/share | 2023-09-17T13:11:07.000Z | [
"region:us"
] | fbw | null | null | null | 0 | 0 | share to 大哥 |
BangumiBase/shirobako | 2023-09-30T12:10:33.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Shirobako
This is the image base of bangumi Shirobako, we detected 52 characters, 3771 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 66 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 18 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 6 | [Download](2/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 3 | 12 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 511 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 205 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 22 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 27 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 42 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 66 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 10 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 90 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 115 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 21 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 29 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 57 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 29 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 54 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 24 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 17 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 16 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 16 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 18 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 764 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 112 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 126 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 18 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 49 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 20 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 164 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 17 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 41 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 68 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 116 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 100 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 20 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 23 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 33 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 132 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 33 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 7 | [Download](40/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 41 | 21 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 111 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 41 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 16 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 11 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 18 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 9 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 32 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 9 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 6 | [Download](50/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 183 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
open-llm-leaderboard/details_Dampish__Dante-2.8B | 2023-09-17T13:26:41.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of Dampish/Dante-2.8B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Dampish/Dante-2.8B](https://huggingface.co/Dampish/Dante-2.8B) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Dampish__Dante-2.8B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T13:26:29.842810](https://huggingface.co/datasets/open-llm-leaderboard/details_Dampish__Dante-2.8B/blob/main/results_2023-09-17T13-26-29.842810.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 \"em\": 0.001363255033557047,\n\
\ \"em_stderr\": 0.00037786091964607033,\n \"f1\": 0.0017051174496644293,\n\
\ \"f1_stderr\": 0.00040455681041866965,\n \"acc\": 0.255327545382794,\n\
\ \"acc_stderr\": 0.007024647268145198\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001363255033557047,\n \"em_stderr\": 0.00037786091964607033,\n\
\ \"f1\": 0.0017051174496644293,\n \"f1_stderr\": 0.00040455681041866965\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.510655090765588,\n\
\ \"acc_stderr\": 0.014049294536290396\n }\n}\n```"
repo_url: https://huggingface.co/Dampish/Dante-2.8B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T13_26_29.842810
path:
- '**/details_harness|drop|3_2023-09-17T13-26-29.842810.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T13-26-29.842810.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T13_26_29.842810
path:
- '**/details_harness|gsm8k|5_2023-09-17T13-26-29.842810.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T13-26-29.842810.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T13_26_29.842810
path:
- '**/details_harness|winogrande|5_2023-09-17T13-26-29.842810.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T13-26-29.842810.parquet'
- config_name: results
data_files:
- split: 2023_09_17T13_26_29.842810
path:
- results_2023-09-17T13-26-29.842810.parquet
- split: latest
path:
- results_2023-09-17T13-26-29.842810.parquet
---
# Dataset Card for Evaluation run of Dampish/Dante-2.8B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Dampish/Dante-2.8B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Dampish/Dante-2.8B](https://huggingface.co/Dampish/Dante-2.8B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_Dampish__Dante-2.8B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T13:26:29.842810](https://huggingface.co/datasets/open-llm-leaderboard/details_Dampish__Dante-2.8B/blob/main/results_2023-09-17T13-26-29.842810.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": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964607033,
"f1": 0.0017051174496644293,
"f1_stderr": 0.00040455681041866965,
"acc": 0.255327545382794,
"acc_stderr": 0.007024647268145198
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964607033,
"f1": 0.0017051174496644293,
"f1_stderr": 0.00040455681041866965
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.510655090765588,
"acc_stderr": 0.014049294536290396
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
asdvawe/qwevqwevqwev | 2023-09-18T12:56:14.000Z | [
"region:us"
] | asdvawe | null | null | null | 0 | 0 | Entry not found |
Admin08077/K | 2023-09-17T13:50:37.000Z | [
"license:other",
"region:us"
] | Admin08077 | null | null | null | 0 | 0 | ---
license: other
---
|
junjuice0/15k | 2023-09-18T11:43:21.000Z | [
"region:us"
] | junjuice0 | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_FabbriSimo01__Bloom_1b_Quantized | 2023-09-17T14:42:06.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of FabbriSimo01/Bloom_1b_Quantized
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [FabbriSimo01/Bloom_1b_Quantized](https://huggingface.co/FabbriSimo01/Bloom_1b_Quantized)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FabbriSimo01__Bloom_1b_Quantized\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T14:41:55.154995](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__Bloom_1b_Quantized/blob/main/results_2023-09-17T14-41-55.154995.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 \"em\": 0.0016778523489932886,\n\
\ \"em_stderr\": 0.00041913301788268413,\n \"f1\": 0.047125629194631036,\n\
\ \"f1_stderr\": 0.0012660847237774002,\n \"acc\": 0.27897440899296483,\n\
\ \"acc_stderr\": 0.007517237128084831\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268413,\n\
\ \"f1\": 0.047125629194631036,\n \"f1_stderr\": 0.0012660847237774002\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \
\ \"acc_stderr\": 0.0010717793485492627\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5564325177584846,\n \"acc_stderr\": 0.0139626949076204\n\
\ }\n}\n```"
repo_url: https://huggingface.co/FabbriSimo01/Bloom_1b_Quantized
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T14_41_55.154995
path:
- '**/details_harness|drop|3_2023-09-17T14-41-55.154995.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T14-41-55.154995.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T14_41_55.154995
path:
- '**/details_harness|gsm8k|5_2023-09-17T14-41-55.154995.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T14-41-55.154995.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T14_41_55.154995
path:
- '**/details_harness|winogrande|5_2023-09-17T14-41-55.154995.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T14-41-55.154995.parquet'
- config_name: results
data_files:
- split: 2023_09_17T14_41_55.154995
path:
- results_2023-09-17T14-41-55.154995.parquet
- split: latest
path:
- results_2023-09-17T14-41-55.154995.parquet
---
# Dataset Card for Evaluation run of FabbriSimo01/Bloom_1b_Quantized
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/FabbriSimo01/Bloom_1b_Quantized
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [FabbriSimo01/Bloom_1b_Quantized](https://huggingface.co/FabbriSimo01/Bloom_1b_Quantized) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_FabbriSimo01__Bloom_1b_Quantized",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T14:41:55.154995](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__Bloom_1b_Quantized/blob/main/results_2023-09-17T14-41-55.154995.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": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788268413,
"f1": 0.047125629194631036,
"f1_stderr": 0.0012660847237774002,
"acc": 0.27897440899296483,
"acc_stderr": 0.007517237128084831
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788268413,
"f1": 0.047125629194631036,
"f1_stderr": 0.0012660847237774002
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492627
},
"harness|winogrande|5": {
"acc": 0.5564325177584846,
"acc_stderr": 0.0139626949076204
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_jphme__Llama-2-13b-chat-german | 2023-09-17T15:03:23.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of jphme/Llama-2-13b-chat-german
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jphme/Llama-2-13b-chat-german](https://huggingface.co/jphme/Llama-2-13b-chat-german)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jphme__Llama-2-13b-chat-german\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T15:03:11.382260](https://huggingface.co/datasets/open-llm-leaderboard/details_jphme__Llama-2-13b-chat-german/blob/main/results_2023-09-17T15-03-11.382260.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 \"em\": 0.006606543624161074,\n\
\ \"em_stderr\": 0.000829635738992222,\n \"f1\": 0.06547399328859073,\n\
\ \"f1_stderr\": 0.0015176277275461638,\n \"acc\": 0.45063287882224046,\n\
\ \"acc_stderr\": 0.01068787508123321\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.006606543624161074,\n \"em_stderr\": 0.000829635738992222,\n\
\ \"f1\": 0.06547399328859073,\n \"f1_stderr\": 0.0015176277275461638\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13646702047005307,\n \
\ \"acc_stderr\": 0.00945574199881554\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7647987371744278,\n \"acc_stderr\": 0.01192000816365088\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jphme/Llama-2-13b-chat-german
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T15_03_11.382260
path:
- '**/details_harness|drop|3_2023-09-17T15-03-11.382260.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T15-03-11.382260.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T15_03_11.382260
path:
- '**/details_harness|gsm8k|5_2023-09-17T15-03-11.382260.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T15-03-11.382260.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T15_03_11.382260
path:
- '**/details_harness|winogrande|5_2023-09-17T15-03-11.382260.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T15-03-11.382260.parquet'
- config_name: results
data_files:
- split: 2023_09_17T15_03_11.382260
path:
- results_2023-09-17T15-03-11.382260.parquet
- split: latest
path:
- results_2023-09-17T15-03-11.382260.parquet
---
# Dataset Card for Evaluation run of jphme/Llama-2-13b-chat-german
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jphme/Llama-2-13b-chat-german
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [jphme/Llama-2-13b-chat-german](https://huggingface.co/jphme/Llama-2-13b-chat-german) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_jphme__Llama-2-13b-chat-german",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T15:03:11.382260](https://huggingface.co/datasets/open-llm-leaderboard/details_jphme__Llama-2-13b-chat-german/blob/main/results_2023-09-17T15-03-11.382260.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": {
"em": 0.006606543624161074,
"em_stderr": 0.000829635738992222,
"f1": 0.06547399328859073,
"f1_stderr": 0.0015176277275461638,
"acc": 0.45063287882224046,
"acc_stderr": 0.01068787508123321
},
"harness|drop|3": {
"em": 0.006606543624161074,
"em_stderr": 0.000829635738992222,
"f1": 0.06547399328859073,
"f1_stderr": 0.0015176277275461638
},
"harness|gsm8k|5": {
"acc": 0.13646702047005307,
"acc_stderr": 0.00945574199881554
},
"harness|winogrande|5": {
"acc": 0.7647987371744278,
"acc_stderr": 0.01192000816365088
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_chavinlo__alpaca-native | 2023-09-21T20:24:38.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of chavinlo/alpaca-native
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chavinlo/alpaca-native](https://huggingface.co/chavinlo/alpaca-native) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chavinlo__alpaca-native\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-21T20:23:20.255556](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__alpaca-native/blob/main/results_2023-09-21T20-23-20.255556.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\": 0.41927597389078103,\n\
\ \"acc_stderr\": 0.035302205782678654,\n \"acc_norm\": 0.42235476219088836,\n\
\ \"acc_norm_stderr\": 0.035290265393035695,\n \"mc1\": 0.2484700122399021,\n\
\ \"mc1_stderr\": 0.015127427096520674,\n \"mc2\": 0.3759916250814691,\n\
\ \"mc2_stderr\": 0.015396201572279763\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5127986348122867,\n \"acc_stderr\": 0.014606603181012538,\n\
\ \"acc_norm\": 0.5204778156996587,\n \"acc_norm_stderr\": 0.01459913135303501\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5959968133837881,\n\
\ \"acc_stderr\": 0.004896952378506926,\n \"acc_norm\": 0.7699661422027485,\n\
\ \"acc_norm_stderr\": 0.004199941217549452\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\
\ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.45925925925925926,\n\
\ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.3618421052631579,\n \"acc_stderr\": 0.03910525752849724,\n\
\ \"acc_norm\": 0.3618421052631579,\n \"acc_norm_stderr\": 0.03910525752849724\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.46,\n\
\ \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.44150943396226416,\n \"acc_stderr\": 0.030561590426731837,\n\
\ \"acc_norm\": 0.44150943396226416,\n \"acc_norm_stderr\": 0.030561590426731837\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3819444444444444,\n\
\ \"acc_stderr\": 0.040629907841466674,\n \"acc_norm\": 0.3819444444444444,\n\
\ \"acc_norm_stderr\": 0.040629907841466674\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"\
acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\
: 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3815028901734104,\n\
\ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.3815028901734104,\n\
\ \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237656,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237656\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.37446808510638296,\n \"acc_stderr\": 0.03163910665367291,\n\
\ \"acc_norm\": 0.37446808510638296,\n \"acc_norm_stderr\": 0.03163910665367291\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\
\ \"acc_stderr\": 0.040969851398436716,\n \"acc_norm\": 0.2543859649122807,\n\
\ \"acc_norm_stderr\": 0.040969851398436716\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.36551724137931035,\n \"acc_stderr\": 0.040131241954243856,\n\
\ \"acc_norm\": 0.36551724137931035,\n \"acc_norm_stderr\": 0.040131241954243856\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.28835978835978837,\n \"acc_stderr\": 0.023330654054535903,\n \"\
acc_norm\": 0.28835978835978837,\n \"acc_norm_stderr\": 0.023330654054535903\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n\
\ \"acc_stderr\": 0.03852273364924314,\n \"acc_norm\": 0.24603174603174602,\n\
\ \"acc_norm_stderr\": 0.03852273364924314\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.4290322580645161,\n \"acc_stderr\": 0.02815603653823321,\n \"\
acc_norm\": 0.4290322580645161,\n \"acc_norm_stderr\": 0.02815603653823321\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3103448275862069,\n \"acc_stderr\": 0.03255086769970103,\n \"\
acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.03255086769970103\n\
\ },\n \"harness|hendrycksTest-high_school_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-high_school_european_history|5\"\
: {\n \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.038956580652718446,\n\
\ \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.038956580652718446\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.4797979797979798,\n \"acc_stderr\": 0.035594435655639196,\n \"\
acc_norm\": 0.4797979797979798,\n \"acc_norm_stderr\": 0.035594435655639196\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6062176165803109,\n \"acc_stderr\": 0.035260770955482405,\n\
\ \"acc_norm\": 0.6062176165803109,\n \"acc_norm_stderr\": 0.035260770955482405\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.3871794871794872,\n \"acc_stderr\": 0.024697216930878948,\n\
\ \"acc_norm\": 0.3871794871794872,\n \"acc_norm_stderr\": 0.024697216930878948\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26296296296296295,\n \"acc_stderr\": 0.02684205787383371,\n \
\ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.02684205787383371\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.031041941304059295,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.031041941304059295\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\
acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.544954128440367,\n \"acc_stderr\": 0.021350503090925173,\n \"\
acc_norm\": 0.544954128440367,\n \"acc_norm_stderr\": 0.021350503090925173\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.375,\n \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.375,\n\
\ \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.5343137254901961,\n \"acc_stderr\": 0.03501038327635897,\n\
\ \"acc_norm\": 0.5343137254901961,\n \"acc_norm_stderr\": 0.03501038327635897\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.5654008438818565,\n \"acc_stderr\": 0.03226759995510145,\n \
\ \"acc_norm\": 0.5654008438818565,\n \"acc_norm_stderr\": 0.03226759995510145\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5022421524663677,\n\
\ \"acc_stderr\": 0.03355746535223263,\n \"acc_norm\": 0.5022421524663677,\n\
\ \"acc_norm_stderr\": 0.03355746535223263\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.4122137404580153,\n \"acc_stderr\": 0.04317171194870254,\n\
\ \"acc_norm\": 0.4122137404580153,\n \"acc_norm_stderr\": 0.04317171194870254\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.5454545454545454,\n \"acc_stderr\": 0.045454545454545484,\n \"\
acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.045454545454545484\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04803752235190192,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04803752235190192\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3987730061349693,\n \"acc_stderr\": 0.03847021420456025,\n\
\ \"acc_norm\": 0.3987730061349693,\n \"acc_norm_stderr\": 0.03847021420456025\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.36607142857142855,\n\
\ \"acc_stderr\": 0.0457237235873743,\n \"acc_norm\": 0.36607142857142855,\n\
\ \"acc_norm_stderr\": 0.0457237235873743\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.47572815533980584,\n \"acc_stderr\": 0.049449010929737795,\n\
\ \"acc_norm\": 0.47572815533980584,\n \"acc_norm_stderr\": 0.049449010929737795\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6068376068376068,\n\
\ \"acc_stderr\": 0.03199957924651047,\n \"acc_norm\": 0.6068376068376068,\n\
\ \"acc_norm_stderr\": 0.03199957924651047\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\"\
: {\n \"acc\": 0.5504469987228607,\n \"acc_stderr\": 0.017788725283507337,\n\
\ \"acc_norm\": 0.5504469987228607,\n \"acc_norm_stderr\": 0.017788725283507337\n\
\ },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.42485549132947975,\n\
\ \"acc_stderr\": 0.026613350840261736,\n \"acc_norm\": 0.42485549132947975,\n\
\ \"acc_norm_stderr\": 0.026613350840261736\n },\n \"harness|hendrycksTest-moral_scenarios|5\"\
: {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n\
\ \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n\
\ },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.4117647058823529,\n\
\ \"acc_stderr\": 0.028180596328259293,\n \"acc_norm\": 0.4117647058823529,\n\
\ \"acc_norm_stderr\": 0.028180596328259293\n },\n \"harness|hendrycksTest-philosophy|5\"\
: {\n \"acc\": 0.4662379421221865,\n \"acc_stderr\": 0.028333277109562793,\n\
\ \"acc_norm\": 0.4662379421221865,\n \"acc_norm_stderr\": 0.028333277109562793\n\
\ },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.4722222222222222,\n\
\ \"acc_stderr\": 0.027777777777777804,\n \"acc_norm\": 0.4722222222222222,\n\
\ \"acc_norm_stderr\": 0.027777777777777804\n },\n \"harness|hendrycksTest-professional_accounting|5\"\
: {\n \"acc\": 0.30851063829787234,\n \"acc_stderr\": 0.027553366165101362,\n\
\ \"acc_norm\": 0.30851063829787234,\n \"acc_norm_stderr\": 0.027553366165101362\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3213820078226858,\n\
\ \"acc_stderr\": 0.011927581352265076,\n \"acc_norm\": 0.3213820078226858,\n\
\ \"acc_norm_stderr\": 0.011927581352265076\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.40441176470588236,\n \"acc_stderr\": 0.029812630701569743,\n\
\ \"acc_norm\": 0.40441176470588236,\n \"acc_norm_stderr\": 0.029812630701569743\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.3790849673202614,\n \"acc_stderr\": 0.019627444748412232,\n \
\ \"acc_norm\": 0.3790849673202614,\n \"acc_norm_stderr\": 0.019627444748412232\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.44545454545454544,\n\
\ \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.44545454545454544,\n\
\ \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.40408163265306124,\n \"acc_stderr\": 0.031414708025865885,\n\
\ \"acc_norm\": 0.40408163265306124,\n \"acc_norm_stderr\": 0.031414708025865885\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.472636815920398,\n\
\ \"acc_stderr\": 0.03530235517334682,\n \"acc_norm\": 0.472636815920398,\n\
\ \"acc_norm_stderr\": 0.03530235517334682\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4036144578313253,\n\
\ \"acc_stderr\": 0.038194861407583984,\n \"acc_norm\": 0.4036144578313253,\n\
\ \"acc_norm_stderr\": 0.038194861407583984\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.03829509868994727,\n\
\ \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.03829509868994727\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2484700122399021,\n\
\ \"mc1_stderr\": 0.015127427096520674,\n \"mc2\": 0.3759916250814691,\n\
\ \"mc2_stderr\": 0.015396201572279763\n }\n}\n```"
repo_url: https://huggingface.co/chavinlo/alpaca-native
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: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|arc:challenge|25_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T15_14_48.848140
path:
- '**/details_harness|drop|3_2023-09-17T15-14-48.848140.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T15-14-48.848140.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T15_14_48.848140
path:
- '**/details_harness|gsm8k|5_2023-09-17T15-14-48.848140.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T15-14-48.848140.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hellaswag|10_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T20-23-20.255556.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_21T20_23_20.255556
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-21T20-23-20.255556.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-21T20-23-20.255556.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T15_14_48.848140
path:
- '**/details_harness|winogrande|5_2023-09-17T15-14-48.848140.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T15-14-48.848140.parquet'
- config_name: results
data_files:
- split: 2023_09_17T15_14_48.848140
path:
- results_2023-09-17T15-14-48.848140.parquet
- split: 2023_09_21T20_23_20.255556
path:
- results_2023-09-21T20-23-20.255556.parquet
- split: latest
path:
- results_2023-09-21T20-23-20.255556.parquet
---
# Dataset Card for Evaluation run of chavinlo/alpaca-native
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chavinlo/alpaca-native
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [chavinlo/alpaca-native](https://huggingface.co/chavinlo/alpaca-native) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 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 agregated 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_chavinlo__alpaca-native",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-21T20:23:20.255556](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__alpaca-native/blob/main/results_2023-09-21T20-23-20.255556.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": 0.41927597389078103,
"acc_stderr": 0.035302205782678654,
"acc_norm": 0.42235476219088836,
"acc_norm_stderr": 0.035290265393035695,
"mc1": 0.2484700122399021,
"mc1_stderr": 0.015127427096520674,
"mc2": 0.3759916250814691,
"mc2_stderr": 0.015396201572279763
},
"harness|arc:challenge|25": {
"acc": 0.5127986348122867,
"acc_stderr": 0.014606603181012538,
"acc_norm": 0.5204778156996587,
"acc_norm_stderr": 0.01459913135303501
},
"harness|hellaswag|10": {
"acc": 0.5959968133837881,
"acc_stderr": 0.004896952378506926,
"acc_norm": 0.7699661422027485,
"acc_norm_stderr": 0.004199941217549452
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.45925925925925926,
"acc_stderr": 0.04304979692464242,
"acc_norm": 0.45925925925925926,
"acc_norm_stderr": 0.04304979692464242
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3618421052631579,
"acc_stderr": 0.03910525752849724,
"acc_norm": 0.3618421052631579,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.44150943396226416,
"acc_stderr": 0.030561590426731837,
"acc_norm": 0.44150943396226416,
"acc_norm_stderr": 0.030561590426731837
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.3819444444444444,
"acc_stderr": 0.040629907841466674,
"acc_norm": 0.3819444444444444,
"acc_norm_stderr": 0.040629907841466674
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3815028901734104,
"acc_stderr": 0.03703851193099521,
"acc_norm": 0.3815028901734104,
"acc_norm_stderr": 0.03703851193099521
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237656,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237656
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.37446808510638296,
"acc_stderr": 0.03163910665367291,
"acc_norm": 0.37446808510638296,
"acc_norm_stderr": 0.03163910665367291
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2543859649122807,
"acc_stderr": 0.040969851398436716,
"acc_norm": 0.2543859649122807,
"acc_norm_stderr": 0.040969851398436716
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.36551724137931035,
"acc_stderr": 0.040131241954243856,
"acc_norm": 0.36551724137931035,
"acc_norm_stderr": 0.040131241954243856
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.28835978835978837,
"acc_stderr": 0.023330654054535903,
"acc_norm": 0.28835978835978837,
"acc_norm_stderr": 0.023330654054535903
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.24603174603174602,
"acc_stderr": 0.03852273364924314,
"acc_norm": 0.24603174603174602,
"acc_norm_stderr": 0.03852273364924314
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.4290322580645161,
"acc_stderr": 0.02815603653823321,
"acc_norm": 0.4290322580645161,
"acc_norm_stderr": 0.02815603653823321
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3103448275862069,
"acc_stderr": 0.03255086769970103,
"acc_norm": 0.3103448275862069,
"acc_norm_stderr": 0.03255086769970103
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.5333333333333333,
"acc_stderr": 0.038956580652718446,
"acc_norm": 0.5333333333333333,
"acc_norm_stderr": 0.038956580652718446
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.4797979797979798,
"acc_stderr": 0.035594435655639196,
"acc_norm": 0.4797979797979798,
"acc_norm_stderr": 0.035594435655639196
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6062176165803109,
"acc_stderr": 0.035260770955482405,
"acc_norm": 0.6062176165803109,
"acc_norm_stderr": 0.035260770955482405
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.3871794871794872,
"acc_stderr": 0.024697216930878948,
"acc_norm": 0.3871794871794872,
"acc_norm_stderr": 0.024697216930878948
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26296296296296295,
"acc_stderr": 0.02684205787383371,
"acc_norm": 0.26296296296296295,
"acc_norm_stderr": 0.02684205787383371
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.031041941304059295,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.031041941304059295
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31125827814569534,
"acc_stderr": 0.03780445850526733,
"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526733
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.544954128440367,
"acc_stderr": 0.021350503090925173,
"acc_norm": 0.544954128440367,
"acc_norm_stderr": 0.021350503090925173
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.375,
"acc_stderr": 0.033016908987210894,
"acc_norm": 0.375,
"acc_norm_stderr": 0.033016908987210894
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5343137254901961,
"acc_stderr": 0.03501038327635897,
"acc_norm": 0.5343137254901961,
"acc_norm_stderr": 0.03501038327635897
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.5654008438818565,
"acc_stderr": 0.03226759995510145,
"acc_norm": 0.5654008438818565,
"acc_norm_stderr": 0.03226759995510145
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5022421524663677,
"acc_stderr": 0.03355746535223263,
"acc_norm": 0.5022421524663677,
"acc_norm_stderr": 0.03355746535223263
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.4122137404580153,
"acc_stderr": 0.04317171194870254,
"acc_norm": 0.4122137404580153,
"acc_norm_stderr": 0.04317171194870254
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.5454545454545454,
"acc_stderr": 0.045454545454545484,
"acc_norm": 0.5454545454545454,
"acc_norm_stderr": 0.045454545454545484
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04803752235190192,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04803752235190192
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.3987730061349693,
"acc_stderr": 0.03847021420456025,
"acc_norm": 0.3987730061349693,
"acc_norm_stderr": 0.03847021420456025
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.36607142857142855,
"acc_stderr": 0.0457237235873743,
"acc_norm": 0.36607142857142855,
"acc_norm_stderr": 0.0457237235873743
},
"harness|hendrycksTest-management|5": {
"acc": 0.47572815533980584,
"acc_stderr": 0.049449010929737795,
"acc_norm": 0.47572815533980584,
"acc_norm_stderr": 0.049449010929737795
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.6068376068376068,
"acc_stderr": 0.03199957924651047,
"acc_norm": 0.6068376068376068,
"acc_norm_stderr": 0.03199957924651047
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.5504469987228607,
"acc_stderr": 0.017788725283507337,
"acc_norm": 0.5504469987228607,
"acc_norm_stderr": 0.017788725283507337
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.42485549132947975,
"acc_stderr": 0.026613350840261736,
"acc_norm": 0.42485549132947975,
"acc_norm_stderr": 0.026613350840261736
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2424581005586592,
"acc_stderr": 0.014333522059217889,
"acc_norm": 0.2424581005586592,
"acc_norm_stderr": 0.014333522059217889
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.028180596328259293,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.028180596328259293
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.4662379421221865,
"acc_stderr": 0.028333277109562793,
"acc_norm": 0.4662379421221865,
"acc_norm_stderr": 0.028333277109562793
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.027777777777777804,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.027777777777777804
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.30851063829787234,
"acc_stderr": 0.027553366165101362,
"acc_norm": 0.30851063829787234,
"acc_norm_stderr": 0.027553366165101362
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3213820078226858,
"acc_stderr": 0.011927581352265076,
"acc_norm": 0.3213820078226858,
"acc_norm_stderr": 0.011927581352265076
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.40441176470588236,
"acc_stderr": 0.029812630701569743,
"acc_norm": 0.40441176470588236,
"acc_norm_stderr": 0.029812630701569743
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.3790849673202614,
"acc_stderr": 0.019627444748412232,
"acc_norm": 0.3790849673202614,
"acc_norm_stderr": 0.019627444748412232
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.44545454545454544,
"acc_stderr": 0.047605488214603246,
"acc_norm": 0.44545454545454544,
"acc_norm_stderr": 0.047605488214603246
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.40408163265306124,
"acc_stderr": 0.031414708025865885,
"acc_norm": 0.40408163265306124,
"acc_norm_stderr": 0.031414708025865885
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.472636815920398,
"acc_stderr": 0.03530235517334682,
"acc_norm": 0.472636815920398,
"acc_norm_stderr": 0.03530235517334682
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4036144578313253,
"acc_stderr": 0.038194861407583984,
"acc_norm": 0.4036144578313253,
"acc_norm_stderr": 0.038194861407583984
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.03829509868994727,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.03829509868994727
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2484700122399021,
"mc1_stderr": 0.015127427096520674,
"mc2": 0.3759916250814691,
"mc2_stderr": 0.015396201572279763
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
dhenypatungka/windi-dataset | 2023-09-17T15:30:29.000Z | [
"region:us"
] | dhenypatungka | null | null | null | 0 | 0 | Entry not found |
suf-yaan123/ubaid | 2023-09-17T15:41:39.000Z | [
"license:openrail",
"region:us"
] | suf-yaan123 | null | null | null | 0 | 0 | ---
license: openrail
---
|
lejosazu/datasets | 2023-09-21T15:35:04.000Z | [
"region:us"
] | lejosazu | null | null | null | 0 | 0 | Entry not found |
ayoubkirouane/med_en2es | 2023-09-17T16:59:16.000Z | [
"region:us"
] | ayoubkirouane | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: translation
dtype: string
splits:
- name: train
num_bytes: 49128890
num_examples: 285584
download_size: 27861710
dataset_size: 49128890
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_en2es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ayoubkirouane/med_en2fr | 2023-09-17T17:04:10.000Z | [
"region:us"
] | ayoubkirouane | null | null | null | 0 | 0 | Entry not found |
DaisyStar004/guanaco-llama2-1k | 2023-09-17T17:04:30.000Z | [
"region:us"
] | DaisyStar004 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Corianas__256_5epoch | 2023-09-17T17:10:55.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of Corianas/256_5epoch
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Corianas/256_5epoch](https://huggingface.co/Corianas/256_5epoch) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Corianas__256_5epoch\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T17:10:44.545164](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__256_5epoch/blob/main/results_2023-09-17T17-10-44.545164.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 \"em\": 0.006082214765100671,\n\
\ \"em_stderr\": 0.0007962432393028846,\n \"f1\": 0.04929320469798652,\n\
\ \"f1_stderr\": 0.0015028533751229739,\n \"acc\": 0.26475206337105733,\n\
\ \"acc_stderr\": 0.0076718947223475545\n },\n \"harness|drop|3\":\
\ {\n \"em\": 0.006082214765100671,\n \"em_stderr\": 0.0007962432393028846,\n\
\ \"f1\": 0.04929320469798652,\n \"f1_stderr\": 0.0015028533751229739\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.002274450341167551,\n \
\ \"acc_stderr\": 0.0013121578148674133\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5272296764009471,\n \"acc_stderr\": 0.014031631629827696\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Corianas/256_5epoch
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T17_10_44.545164
path:
- '**/details_harness|drop|3_2023-09-17T17-10-44.545164.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T17-10-44.545164.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T17_10_44.545164
path:
- '**/details_harness|gsm8k|5_2023-09-17T17-10-44.545164.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T17-10-44.545164.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T17_10_44.545164
path:
- '**/details_harness|winogrande|5_2023-09-17T17-10-44.545164.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T17-10-44.545164.parquet'
- config_name: results
data_files:
- split: 2023_09_17T17_10_44.545164
path:
- results_2023-09-17T17-10-44.545164.parquet
- split: latest
path:
- results_2023-09-17T17-10-44.545164.parquet
---
# Dataset Card for Evaluation run of Corianas/256_5epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Corianas/256_5epoch
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Corianas/256_5epoch](https://huggingface.co/Corianas/256_5epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_Corianas__256_5epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T17:10:44.545164](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__256_5epoch/blob/main/results_2023-09-17T17-10-44.545164.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": {
"em": 0.006082214765100671,
"em_stderr": 0.0007962432393028846,
"f1": 0.04929320469798652,
"f1_stderr": 0.0015028533751229739,
"acc": 0.26475206337105733,
"acc_stderr": 0.0076718947223475545
},
"harness|drop|3": {
"em": 0.006082214765100671,
"em_stderr": 0.0007962432393028846,
"f1": 0.04929320469798652,
"f1_stderr": 0.0015028533751229739
},
"harness|gsm8k|5": {
"acc": 0.002274450341167551,
"acc_stderr": 0.0013121578148674133
},
"harness|winogrande|5": {
"acc": 0.5272296764009471,
"acc_stderr": 0.014031631629827696
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_bofenghuang__vigogne-2-13b-instruct | 2023-09-17T17:11:53.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of bofenghuang/vigogne-2-13b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bofenghuang/vigogne-2-13b-instruct](https://huggingface.co/bofenghuang/vigogne-2-13b-instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bofenghuang__vigogne-2-13b-instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T17:11:41.679174](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-2-13b-instruct/blob/main/results_2023-09-17T17-11-41.679174.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 \"em\": 0.32791526845637586,\n\
\ \"em_stderr\": 0.004807646038011016,\n \"f1\": 0.3836671560402693,\n\
\ \"f1_stderr\": 0.00469015048706981,\n \"acc\": 0.3969753580269667,\n\
\ \"acc_stderr\": 0.007832281220307026\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.32791526845637586,\n \"em_stderr\": 0.004807646038011016,\n\
\ \"f1\": 0.3836671560402693,\n \"f1_stderr\": 0.00469015048706981\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02047005307050796,\n \
\ \"acc_stderr\": 0.003900413385915718\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698334\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bofenghuang/vigogne-2-13b-instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T17_11_41.679174
path:
- '**/details_harness|drop|3_2023-09-17T17-11-41.679174.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T17-11-41.679174.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T17_11_41.679174
path:
- '**/details_harness|gsm8k|5_2023-09-17T17-11-41.679174.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T17-11-41.679174.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T17_11_41.679174
path:
- '**/details_harness|winogrande|5_2023-09-17T17-11-41.679174.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T17-11-41.679174.parquet'
- config_name: results
data_files:
- split: 2023_09_17T17_11_41.679174
path:
- results_2023-09-17T17-11-41.679174.parquet
- split: latest
path:
- results_2023-09-17T17-11-41.679174.parquet
---
# Dataset Card for Evaluation run of bofenghuang/vigogne-2-13b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bofenghuang/vigogne-2-13b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [bofenghuang/vigogne-2-13b-instruct](https://huggingface.co/bofenghuang/vigogne-2-13b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_bofenghuang__vigogne-2-13b-instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T17:11:41.679174](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-2-13b-instruct/blob/main/results_2023-09-17T17-11-41.679174.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": {
"em": 0.32791526845637586,
"em_stderr": 0.004807646038011016,
"f1": 0.3836671560402693,
"f1_stderr": 0.00469015048706981,
"acc": 0.3969753580269667,
"acc_stderr": 0.007832281220307026
},
"harness|drop|3": {
"em": 0.32791526845637586,
"em_stderr": 0.004807646038011016,
"f1": 0.3836671560402693,
"f1_stderr": 0.00469015048706981
},
"harness|gsm8k|5": {
"acc": 0.02047005307050796,
"acc_stderr": 0.003900413385915718
},
"harness|winogrande|5": {
"acc": 0.7734806629834254,
"acc_stderr": 0.011764149054698334
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
DaisyStar004/Transformed_data | 2023-09-18T01:50:51.000Z | [
"region:us"
] | DaisyStar004 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 385155
num_examples: 607
download_size: 211261
dataset_size: 385155
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Transformed_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jpbello/gtzan_all_preprocessed | 2023-09-17T17:55:40.000Z | [
"region:us"
] | jpbello | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': blues
'1': classical
'2': country
'3': disco
'4': hiphop
'5': jazz
'6': metal
'7': pop
'8': reggae
'9': rock
- name: input_values
sequence: float32
- name: attention_mask
sequence: int32
splits:
- name: train
num_bytes: 3452159816
num_examples: 899
- name: test
num_bytes: 384000696
num_examples: 100
download_size: 1923103923
dataset_size: 3836160512
---
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ptx0/BG20K | 2023-09-18T00:59:50.000Z | [
"region:us"
] | ptx0 | null | null | null | 0 | 0 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# pseudo's BG20K-COCO dataset
## Dataset Description
- **Homepage:** https://paperswithcode.com/dataset/bg-20k
- **Repository:** https://github.com/JizhiziLi/GFM
- **Paper:** https://paperswithcode.com/dataset/bg-20k
### Dataset Summary
This is the BG20K dataset, captioned using the BLIP2 model `git-coco-large`.
BG20K is a dataset of non-salient objects, though some animals and silhouettes may have slipped through (see `/train/s` directory).
The captions have been partially validated as being highly accurate. Locations tend to be named correctly.
|
open-llm-leaderboard/details_KnutJaegersberg__gpt-2-xl-EvolInstruct | 2023-09-17T18:03:08.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of KnutJaegersberg/gpt-2-xl-EvolInstruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KnutJaegersberg/gpt-2-xl-EvolInstruct](https://huggingface.co/KnutJaegersberg/gpt-2-xl-EvolInstruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__gpt-2-xl-EvolInstruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T18:02:57.671011](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__gpt-2-xl-EvolInstruct/blob/main/results_2023-09-17T18-02-57.671011.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 \"em\": 0.0045092281879194635,\n\
\ \"em_stderr\": 0.000686134689909505,\n \"f1\": 0.039052013422818846,\n\
\ \"f1_stderr\": 0.0012293007940162644,\n \"acc\": 0.26831931822737687,\n\
\ \"acc_stderr\": 0.007544776234715419\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0045092281879194635,\n \"em_stderr\": 0.000686134689909505,\n\
\ \"f1\": 0.039052013422818846,\n \"f1_stderr\": 0.0012293007940162644\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \
\ \"acc_stderr\": 0.0010717793485492619\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5351223362273086,\n \"acc_stderr\": 0.014017773120881576\n\
\ }\n}\n```"
repo_url: https://huggingface.co/KnutJaegersberg/gpt-2-xl-EvolInstruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T18_02_57.671011
path:
- '**/details_harness|drop|3_2023-09-17T18-02-57.671011.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T18-02-57.671011.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T18_02_57.671011
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-02-57.671011.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-02-57.671011.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T18_02_57.671011
path:
- '**/details_harness|winogrande|5_2023-09-17T18-02-57.671011.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T18-02-57.671011.parquet'
- config_name: results
data_files:
- split: 2023_09_17T18_02_57.671011
path:
- results_2023-09-17T18-02-57.671011.parquet
- split: latest
path:
- results_2023-09-17T18-02-57.671011.parquet
---
# Dataset Card for Evaluation run of KnutJaegersberg/gpt-2-xl-EvolInstruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/gpt-2-xl-EvolInstruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [KnutJaegersberg/gpt-2-xl-EvolInstruct](https://huggingface.co/KnutJaegersberg/gpt-2-xl-EvolInstruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_KnutJaegersberg__gpt-2-xl-EvolInstruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T18:02:57.671011](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__gpt-2-xl-EvolInstruct/blob/main/results_2023-09-17T18-02-57.671011.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": {
"em": 0.0045092281879194635,
"em_stderr": 0.000686134689909505,
"f1": 0.039052013422818846,
"f1_stderr": 0.0012293007940162644,
"acc": 0.26831931822737687,
"acc_stderr": 0.007544776234715419
},
"harness|drop|3": {
"em": 0.0045092281879194635,
"em_stderr": 0.000686134689909505,
"f1": 0.039052013422818846,
"f1_stderr": 0.0012293007940162644
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492619
},
"harness|winogrande|5": {
"acc": 0.5351223362273086,
"acc_stderr": 0.014017773120881576
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
ardneebwar/gtzan_all_preprocessed | 2023-09-18T10:41:16.000Z | [
"region:us"
] | ardneebwar | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': blues
'1': classical
'2': country
'3': disco
'4': hiphop
'5': jazz
'6': metal
'7': pop
'8': reggae
'9': rock
- name: input_values
sequence: float32
- name: attention_mask
sequence: int32
splits:
- name: train
num_bytes: 3452159816
num_examples: 899
- name: test
num_bytes: 384000696
num_examples: 100
download_size: 0
dataset_size: 3836160512
---
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
marasama/nva-uzukikou | 2023-09-17T18:19:51.000Z | [
"region:us"
] | marasama | null | null | null | 0 | 0 | Entry not found |
Fredithefish/GodLLaMA | 2023-09-17T18:26:14.000Z | [
"region:us"
] | Fredithefish | null | null | null | 0 | 0 | Entry not found |
uellaaaa/praci | 2023-09-17T18:30:28.000Z | [
"language:it",
"region:us"
] | uellaaaa | null | null | null | 0 | 0 | ---
language:
- it
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_ikala__bloom-zh-3b-chat | 2023-09-17T18:43:53.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of ikala/bloom-zh-3b-chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ikala/bloom-zh-3b-chat](https://huggingface.co/ikala/bloom-zh-3b-chat) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ikala__bloom-zh-3b-chat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T18:43:41.397434](https://huggingface.co/datasets/open-llm-leaderboard/details_ikala__bloom-zh-3b-chat/blob/main/results_2023-09-17T18-43-41.397434.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 \"em\": 0.08022231543624161,\n\
\ \"em_stderr\": 0.0027818178017908015,\n \"f1\": 0.1465918624161071,\n\
\ \"f1_stderr\": 0.003030605237968897,\n \"acc\": 0.2954867628904967,\n\
\ \"acc_stderr\": 0.007847263403599461\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08022231543624161,\n \"em_stderr\": 0.0027818178017908015,\n\
\ \"f1\": 0.1465918624161071,\n \"f1_stderr\": 0.003030605237968897\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.004548900682335102,\n \
\ \"acc_stderr\": 0.0018535550440036198\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5864246250986582,\n \"acc_stderr\": 0.013840971763195304\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ikala/bloom-zh-3b-chat
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T18_43_41.397434
path:
- '**/details_harness|drop|3_2023-09-17T18-43-41.397434.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T18-43-41.397434.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T18_43_41.397434
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-43-41.397434.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-43-41.397434.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T18_43_41.397434
path:
- '**/details_harness|winogrande|5_2023-09-17T18-43-41.397434.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T18-43-41.397434.parquet'
- config_name: results
data_files:
- split: 2023_09_17T18_43_41.397434
path:
- results_2023-09-17T18-43-41.397434.parquet
- split: latest
path:
- results_2023-09-17T18-43-41.397434.parquet
---
# Dataset Card for Evaluation run of ikala/bloom-zh-3b-chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ikala/bloom-zh-3b-chat
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [ikala/bloom-zh-3b-chat](https://huggingface.co/ikala/bloom-zh-3b-chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_ikala__bloom-zh-3b-chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T18:43:41.397434](https://huggingface.co/datasets/open-llm-leaderboard/details_ikala__bloom-zh-3b-chat/blob/main/results_2023-09-17T18-43-41.397434.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": {
"em": 0.08022231543624161,
"em_stderr": 0.0027818178017908015,
"f1": 0.1465918624161071,
"f1_stderr": 0.003030605237968897,
"acc": 0.2954867628904967,
"acc_stderr": 0.007847263403599461
},
"harness|drop|3": {
"em": 0.08022231543624161,
"em_stderr": 0.0027818178017908015,
"f1": 0.1465918624161071,
"f1_stderr": 0.003030605237968897
},
"harness|gsm8k|5": {
"acc": 0.004548900682335102,
"acc_stderr": 0.0018535550440036198
},
"harness|winogrande|5": {
"acc": 0.5864246250986582,
"acc_stderr": 0.013840971763195304
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora | 2023-09-17T19:01:05.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of Harshvir/LaMini-Neo-1.3B-Mental-Health_lora
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Harshvir/LaMini-Neo-1.3B-Mental-Health_lora](https://huggingface.co/Harshvir/LaMini-Neo-1.3B-Mental-Health_lora)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T19:00:53.771505](https://huggingface.co/datasets/open-llm-leaderboard/details_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora/blob/main/results_2023-09-17T19-00-53.771505.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 \"em\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\
acc\": 0.24585635359116023,\n \"acc_stderr\": 0.007025277661412099\n },\n\
\ \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\
\ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.49171270718232046,\n \"acc_stderr\": 0.014050555322824197\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Harshvir/LaMini-Neo-1.3B-Mental-Health_lora
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T19_00_53.771505
path:
- '**/details_harness|drop|3_2023-09-17T19-00-53.771505.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T19-00-53.771505.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T19_00_53.771505
path:
- '**/details_harness|gsm8k|5_2023-09-17T19-00-53.771505.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T19-00-53.771505.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T19_00_53.771505
path:
- '**/details_harness|winogrande|5_2023-09-17T19-00-53.771505.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T19-00-53.771505.parquet'
- config_name: results
data_files:
- split: 2023_09_17T19_00_53.771505
path:
- results_2023-09-17T19-00-53.771505.parquet
- split: latest
path:
- results_2023-09-17T19-00-53.771505.parquet
---
# Dataset Card for Evaluation run of Harshvir/LaMini-Neo-1.3B-Mental-Health_lora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Harshvir/LaMini-Neo-1.3B-Mental-Health_lora
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Harshvir/LaMini-Neo-1.3B-Mental-Health_lora](https://huggingface.co/Harshvir/LaMini-Neo-1.3B-Mental-Health_lora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T19:00:53.771505](https://huggingface.co/datasets/open-llm-leaderboard/details_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora/blob/main/results_2023-09-17T19-00-53.771505.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": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0,
"acc": 0.24585635359116023,
"acc_stderr": 0.007025277661412099
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.49171270718232046,
"acc_stderr": 0.014050555322824197
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora-v2 | 2023-09-17T19:43:40.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of lvkaokao/llama2-7b-hf-chat-lora-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lvkaokao/llama2-7b-hf-chat-lora-v2](https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T19:43:28.899115](https://huggingface.co/datasets/open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora-v2/blob/main/results_2023-09-17T19-43-28.899115.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 \"em\": 0.25723573825503354,\n\
\ \"em_stderr\": 0.004476419757548592,\n \"f1\": 0.31864408557046997,\n\
\ \"f1_stderr\": 0.004427420085857621,\n \"acc\": 0.42871444189201235,\n\
\ \"acc_stderr\": 0.010374814363571815\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.25723573825503354,\n \"em_stderr\": 0.004476419757548592,\n\
\ \"f1\": 0.31864408557046997,\n \"f1_stderr\": 0.004427420085857621\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10841546626231995,\n \
\ \"acc_stderr\": 0.008563852506627476\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7490134175217048,\n \"acc_stderr\": 0.012185776220516155\n\
\ }\n}\n```"
repo_url: https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora-v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T19_43_28.899115
path:
- '**/details_harness|drop|3_2023-09-17T19-43-28.899115.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T19-43-28.899115.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T19_43_28.899115
path:
- '**/details_harness|gsm8k|5_2023-09-17T19-43-28.899115.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T19-43-28.899115.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T19_43_28.899115
path:
- '**/details_harness|winogrande|5_2023-09-17T19-43-28.899115.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T19-43-28.899115.parquet'
- config_name: results
data_files:
- split: 2023_09_17T19_43_28.899115
path:
- results_2023-09-17T19-43-28.899115.parquet
- split: latest
path:
- results_2023-09-17T19-43-28.899115.parquet
---
# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [lvkaokao/llama2-7b-hf-chat-lora-v2](https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_lvkaokao__llama2-7b-hf-chat-lora-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T19:43:28.899115](https://huggingface.co/datasets/open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora-v2/blob/main/results_2023-09-17T19-43-28.899115.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": {
"em": 0.25723573825503354,
"em_stderr": 0.004476419757548592,
"f1": 0.31864408557046997,
"f1_stderr": 0.004427420085857621,
"acc": 0.42871444189201235,
"acc_stderr": 0.010374814363571815
},
"harness|drop|3": {
"em": 0.25723573825503354,
"em_stderr": 0.004476419757548592,
"f1": 0.31864408557046997,
"f1_stderr": 0.004427420085857621
},
"harness|gsm8k|5": {
"acc": 0.10841546626231995,
"acc_stderr": 0.008563852506627476
},
"harness|winogrande|5": {
"acc": 0.7490134175217048,
"acc_stderr": 0.012185776220516155
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
AescF/gtzan_all_preprocessed | 2023-09-17T19:46:55.000Z | [
"region:us"
] | AescF | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': blues
'1': classical
'2': country
'3': disco
'4': hiphop
'5': jazz
'6': metal
'7': pop
'8': reggae
'9': rock
- name: input_values
sequence: float32
- name: attention_mask
sequence: int32
splits:
- name: train
num_bytes: 3452159816
num_examples: 899
- name: test
num_bytes: 384000696
num_examples: 100
download_size: 1923103923
dataset_size: 3836160512
---
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bongo2112/harmonize-SDxl-Styled-Output-Selected | 2023-09-18T03:35:38.000Z | [
"region:us"
] | bongo2112 | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_nkpz__llama2-22b-chat-wizard-uncensored | 2023-09-17T20:13:15.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of nkpz/llama2-22b-chat-wizard-uncensored
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [nkpz/llama2-22b-chat-wizard-uncensored](https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated 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\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_nkpz__llama2-22b-chat-wizard-uncensored\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T20:13:04.484783](https://huggingface.co/datasets/open-llm-leaderboard/details_nkpz__llama2-22b-chat-wizard-uncensored/blob/main/results_2023-09-17T20-13-04.484783.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 \"em\": 0.047399328859060404,\n\
\ \"em_stderr\": 0.002176111725660241,\n \"f1\": 0.10403313758389295,\n\
\ \"f1_stderr\": 0.0024782296933352054,\n \"acc\": 0.406947395631691,\n\
\ \"acc_stderr\": 0.010758553304204539\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.047399328859060404,\n \"em_stderr\": 0.002176111725660241,\n\
\ \"f1\": 0.10403313758389295,\n \"f1_stderr\": 0.0024782296933352054\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11144806671721001,\n \
\ \"acc_stderr\": 0.008668021353794427\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7024467245461721,\n \"acc_stderr\": 0.01284908525461465\n\
\ }\n}\n```"
repo_url: https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T20_13_04.484783
path:
- '**/details_harness|drop|3_2023-09-17T20-13-04.484783.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T20-13-04.484783.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T20_13_04.484783
path:
- '**/details_harness|gsm8k|5_2023-09-17T20-13-04.484783.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T20-13-04.484783.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T20_13_04.484783
path:
- '**/details_harness|winogrande|5_2023-09-17T20-13-04.484783.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T20-13-04.484783.parquet'
- config_name: results
data_files:
- split: 2023_09_17T20_13_04.484783
path:
- results_2023-09-17T20-13-04.484783.parquet
- split: latest
path:
- results_2023-09-17T20-13-04.484783.parquet
---
# Dataset Card for Evaluation run of nkpz/llama2-22b-chat-wizard-uncensored
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [nkpz/llama2-22b-chat-wizard-uncensored](https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_nkpz__llama2-22b-chat-wizard-uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T20:13:04.484783](https://huggingface.co/datasets/open-llm-leaderboard/details_nkpz__llama2-22b-chat-wizard-uncensored/blob/main/results_2023-09-17T20-13-04.484783.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": {
"em": 0.047399328859060404,
"em_stderr": 0.002176111725660241,
"f1": 0.10403313758389295,
"f1_stderr": 0.0024782296933352054,
"acc": 0.406947395631691,
"acc_stderr": 0.010758553304204539
},
"harness|drop|3": {
"em": 0.047399328859060404,
"em_stderr": 0.002176111725660241,
"f1": 0.10403313758389295,
"f1_stderr": 0.0024782296933352054
},
"harness|gsm8k|5": {
"acc": 0.11144806671721001,
"acc_stderr": 0.008668021353794427
},
"harness|winogrande|5": {
"acc": 0.7024467245461721,
"acc_stderr": 0.01284908525461465
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
Abdelkareem/arabic_articles | 2023-09-17T20:14:25.000Z | [
"region:us"
] | Abdelkareem | null | null | null | 0 | 0 | Entry not found |
josedanielaromi/Gre2007 | 2023-09-17T20:17:04.000Z | [
"region:us"
] | josedanielaromi | null | null | null | 0 | 0 | Entry not found |
Brthy467/bagi_rv2 | 2023-09-17T20:39:20.000Z | [
"region:us"
] | Brthy467 | null | null | null | 0 | 0 | Entry not found |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.