id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
Lordaniket/testing_dataset_ps | 2023-10-16T08:52:25.000Z | [
"region:us"
] | Lordaniket | null | null | 0 | 0 | 2023-10-16T08:52:25 | Entry not found | 15 | [
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Irfan-Ali-2023/train | 2023-10-16T09:29:38.000Z | [
"region:us"
] | Irfan-Ali-2023 | null | null | 0 | 0 | 2023-10-16T09:28:30 | Entry not found | 15 | [
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thangvip/medical-data | 2023-10-16T09:29:26.000Z | [
"region:us"
] | thangvip | null | null | 0 | 0 | 2023-10-16T09:29:24 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9331012
num_examples: 603
download_size: 4263217
dataset_size: 9331012
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "medical-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 439 | [
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... |
vietlegalqa/xquad_vi_masked | 2023-10-16T09:30:09.000Z | [
"region:us"
] | vietlegalqa | null | null | 0 | 0 | 2023-10-16T09:30:03 | ---
dataset_info:
features:
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: context
dtype: string
- name: masked_context
dtype: string
- name: id
dtype: string
- name: question
dtype: string
- name: title
dtype: string
- name: answer_start
sequence: int64
- name: answer_text
sequence: string
splits:
- name: train
num_bytes: 2827520
num_examples: 1190
download_size: 496644
dataset_size: 2827520
---
# Dataset Card for "xquad_vi_masked"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 707 | [
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open-llm-leaderboard/details_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload | 2023-10-16T09:37:36.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T09:37:28 | ---
pretty_name: Evaluation run of Lajonbot/WizardLM-13B-V1.2-PL-lora_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Lajonbot/WizardLM-13B-V1.2-PL-lora_unload](https://huggingface.co/Lajonbot/WizardLM-13B-V1.2-PL-lora_unload)\
\ 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_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T09:37:24.771314](https://huggingface.co/datasets/open-llm-leaderboard/details_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload/blob/main/results_2023-10-16T09-37-24.771314.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\": 0.003984899328859061,\n\
\ \"em_stderr\": 0.0006451805848102423,\n \"f1\": 0.06672923657718131,\n\
\ \"f1_stderr\": 0.0015525464124355034,\n \"acc\": 0.41089372554487175,\n\
\ \"acc_stderr\": 0.010708286080716344\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003984899328859061,\n \"em_stderr\": 0.0006451805848102423,\n\
\ \"f1\": 0.06672923657718131,\n \"f1_stderr\": 0.0015525464124355034\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11144806671721001,\n \
\ \"acc_stderr\": 0.008668021353794427\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7103393843725335,\n \"acc_stderr\": 0.012748550807638263\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Lajonbot/WizardLM-13B-V1.2-PL-lora_unload
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_10_16T09_37_24.771314
path:
- '**/details_harness|drop|3_2023-10-16T09-37-24.771314.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T09-37-24.771314.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T09_37_24.771314
path:
- '**/details_harness|gsm8k|5_2023-10-16T09-37-24.771314.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T09-37-24.771314.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T09_37_24.771314
path:
- '**/details_harness|winogrande|5_2023-10-16T09-37-24.771314.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T09-37-24.771314.parquet'
- config_name: results
data_files:
- split: 2023_10_16T09_37_24.771314
path:
- results_2023-10-16T09-37-24.771314.parquet
- split: latest
path:
- results_2023-10-16T09-37-24.771314.parquet
---
# Dataset Card for Evaluation run of Lajonbot/WizardLM-13B-V1.2-PL-lora_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Lajonbot/WizardLM-13B-V1.2-PL-lora_unload
- **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 [Lajonbot/WizardLM-13B-V1.2-PL-lora_unload](https://huggingface.co/Lajonbot/WizardLM-13B-V1.2-PL-lora_unload) 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_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T09:37:24.771314](https://huggingface.co/datasets/open-llm-leaderboard/details_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload/blob/main/results_2023-10-16T09-37-24.771314.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": 0.003984899328859061,
"em_stderr": 0.0006451805848102423,
"f1": 0.06672923657718131,
"f1_stderr": 0.0015525464124355034,
"acc": 0.41089372554487175,
"acc_stderr": 0.010708286080716344
},
"harness|drop|3": {
"em": 0.003984899328859061,
"em_stderr": 0.0006451805848102423,
"f1": 0.06672923657718131,
"f1_stderr": 0.0015525464124355034
},
"harness|gsm8k|5": {
"acc": 0.11144806671721001,
"acc_stderr": 0.008668021353794427
},
"harness|winogrande|5": {
"acc": 0.7103393843725335,
"acc_stderr": 0.012748550807638263
}
}
```
### 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] | 7,409 | [
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... |
JL-er/train | 2023-10-16T10:07:00.000Z | [
"region:us"
] | JL-er | null | null | 1 | 0 | 2023-10-16T09:49:45 | Entry not found | 15 | [
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haonanqqq/AgriSFT_60k | 2023-10-16T10:25:09.000Z | [
"region:us"
] | haonanqqq | null | null | 0 | 0 | 2023-10-16T10:18:42 | # 六万条农业微调数据 | 11 | [
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AliAsh/digikala_translated_small_5m | 2023-10-16T11:32:22.000Z | [
"size_categories:1B<n<10B",
"language:fa",
"region:us"
] | AliAsh | null | null | 1 | 0 | 2023-10-16T10:46:46 | ---
language:
- fa
pretty_name: digikala-5m
size_categories:
- 1B<n<10B
---
# Digikala Dataset Small 5m
- digikala product titles translated by standard google translate api
- category and brand english translation might be invalid but title_en checked
- | 257 | [
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open-llm-leaderboard/details_microsoft__CodeGPT-small-py | 2023-10-16T10:55:35.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T10:55:24 | ---
pretty_name: Evaluation run of microsoft/CodeGPT-small-py
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [microsoft/CodeGPT-small-py](https://huggingface.co/microsoft/CodeGPT-small-py)\
\ 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_microsoft__CodeGPT-small-py\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T10:55:21.745604](https://huggingface.co/datasets/open-llm-leaderboard/details_microsoft__CodeGPT-small-py/blob/main/results_2023-10-16T10-55-21.745604.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\": 0.00388003355704698,\n\
\ \"em_stderr\": 0.0006366682825519956,\n \"f1\": 0.016416736577181235,\n\
\ \"f1_stderr\": 0.0008900949322041355,\n \"acc\": 0.24388318863456984,\n\
\ \"acc_stderr\": 0.007024139410202808\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.00388003355704698,\n \"em_stderr\": 0.0006366682825519956,\n\
\ \"f1\": 0.016416736577181235,\n \"f1_stderr\": 0.0008900949322041355\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.4877663772691397,\n\
\ \"acc_stderr\": 0.014048278820405616\n }\n}\n```"
repo_url: https://huggingface.co/microsoft/CodeGPT-small-py
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_10_16T10_55_21.745604
path:
- '**/details_harness|drop|3_2023-10-16T10-55-21.745604.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T10-55-21.745604.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T10_55_21.745604
path:
- '**/details_harness|gsm8k|5_2023-10-16T10-55-21.745604.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T10-55-21.745604.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T10_55_21.745604
path:
- '**/details_harness|winogrande|5_2023-10-16T10-55-21.745604.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T10-55-21.745604.parquet'
- config_name: results
data_files:
- split: 2023_10_16T10_55_21.745604
path:
- results_2023-10-16T10-55-21.745604.parquet
- split: latest
path:
- results_2023-10-16T10-55-21.745604.parquet
---
# Dataset Card for Evaluation run of microsoft/CodeGPT-small-py
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/microsoft/CodeGPT-small-py
- **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 [microsoft/CodeGPT-small-py](https://huggingface.co/microsoft/CodeGPT-small-py) 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_microsoft__CodeGPT-small-py",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T10:55:21.745604](https://huggingface.co/datasets/open-llm-leaderboard/details_microsoft__CodeGPT-small-py/blob/main/results_2023-10-16T10-55-21.745604.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": 0.00388003355704698,
"em_stderr": 0.0006366682825519956,
"f1": 0.016416736577181235,
"f1_stderr": 0.0008900949322041355,
"acc": 0.24388318863456984,
"acc_stderr": 0.007024139410202808
},
"harness|drop|3": {
"em": 0.00388003355704698,
"em_stderr": 0.0006366682825519956,
"f1": 0.016416736577181235,
"f1_stderr": 0.0008900949322041355
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.4877663772691397,
"acc_stderr": 0.014048278820405616
}
}
```
### 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] | 7,158 | [
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0.026824951171875,
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copenlu/spanex | 2023-10-16T13:12:23.000Z | [
"region:us"
] | copenlu | null | null | 1 | 0 | 2023-10-16T11:11:41 | SpanEx consists of 7071 instances annotated for span interactions.
SpanEx is the first dataset with human phrase-level interaction explanations with explicit labels for interaction types.
Moreover, SpanEx is annotated by three annotators, which opens new avenues for studies of human explanation agreement -- an understudied area in the explainability literature.
Our study reveals that while human annotators often agree on span interactions, they also offer complementary reasons for a prediction, collectively providing a comprehensive set of reasons for a prediction.
We collect explanations of span interactions for NLI on the SNLI dataset and for FC on the FEVER dataset.
---
license: mit
configs:
- config_name: snli_extended
data_files:
- split: test
path: snli_extended.jsonl
- config_name: fever_extended
data_files:
- split: test
path: fever_extended.jsonl
- config_name: snli
data_files:
- split: test
path: snli.jsonl
- config_name: fever
data_files:
- split: test
path: fever.jsonl
task_categories:
- text-classification
language:
- en
tags:
- fact-checking
- nli
- explainability
pretty_name: SpanEx
size_categories:
- 1K<n<10K
--- | 1,180 | [
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Gracechy/Ultimate_RVC_Dataset | 2023-10-16T11:15:50.000Z | [
"region:us"
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sayan1101/less_sum_sft | 2023-10-16T11:26:35.000Z | [
"region:us"
] | sayan1101 | null | null | 0 | 0 | 2023-10-16T11:26:35 | Entry not found | 15 | [
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silaseic/pokemon | 2023-10-16T11:30:54.000Z | [
"license:unknown",
"region:us"
] | silaseic | null | null | 0 | 0 | 2023-10-16T11:29:11 | ---
license: unknown
---
Dataset from
https://www.kaggle.com/datasets/rounakbanik/pokemon
| 91 | [
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lunaluan/chat_history5 | 2023-11-03T01:23:13.000Z | [
"region:us"
] | lunaluan | null | null | 0 | 0 | 2023-10-16T12:11:38 | Entry not found | 15 | [
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maurya/alpaca_motivations_sommaires_titres | 2023-10-16T13:15:18.000Z | [
"region:us"
] | maurya | null | null | 0 | 0 | 2023-10-16T13:06:43 | Entry not found | 15 | [
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pallavjparikh/nr | 2023-10-16T13:20:40.000Z | [
"region:us"
] | pallavjparikh | null | null | 0 | 0 | 2023-10-16T13:19:32 | Entry not found | 15 | [
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jin05102518/KO_EN | 2023-10-16T13:35:55.000Z | [
"region:us"
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pclerc/test1 | 2023-10-16T13:53:18.000Z | [
"task_categories:question-answering",
"size_categories:1M<n<10M",
"language:fr",
"license:eupl-1.1",
"region:us"
] | pclerc | null | null | 0 | 0 | 2023-10-16T13:45:23 | ---
license: eupl-1.1
task_categories:
- question-answering
language:
- fr
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | 4,467 | [
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jin05102518/KO_EN_QA | 2023-10-16T13:56:08.000Z | [
"region:us"
] | jin05102518 | null | null | 0 | 0 | 2023-10-16T13:52:53 | Entry not found | 15 | [
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OdiaGenAI/sentiment_analysis_hindi | 2023-10-16T14:34:14.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:hi",
"region:us"
] | OdiaGenAI | null | null | 0 | 0 | 2023-10-16T14:22:08 | ---
task_categories:
- text-classification
language:
- hi
size_categories:
- 1K<n<10K
---
Conventions followed to decide the polarity: -
- labels consisting of a single value are left undisturbed, i.e. if label = 'pos', then it'll be pos
- labels consisting of multiple values separated by '&' are processed. If all the labels are the same ('pos&pos&pos' or 'neg&neg'), then the shortened form of the multiple label is assigned as the final label. For example, if label = 'pos&pos&pos', then final label will be 'pos'.
- labels consisting of mixed values ('pos&neg&pos' or 'neg&neu&pos') are rejected.
Contributors: -
- Kusumlata Patiyal | 640 | [
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open-llm-leaderboard/details_ajibawa-2023__scarlett-7b | 2023-10-28T20:24:56.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T14:25:25 | ---
pretty_name: Evaluation run of ajibawa-2023/scarlett-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ajibawa-2023/scarlett-7b](https://huggingface.co/ajibawa-2023/scarlett-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 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_ajibawa-2023__scarlett-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T20:24:47.914205](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__scarlett-7b/blob/main/results_2023-10-28T20-24-47.914205.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\": 0.06103187919463087,\n\
\ \"em_stderr\": 0.002451565190705489,\n \"f1\": 0.12157193791946294,\n\
\ \"f1_stderr\": 0.002704445932722437,\n \"acc\": 0.3622108542921648,\n\
\ \"acc_stderr\": 0.007057235105359207\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.06103187919463087,\n \"em_stderr\": 0.002451565190705489,\n\
\ \"f1\": 0.12157193791946294,\n \"f1_stderr\": 0.002704445932722437\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \
\ \"acc_stderr\": 0.0015145735612245386\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7213891081294396,\n \"acc_stderr\": 0.012599896649493875\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ajibawa-2023/scarlett-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_10_16T14_25_21.080350
path:
- '**/details_harness|drop|3_2023-10-16T14-25-21.080350.parquet'
- split: 2023_10_28T20_24_47.914205
path:
- '**/details_harness|drop|3_2023-10-28T20-24-47.914205.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T20-24-47.914205.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T14_25_21.080350
path:
- '**/details_harness|gsm8k|5_2023-10-16T14-25-21.080350.parquet'
- split: 2023_10_28T20_24_47.914205
path:
- '**/details_harness|gsm8k|5_2023-10-28T20-24-47.914205.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T20-24-47.914205.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T14_25_21.080350
path:
- '**/details_harness|winogrande|5_2023-10-16T14-25-21.080350.parquet'
- split: 2023_10_28T20_24_47.914205
path:
- '**/details_harness|winogrande|5_2023-10-28T20-24-47.914205.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T20-24-47.914205.parquet'
- config_name: results
data_files:
- split: 2023_10_16T14_25_21.080350
path:
- results_2023-10-16T14-25-21.080350.parquet
- split: 2023_10_28T20_24_47.914205
path:
- results_2023-10-28T20-24-47.914205.parquet
- split: latest
path:
- results_2023-10-28T20-24-47.914205.parquet
---
# Dataset Card for Evaluation run of ajibawa-2023/scarlett-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/scarlett-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 [ajibawa-2023/scarlett-7b](https://huggingface.co/ajibawa-2023/scarlett-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 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_ajibawa-2023__scarlett-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T20:24:47.914205](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__scarlett-7b/blob/main/results_2023-10-28T20-24-47.914205.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": 0.06103187919463087,
"em_stderr": 0.002451565190705489,
"f1": 0.12157193791946294,
"f1_stderr": 0.002704445932722437,
"acc": 0.3622108542921648,
"acc_stderr": 0.007057235105359207
},
"harness|drop|3": {
"em": 0.06103187919463087,
"em_stderr": 0.002451565190705489,
"f1": 0.12157193791946294,
"f1_stderr": 0.002704445932722437
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245386
},
"harness|winogrande|5": {
"acc": 0.7213891081294396,
"acc_stderr": 0.012599896649493875
}
}
```
### 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] | 7,650 | [
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lilysmith98/audio_clips_wav | 2023-10-16T14:41:55.000Z | [
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Arwel/test | 2023-10-16T14:51:34.000Z | [
"region:us"
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ombhojane/plantds | 2023-10-16T15:05:49.000Z | [
"region:us"
] | ombhojane | null | null | 0 | 0 | 2023-10-16T15:05:49 | Entry not found | 15 | [
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TSjB/qm_ru_265718 | 2023-10-16T15:14:39.000Z | [
"language:krc",
"language:ru",
"license:cc-by-nc-sa-4.0",
"region:us"
] | TSjB | null | null | 0 | 0 | 2023-10-16T15:13:12 | ---
license: cc-by-nc-sa-4.0
language:
- krc
- ru
---
265718 parallel sentences between russian and Qarachay-Malqar languages.
Because of dialects of Qarachay-Malqar language and diphthong change some letter on latin:
b - б/п/ф
w - ў
q - къ
g - гъ
n - нг
Taken from: Alan nart epose, Qarachay-Malqar folklore set, films, Kuliev's poems, phrasebook, Uzden codex of the Qarachay-Malqar, Koran, gospel, psalter, book of the prophet Jonah, book of the prophet Daniel, Ruth, Esther, Qarachay-Malqar dictionary. | 523 | [
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-0.0115203857421875,
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0.023040771484375,
-0.004688262939453125,
0.009033203125,
0.0144195556640625,
-0.0261383056640625,
0.0108795166015625,
0.0418701171875,
-0.03753662109375,
-0.02862548828125,
-0.0199127197265625,
0.028... |
ailearningcorner/sample_data | 2023-10-16T15:19:07.000Z | [
"region:us"
] | ailearningcorner | null | null | 0 | 0 | 2023-10-16T15:16:04 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: ID
dtype: int64
- name: ' Student'
dtype: string
splits:
- name: train
num_bytes: 128.1
num_examples: 7
- name: test
num_bytes: 54.9
num_examples: 3
download_size: 2655
dataset_size: 183.0
---
# Dataset Card for "sample_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 558 | [
[
-0.04010009765625,
-0.0209197998046875,
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-0.0176849365234375,
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0.0170745849609375,
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0.061553955078125,
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-0.0640869140625,
-0.055511474609375,
-0.03378295898437... |
Pampkinus/Mr-Beast | 2023-10-16T15:18:56.000Z | [
"license:openrail",
"region:us"
] | Pampkinus | null | null | 0 | 0 | 2023-10-16T15:16:33 | ---
license: openrail
---
Faceset of the youtuber MrBeast, 5252 images (JPG) | 76 | [
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-0.0275115966796875,
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0.01006317138671875,
0.003662109375,
0.054351806640625,
0.06121826171875,
-0.0797119140625,
-0.0005011558532714844,
-0.05450439453125,
-0.002037048... |
lc-col/bigearthnet | 2023-10-23T12:36:06.000Z | [
"task_categories:image-classification",
"size_categories:100K<n<1M",
"region:us"
] | lc-col | null | null | 0 | 0 | 2023-10-16T15:18:25 | ---
task_categories:
- image-classification
pretty_name: BigEarthNet
size_categories:
- 100K<n<1M
---
# BigEarthNet - HDF5 version
This repository contains an export of the existing BigEarthNet dataset in HDF5 format. All Sentinel-2 acquisitions are exported according to TorchGeo's dataset (120x120 pixels resolution).
Sentinel-1 is not contained in this repository for the moment.
CSV files contain for each satellite acquisition the corresponding HDF5 file and the index.
A PyTorch dataset class which can be used to iterate over this dataset can be found [here](https://github.com/lccol/bigearthnet-conversion), as well as the script used to convert it into HDF5 format. | 675 | [
[
-0.0401611328125,
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0.0015382766723632812,
-0.02789306640625,
-0.0081329345703125,
-0.00020313262939453125,
-0.02789306640625,
0.02178955078125,
0.060028076171875,
-0.0601806640625,
-0.0716552734375,
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0... |
open-llm-leaderboard/details_ajibawa-2023__carl-33b | 2023-10-25T06:29:58.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T15:30:46 | ---
pretty_name: Evaluation run of ajibawa-2023/carl-33b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ajibawa-2023/carl-33b](https://huggingface.co/ajibawa-2023/carl-33b) 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_ajibawa-2023__carl-33b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T06:29:50.391928](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__carl-33b/blob/main/results_2023-10-25T06-29-50.391928.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\": 0.4434773489932886,\n\
\ \"em_stderr\": 0.005087644945149476,\n \"f1\": 0.48920616610738366,\n\
\ \"f1_stderr\": 0.004915552047694347,\n \"acc\": 0.4130577743896054,\n\
\ \"acc_stderr\": 0.009343755992304432\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.4434773489932886,\n \"em_stderr\": 0.005087644945149476,\n\
\ \"f1\": 0.48920616610738366,\n \"f1_stderr\": 0.004915552047694347\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06368460955269144,\n \
\ \"acc_stderr\": 0.006726213078805715\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803146\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ajibawa-2023/carl-33b
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_10_16T15_30_43.173459
path:
- '**/details_harness|drop|3_2023-10-16T15-30-43.173459.parquet'
- split: 2023_10_25T06_29_50.391928
path:
- '**/details_harness|drop|3_2023-10-25T06-29-50.391928.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T06-29-50.391928.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T15_30_43.173459
path:
- '**/details_harness|gsm8k|5_2023-10-16T15-30-43.173459.parquet'
- split: 2023_10_25T06_29_50.391928
path:
- '**/details_harness|gsm8k|5_2023-10-25T06-29-50.391928.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T06-29-50.391928.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T15_30_43.173459
path:
- '**/details_harness|winogrande|5_2023-10-16T15-30-43.173459.parquet'
- split: 2023_10_25T06_29_50.391928
path:
- '**/details_harness|winogrande|5_2023-10-25T06-29-50.391928.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T06-29-50.391928.parquet'
- config_name: results
data_files:
- split: 2023_10_16T15_30_43.173459
path:
- results_2023-10-16T15-30-43.173459.parquet
- split: 2023_10_25T06_29_50.391928
path:
- results_2023-10-25T06-29-50.391928.parquet
- split: latest
path:
- results_2023-10-25T06-29-50.391928.parquet
---
# Dataset Card for Evaluation run of ajibawa-2023/carl-33b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/carl-33b
- **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 [ajibawa-2023/carl-33b](https://huggingface.co/ajibawa-2023/carl-33b) 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_ajibawa-2023__carl-33b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T06:29:50.391928](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__carl-33b/blob/main/results_2023-10-25T06-29-50.391928.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": 0.4434773489932886,
"em_stderr": 0.005087644945149476,
"f1": 0.48920616610738366,
"f1_stderr": 0.004915552047694347,
"acc": 0.4130577743896054,
"acc_stderr": 0.009343755992304432
},
"harness|drop|3": {
"em": 0.4434773489932886,
"em_stderr": 0.005087644945149476,
"f1": 0.48920616610738366,
"f1_stderr": 0.004915552047694347
},
"harness|gsm8k|5": {
"acc": 0.06368460955269144,
"acc_stderr": 0.006726213078805715
},
"harness|winogrande|5": {
"acc": 0.7624309392265194,
"acc_stderr": 0.011961298905803146
}
}
```
### 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] | 7,606 | [
[
-0.037628173828125,
-0.048126220703125,
0.01959228515625,
0.0219879150390625,
-0.00865936279296875,
0.0073699951171875,
-0.0303497314453125,
-0.018890380859375,
0.02667236328125,
0.0374755859375,
-0.050872802734375,
-0.07177734375,
-0.0452880859375,
0.013755... |
open-llm-leaderboard/details_lmsys__longchat-7b-v1.5-32k | 2023-10-16T16:20:45.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T16:20:37 | ---
pretty_name: Evaluation run of lmsys/longchat-7b-v1.5-32k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lmsys/longchat-7b-v1.5-32k](https://huggingface.co/lmsys/longchat-7b-v1.5-32k)\
\ 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_lmsys__longchat-7b-v1.5-32k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T16:20:33.188247](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__longchat-7b-v1.5-32k/blob/main/results_2023-10-16T16-20-33.188247.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\": 0.08252936241610738,\n\
\ \"em_stderr\": 0.0028179934761829416,\n \"f1\": 0.1372829278523486,\n\
\ \"f1_stderr\": 0.0030245592633561815,\n \"acc\": 0.3672124310289838,\n\
\ \"acc_stderr\": 0.009455449816488642\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08252936241610738,\n \"em_stderr\": 0.0028179934761829416,\n\
\ \"f1\": 0.1372829278523486,\n \"f1_stderr\": 0.0030245592633561815\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.047763457164518575,\n \
\ \"acc_stderr\": 0.005874387536229305\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6866614048934491,\n \"acc_stderr\": 0.01303651209674798\n\
\ }\n}\n```"
repo_url: https://huggingface.co/lmsys/longchat-7b-v1.5-32k
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_10_16T16_20_33.188247
path:
- '**/details_harness|drop|3_2023-10-16T16-20-33.188247.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T16-20-33.188247.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T16_20_33.188247
path:
- '**/details_harness|gsm8k|5_2023-10-16T16-20-33.188247.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T16-20-33.188247.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T16_20_33.188247
path:
- '**/details_harness|winogrande|5_2023-10-16T16-20-33.188247.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T16-20-33.188247.parquet'
- config_name: results
data_files:
- split: 2023_10_16T16_20_33.188247
path:
- results_2023-10-16T16-20-33.188247.parquet
- split: latest
path:
- results_2023-10-16T16-20-33.188247.parquet
---
# Dataset Card for Evaluation run of lmsys/longchat-7b-v1.5-32k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lmsys/longchat-7b-v1.5-32k
- **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 [lmsys/longchat-7b-v1.5-32k](https://huggingface.co/lmsys/longchat-7b-v1.5-32k) 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_lmsys__longchat-7b-v1.5-32k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T16:20:33.188247](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__longchat-7b-v1.5-32k/blob/main/results_2023-10-16T16-20-33.188247.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": 0.08252936241610738,
"em_stderr": 0.0028179934761829416,
"f1": 0.1372829278523486,
"f1_stderr": 0.0030245592633561815,
"acc": 0.3672124310289838,
"acc_stderr": 0.009455449816488642
},
"harness|drop|3": {
"em": 0.08252936241610738,
"em_stderr": 0.0028179934761829416,
"f1": 0.1372829278523486,
"f1_stderr": 0.0030245592633561815
},
"harness|gsm8k|5": {
"acc": 0.047763457164518575,
"acc_stderr": 0.005874387536229305
},
"harness|winogrande|5": {
"acc": 0.6866614048934491,
"acc_stderr": 0.01303651209674798
}
}
```
### 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] | 7,219 | [
[
-0.0244293212890625,
-0.05096435546875,
0.0191192626953125,
0.0225677490234375,
-0.0162353515625,
0.00994873046875,
-0.031402587890625,
-0.0234527587890625,
0.038238525390625,
0.04144287109375,
-0.051422119140625,
-0.064453125,
-0.03997802734375,
0.017745971... |
open-llm-leaderboard/details_porkorbeef__Llama-2-13b-12_153950 | 2023-10-25T19:51:29.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T16:23:07 | ---
pretty_name: Evaluation run of porkorbeef/Llama-2-13b-12_153950
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [porkorbeef/Llama-2-13b-12_153950](https://huggingface.co/porkorbeef/Llama-2-13b-12_153950)\
\ 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_porkorbeef__Llama-2-13b-12_153950\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T19:51:17.489031](https://huggingface.co/datasets/open-llm-leaderboard/details_porkorbeef__Llama-2-13b-12_153950/blob/main/results_2023-10-25T19-51-17.489031.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\": 5.76761744966443e-05,\n \"f1_stderr\"\
: 1.4707528558078046e-05,\n \"acc\": 0.26558800315706393,\n \"acc_stderr\"\
: 0.007012571320319756\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 5.76761744966443e-05,\n \"\
f1_stderr\": 1.4707528558078046e-05\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5311760063141279,\n \"acc_stderr\": 0.014025142640639513\n\
\ }\n}\n```"
repo_url: https://huggingface.co/porkorbeef/Llama-2-13b-12_153950
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_10_16T16_23_02.920553
path:
- '**/details_harness|drop|3_2023-10-16T16-23-02.920553.parquet'
- split: 2023_10_25T19_51_17.489031
path:
- '**/details_harness|drop|3_2023-10-25T19-51-17.489031.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T19-51-17.489031.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T16_23_02.920553
path:
- '**/details_harness|gsm8k|5_2023-10-16T16-23-02.920553.parquet'
- split: 2023_10_25T19_51_17.489031
path:
- '**/details_harness|gsm8k|5_2023-10-25T19-51-17.489031.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T19-51-17.489031.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T16_23_02.920553
path:
- '**/details_harness|winogrande|5_2023-10-16T16-23-02.920553.parquet'
- split: 2023_10_25T19_51_17.489031
path:
- '**/details_harness|winogrande|5_2023-10-25T19-51-17.489031.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T19-51-17.489031.parquet'
- config_name: results
data_files:
- split: 2023_10_16T16_23_02.920553
path:
- results_2023-10-16T16-23-02.920553.parquet
- split: 2023_10_25T19_51_17.489031
path:
- results_2023-10-25T19-51-17.489031.parquet
- split: latest
path:
- results_2023-10-25T19-51-17.489031.parquet
---
# Dataset Card for Evaluation run of porkorbeef/Llama-2-13b-12_153950
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/porkorbeef/Llama-2-13b-12_153950
- **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 [porkorbeef/Llama-2-13b-12_153950](https://huggingface.co/porkorbeef/Llama-2-13b-12_153950) 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_porkorbeef__Llama-2-13b-12_153950",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T19:51:17.489031](https://huggingface.co/datasets/open-llm-leaderboard/details_porkorbeef__Llama-2-13b-12_153950/blob/main/results_2023-10-25T19-51-17.489031.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": 5.76761744966443e-05,
"f1_stderr": 1.4707528558078046e-05,
"acc": 0.26558800315706393,
"acc_stderr": 0.007012571320319756
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 5.76761744966443e-05,
"f1_stderr": 1.4707528558078046e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5311760063141279,
"acc_stderr": 0.014025142640639513
}
}
```
### 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] | 7,549 | [
[
-0.02618408203125,
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0.01536560058... |
open-llm-leaderboard/details_bigscience__bloom-1b7 | 2023-10-16T16:35:40.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T16:35:31 | ---
pretty_name: Evaluation run of bigscience/bloom-1b7
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) 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_bigscience__bloom-1b7\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T16:35:28.358737](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloom-1b7/blob/main/results_2023-10-16T16-35-28.358737.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\": 0.0009437919463087249,\n\
\ \"em_stderr\": 0.000314465311941353,\n \"f1\": 0.050256921140939666,\n\
\ \"f1_stderr\": 0.0012661427361730828,\n \"acc\": 0.28246387417700025,\n\
\ \"acc_stderr\": 0.007901602410009659\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0009437919463087249,\n \"em_stderr\": 0.000314465311941353,\n\
\ \"f1\": 0.050256921140939666,\n \"f1_stderr\": 0.0012661427361730828\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.004548900682335102,\n \
\ \"acc_stderr\": 0.0018535550440036202\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5603788476716653,\n \"acc_stderr\": 0.013949649776015698\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bigscience/bloom-1b7
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_10_16T16_35_28.358737
path:
- '**/details_harness|drop|3_2023-10-16T16-35-28.358737.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T16-35-28.358737.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T16_35_28.358737
path:
- '**/details_harness|gsm8k|5_2023-10-16T16-35-28.358737.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T16-35-28.358737.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T16_35_28.358737
path:
- '**/details_harness|winogrande|5_2023-10-16T16-35-28.358737.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T16-35-28.358737.parquet'
- config_name: results
data_files:
- split: 2023_10_16T16_35_28.358737
path:
- results_2023-10-16T16-35-28.358737.parquet
- split: latest
path:
- results_2023-10-16T16-35-28.358737.parquet
---
# Dataset Card for Evaluation run of bigscience/bloom-1b7
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigscience/bloom-1b7
- **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 [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) 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_bigscience__bloom-1b7",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T16:35:28.358737](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloom-1b7/blob/main/results_2023-10-16T16-35-28.358737.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": 0.0009437919463087249,
"em_stderr": 0.000314465311941353,
"f1": 0.050256921140939666,
"f1_stderr": 0.0012661427361730828,
"acc": 0.28246387417700025,
"acc_stderr": 0.007901602410009659
},
"harness|drop|3": {
"em": 0.0009437919463087249,
"em_stderr": 0.000314465311941353,
"f1": 0.050256921140939666,
"f1_stderr": 0.0012661427361730828
},
"harness|gsm8k|5": {
"acc": 0.004548900682335102,
"acc_stderr": 0.0018535550440036202
},
"harness|winogrande|5": {
"acc": 0.5603788476716653,
"acc_stderr": 0.013949649776015698
}
}
```
### 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] | 7,165 | [
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-0.02362060546875,
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... |
DataProvenanceInitiative/niv2_submix_original | 2023-10-16T17:35:49.000Z | [
"region:us"
] | DataProvenanceInitiative | null | null | 0 | 0 | 2023-10-16T17:32:45 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 13104211362
num_examples: 10066896
download_size: 7612945130
dataset_size: 13104211362
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "niv2_submix_original"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 621 | [
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asoria/test_without_revision | 2023-10-16T17:38:17.000Z | [
"region:us"
] | asoria | null | null | 0 | 0 | 2023-10-16T17:38:17 | Entry not found | 15 | [
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0.0379... |
naqashafzal/traning | 2023-10-16T17:40:25.000Z | [
"region:us"
] | naqashafzal | null | null | 0 | 0 | 2023-10-16T17:39:34 | Entry not found | 15 | [
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0.0379... |
safgasgfsa/SeiyaRyuuguuinCautiousHeroRVC2 | 2023-10-16T17:58:41.000Z | [
"region:us"
] | safgasgfsa | null | null | 0 | 0 | 2023-10-16T17:57:58 | Entry not found | 15 | [
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hippocrates/2018n2c2_RE_train | 2023-10-16T18:04:58.000Z | [
"region:us"
] | hippocrates | null | null | 0 | 0 | 2023-10-16T18:04:53 | Entry not found | 15 | [
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hippocrates/2018n2c2_RE_test | 2023-10-16T18:12:07.000Z | [
"region:us"
] | hippocrates | null | null | 0 | 0 | 2023-10-16T18:12:04 | Entry not found | 15 | [
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0.0379... |
qureshiu/embeddings_alpaca_aci | 2023-10-16T18:41:00.000Z | [
"region:us"
] | qureshiu | null | null | 0 | 0 | 2023-10-16T18:39:52 | Entry not found | 15 | [
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stepkurniawan/RAGAS_50 | 2023-10-16T19:18:54.000Z | [
"language:en",
"license:mit",
"climate",
"region:us"
] | stepkurniawan | null | null | 0 | 0 | 2023-10-16T19:17:32 | ---
license: mit
language:
- en
tags:
- climate
---
This is the table to evaluate RAGAS using 50 questions from the whole dataset | 130 | [
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jorgego/repo_test | 2023-10-16T19:28:33.000Z | [
"region:us"
] | jorgego | null | null | 0 | 0 | 2023-10-16T19:28:33 | Entry not found | 15 | [
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orgcatorg/russia-ukraine-aljazeera | 2023-10-16T19:47:01.000Z | [
"region:us"
] | orgcatorg | null | null | 0 | 0 | 2023-10-16T19:31:51 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: headline
dtype: string
- name: url
dtype: string
- name: articleBody
dtype: string
- name: imageCaption
dtype: string
- name: datePublished
dtype: string
splits:
- name: train
num_bytes: 37433318.0
num_examples: 464
download_size: 36575736
dataset_size: 37433318.0
---
# Dataset Card for "russia-ukraine-aljazeera"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 650 | [
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dsfsi/daily-news-dikgang | 2023-10-26T07:21:04.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:tn",
"license:cc-by-sa-4.0",
"arxiv:2310.09141",
"region:us"
] | dsfsi | null | null | 1 | 0 | 2023-10-16T19:32:56 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- tn
size_categories:
- 1K<n<10K
---
# Daily News Dikgang
[](https://arxiv.org/abs/2310.09141)
Give Feedback 📑: [DSFSI Resource Feedback Form](https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/formResponse)
## About dataset
The dataset contains annotated categorised data from Dikgang - Daily News [https://dailynews.gov.bw/news-list/srccategory/10](https://dailynews.gov.bw/news-list/srccategory/10). The data is in setswana.
See the [Data Statement](DataStatementPuoBERTaDailyNewsDikgang.pdf) for foll details.
Disclaimer
-------
This dataset contains machine-readable data extracted from online news articles, from [https://dailynews.gov.bw/news-list/srccategory/10](https://dailynews.gov.bw/news-list/srccategory/10), provided by the Botswana Government. While efforts were made to ensure the accuracy and completeness of this data, there may be errors or discrepancies between the original publications and this dataset. No warranties, guarantees or representations are given in relation to the information contained in the dataset. The members of the Data Science for Societal Impact Research Group bear no responsibility and/or liability for any such errors or discrepancies in this dataset. The Botswana Government bears no responsibility and/or liability for any such errors or discrepancies in this dataset. It is recommended that users verify all information contained herein before making decisions based upon this information.
Authors
-------
- Vukosi Marivate - [@vukosi](https://twitter.com/vukosi)
- Valencia Wagner
Citation
--------
Bibtex Reference
```
@inproceedings{marivate2023puoberta,
title = {PuoBERTa: Training and evaluation of a curated language model for Setswana},
author = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai},
year = {2023},
booktitle= {SACAIR 2023 (To Appear)},
keywords = {NLP},
preprint_url = {https://arxiv.org/abs/2310.09141},
dataset_url = {https://github.com/dsfsi/PuoBERTa},
software_url = {https://huggingface.co/dsfsi/PuoBERTa}
}
```
Licences
-------
The license of the News Categorisation dataset is in CC-BY-SA-4.0. the monolingual data have difference licenses depending on the news website license
* License for Data - [CC-BY-SA-4.0](LICENSE.data.md) | 2,484 | [
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open-llm-leaderboard/details_dvruette__oasst-llama-13b-2-epochs | 2023-10-16T19:36:03.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T19:35:55 | ---
pretty_name: Evaluation run of dvruette/oasst-llama-13b-2-epochs
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dvruette/oasst-llama-13b-2-epochs](https://huggingface.co/dvruette/oasst-llama-13b-2-epochs)\
\ 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_dvruette__oasst-llama-13b-2-epochs\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T19:35:51.495118](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-llama-13b-2-epochs/blob/main/results_2023-10-16T19-35-51.495118.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\": 0.04299496644295302,\n\
\ \"em_stderr\": 0.002077330365557692,\n \"f1\": 0.10806732382550314,\n\
\ \"f1_stderr\": 0.0024113571936826787,\n \"acc\": 0.42569171474168144,\n\
\ \"acc_stderr\": 0.00971706467249931\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.04299496644295302,\n \"em_stderr\": 0.002077330365557692,\n\
\ \"f1\": 0.10806732382550314,\n \"f1_stderr\": 0.0024113571936826787\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08263836239575435,\n \
\ \"acc_stderr\": 0.007584089220148114\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dvruette/oasst-llama-13b-2-epochs
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_10_16T19_35_51.495118
path:
- '**/details_harness|drop|3_2023-10-16T19-35-51.495118.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T19-35-51.495118.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T19_35_51.495118
path:
- '**/details_harness|gsm8k|5_2023-10-16T19-35-51.495118.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T19-35-51.495118.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T19_35_51.495118
path:
- '**/details_harness|winogrande|5_2023-10-16T19-35-51.495118.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T19-35-51.495118.parquet'
- config_name: results
data_files:
- split: 2023_10_16T19_35_51.495118
path:
- results_2023-10-16T19-35-51.495118.parquet
- split: latest
path:
- results_2023-10-16T19-35-51.495118.parquet
---
# Dataset Card for Evaluation run of dvruette/oasst-llama-13b-2-epochs
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dvruette/oasst-llama-13b-2-epochs
- **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 [dvruette/oasst-llama-13b-2-epochs](https://huggingface.co/dvruette/oasst-llama-13b-2-epochs) 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_dvruette__oasst-llama-13b-2-epochs",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T19:35:51.495118](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-llama-13b-2-epochs/blob/main/results_2023-10-16T19-35-51.495118.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": 0.04299496644295302,
"em_stderr": 0.002077330365557692,
"f1": 0.10806732382550314,
"f1_stderr": 0.0024113571936826787,
"acc": 0.42569171474168144,
"acc_stderr": 0.00971706467249931
},
"harness|drop|3": {
"em": 0.04299496644295302,
"em_stderr": 0.002077330365557692,
"f1": 0.10806732382550314,
"f1_stderr": 0.0024113571936826787
},
"harness|gsm8k|5": {
"acc": 0.08263836239575435,
"acc_stderr": 0.007584089220148114
},
"harness|winogrande|5": {
"acc": 0.7687450670876085,
"acc_stderr": 0.01185004012485051
}
}
```
### 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] | 7,301 | [
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Shengjie/WildCamera | 2023-10-26T18:40:39.000Z | [
"region:us"
] | Shengjie | null | null | 0 | 0 | 2023-10-16T20:03:23 | Entry not found | 15 | [
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SkunkworksAI/BakLLaVA-1-FT | 2023-10-16T20:15:12.000Z | [
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] | SkunkworksAI | null | null | 4 | 0 | 2023-10-16T20:15:12 | Entry not found | 15 | [
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helenqu/astro-classification-redshifts | 2023-10-16T21:33:53.000Z | [
"size_categories:100K<n<1M",
"license:mit",
"time series",
"astrophysics",
"pretraining",
"connect-later",
"arxiv:1810.00001",
"arxiv:1903.11756",
"region:us"
] | helenqu | null | null | 0 | 0 | 2023-10-16T20:33:04 | ---
license: mit
tags:
- time series
- astrophysics
- pretraining
- connect-later
size_categories:
- 100K<n<1M
---
# AstroClassification and Redshifts Datasets
<!-- Provide a quick summary of the dataset. -->
This dataset was used for the AstroClassification and Redshifts introduced in [Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations](). This is a dataset of simulated astronomical time-series (e.g., supernovae, active galactic nuclei), and the task is to classify the object type (AstroClassification) or predict the object's redshift (Redshifts).
- **Repository:** https://github.com/helenqu/connect-later
- **Paper:** will be updated
- **Point of Contact: Helen Qu (<helenqu@sas.upenn.edu>)**
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
- **object_id**: unique object identifier
- **times_wv**: 2D array of shape (N, 2) containing the observation times (modified Julian days, MJD) and filter (wavelength in nm) for each observation, N=number of observations
- **lightcurve**: 2D array of shape (N, 2) containing the flux (arbitrary units) and flux error for each observation
- **label**: integer representing the class of the object (see below for details)
- **redshift**: redshift of the object
## Dataset Creation
### Source Data
This is a modified version of the dataset from the 2018 Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) Kaggle competition
The original Kaggle competition can be found [here](https://www.kaggle.com/c/PLAsTiCC-2018). [This note](https://arxiv.org/abs/1810.00001) from the competition describes the dataset in detail. Astronomers may be interested in [this paper](https://arxiv.org/abs/1903.11756) describing the simulations used to generate the data.
- **Train**: 80% of the original PLAsTiCC training set augmented using the redshifting targeted augmentation described in the Connect Later paper
- **Validation**: Remaining 20% of the original PLAsTiCC training set, *not* augmented or modified
- **Test**: Subset of 10,000 objects randomly selected from the PLAsTiCC test set
### Object Types
```
0: microlens-single
1: tidal disruption event (TDE)
2: eclipsing binary (EB)
3: type II supernova (SNII)
4: peculiar type Ia supernova (SNIax)
5: Mira variable
6: type Ibc supernova(SNIbc)
7: kilonova (KN)
8: M-dwarf
9: peculiar type Ia supernova (SNIa-91bg)
10: active galactic nuclei (AGN)
11: type Ia supernova (SNIa)
12: RR-Lyrae (RRL)
13: superluminous supernova (SLSN-I)
14: 5 "anomalous" types that are not present in training set: microlens-binary, intermediate luminosity optical transient (ILOT), calcium-rich transient (CaRT), pair instability supernova (PISN), microlens-string
```
## Citation
will be updated | 2,930 | [
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autoevaluate/autoeval-eval-squad-plain_text-7a69af-95554146403 | 2023-10-16T21:23:22.000Z | [
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open-llm-leaderboard/details_ajibawa-2023__scarlett-33b | 2023-10-16T22:35:46.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T22:35:37 | ---
pretty_name: Evaluation run of ajibawa-2023/scarlett-33b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ajibawa-2023/scarlett-33b](https://huggingface.co/ajibawa-2023/scarlett-33b)\
\ 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_ajibawa-2023__scarlett-33b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T22:35:33.432949](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__scarlett-33b/blob/main/results_2023-10-16T22-35-33.432949.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\": 0.3665058724832215,\n\
\ \"em_stderr\": 0.004934593891762348,\n \"f1\": 0.43883598993288797,\n\
\ \"f1_stderr\": 0.004751167980569885,\n \"acc\": 0.39800367765635275,\n\
\ \"acc_stderr\": 0.008206189612832142\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3665058724832215,\n \"em_stderr\": 0.004934593891762348,\n\
\ \"f1\": 0.43883598993288797,\n \"f1_stderr\": 0.004751167980569885\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.028051554207733132,\n \
\ \"acc_stderr\": 0.00454822953383635\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827933\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ajibawa-2023/scarlett-33b
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_10_16T22_35_33.432949
path:
- '**/details_harness|drop|3_2023-10-16T22-35-33.432949.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T22-35-33.432949.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T22_35_33.432949
path:
- '**/details_harness|gsm8k|5_2023-10-16T22-35-33.432949.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T22-35-33.432949.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T22_35_33.432949
path:
- '**/details_harness|winogrande|5_2023-10-16T22-35-33.432949.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T22-35-33.432949.parquet'
- config_name: results
data_files:
- split: 2023_10_16T22_35_33.432949
path:
- results_2023-10-16T22-35-33.432949.parquet
- split: latest
path:
- results_2023-10-16T22-35-33.432949.parquet
---
# Dataset Card for Evaluation run of ajibawa-2023/scarlett-33b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/scarlett-33b
- **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 [ajibawa-2023/scarlett-33b](https://huggingface.co/ajibawa-2023/scarlett-33b) 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_ajibawa-2023__scarlett-33b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T22:35:33.432949](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__scarlett-33b/blob/main/results_2023-10-16T22-35-33.432949.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": 0.3665058724832215,
"em_stderr": 0.004934593891762348,
"f1": 0.43883598993288797,
"f1_stderr": 0.004751167980569885,
"acc": 0.39800367765635275,
"acc_stderr": 0.008206189612832142
},
"harness|drop|3": {
"em": 0.3665058724832215,
"em_stderr": 0.004934593891762348,
"f1": 0.43883598993288797,
"f1_stderr": 0.004751167980569885
},
"harness|gsm8k|5": {
"acc": 0.028051554207733132,
"acc_stderr": 0.00454822953383635
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827933
}
}
```
### 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] | 7,201 | [
[
-0.035491943359375,
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open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b | 2023-10-16T23:19:31.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-16T23:19:21 | ---
pretty_name: Evaluation run of Gryphe/MythoMax-L2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) 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_Gryphe__MythoMax-L2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T23:19:17.622542](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b/blob/main/results_2023-10-16T23-19-17.622542.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\": 0.13433305369127516,\n\
\ \"em_stderr\": 0.00349225954139751,\n \"f1\": 0.20734689597315364,\n\
\ \"f1_stderr\": 0.003631918882586114,\n \"acc\": 0.42119517249261446,\n\
\ \"acc_stderr\": 0.010012961564157645\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.13433305369127516,\n \"em_stderr\": 0.00349225954139751,\n\
\ \"f1\": 0.20734689597315364,\n \"f1_stderr\": 0.003631918882586114\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09021986353297953,\n \
\ \"acc_stderr\": 0.00789153710844994\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.01213438601986535\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Gryphe/MythoMax-L2-13b
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_10_16T23_19_17.622542
path:
- '**/details_harness|drop|3_2023-10-16T23-19-17.622542.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T23-19-17.622542.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T23_19_17.622542
path:
- '**/details_harness|gsm8k|5_2023-10-16T23-19-17.622542.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T23-19-17.622542.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T23_19_17.622542
path:
- '**/details_harness|winogrande|5_2023-10-16T23-19-17.622542.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T23-19-17.622542.parquet'
- config_name: results
data_files:
- split: 2023_10_16T23_19_17.622542
path:
- results_2023-10-16T23-19-17.622542.parquet
- split: latest
path:
- results_2023-10-16T23-19-17.622542.parquet
---
# Dataset Card for Evaluation run of Gryphe/MythoMax-L2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Gryphe/MythoMax-L2-13b
- **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 [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) 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_Gryphe__MythoMax-L2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T23:19:17.622542](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b/blob/main/results_2023-10-16T23-19-17.622542.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": 0.13433305369127516,
"em_stderr": 0.00349225954139751,
"f1": 0.20734689597315364,
"f1_stderr": 0.003631918882586114,
"acc": 0.42119517249261446,
"acc_stderr": 0.010012961564157645
},
"harness|drop|3": {
"em": 0.13433305369127516,
"em_stderr": 0.00349225954139751,
"f1": 0.20734689597315364,
"f1_stderr": 0.003631918882586114
},
"harness|gsm8k|5": {
"acc": 0.09021986353297953,
"acc_stderr": 0.00789153710844994
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.01213438601986535
}
}
```
### 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] | 7,161 | [
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0.01151... |
nuprl-staging/longtest_benchmark | 2023-10-16T23:39:54.000Z | [
"region:us"
] | nuprl-staging | null | null | 0 | 0 | 2023-10-16T23:36:15 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: target_tests
dtype: string
- name: canonical_prompt
dtype: string
- name: canonical_solution
dtype: string
- name: size
dtype: int64
splits:
- name: train
num_bytes: 623100
num_examples: 24
download_size: 0
dataset_size: 623100
---
# Dataset Card for "longtest_benchmark"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 517 | [
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0.028350830078125,
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HengJi/human_faces | 2023-10-17T00:01:12.000Z | [
"region:us"
] | HengJi | null | null | 0 | 0 | 2023-10-16T23:59:43 | Entry not found | 15 | [
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0.0379... |
peterbeamish/environment-yaml-sorted | 2023-10-17T00:34:36.000Z | [
"region:us"
] | peterbeamish | null | null | 0 | 0 | 2023-10-17T00:19:26 | Entry not found | 15 | [
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0.0379... |
CodingCoda/AI52.3k | 2023-10-17T00:45:22.000Z | [
"region:us"
] | CodingCoda | null | null | 0 | 0 | 2023-10-17T00:45:22 | Entry not found | 15 | [
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0.016998291015625,
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0.0379... |
Aveygo/AI52.3K | 2023-10-17T00:46:31.000Z | [
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Cesar42/EmotionTrainLlama2_16k | 2023-10-17T03:32:45.000Z | [
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open-llm-leaderboard/details_MBZUAI__LaMini-GPT-774M | 2023-10-17T01:05:35.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T01:05:26 | ---
pretty_name: Evaluation run of MBZUAI/LaMini-GPT-774M
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [MBZUAI/LaMini-GPT-774M](https://huggingface.co/MBZUAI/LaMini-GPT-774M) 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-GPT-774M\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T01:05:23.378180](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__LaMini-GPT-774M/blob/main/results_2023-10-17T01-05-23.378180.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\": 0.03544463087248322,\n\
\ \"em_stderr\": 0.0018935573437954087,\n \"f1\": 0.12509857382550346,\n\
\ \"f1_stderr\": 0.0025549304231766066,\n \"acc\": 0.2829518547750592,\n\
\ \"acc_stderr\": 0.006964941277847027\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.03544463087248322,\n \"em_stderr\": 0.0018935573437954087,\n\
\ \"f1\": 0.12509857382550346,\n \"f1_stderr\": 0.0025549304231766066\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5659037095501184,\n\
\ \"acc_stderr\": 0.013929882555694054\n }\n}\n```"
repo_url: https://huggingface.co/MBZUAI/LaMini-GPT-774M
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_10_17T01_05_23.378180
path:
- '**/details_harness|drop|3_2023-10-17T01-05-23.378180.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T01-05-23.378180.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T01_05_23.378180
path:
- '**/details_harness|gsm8k|5_2023-10-17T01-05-23.378180.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T01-05-23.378180.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T01_05_23.378180
path:
- '**/details_harness|winogrande|5_2023-10-17T01-05-23.378180.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T01-05-23.378180.parquet'
- config_name: results
data_files:
- split: 2023_10_17T01_05_23.378180
path:
- results_2023-10-17T01-05-23.378180.parquet
- split: latest
path:
- results_2023-10-17T01-05-23.378180.parquet
---
# Dataset Card for Evaluation run of MBZUAI/LaMini-GPT-774M
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/MBZUAI/LaMini-GPT-774M
- **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-GPT-774M](https://huggingface.co/MBZUAI/LaMini-GPT-774M) 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-GPT-774M",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T01:05:23.378180](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__LaMini-GPT-774M/blob/main/results_2023-10-17T01-05-23.378180.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": 0.03544463087248322,
"em_stderr": 0.0018935573437954087,
"f1": 0.12509857382550346,
"f1_stderr": 0.0025549304231766066,
"acc": 0.2829518547750592,
"acc_stderr": 0.006964941277847027
},
"harness|drop|3": {
"em": 0.03544463087248322,
"em_stderr": 0.0018935573437954087,
"f1": 0.12509857382550346,
"f1_stderr": 0.0025549304231766066
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5659037095501184,
"acc_stderr": 0.013929882555694054
}
}
```
### 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] | 7,104 | [
[
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0.01... |
Minglii/A_QthenA_4096 | 2023-10-17T01:31:40.000Z | [
"region:us"
] | Minglii | null | null | 0 | 0 | 2023-10-17T01:24:48 | ---
dataset_info:
features:
- name: data
struct:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 359881748
num_examples: 52002
download_size: 119164182
dataset_size: 359881748
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "A_QthenA_4096"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 598 | [
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mesolitica/translated-python-evol-instruct-51k | 2023-10-17T01:41:07.000Z | [
"region:us"
] | mesolitica | null | null | 0 | 0 | 2023-10-17T01:38:20 | Entry not found | 15 | [
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biodigital2/address-fixed-4 | 2023-10-17T01:56:31.000Z | [
"region:us"
] | biodigital2 | null | null | 0 | 0 | 2023-10-17T01:56:07 | Entry not found | 15 | [
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SebastiantorresVFX/Comic_Book_Reg_Images_SDXL | 2023-10-24T05:12:41.000Z | [
"region:us"
] | SebastiantorresVFX | null | null | 0 | 0 | 2023-10-17T02:01:51 | Entry not found | 15 | [
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phanvancongthanh/pubchem-kinases | 2023-10-17T07:55:49.000Z | [
"region:us"
] | phanvancongthanh | null | null | 0 | 0 | 2023-10-17T02:11:50 | Entry not found | 15 | [
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TearGosling/redpj_sample_binidx | 2023-10-17T03:02:53.000Z | [
"region:us"
] | TearGosling | null | null | 0 | 0 | 2023-10-17T03:02:53 | Entry not found | 15 | [
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twodgirl/soda-fin | 2023-10-17T16:07:20.000Z | [
"task_categories:text-generation",
"language:fi",
"suomi",
"region:us"
] | twodgirl | null | null | 0 | 0 | 2023-10-17T03:15:00 | ---
language:
- fi
tags:
- suomi
task_categories:
- text-generation
---
Machine translation of the [SODA dataset.](https://huggingface.co/datasets/allenai/soda) | 161 | [
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twodgirl/soda-est | 2023-10-17T16:03:33.000Z | [
"task_categories:text-generation",
"language:et",
"license:cc-by-4.0",
"eesti",
"region:us"
] | twodgirl | null | null | 0 | 0 | 2023-10-17T03:30:47 | ---
language:
- et
tags:
- eesti
task_categories:
- text-generation
license: cc-by-4.0
---
Machine translation of the [SODA dataset.](https://huggingface.co/datasets/allenai/soda) | 180 | [
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twodgirl/soda-mag | 2023-10-17T03:37:44.000Z | [
"task_categories:text-generation",
"license:cc-by-4.0",
"magyar",
"region:us"
] | twodgirl | null | null | 0 | 0 | 2023-10-17T03:35:06 | ---
tags:
- magyar
task_categories:
- text-generation
license: cc-by-4.0
---
Machine translation of the [SODA dataset.](https://huggingface.co/datasets/allenai/soda) | 166 | [
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Rudigus/persona-voices | 2023-10-18T00:44:19.000Z | [
"region:us"
] | Rudigus | null | null | 0 | 0 | 2023-10-17T04:06:45 | Entry not found | 15 | [
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open-llm-leaderboard/details_Aeala__Enterredaas-33b | 2023-10-17T04:24:29.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T04:24:21 | ---
pretty_name: Evaluation run of Aeala/Enterredaas-33b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aeala/Enterredaas-33b](https://huggingface.co/Aeala/Enterredaas-33b) 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_Aeala__Enterredaas-33b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T04:24:16.762833](https://huggingface.co/datasets/open-llm-leaderboard/details_Aeala__Enterredaas-33b/blob/main/results_2023-10-17T04-24-16.762833.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\": 0.001572986577181208,\n\
\ \"em_stderr\": 0.00040584511324177344,\n \"f1\": 0.06232487416107388,\n\
\ \"f1_stderr\": 0.0013590473373823627,\n \"acc\": 0.47496578750374735,\n\
\ \"acc_stderr\": 0.010824257783821654\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001572986577181208,\n \"em_stderr\": 0.00040584511324177344,\n\
\ \"f1\": 0.06232487416107388,\n \"f1_stderr\": 0.0013590473373823627\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.16224412433661864,\n \
\ \"acc_stderr\": 0.010155130880393526\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7876874506708761,\n \"acc_stderr\": 0.011493384687249784\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aeala/Enterredaas-33b
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_10_17T04_24_16.762833
path:
- '**/details_harness|drop|3_2023-10-17T04-24-16.762833.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T04-24-16.762833.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T04_24_16.762833
path:
- '**/details_harness|gsm8k|5_2023-10-17T04-24-16.762833.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T04-24-16.762833.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T04_24_16.762833
path:
- '**/details_harness|winogrande|5_2023-10-17T04-24-16.762833.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T04-24-16.762833.parquet'
- config_name: results
data_files:
- split: 2023_10_17T04_24_16.762833
path:
- results_2023-10-17T04-24-16.762833.parquet
- split: latest
path:
- results_2023-10-17T04-24-16.762833.parquet
---
# Dataset Card for Evaluation run of Aeala/Enterredaas-33b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aeala/Enterredaas-33b
- **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 [Aeala/Enterredaas-33b](https://huggingface.co/Aeala/Enterredaas-33b) 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_Aeala__Enterredaas-33b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T04:24:16.762833](https://huggingface.co/datasets/open-llm-leaderboard/details_Aeala__Enterredaas-33b/blob/main/results_2023-10-17T04-24-16.762833.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": 0.001572986577181208,
"em_stderr": 0.00040584511324177344,
"f1": 0.06232487416107388,
"f1_stderr": 0.0013590473373823627,
"acc": 0.47496578750374735,
"acc_stderr": 0.010824257783821654
},
"harness|drop|3": {
"em": 0.001572986577181208,
"em_stderr": 0.00040584511324177344,
"f1": 0.06232487416107388,
"f1_stderr": 0.0013590473373823627
},
"harness|gsm8k|5": {
"acc": 0.16224412433661864,
"acc_stderr": 0.010155130880393526
},
"harness|winogrande|5": {
"acc": 0.7876874506708761,
"acc_stderr": 0.011493384687249784
}
}
```
### 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] | 7,173 | [
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matrixportable/matrixportableheaterbenefit | 2023-10-17T05:08:33.000Z | [
"region:us"
] | matrixportable | null | null | 0 | 0 | 2023-10-17T05:07:20 | **➢** **Product Name – [Matrix Portable Heater](https://www.facebook.com/people/Matrix-Portable-Heater/61552593236365/)**
**➢ Rating -** ★★★★★ (4.9)
**➢ Where to Buy (Sale Live) – [Click Here](https://www.glitco.com/matrix-portable-heater/)**
### [Matrix Portable Heater: Your Compact Solution for Efficient Heating](https://www.glitco.com/matrix-portable-heater/)
**Brand:** [Matrix Industrial Products](https://www.facebook.com/people/Matrix-Portable-Heater/61552593236365/)
**Special feature:** Portable
**Power source:** Battery Powered
**Heating method:** Convection
**Mounting type:** Floor Mount
**Burner type:** Radiant
**Item weight:** 3.24 Pounds
**Heat output:** 5200 British Thermal Units
[.png)](https://www.glitco.com/matrix-portable-heater/)
### **[Matrix Portable Heater Reviews](https://www.glitco.com/matrix-portable-heater/)**
The Matrix Portable Heater is revolutionizing the way we think about personal heating solutions. In an age where portability and energy efficiency are paramount, this sleek and compact heater offers a reliable, energy-efficient, and convenient way to stay warm in any room of your home. In this article, we'll explore the many advantages of the **[Matrix Portable Heater](https://www.facebook.com/people/Matrix-Portable-Heater/61552593236365/)** and why it's becoming a popular choice for those seeking warmth and comfort without the need for a bulky, traditional space heater.
### **Features & Details**
* **Fast Heating-** This Handy Heater comes with Ceramic heating element creates energy effective warmth snappily in 3 seconds. SaiEllin Heater room warmer is excellent for close range warming.
* **Overheat protection PTC ceramic element is tone-** regulating With the design ofover-heat protection for thermal control. It's an air cracker heater which has an inbuilt addict to throw air.
* **Compact Design-** It's compact enough to take anywhere, Great for the trailer or give it to the kiddies for their council dorm apartments. Mini Room Heater has malleable temperature and speed.
* Megahit for hot room and cracker for downtime is a small room heater which comes with LED Screen and buttons to help set the Temperature both in Celsius and Fahrenheit and help acclimate addict speed.
### **Benefits Of [Matrix Portable Heater](https://www.glitco.com/matrix-portable-heater/)**
malleable Temperature These heaters frequently come with temperature controls, allowing you to set the asked position of warmth.
Safety Features numerous movable heaters have safety features like overheat protection and tip- over switches to help accidents.
Energy Efficiency Some models are designed to be energy-effective, which can help reduce heating costs.
Portability These heaters are easy to move around, thanks to their compact size and occasionally erected- in handles.
Different Heating styles movable heaters can use colorful heating styles, including ceramic, radiant, or convection heating.
[.png)](https://www.glitco.com/matrix-portable-heater/)
### **Compact Design**
One of the standout features of the [**Matrix Portable Heater**](https://www.glitco.com/matrix-portable-heater/) is its compact design. Measuring just a few inches in height and width, this heater is easily transportable, making it ideal for use in various rooms within your home. Whether you're looking to heat your bedroom, living room, or even a home office, the Matrix Portable Heater can seamlessly fit into your space without being obtrusive. Its sleek design allows it to blend into any decor, making it an unobtrusive addition to your home.
### **Efficient Heating**
Matrix Portable Heater doesn't compromise on heating efficiency. Despite its small size, it packs a punch when it comes to producing warmth. With multiple heating settings and an adjustable thermostat, you can customize the temperature to your liking. This heater employs innovative heating technology to distribute warmth evenly, ensuring that every corner of your room is heated effectively. You'll no longer have to huddle near a traditional, inefficient space heater to stay warm.
### **Energy Efficiency**
Energy efficiency is a significant concern for today's environmentally-conscious consumers. The **[Matrix Portable Heater](https://www.glitco.com/matrix-portable-heater/)** is designed with this in mind. It utilizes the latest energy-saving technology, ensuring that you can enjoy a warm and comfortable space without the guilt of high energy bills. By efficiently heating the area you need, it eliminates the waste associated with heating an entire room. The heater also features a built-in timer, which allows you to schedule heating periods, further reducing energy consumption.
### **Safety Features**
Safety is a top priority when it comes to any heating device, and the **[Matrix Portable Heater](https://www.facebook.com/people/Matrix-Portable-Heater/61552593236365/)** doesn't disappoint. It comes equipped with various safety features, including overheat protection and a tip-over switch. These mechanisms ensure that the heater automatically shuts off if it overheats or gets accidentally knocked over. This not only prevents accidents but also provides peace of mind for users.
### **Noise-Free Operation**
Traditional space heaters can be noisy and disruptive, making it difficult to concentrate, relax, or sleep in a quiet environment. The **[Matrix Portable Heater](https://www.glitco.com/matrix-portable-heater/)** is designed with silent operation in mind. It operates virtually noise-free, allowing you to enjoy the warmth it provides without any distractions. Whether you're working, reading, or watching TV, this heater won't disrupt your peace and quiet.
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### **Conclusion**
The **[Matrix Portable Heater](https://www.glitco.com/matrix-portable-heater/)** offers a modern and efficient solution for personal heating needs. Its compact design, energy efficiency, and safety features make it a top choice for anyone seeking a convenient and effective way to stay warm during the colder months. Whether you want to heat your bedroom, living room, or workspace, this portable heater offers a versatile and unobtrusive solution. Say goodbye to bulky space heaters and hello to the future of heating with the Matrix Portable Heater. Stay warm, stay comfortable, and stay energy-efficient. | 7,258 | [
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vietlegalqa/vi_quad2 | 2023-10-17T05:07:53.000Z | [
"region:us"
] | vietlegalqa | null | null | 0 | 0 | 2023-10-17T05:07:41 | ---
dataset_info:
features:
- name: context
dtype: string
- name: masked_context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: plausible_answer
dtype: string
splits:
- name: train
num_bytes: 39809840
num_examples: 19920
download_size: 7455118
dataset_size: 39809840
---
# Dataset Card for "vi_quad2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 516 | [
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open-llm-leaderboard/details_meta-llama__Llama-2-70b-chat-hf | 2023-10-17T05:07:55.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T05:07:46 | ---
pretty_name: Evaluation run of meta-llama/Llama-2-70b-chat-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)\
\ 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_meta-llama__Llama-2-70b-chat-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T05:07:42.486452](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-chat-hf/blob/main/results_2023-10-17T05-07-42.486452.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\": 0.040373322147651006,\n\
\ \"em_stderr\": 0.0020157564185176837,\n \"f1\": 0.1050272651006715,\n\
\ \"f1_stderr\": 0.0023756238577676155,\n \"acc\": 0.5359600711595986,\n\
\ \"acc_stderr\": 0.011658939983913113\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.040373322147651006,\n \"em_stderr\": 0.0020157564185176837,\n\
\ \"f1\": 0.1050272651006715,\n \"f1_stderr\": 0.0023756238577676155\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.266868840030326,\n \
\ \"acc_stderr\": 0.012183780551887957\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938268\n\
\ }\n}\n```"
repo_url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
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_10_17T05_07_42.486452
path:
- '**/details_harness|drop|3_2023-10-17T05-07-42.486452.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T05-07-42.486452.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T05_07_42.486452
path:
- '**/details_harness|gsm8k|5_2023-10-17T05-07-42.486452.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T05-07-42.486452.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T05_07_42.486452
path:
- '**/details_harness|winogrande|5_2023-10-17T05-07-42.486452.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T05-07-42.486452.parquet'
- config_name: results
data_files:
- split: 2023_10_17T05_07_42.486452
path:
- results_2023-10-17T05-07-42.486452.parquet
- split: latest
path:
- results_2023-10-17T05-07-42.486452.parquet
---
# Dataset Card for Evaluation run of meta-llama/Llama-2-70b-chat-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
- **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 [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) 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_meta-llama__Llama-2-70b-chat-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T05:07:42.486452](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-chat-hf/blob/main/results_2023-10-17T05-07-42.486452.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": 0.040373322147651006,
"em_stderr": 0.0020157564185176837,
"f1": 0.1050272651006715,
"f1_stderr": 0.0023756238577676155,
"acc": 0.5359600711595986,
"acc_stderr": 0.011658939983913113
},
"harness|drop|3": {
"em": 0.040373322147651006,
"em_stderr": 0.0020157564185176837,
"f1": 0.1050272651006715,
"f1_stderr": 0.0023756238577676155
},
"harness|gsm8k|5": {
"acc": 0.266868840030326,
"acc_stderr": 0.012183780551887957
},
"harness|winogrande|5": {
"acc": 0.8050513022888713,
"acc_stderr": 0.011134099415938268
}
}
```
### 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] | 7,268 | [
[
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wal14567/test-xray-covid | 2023-10-17T06:19:46.000Z | [
"region:us"
] | wal14567 | null | null | 0 | 0 | 2023-10-17T06:19:46 | Entry not found | 15 | [
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open-llm-leaderboard/details_bofenghuang__vigogne-33b-instruct | 2023-10-17T06:48:29.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T06:48:21 | ---
pretty_name: Evaluation run of bofenghuang/vigogne-33b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bofenghuang/vigogne-33b-instruct](https://huggingface.co/bofenghuang/vigogne-33b-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-33b-instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T06:48:17.282592](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-33b-instruct/blob/main/results_2023-10-17T06-48-17.282592.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\": 0.4092911073825503,\n\
\ \"em_stderr\": 0.005035499534676373,\n \"f1\": 0.47988779362416334,\n\
\ \"f1_stderr\": 0.004806379711128169,\n \"acc\": 0.4499623916853611,\n\
\ \"acc_stderr\": 0.010072884519008809\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.4092911073825503,\n \"em_stderr\": 0.005035499534676373,\n\
\ \"f1\": 0.47988779362416334,\n \"f1_stderr\": 0.004806379711128169\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11144806671721001,\n \
\ \"acc_stderr\": 0.008668021353794433\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7884767166535123,\n \"acc_stderr\": 0.011477747684223187\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bofenghuang/vigogne-33b-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_10_17T06_48_17.282592
path:
- '**/details_harness|drop|3_2023-10-17T06-48-17.282592.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T06-48-17.282592.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T06_48_17.282592
path:
- '**/details_harness|gsm8k|5_2023-10-17T06-48-17.282592.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T06-48-17.282592.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T06_48_17.282592
path:
- '**/details_harness|winogrande|5_2023-10-17T06-48-17.282592.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T06-48-17.282592.parquet'
- config_name: results
data_files:
- split: 2023_10_17T06_48_17.282592
path:
- results_2023-10-17T06-48-17.282592.parquet
- split: latest
path:
- results_2023-10-17T06-48-17.282592.parquet
---
# Dataset Card for Evaluation run of bofenghuang/vigogne-33b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bofenghuang/vigogne-33b-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-33b-instruct](https://huggingface.co/bofenghuang/vigogne-33b-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-33b-instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T06:48:17.282592](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-33b-instruct/blob/main/results_2023-10-17T06-48-17.282592.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": 0.4092911073825503,
"em_stderr": 0.005035499534676373,
"f1": 0.47988779362416334,
"f1_stderr": 0.004806379711128169,
"acc": 0.4499623916853611,
"acc_stderr": 0.010072884519008809
},
"harness|drop|3": {
"em": 0.4092911073825503,
"em_stderr": 0.005035499534676373,
"f1": 0.47988779362416334,
"f1_stderr": 0.004806379711128169
},
"harness|gsm8k|5": {
"acc": 0.11144806671721001,
"acc_stderr": 0.008668021353794433
},
"harness|winogrande|5": {
"acc": 0.7884767166535123,
"acc_stderr": 0.011477747684223187
}
}
```
### 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] | 7,283 | [
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HemanthKumarK/Skindata | 2023-10-17T07:23:54.000Z | [
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nekofura/project_tera | 2023-10-17T08:58:31.000Z | [
"region:us"
] | nekofura | null | null | 0 | 0 | 2023-10-17T07:28:03 | Entry not found | 15 | [
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autoevaluate/autoeval-eval-acronym_identification-default-9578d4-95645146430 | 2023-10-17T07:31:02.000Z | [
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ZahyBnaya/zahy_repo | 2023-10-17T07:39:07.000Z | [
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nekofura/cutter | 2023-10-17T08:25:03.000Z | [
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open-llm-leaderboard/details_Aspik101__30B-Lazarus-instruct-PL-lora_unload | 2023-10-17T08:13:36.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T08:13:28 | ---
pretty_name: Evaluation run of Aspik101/30B-Lazarus-instruct-PL-lora_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aspik101/30B-Lazarus-instruct-PL-lora_unload](https://huggingface.co/Aspik101/30B-Lazarus-instruct-PL-lora_unload)\
\ 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_Aspik101__30B-Lazarus-instruct-PL-lora_unload\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T08:13:24.195120](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__30B-Lazarus-instruct-PL-lora_unload/blob/main/results_2023-10-17T08-13-24.195120.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\": 0.01164010067114094,\n\
\ \"em_stderr\": 0.0010984380734032925,\n \"f1\": 0.07800545302013438,\n\
\ \"f1_stderr\": 0.0017935902090569574,\n \"acc\": 0.4522835158298991,\n\
\ \"acc_stderr\": 0.010087630088457804\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01164010067114094,\n \"em_stderr\": 0.0010984380734032925,\n\
\ \"f1\": 0.07800545302013438,\n \"f1_stderr\": 0.0017935902090569574\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11372251705837756,\n \
\ \"acc_stderr\": 0.008744810131034036\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7908445146014207,\n \"acc_stderr\": 0.01143045004588157\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aspik101/30B-Lazarus-instruct-PL-lora_unload
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_10_17T08_13_24.195120
path:
- '**/details_harness|drop|3_2023-10-17T08-13-24.195120.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T08-13-24.195120.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T08_13_24.195120
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-13-24.195120.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-13-24.195120.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T08_13_24.195120
path:
- '**/details_harness|winogrande|5_2023-10-17T08-13-24.195120.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T08-13-24.195120.parquet'
- config_name: results
data_files:
- split: 2023_10_17T08_13_24.195120
path:
- results_2023-10-17T08-13-24.195120.parquet
- split: latest
path:
- results_2023-10-17T08-13-24.195120.parquet
---
# Dataset Card for Evaluation run of Aspik101/30B-Lazarus-instruct-PL-lora_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aspik101/30B-Lazarus-instruct-PL-lora_unload
- **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 [Aspik101/30B-Lazarus-instruct-PL-lora_unload](https://huggingface.co/Aspik101/30B-Lazarus-instruct-PL-lora_unload) 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_Aspik101__30B-Lazarus-instruct-PL-lora_unload",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T08:13:24.195120](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__30B-Lazarus-instruct-PL-lora_unload/blob/main/results_2023-10-17T08-13-24.195120.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": 0.01164010067114094,
"em_stderr": 0.0010984380734032925,
"f1": 0.07800545302013438,
"f1_stderr": 0.0017935902090569574,
"acc": 0.4522835158298991,
"acc_stderr": 0.010087630088457804
},
"harness|drop|3": {
"em": 0.01164010067114094,
"em_stderr": 0.0010984380734032925,
"f1": 0.07800545302013438,
"f1_stderr": 0.0017935902090569574
},
"harness|gsm8k|5": {
"acc": 0.11372251705837756,
"acc_stderr": 0.008744810131034036
},
"harness|winogrande|5": {
"acc": 0.7908445146014207,
"acc_stderr": 0.01143045004588157
}
}
```
### 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] | 7,437 | [
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LeonGuertler/ccc-custom-coco | 2023-10-17T08:16:57.000Z | [
"region:us"
] | LeonGuertler | null | null | 0 | 0 | 2023-10-17T08:15:26 | Entry not found | 15 | [
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open-llm-leaderboard/details_teknium__OpenHermes-2-Mistral-7B | 2023-10-17T08:22:47.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T08:22:35 | ---
pretty_name: Evaluation run of teknium/OpenHermes-2-Mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [teknium/OpenHermes-2-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2-Mistral-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_teknium__OpenHermes-2-Mistral-7B_public\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-10-17T08:19:50.329623](https://huggingface.co/datasets/open-llm-leaderboard/details_teknium__OpenHermes-2-Mistral-7B_public/blob/main/results_2023-10-17T08-19-50.329623.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\": 0.6340923864588642,\n\
\ \"acc_stderr\": 0.03292343427112481,\n \"acc_norm\": 0.6379910781883433,\n\
\ \"acc_norm_stderr\": 0.03290093486621529,\n \"mc1\": 0.3329253365973072,\n\
\ \"mc1_stderr\": 0.016497402382012052,\n \"mc2\": 0.5024236235238323,\n\
\ \"mc2_stderr\": 0.015034918880371569\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6006825938566553,\n \"acc_stderr\": 0.014312094557946716,\n\
\ \"acc_norm\": 0.6305460750853242,\n \"acc_norm_stderr\": 0.014104578366491887\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6379207329217288,\n\
\ \"acc_stderr\": 0.004796193584930074,\n \"acc_norm\": 0.8380800637323242,\n\
\ \"acc_norm_stderr\": 0.0036762448867232607\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n\
\ \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.028815615713432115,\n\
\ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.028815615713432115\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\"\
: {\n \"acc\": 0.6011560693641619,\n \"acc_stderr\": 0.037336266553835096,\n\
\ \"acc_norm\": 0.6011560693641619,\n \"acc_norm_stderr\": 0.037336266553835096\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n\
\ \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n\
\ \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\
\ 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n \"\
acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\
\ \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n\
\ \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055266,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055266\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7516129032258064,\n\
\ \"acc_stderr\": 0.02458002892148101,\n \"acc_norm\": 0.7516129032258064,\n\
\ \"acc_norm_stderr\": 0.02458002892148101\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.02486499515976775,\n \
\ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.02486499515976775\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606648,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606648\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.03149930577784906,\n \
\ \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.03149930577784906\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8348623853211009,\n \"acc_stderr\": 0.01591955782997604,\n \"\
acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.01591955782997604\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8235294117647058,\n\
\ \"acc_stderr\": 0.026756401538078962,\n \"acc_norm\": 0.8235294117647058,\n\
\ \"acc_norm_stderr\": 0.026756401538078962\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n\
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\
\ \"acc_stderr\": 0.030769352008229146,\n \"acc_norm\": 0.6995515695067265,\n\
\ \"acc_norm_stderr\": 0.030769352008229146\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8326947637292464,\n\
\ \"acc_stderr\": 0.013347327202920332,\n \"acc_norm\": 0.8326947637292464,\n\
\ \"acc_norm_stderr\": 0.013347327202920332\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508297,\n\
\ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508297\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3642458100558659,\n\
\ \"acc_stderr\": 0.016094338768474596,\n \"acc_norm\": 0.3642458100558659,\n\
\ \"acc_norm_stderr\": 0.016094338768474596\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.02505850331695814,\n\
\ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.02505850331695814\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n\
\ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\
: {\n \"acc\": 0.46870925684485004,\n \"acc_stderr\": 0.01274520462608314,\n\
\ \"acc_norm\": 0.46870925684485004,\n \"acc_norm_stderr\": 0.01274520462608314\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681393,\n \"\
acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681393\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6617647058823529,\n \"acc_stderr\": 0.01913994374848703,\n \
\ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.01913994374848703\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3329253365973072,\n\
\ \"mc1_stderr\": 0.016497402382012052,\n \"mc2\": 0.5024236235238323,\n\
\ \"mc2_stderr\": 0.015034918880371569\n }\n}\n```"
repo_url: https://huggingface.co/teknium/OpenHermes-2-Mistral-7B
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_10_17T08_19_50.329623
path:
- '**/details_harness|arc:challenge|25_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hellaswag|10_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-17T08-19-50.329623.parquet'
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- '**/details_harness|hendrycksTest-college_biology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-17T08-19-50.329623.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-17T08-19-50.329623.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-17T08-19-50.329623.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-17T08-19-50.329623.parquet'
- config_name: results
data_files:
- split: 2023_10_17T08_19_50.329623
path:
- results_2023-10-17T08-19-50.329623.parquet
- split: latest
path:
- results_2023-10-17T08-19-50.329623.parquet
---
# Dataset Card for Evaluation run of teknium/OpenHermes-2-Mistral-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/teknium/OpenHermes-2-Mistral-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 [teknium/OpenHermes-2-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_teknium__OpenHermes-2-Mistral-7B_public",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T08:19:50.329623](https://huggingface.co/datasets/open-llm-leaderboard/details_teknium__OpenHermes-2-Mistral-7B_public/blob/main/results_2023-10-17T08-19-50.329623.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": 0.6340923864588642,
"acc_stderr": 0.03292343427112481,
"acc_norm": 0.6379910781883433,
"acc_norm_stderr": 0.03290093486621529,
"mc1": 0.3329253365973072,
"mc1_stderr": 0.016497402382012052,
"mc2": 0.5024236235238323,
"mc2_stderr": 0.015034918880371569
},
"harness|arc:challenge|25": {
"acc": 0.6006825938566553,
"acc_stderr": 0.014312094557946716,
"acc_norm": 0.6305460750853242,
"acc_norm_stderr": 0.014104578366491887
},
"harness|hellaswag|10": {
"acc": 0.6379207329217288,
"acc_stderr": 0.004796193584930074,
"acc_norm": 0.8380800637323242,
"acc_norm_stderr": 0.0036762448867232607
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7171052631578947,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.7171052631578947,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"acc_stderr": 0.028815615713432115,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.028815615713432115
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.0358687928008034,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.0358687928008034
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6011560693641619,
"acc_stderr": 0.037336266553835096,
"acc_norm": 0.6011560693641619,
"acc_norm_stderr": 0.037336266553835096
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.03243618636108101,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108101
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.04697085136647863,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.04697085136647863
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.025355741263055266,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.025355741263055266
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.0442626668137991,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.0442626668137991
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_biology|5": {
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}
```
### 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] | 64,888 | [
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open-llm-leaderboard/details_uukuguy__speechless-hermes-coig-lite-13b | 2023-10-18T15:02:00.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T08:26:05 | ---
pretty_name: Evaluation run of uukuguy/speechless-hermes-coig-lite-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-hermes-coig-lite-13b](https://huggingface.co/uukuguy/speechless-hermes-coig-lite-13b)\
\ 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_uukuguy__speechless-hermes-coig-lite-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T15:01:47.854586](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-hermes-coig-lite-13b/blob/main/results_2023-10-18T15-01-47.854586.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\": 0.3490981543624161,\n\
\ \"em_stderr\": 0.004881701038810246,\n \"f1\": 0.39497588087248336,\n\
\ \"f1_stderr\": 0.004768097534076323,\n \"acc\": 0.44193958375344744,\n\
\ \"acc_stderr\": 0.009875116542645869\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3490981543624161,\n \"em_stderr\": 0.004881701038810246,\n\
\ \"f1\": 0.39497588087248336,\n \"f1_stderr\": 0.004768097534076323\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09855951478392722,\n \
\ \"acc_stderr\": 0.008210320350946338\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7853196527229677,\n \"acc_stderr\": 0.011539912734345398\n\
\ }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-hermes-coig-lite-13b
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_10_17T08_26_01.591650
path:
- '**/details_harness|drop|3_2023-10-17T08-26-01.591650.parquet'
- split: 2023_10_18T15_01_47.854586
path:
- '**/details_harness|drop|3_2023-10-18T15-01-47.854586.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T15-01-47.854586.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T08_26_01.591650
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-26-01.591650.parquet'
- split: 2023_10_18T15_01_47.854586
path:
- '**/details_harness|gsm8k|5_2023-10-18T15-01-47.854586.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T15-01-47.854586.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T08_26_01.591650
path:
- '**/details_harness|winogrande|5_2023-10-17T08-26-01.591650.parquet'
- split: 2023_10_18T15_01_47.854586
path:
- '**/details_harness|winogrande|5_2023-10-18T15-01-47.854586.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T15-01-47.854586.parquet'
- config_name: results
data_files:
- split: 2023_10_17T08_26_01.591650
path:
- results_2023-10-17T08-26-01.591650.parquet
- split: 2023_10_18T15_01_47.854586
path:
- results_2023-10-18T15-01-47.854586.parquet
- split: latest
path:
- results_2023-10-18T15-01-47.854586.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-hermes-coig-lite-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-hermes-coig-lite-13b
- **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 [uukuguy/speechless-hermes-coig-lite-13b](https://huggingface.co/uukuguy/speechless-hermes-coig-lite-13b) 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_uukuguy__speechless-hermes-coig-lite-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T15:01:47.854586](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-hermes-coig-lite-13b/blob/main/results_2023-10-18T15-01-47.854586.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": 0.3490981543624161,
"em_stderr": 0.004881701038810246,
"f1": 0.39497588087248336,
"f1_stderr": 0.004768097534076323,
"acc": 0.44193958375344744,
"acc_stderr": 0.009875116542645869
},
"harness|drop|3": {
"em": 0.3490981543624161,
"em_stderr": 0.004881701038810246,
"f1": 0.39497588087248336,
"f1_stderr": 0.004768097534076323
},
"harness|gsm8k|5": {
"acc": 0.09855951478392722,
"acc_stderr": 0.008210320350946338
},
"harness|winogrande|5": {
"acc": 0.7853196527229677,
"acc_stderr": 0.011539912734345398
}
}
```
### 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] | 7,824 | [
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Hoga2/Ukiyoe | 2023-10-17T08:35:59.000Z | [
"region:us"
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Back-up/test-QA | 2023-10-17T08:51:00.000Z | [
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miulab/SalesBot2.0 | 2023-10-17T09:11:04.000Z | [
"region:us"
] | miulab | null | null | 0 | 0 | 2023-10-17T08:55:41 | Entry not found | 15 | [
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open-llm-leaderboard/details_quantumaikr__llama-2-70b-fb16-korean | 2023-10-17T08:56:36.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T08:56:28 | ---
pretty_name: Evaluation run of quantumaikr/llama-2-70b-fb16-korean
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [quantumaikr/llama-2-70b-fb16-korean](https://huggingface.co/quantumaikr/llama-2-70b-fb16-korean)\
\ 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_quantumaikr__llama-2-70b-fb16-korean\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T08:56:24.573395](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__llama-2-70b-fb16-korean/blob/main/results_2023-10-17T08-56-24.573395.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\": 0.0041946308724832215,\n\
\ \"em_stderr\": 0.0006618716168266237,\n \"f1\": 0.07418729026845645,\n\
\ \"f1_stderr\": 0.0015820737575191846,\n \"acc\": 0.5583664886878857,\n\
\ \"acc_stderr\": 0.011574854481074981\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0041946308724832215,\n \"em_stderr\": 0.0006618716168266237,\n\
\ \"f1\": 0.07418729026845645,\n \"f1_stderr\": 0.0015820737575191846\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.29037149355572406,\n \
\ \"acc_stderr\": 0.012503592481818962\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8263614838200474,\n \"acc_stderr\": 0.010646116480331\n\
\ }\n}\n```"
repo_url: https://huggingface.co/quantumaikr/llama-2-70b-fb16-korean
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_10_17T08_56_24.573395
path:
- '**/details_harness|drop|3_2023-10-17T08-56-24.573395.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T08-56-24.573395.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T08_56_24.573395
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-56-24.573395.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-56-24.573395.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T08_56_24.573395
path:
- '**/details_harness|winogrande|5_2023-10-17T08-56-24.573395.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T08-56-24.573395.parquet'
- config_name: results
data_files:
- split: 2023_10_17T08_56_24.573395
path:
- results_2023-10-17T08-56-24.573395.parquet
- split: latest
path:
- results_2023-10-17T08-56-24.573395.parquet
---
# Dataset Card for Evaluation run of quantumaikr/llama-2-70b-fb16-korean
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/quantumaikr/llama-2-70b-fb16-korean
- **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 [quantumaikr/llama-2-70b-fb16-korean](https://huggingface.co/quantumaikr/llama-2-70b-fb16-korean) 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_quantumaikr__llama-2-70b-fb16-korean",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T08:56:24.573395](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__llama-2-70b-fb16-korean/blob/main/results_2023-10-17T08-56-24.573395.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": 0.0041946308724832215,
"em_stderr": 0.0006618716168266237,
"f1": 0.07418729026845645,
"f1_stderr": 0.0015820737575191846,
"acc": 0.5583664886878857,
"acc_stderr": 0.011574854481074981
},
"harness|drop|3": {
"em": 0.0041946308724832215,
"em_stderr": 0.0006618716168266237,
"f1": 0.07418729026845645,
"f1_stderr": 0.0015820737575191846
},
"harness|gsm8k|5": {
"acc": 0.29037149355572406,
"acc_stderr": 0.012503592481818962
},
"harness|winogrande|5": {
"acc": 0.8263614838200474,
"acc_stderr": 0.010646116480331
}
}
```
### 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] | 7,333 | [
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-0.022064208984375,
-0.051605224609375,
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-0.0199737548828125,
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0.031036376953125,
0.03662109375,
-0.04388427734375,
-0.06561279296875,
-0.045562744140625,
0... |
kroshan/kroshan2323 | 2023-10-17T09:11:27.000Z | [
"region:us"
] | kroshan | null | null | 0 | 0 | 2023-10-17T09:10:48 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01494598388671875,
0.057159423828125,
0.02880859375,
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0.0170135498046875,
-0.05206298828125,
-0.01494598388671875,
-0.06036376953125,
0.03... |
sengunsipahi/civit_10k_1line | 2023-10-17T09:35:40.000Z | [
"region:us"
] | sengunsipahi | null | null | 0 | 0 | 2023-10-17T09:35:11 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01494598388671875,
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0.02880859375,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005077362060546875,
0.051361083984375,
0.0170135498046875,
-0.05206298828125,
-0.01494598388671875,
-0.06036376953125,
0.03... |
open-llm-leaderboard/details_chargoddard__platypus-2-22b-relora | 2023-10-17T09:49:05.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T09:48:57 | ---
pretty_name: Evaluation run of chargoddard/platypus-2-22b-relora
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/platypus-2-22b-relora](https://huggingface.co/chargoddard/platypus-2-22b-relora)\
\ 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_chargoddard__platypus-2-22b-relora\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T09:48:53.081759](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__platypus-2-22b-relora/blob/main/results_2023-10-17T09-48-53.081759.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\": 0.38443791946308725,\n\
\ \"em_stderr\": 0.004981827548218364,\n \"f1\": 0.42459836409396046,\n\
\ \"f1_stderr\": 0.004867799120548586,\n \"acc\": 0.4197198614386422,\n\
\ \"acc_stderr\": 0.009300550123366277\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.38443791946308725,\n \"em_stderr\": 0.004981827548218364,\n\
\ \"f1\": 0.42459836409396046,\n \"f1_stderr\": 0.004867799120548586\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06595905989385899,\n \
\ \"acc_stderr\": 0.006836951192034225\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698329\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/platypus-2-22b-relora
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_10_17T09_48_53.081759
path:
- '**/details_harness|drop|3_2023-10-17T09-48-53.081759.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T09-48-53.081759.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T09_48_53.081759
path:
- '**/details_harness|gsm8k|5_2023-10-17T09-48-53.081759.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T09-48-53.081759.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T09_48_53.081759
path:
- '**/details_harness|winogrande|5_2023-10-17T09-48-53.081759.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T09-48-53.081759.parquet'
- config_name: results
data_files:
- split: 2023_10_17T09_48_53.081759
path:
- results_2023-10-17T09-48-53.081759.parquet
- split: latest
path:
- results_2023-10-17T09-48-53.081759.parquet
---
# Dataset Card for Evaluation run of chargoddard/platypus-2-22b-relora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/platypus-2-22b-relora
- **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 [chargoddard/platypus-2-22b-relora](https://huggingface.co/chargoddard/platypus-2-22b-relora) 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_chargoddard__platypus-2-22b-relora",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T09:48:53.081759](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__platypus-2-22b-relora/blob/main/results_2023-10-17T09-48-53.081759.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": 0.38443791946308725,
"em_stderr": 0.004981827548218364,
"f1": 0.42459836409396046,
"f1_stderr": 0.004867799120548586,
"acc": 0.4197198614386422,
"acc_stderr": 0.009300550123366277
},
"harness|drop|3": {
"em": 0.38443791946308725,
"em_stderr": 0.004981827548218364,
"f1": 0.42459836409396046,
"f1_stderr": 0.004867799120548586
},
"harness|gsm8k|5": {
"acc": 0.06595905989385899,
"acc_stderr": 0.006836951192034225
},
"harness|winogrande|5": {
"acc": 0.7734806629834254,
"acc_stderr": 0.011764149054698329
}
}
```
### 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] | 7,299 | [
[
-0.025238037109375,
-0.048553466796875,
0.0160369873046875,
0.016876220703125,
-0.0161590576171875,
0.0125732421875,
-0.0286712646484375,
-0.011199951171875,
0.0338134765625,
0.0423583984375,
-0.05316162109375,
-0.0673828125,
-0.047332763671875,
0.0121536254... |
Qdrant/arxiv-abstracts-instructorxl-embeddings | 2023-10-19T12:58:16.000Z | [
"task_categories:sentence-similarity",
"task_categories:feature-extraction",
"size_categories:1M<n<10M",
"language:en",
"region:us"
] | Qdrant | null | null | 0 | 0 | 2023-10-17T10:21:18 | ---
language:
- en
pretty_name: InstructorXL embeddings of the Arxiv.org abstracts
task_categories:
- sentence-similarity
- feature-extraction
size_categories:
- 1M<n<10M
---
# arxiv-abstracts-instructorxl-embeddings
This dataset contains 768-dimensional embeddings generated from the [arxiv](https://arxiv.org/)
paper abstracts using [InstructorXL](https://huggingface.co/hkunlp/instructor-xl) model. Each
vector has an abstract used to create it, along with the DOI (Digital Object Identifier). The
dataset was created using precomputed embeddings exposed by the [Alexandria Index](https://alex.macrocosm.so/download).
## Generation process
The embeddings have been generated using the following instruction:
```text
Represent the Research Paper abstract for retrieval; Input:
```
The following code snippet shows how to generate embeddings using the InstructorXL model:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-xl')
sentence = "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train."
instruction = "Represent the Research Paper abstract for retrieval; Input:"
embeddings = model.encode([[instruction, sentence]])
``` | 1,688 | [
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-0.03363037109375,
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0.038330078125,
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-0.01145172119140625,
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0.0091705322265625,
0.0310821533203125,
-0.00418853759765625,
-0.0560302734375,
-0.062042236328... |
open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b | 2023-10-17T10:27:46.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-17T10:27:37 | ---
pretty_name: Evaluation run of chargoddard/Chronorctypus-Limarobormes-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/Chronorctypus-Limarobormes-13b](https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b)\
\ 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_chargoddard__Chronorctypus-Limarobormes-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T10:27:33.460587](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b/blob/main/results_2023-10-17T10-27-33.460587.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\": 0.05169882550335571,\n\
\ \"em_stderr\": 0.0022675304823078276,\n \"f1\": 0.17888317953020105,\n\
\ \"f1_stderr\": 0.0028882183973903902,\n \"acc\": 0.39147173871286817,\n\
\ \"acc_stderr\": 0.008785918503769254\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.05169882550335571,\n \"em_stderr\": 0.0022675304823078276,\n\
\ \"f1\": 0.17888317953020105,\n \"f1_stderr\": 0.0028882183973903902\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.03866565579984837,\n \
\ \"acc_stderr\": 0.005310583162098035\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.744277821625888,\n \"acc_stderr\": 0.012261253845440473\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b
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_10_17T10_27_33.460587
path:
- '**/details_harness|drop|3_2023-10-17T10-27-33.460587.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T10-27-33.460587.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T10_27_33.460587
path:
- '**/details_harness|gsm8k|5_2023-10-17T10-27-33.460587.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T10-27-33.460587.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T10_27_33.460587
path:
- '**/details_harness|winogrande|5_2023-10-17T10-27-33.460587.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T10-27-33.460587.parquet'
- config_name: results
data_files:
- split: 2023_10_17T10_27_33.460587
path:
- results_2023-10-17T10-27-33.460587.parquet
- split: latest
path:
- results_2023-10-17T10-27-33.460587.parquet
---
# Dataset Card for Evaluation run of chargoddard/Chronorctypus-Limarobormes-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b
- **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 [chargoddard/Chronorctypus-Limarobormes-13b](https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b) 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_chargoddard__Chronorctypus-Limarobormes-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T10:27:33.460587](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b/blob/main/results_2023-10-17T10-27-33.460587.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": 0.05169882550335571,
"em_stderr": 0.0022675304823078276,
"f1": 0.17888317953020105,
"f1_stderr": 0.0028882183973903902,
"acc": 0.39147173871286817,
"acc_stderr": 0.008785918503769254
},
"harness|drop|3": {
"em": 0.05169882550335571,
"em_stderr": 0.0022675304823078276,
"f1": 0.17888317953020105,
"f1_stderr": 0.0028882183973903902
},
"harness|gsm8k|5": {
"acc": 0.03866565579984837,
"acc_stderr": 0.005310583162098035
},
"harness|winogrande|5": {
"acc": 0.744277821625888,
"acc_stderr": 0.012261253845440473
}
}
```
### 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] | 7,415 | [
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0.01... |
erhwenkuo/pretrain-chinese-zhtw | 2023-10-18T07:23:28.000Z | [
"region:us"
] | erhwenkuo | null | null | 0 | 0 | 2023-10-17T10:49:45 | ---
dataset_info:
features:
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dtype: string
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num_examples: 416105
download_size: 419861369
dataset_size: 815848804
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "pretrain-chinese-zhtw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 637 | [
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Agisight/tyvan-russian-parallel-50k | 2023-10-17T13:40:34.000Z | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:ru",
"language:tyv",
"license:cc-by-sa-4.0",
"region:us"
] | Agisight | null | null | 1 | 0 | 2023-10-17T10:56:36 | ---
license: cc-by-sa-4.0
task_categories:
- translation
language:
- ru
- tyv
size_categories:
- 10K<n<100K
---
A 50K sample from the Russian-Tyvan parallel corpus collected at https://tyvan.ru.
| 196 | [
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autoevaluate/autoeval-eval-aslg_pc12-default-864bef-95687146442 | 2023-10-17T11:25:17.000Z | [
"autotrain",
"evaluation",
"region:us"
] | autoevaluate | null | null | 0 | 0 | 2023-10-17T11:21:05 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- aslg_pc12
eval_info:
task: translation
model: HamdanXI/t5_small_gloss_to_text_merged_dataset
metrics: ['bertscore']
dataset_name: aslg_pc12
dataset_config: default
dataset_split: train
col_mapping:
source: gloss
target: text
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: HamdanXI/t5_small_gloss_to_text_merged_dataset
* Dataset: aslg_pc12
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@HamdanXI](https://huggingface.co/HamdanXI) for evaluating this model. | 864 | [
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acharkq/PubChem324k | 2023-10-17T11:30:20.000Z | [
"region:us"
] | acharkq | null | null | 1 | 0 | 2023-10-17T11:25:14 | Entry not found | 15 | [
[
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0.0379... |
autoevaluate/autoeval-eval-aslg_pc12-default-6f4366-95699146446 | 2023-10-17T12:08:46.000Z | [
"autotrain",
"evaluation",
"region:us"
] | autoevaluate | null | null | 0 | 0 | 2023-10-17T12:04:32 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- aslg_pc12
eval_info:
task: translation
model: HamdanXI/t5_small_aslg_pc12
metrics: ['bertscore', 'comet']
dataset_name: aslg_pc12
dataset_config: default
dataset_split: train
col_mapping:
source: gloss
target: text
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: HamdanXI/t5_small_aslg_pc12
* Dataset: aslg_pc12
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@HamdanXI](https://huggingface.co/HamdanXI) for evaluating this model. | 835 | [
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jiiiM/RoCo-2 | 2023-10-17T12:28:41.000Z | [
"region:us"
] | jiiiM | null | null | 2 | 0 | 2023-10-17T12:14:40 | Entry not found | 15 | [
[
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0.0379... |
johannes-garstenauer/ENN_class_embeddings_dim_512 | 2023-10-17T12:36:34.000Z | [
"region:us"
] | johannes-garstenauer | null | null | 0 | 0 | 2023-10-17T12:36:00 | ---
dataset_info:
features:
- name: last_hs
sequence: float32
- name: label
dtype: int64
splits:
- name: train
num_bytes: 138580320
num_examples: 67272
download_size: 167196918
dataset_size: 138580320
---
# Dataset Card for "ENN_class_embeddings_dim_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 417 | [
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autoevaluate/autoeval-eval-aslg_pc12-default-007f32-95707146449 | 2023-10-17T12:41:11.000Z | [
"autotrain",
"evaluation",
"region:us"
] | autoevaluate | null | null | 0 | 0 | 2023-10-17T12:36:55 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- aslg_pc12
eval_info:
task: translation
model: HamdanXI/t5_small_gloss_merged_dataset_random_0.1
metrics: ['comet', 'bertscore']
dataset_name: aslg_pc12
dataset_config: default
dataset_split: train
col_mapping:
source: gloss
target: text
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: HamdanXI/t5_small_gloss_merged_dataset_random_0.1
* Dataset: aslg_pc12
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@HamdanXI](https://huggingface.co/HamdanXI) for evaluating this model. | 879 | [
[
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