datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
fadeke/clavis_studio_dataset_2 | fadeke | 2023-12-03T11:22:35Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T10:30:19Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 31077171.0
num_examples: 72
download_size: 30449595
dataset_size: 31077171.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
hajar817/CV_10_0_fa_pseudo_labelled_whisper_Lv_fa | hajar817 | 2023-12-03T18:04:41Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T10:45:32Z | ---
dataset_info:
config_name: fa
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 691716796.976
num_examples: 24672
- name: validation
num_bytes: 388112207.79
num_examples: 9937
- name: test
num_bytes: 423810901.777
num_examples: 9937
download_size: 1244669816
dataset_size: 1503639906.543
configs:
- config_name: fa
data_files:
- split: train
path: fa/train-*
- split: validation
path: fa/validation-*
- split: test
path: fa/test-*
---
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|
Deadsg/Batman_AI | Deadsg | 2023-12-03T15:16:00Z | 0 | 0 | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-12-03T10:49:11Z | ---
license: creativeml-openrail-m
---
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|
Udbhav44/qu-bit | Udbhav44 | 2023-12-03T20:58:52Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T11:19:26Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 410889
num_examples: 94
download_size: 100327
dataset_size: 410889
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
jahb57/gpt2_sentence_embeddings | jahb57 | 2023-12-03T11:54:42Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T11:52:24Z | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 3446878
num_examples: 20
download_size: 4068897
dataset_size: 3446878
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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]
|
jahb57/bert-base-uncased_sentence_embeddings | jahb57 | 2023-12-03T11:56:57Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T11:56:55Z | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence:
sequence: float32
- name: pooler_output
sequence:
sequence: float32
splits:
- name: train
num_bytes: 3508478
num_examples: 20
download_size: 4156709
dataset_size: 3508478
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
ahishamm/HAM_db_enhanced_balanced_reduced_50_20_20 | ahishamm | 2023-12-03T12:29:19Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T12:27:54Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': akiec
'1': bcc
'2': bkl
'3': df
'4': mel
'5': nv
'6': vasc
splits:
- name: train
num_bytes: 711274679.4
num_examples: 13895
- name: test
num_bytes: 154019730.828
num_examples: 2996
download_size: 1014069215
dataset_size: 865294410.2279999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
yongtae-jp/UltraChat_jp | yongtae-jp | 2023-12-03T12:56:54Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T12:56:54Z | ---
license: apache-2.0
---
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|
ibleducation/fordham-university | ibleducation | 2023-12-03T13:39:21Z | 0 | 0 | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
]
| [
"question-answering"
]
| 2023-12-03T13:10:50Z | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---
# ibleducation/fordham-university
This dataset contains a set of query and response pairs about Fordham university
Data for the dataset was scrapped from [fordham.edu](https://fordham.edu) using [GptCrawler](https://github.com/BuilderIO/gpt-crawler).
The resulting pages were then converted to query response pairs using GPT-3.5
A total of **2707** data points exist in this dataset. | [
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|
JinglesDados/IzabelLira | JinglesDados | 2023-12-03T14:58:04Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T14:57:30Z | ---
license: openrail
---
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Recruta/PedroAccioly | Recruta | 2023-12-03T15:02:44Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T15:01:52Z | ---
license: openrail
---
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|
KnutJaegersberg/trilobite | KnutJaegersberg | 2023-12-03T15:19:13Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
]
| null | 2023-12-03T15:17:44Z | ---
license: cc-by-nc-4.0
---
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|
open-llm-leaderboard/details_perlthoughts__Chupacabra-7B | open-llm-leaderboard | 2023-12-03T15:21:05Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T15:21:01Z | ---
pretty_name: Evaluation run of perlthoughts/Chupacabra-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [perlthoughts/Chupacabra-7B](https://huggingface.co/perlthoughts/Chupacabra-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_perlthoughts__Chupacabra-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T15:20:58.431709](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B/blob/main/results_2023-12-03T15-20-58.431709.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\": 0.621683093252464,\n\
\ \"acc_stderr\": 0.013358407831777112\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.621683093252464,\n \"acc_stderr\": 0.013358407831777112\n\
\ }\n}\n```"
repo_url: https://huggingface.co/perlthoughts/Chupacabra-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T15_20_58.431709
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-20-58.431709.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-20-58.431709.parquet'
- config_name: results
data_files:
- split: 2023_12_03T15_20_58.431709
path:
- results_2023-12-03T15-20-58.431709.parquet
- split: latest
path:
- results_2023-12-03T15-20-58.431709.parquet
---
# Dataset Card for Evaluation run of perlthoughts/Chupacabra-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/perlthoughts/Chupacabra-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [perlthoughts/Chupacabra-7B](https://huggingface.co/perlthoughts/Chupacabra-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_perlthoughts__Chupacabra-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T15:20:58.431709](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B/blob/main/results_2023-12-03T15-20-58.431709.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": 0.621683093252464,
"acc_stderr": 0.013358407831777112
},
"harness|gsm8k|5": {
"acc": 0.621683093252464,
"acc_stderr": 0.013358407831777112
}
}
```
### 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] | [
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|
hopee4/taina | hopee4 | 2023-12-03T15:23:57Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T15:23:32Z | ---
license: openrail
---
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|
open-llm-leaderboard/details_qblocks__mistral_7b_norobots | open-llm-leaderboard | 2023-12-03T15:25:20Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T15:25:16Z | ---
pretty_name: Evaluation run of qblocks/mistral_7b_norobots
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [qblocks/mistral_7b_norobots](https://huggingface.co/qblocks/mistral_7b_norobots)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_qblocks__mistral_7b_norobots\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T15:25:13.654024](https://huggingface.co/datasets/open-llm-leaderboard/details_qblocks__mistral_7b_norobots/blob/main/results_2023-12-03T15-25-13.654024.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\": 0.38362395754359363,\n\
\ \"acc_stderr\": 0.013394238584938165\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.38362395754359363,\n \"acc_stderr\": 0.013394238584938165\n\
\ }\n}\n```"
repo_url: https://huggingface.co/qblocks/mistral_7b_norobots
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T15_25_13.654024
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-25-13.654024.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-25-13.654024.parquet'
- config_name: results
data_files:
- split: 2023_12_03T15_25_13.654024
path:
- results_2023-12-03T15-25-13.654024.parquet
- split: latest
path:
- results_2023-12-03T15-25-13.654024.parquet
---
# Dataset Card for Evaluation run of qblocks/mistral_7b_norobots
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/qblocks/mistral_7b_norobots
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [qblocks/mistral_7b_norobots](https://huggingface.co/qblocks/mistral_7b_norobots) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_qblocks__mistral_7b_norobots",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T15:25:13.654024](https://huggingface.co/datasets/open-llm-leaderboard/details_qblocks__mistral_7b_norobots/blob/main/results_2023-12-03T15-25-13.654024.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": 0.38362395754359363,
"acc_stderr": 0.013394238584938165
},
"harness|gsm8k|5": {
"acc": 0.38362395754359363,
"acc_stderr": 0.013394238584938165
}
}
```
### 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] | [
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|
hopee4/tainacosta | hopee4 | 2023-12-03T15:27:40Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T15:25:21Z | ---
license: openrail
---
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|
breno30/WaldoMoraes | breno30 | 2023-12-03T15:33:23Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T15:32:55Z | ---
license: openrail
---
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|
malaysia-ai/mosaic-embedding-pairs | malaysia-ai | 2023-12-03T15:47:00Z | 0 | 0 | [
"language:ms",
"region:us"
]
| null | 2023-12-03T15:36:49Z | ---
language:
- ms
---
# Mosaic format for embedding task text pair dataset
This repository is to store dataset shards using mosaic format.
1. prepared at https://github.com/mesolitica/llama2-embedding/blob/main/notebooks/combine-embedding.ipynb
## how-to
1. git clone,
```bash
git lfs clone https://huggingface.co/datasets/malaysia-ai/mosaic-embedding-pairs
```
2. load it,
```python
from streaming import LocalDataset
from streaming.base.format.mds.encodings import Encoding, _encodings
import json
class ListStr(Encoding):
def encode(self, obj):
return json.dumps(obj).encode()
def decode(self, data):
return json.loads(data)
_encodings['liststr'] = ListStr
dataset = LocalDataset('mosaic-embedding-pairs')
len(dataset)
``` | [
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|
openerotica/lyric-analysis | openerotica | 2023-12-03T16:33:22Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T16:11:53Z | ---
license: apache-2.0
---
This dataset was an attempt to reverse engineer song lyrics into training data using GPT-turbo. The datset was supposed to be much bigger, but I sufferd a catastrophic crash during the processing and was only able to recover a small portion. This is what I was able to salvage, and it still definitely needs some post processing. You might be better off just stating over from scratch, but I didn't want to throw this away if somebody can salvage it for something. | [
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|
michaelyhuang23/autodiagram | michaelyhuang23 | 2023-12-03T16:56:50Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-03T16:21:11Z | ---
license: mit
dataset_info:
features:
- name: images
dtype: image
- name: tex
dtype: string
splits:
- name: train
num_bytes: 1698749121.0
num_examples: 24000
- name: validation
num_bytes: 419016962.0
num_examples: 6000
download_size: 1577024344
dataset_size: 2117766083.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
open-llm-leaderboard/details_amazingvince__zephyr-smol_llama-100m-dpo-full | open-llm-leaderboard | 2023-12-03T16:25:58Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T16:25:53Z | ---
pretty_name: Evaluation run of amazingvince/zephyr-smol_llama-100m-dpo-full
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [amazingvince/zephyr-smol_llama-100m-dpo-full](https://huggingface.co/amazingvince/zephyr-smol_llama-100m-dpo-full)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_amazingvince__zephyr-smol_llama-100m-dpo-full\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T16:25:51.768387](https://huggingface.co/datasets/open-llm-leaderboard/details_amazingvince__zephyr-smol_llama-100m-dpo-full/blob/main/results_2023-12-03T16-25-51.768387.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\": 0.006823351023502654,\n\
\ \"acc_stderr\": 0.002267537102254512\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.006823351023502654,\n \"acc_stderr\": 0.002267537102254512\n\
\ }\n}\n```"
repo_url: https://huggingface.co/amazingvince/zephyr-smol_llama-100m-dpo-full
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T16_25_51.768387
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-25-51.768387.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-25-51.768387.parquet'
- config_name: results
data_files:
- split: 2023_12_03T16_25_51.768387
path:
- results_2023-12-03T16-25-51.768387.parquet
- split: latest
path:
- results_2023-12-03T16-25-51.768387.parquet
---
# Dataset Card for Evaluation run of amazingvince/zephyr-smol_llama-100m-dpo-full
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/amazingvince/zephyr-smol_llama-100m-dpo-full
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [amazingvince/zephyr-smol_llama-100m-dpo-full](https://huggingface.co/amazingvince/zephyr-smol_llama-100m-dpo-full) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_amazingvince__zephyr-smol_llama-100m-dpo-full",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T16:25:51.768387](https://huggingface.co/datasets/open-llm-leaderboard/details_amazingvince__zephyr-smol_llama-100m-dpo-full/blob/main/results_2023-12-03T16-25-51.768387.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": 0.006823351023502654,
"acc_stderr": 0.002267537102254512
},
"harness|gsm8k|5": {
"acc": 0.006823351023502654,
"acc_stderr": 0.002267537102254512
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_allenai__digital-socrates-13b | open-llm-leaderboard | 2023-12-03T16:32:11Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T16:32:06Z | ---
pretty_name: Evaluation run of allenai/digital-socrates-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [allenai/digital-socrates-13b](https://huggingface.co/allenai/digital-socrates-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_allenai__digital-socrates-13b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T16:32:03.154791](https://huggingface.co/datasets/open-llm-leaderboard/details_allenai__digital-socrates-13b/blob/main/results_2023-12-03T16-32-03.154791.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\": 0.29492039423805916,\n\
\ \"acc_stderr\": 0.012560698010954769\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.29492039423805916,\n \"acc_stderr\": 0.012560698010954769\n\
\ }\n}\n```"
repo_url: https://huggingface.co/allenai/digital-socrates-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T16_32_03.154791
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-32-03.154791.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-32-03.154791.parquet'
- config_name: results
data_files:
- split: 2023_12_03T16_32_03.154791
path:
- results_2023-12-03T16-32-03.154791.parquet
- split: latest
path:
- results_2023-12-03T16-32-03.154791.parquet
---
# Dataset Card for Evaluation run of allenai/digital-socrates-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/allenai/digital-socrates-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [allenai/digital-socrates-13b](https://huggingface.co/allenai/digital-socrates-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_allenai__digital-socrates-13b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T16:32:03.154791](https://huggingface.co/datasets/open-llm-leaderboard/details_allenai__digital-socrates-13b/blob/main/results_2023-12-03T16-32-03.154791.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": 0.29492039423805916,
"acc_stderr": 0.012560698010954769
},
"harness|gsm8k|5": {
"acc": 0.29492039423805916,
"acc_stderr": 0.012560698010954769
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_allenai__digital-socrates-7b | open-llm-leaderboard | 2023-12-03T16:35:24Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T16:35:20Z | ---
pretty_name: Evaluation run of allenai/digital-socrates-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [allenai/digital-socrates-7b](https://huggingface.co/allenai/digital-socrates-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_allenai__digital-socrates-7b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T16:35:17.781750](https://huggingface.co/datasets/open-llm-leaderboard/details_allenai__digital-socrates-7b/blob/main/results_2023-12-03T16-35-17.781750.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\": 0.17892342683851403,\n\
\ \"acc_stderr\": 0.010557661392901293\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.17892342683851403,\n \"acc_stderr\": 0.010557661392901293\n\
\ }\n}\n```"
repo_url: https://huggingface.co/allenai/digital-socrates-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T16_35_17.781750
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-35-17.781750.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-35-17.781750.parquet'
- config_name: results
data_files:
- split: 2023_12_03T16_35_17.781750
path:
- results_2023-12-03T16-35-17.781750.parquet
- split: latest
path:
- results_2023-12-03T16-35-17.781750.parquet
---
# Dataset Card for Evaluation run of allenai/digital-socrates-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/allenai/digital-socrates-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [allenai/digital-socrates-7b](https://huggingface.co/allenai/digital-socrates-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_allenai__digital-socrates-7b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T16:35:17.781750](https://huggingface.co/datasets/open-llm-leaderboard/details_allenai__digital-socrates-7b/blob/main/results_2023-12-03T16-35-17.781750.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": 0.17892342683851403,
"acc_stderr": 0.010557661392901293
},
"harness|gsm8k|5": {
"acc": 0.17892342683851403,
"acc_stderr": 0.010557661392901293
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_berkeley-nest__Starling-LM-7B-alpha | open-llm-leaderboard | 2023-12-04T16:01:08Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T16:39:22Z | ---
pretty_name: Evaluation run of berkeley-nest/Starling-LM-7B-alpha
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_berkeley-nest__Starling-LM-7B-alpha\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-04T15:57:32.180703](https://huggingface.co/datasets/open-llm-leaderboard/details_berkeley-nest__Starling-LM-7B-alpha/blob/main/results_2023-12-04T15-57-32.180703.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\": 0.6484620563320449,\n\
\ \"acc_stderr\": 0.03189811561189118,\n \"acc_norm\": 0.6499663332374793,\n\
\ \"acc_norm_stderr\": 0.03254094389407915,\n \"mc1\": 0.3047735618115055,\n\
\ \"mc1_stderr\": 0.01611412415688245,\n \"mc2\": 0.463936332301049,\n\
\ \"mc2_stderr\": 0.015153266555511496\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5998293515358362,\n \"acc_stderr\": 0.014317197787809172,\n\
\ \"acc_norm\": 0.6382252559726962,\n \"acc_norm_stderr\": 0.01404195794503808\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.665803624775941,\n\
\ \"acc_stderr\": 0.004707447244200621,\n \"acc_norm\": 0.8490340569607648,\n\
\ \"acc_norm_stderr\": 0.0035728399695219874\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\
\ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.035146974678623884,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.035146974678623884\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.032500536843658404,\n\
\ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.032500536843658404\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894444,\n \"\
acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894444\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n\
\ \"acc_stderr\": 0.023157879349083525,\n \"acc_norm\": 0.7903225806451613,\n\
\ \"acc_norm_stderr\": 0.023157879349083525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n\
\ \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.028606204289229865,\n \"\
acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229865\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\
\ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \
\ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\
acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078966,\n\
\ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078966\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8270042194092827,\n \"acc_stderr\": 0.024621562866768434,\n \
\ \"acc_norm\": 0.8270042194092827,\n \"acc_norm_stderr\": 0.024621562866768434\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7130044843049327,\n\
\ \"acc_stderr\": 0.030360379710291947,\n \"acc_norm\": 0.7130044843049327,\n\
\ \"acc_norm_stderr\": 0.030360379710291947\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.0345727283691767,\n \"acc_norm\"\
: 0.8264462809917356,\n \"acc_norm_stderr\": 0.0345727283691767\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573973,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573973\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128138\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\
\ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\
\ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258176,\n\
\ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258176\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.47039106145251397,\n\
\ \"acc_stderr\": 0.016693154927383557,\n \"acc_norm\": 0.47039106145251397,\n\
\ \"acc_norm_stderr\": 0.016693154927383557\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729484,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729484\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\
\ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\
\ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n\
\ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \
\ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4641460234680574,\n\
\ \"acc_stderr\": 0.012737361318730581,\n \"acc_norm\": 0.4641460234680574,\n\
\ \"acc_norm_stderr\": 0.012737361318730581\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.028064998167040094,\n\
\ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.028064998167040094\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6633986928104575,\n \"acc_stderr\": 0.019117213911495144,\n \
\ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.019117213911495144\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128448,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128448\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578334,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578334\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.032659863237109066,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.032659863237109066\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3047735618115055,\n\
\ \"mc1_stderr\": 0.01611412415688245,\n \"mc2\": 0.463936332301049,\n\
\ \"mc2_stderr\": 0.015153266555511496\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8058405682715075,\n \"acc_stderr\": 0.01111698339239267\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6239575435936315,\n \
\ \"acc_stderr\": 0.013342532064849765\n }\n}\n```"
repo_url: https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|arc:challenge|25_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T16_39_17.349427
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-39-17.349427.parquet'
- split: 2023_12_03T16_39_42.112189
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-39-42.112189.parquet'
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|gsm8k|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hellaswag|10_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T15-57-32.180703.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T15-57-32.180703.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_04T15_57_32.180703
path:
- '**/details_harness|winogrande|5_2023-12-04T15-57-32.180703.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-04T15-57-32.180703.parquet'
- config_name: results
data_files:
- split: 2023_12_03T16_39_17.349427
path:
- results_2023-12-03T16-39-17.349427.parquet
- split: 2023_12_03T16_39_42.112189
path:
- results_2023-12-03T16-39-42.112189.parquet
- split: 2023_12_04T15_57_32.180703
path:
- results_2023-12-04T15-57-32.180703.parquet
- split: latest
path:
- results_2023-12-04T15-57-32.180703.parquet
---
# Dataset Card for Evaluation run of berkeley-nest/Starling-LM-7B-alpha
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_berkeley-nest__Starling-LM-7B-alpha",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T15:57:32.180703](https://huggingface.co/datasets/open-llm-leaderboard/details_berkeley-nest__Starling-LM-7B-alpha/blob/main/results_2023-12-04T15-57-32.180703.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": 0.6484620563320449,
"acc_stderr": 0.03189811561189118,
"acc_norm": 0.6499663332374793,
"acc_norm_stderr": 0.03254094389407915,
"mc1": 0.3047735618115055,
"mc1_stderr": 0.01611412415688245,
"mc2": 0.463936332301049,
"mc2_stderr": 0.015153266555511496
},
"harness|arc:challenge|25": {
"acc": 0.5998293515358362,
"acc_stderr": 0.014317197787809172,
"acc_norm": 0.6382252559726962,
"acc_norm_stderr": 0.01404195794503808
},
"harness|hellaswag|10": {
"acc": 0.665803624775941,
"acc_stderr": 0.004707447244200621,
"acc_norm": 0.8490340569607648,
"acc_norm_stderr": 0.0035728399695219874
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6867924528301886,
"acc_stderr": 0.028544793319055326,
"acc_norm": 0.6867924528301886,
"acc_norm_stderr": 0.028544793319055326
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.035146974678623884,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.035146974678623884
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5531914893617021,
"acc_stderr": 0.032500536843658404,
"acc_norm": 0.5531914893617021,
"acc_norm_stderr": 0.032500536843658404
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.04122737111370332,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370332
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41005291005291006,
"acc_stderr": 0.02533120243894444,
"acc_norm": 0.41005291005291006,
"acc_norm_stderr": 0.02533120243894444
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5238095238095238,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.5238095238095238,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932269,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932269
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7903225806451613,
"acc_stderr": 0.023157879349083525,
"acc_norm": 0.7903225806451613,
"acc_norm_stderr": 0.023157879349083525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.46798029556650245,
"acc_stderr": 0.035107665979592154,
"acc_norm": 0.46798029556650245,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009181,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009181
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.797979797979798,
"acc_stderr": 0.028606204289229865,
"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.028606204289229865
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9119170984455959,
"acc_stderr": 0.02045374660160103,
"acc_norm": 0.9119170984455959,
"acc_norm_stderr": 0.02045374660160103
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.676923076923077,
"acc_stderr": 0.02371088850197057,
"acc_norm": 0.676923076923077,
"acc_norm_stderr": 0.02371088850197057
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.028493465091028593,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028593
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.0302839955258844,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.0302839955258844
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.03958027231121569,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.03958027231121569
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5,
"acc_stderr": 0.034099716973523674,
"acc_norm": 0.5,
"acc_norm_stderr": 0.034099716973523674
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8235294117647058,
"acc_stderr": 0.026756401538078966,
"acc_norm": 0.8235294117647058,
"acc_norm_stderr": 0.026756401538078966
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8270042194092827,
"acc_stderr": 0.024621562866768434,
"acc_norm": 0.8270042194092827,
"acc_norm_stderr": 0.024621562866768434
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7130044843049327,
"acc_stderr": 0.030360379710291947,
"acc_norm": 0.7130044843049327,
"acc_norm_stderr": 0.030360379710291947
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8264462809917356,
"acc_stderr": 0.0345727283691767,
"acc_norm": 0.8264462809917356,
"acc_norm_stderr": 0.0345727283691767
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.042365112580946336,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.042365112580946336
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
"acc_stderr": 0.04684099321077106,
"acc_norm": 0.41964285714285715,
"acc_norm_stderr": 0.04684099321077106
},
"harness|hendrycksTest-management|5": {
"acc": 0.8446601941747572,
"acc_stderr": 0.03586594738573973,
"acc_norm": 0.8446601941747572,
"acc_norm_stderr": 0.03586594738573973
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
"acc_stderr": 0.02158649400128138,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.02158649400128138
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.73,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8263090676883781,
"acc_stderr": 0.01354741565866226,
"acc_norm": 0.8263090676883781,
"acc_norm_stderr": 0.01354741565866226
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7369942196531792,
"acc_stderr": 0.023703099525258176,
"acc_norm": 0.7369942196531792,
"acc_norm_stderr": 0.023703099525258176
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.47039106145251397,
"acc_stderr": 0.016693154927383557,
"acc_norm": 0.47039106145251397,
"acc_norm_stderr": 0.016693154927383557
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7352941176470589,
"acc_stderr": 0.025261691219729484,
"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.025261691219729484
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6977491961414791,
"acc_stderr": 0.02608270069539966,
"acc_norm": 0.6977491961414791,
"acc_norm_stderr": 0.02608270069539966
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7345679012345679,
"acc_stderr": 0.024569223600460845,
"acc_norm": 0.7345679012345679,
"acc_norm_stderr": 0.024569223600460845
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.029752389657427047,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.029752389657427047
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4641460234680574,
"acc_stderr": 0.012737361318730581,
"acc_norm": 0.4641460234680574,
"acc_norm_stderr": 0.012737361318730581
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6911764705882353,
"acc_stderr": 0.028064998167040094,
"acc_norm": 0.6911764705882353,
"acc_norm_stderr": 0.028064998167040094
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6633986928104575,
"acc_stderr": 0.019117213911495144,
"acc_norm": 0.6633986928104575,
"acc_norm_stderr": 0.019117213911495144
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.726530612244898,
"acc_stderr": 0.028535560337128448,
"acc_norm": 0.726530612244898,
"acc_norm_stderr": 0.028535560337128448
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.025538433368578334,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.025538433368578334
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.032659863237109066,
"acc_norm": 0.88,
"acc_norm_stderr": 0.032659863237109066
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.027539122889061456,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061456
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3047735618115055,
"mc1_stderr": 0.01611412415688245,
"mc2": 0.463936332301049,
"mc2_stderr": 0.015153266555511496
},
"harness|winogrande|5": {
"acc": 0.8058405682715075,
"acc_stderr": 0.01111698339239267
},
"harness|gsm8k|5": {
"acc": 0.6239575435936315,
"acc_stderr": 0.013342532064849765
}
}
```
### 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
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### Licensing Information
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### Citation Information
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### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_KnutJaegersberg__Galactica-6.7B-EssayWriter | open-llm-leaderboard | 2023-12-03T16:42:29Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T16:42:25Z | ---
pretty_name: Evaluation run of KnutJaegersberg/Galactica-6.7B-EssayWriter
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KnutJaegersberg/Galactica-6.7B-EssayWriter](https://huggingface.co/KnutJaegersberg/Galactica-6.7B-EssayWriter)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_KnutJaegersberg__Galactica-6.7B-EssayWriter\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T16:42:22.412540](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Galactica-6.7B-EssayWriter/blob/main/results_2023-12-03T16-42-22.412540.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\": 0.034874905231235785,\n\
\ \"acc_stderr\": 0.005053480765022248\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.034874905231235785,\n \"acc_stderr\": 0.005053480765022248\n\
\ }\n}\n```"
repo_url: https://huggingface.co/KnutJaegersberg/Galactica-6.7B-EssayWriter
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T16_42_22.412540
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-42-22.412540.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-42-22.412540.parquet'
- config_name: results
data_files:
- split: 2023_12_03T16_42_22.412540
path:
- results_2023-12-03T16-42-22.412540.parquet
- split: latest
path:
- results_2023-12-03T16-42-22.412540.parquet
---
# Dataset Card for Evaluation run of KnutJaegersberg/Galactica-6.7B-EssayWriter
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/Galactica-6.7B-EssayWriter
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [KnutJaegersberg/Galactica-6.7B-EssayWriter](https://huggingface.co/KnutJaegersberg/Galactica-6.7B-EssayWriter) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_KnutJaegersberg__Galactica-6.7B-EssayWriter",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T16:42:22.412540](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Galactica-6.7B-EssayWriter/blob/main/results_2023-12-03T16-42-22.412540.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": 0.034874905231235785,
"acc_stderr": 0.005053480765022248
},
"harness|gsm8k|5": {
"acc": 0.034874905231235785,
"acc_stderr": 0.005053480765022248
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_speechlessai__speechless-coding-7b-16k-tora | open-llm-leaderboard | 2023-12-03T17:01:01Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:00:57Z | ---
pretty_name: Evaluation run of speechlessai/speechless-coding-7b-16k-tora
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [speechlessai/speechless-coding-7b-16k-tora](https://huggingface.co/speechlessai/speechless-coding-7b-16k-tora)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_speechlessai__speechless-coding-7b-16k-tora\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:00:54.290226](https://huggingface.co/datasets/open-llm-leaderboard/details_speechlessai__speechless-coding-7b-16k-tora/blob/main/results_2023-12-03T17-00-54.290226.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\": 0.17058377558756635,\n\
\ \"acc_stderr\": 0.010360898504733313\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.17058377558756635,\n \"acc_stderr\": 0.010360898504733313\n\
\ }\n}\n```"
repo_url: https://huggingface.co/speechlessai/speechless-coding-7b-16k-tora
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_00_54.290226
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-00-54.290226.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-00-54.290226.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_00_54.290226
path:
- results_2023-12-03T17-00-54.290226.parquet
- split: latest
path:
- results_2023-12-03T17-00-54.290226.parquet
---
# Dataset Card for Evaluation run of speechlessai/speechless-coding-7b-16k-tora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/speechlessai/speechless-coding-7b-16k-tora
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [speechlessai/speechless-coding-7b-16k-tora](https://huggingface.co/speechlessai/speechless-coding-7b-16k-tora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_speechlessai__speechless-coding-7b-16k-tora",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:00:54.290226](https://huggingface.co/datasets/open-llm-leaderboard/details_speechlessai__speechless-coding-7b-16k-tora/blob/main/results_2023-12-03T17-00-54.290226.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": 0.17058377558756635,
"acc_stderr": 0.010360898504733313
},
"harness|gsm8k|5": {
"acc": 0.17058377558756635,
"acc_stderr": 0.010360898504733313
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_NousResearch__Yarn-Mistral-7b-128k | open-llm-leaderboard | 2023-12-03T17:07:35Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:07:10Z | ---
pretty_name: Evaluation run of NousResearch/Yarn-Mistral-7b-128k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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 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_NousResearch__Yarn-Mistral-7b-128k\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:07:28.766997](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__Yarn-Mistral-7b-128k/blob/main/results_2023-12-03T17-07-28.766997.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\": 0.3260045489006823,\n\
\ \"acc_stderr\": 0.012911675645682847\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.3260045489006823,\n \"acc_stderr\": 0.012911675645682847\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_07_07.837997
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-07-07.837997.parquet'
- split: 2023_12_03T17_07_28.766997
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-07-28.766997.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-07-28.766997.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_07_07.837997
path:
- results_2023-12-03T17-07-07.837997.parquet
- split: 2023_12_03T17_07_28.766997
path:
- results_2023-12-03T17-07-28.766997.parquet
- split: latest
path:
- results_2023-12-03T17-07-28.766997.parquet
---
# Dataset Card for Evaluation run of NousResearch/Yarn-Mistral-7b-128k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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 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_NousResearch__Yarn-Mistral-7b-128k",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:07:28.766997](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__Yarn-Mistral-7b-128k/blob/main/results_2023-12-03T17-07-28.766997.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": 0.3260045489006823,
"acc_stderr": 0.012911675645682847
},
"harness|gsm8k|5": {
"acc": 0.3260045489006823,
"acc_stderr": 0.012911675645682847
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_NousResearch__Yarn-Mistral-7b-64k | open-llm-leaderboard | 2023-12-03T17:07:56Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:07:52Z | ---
pretty_name: Evaluation run of NousResearch/Yarn-Mistral-7b-64k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NousResearch/Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_NousResearch__Yarn-Mistral-7b-64k\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:07:49.588193](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__Yarn-Mistral-7b-64k/blob/main/results_2023-12-03T17-07-49.588193.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\": 0.332827899924185,\n\
\ \"acc_stderr\": 0.012979892496598278\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.332827899924185,\n \"acc_stderr\": 0.012979892496598278\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_07_49.588193
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-07-49.588193.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-07-49.588193.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_07_49.588193
path:
- results_2023-12-03T17-07-49.588193.parquet
- split: latest
path:
- results_2023-12-03T17-07-49.588193.parquet
---
# Dataset Card for Evaluation run of NousResearch/Yarn-Mistral-7b-64k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [NousResearch/Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_NousResearch__Yarn-Mistral-7b-64k",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:07:49.588193](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__Yarn-Mistral-7b-64k/blob/main/results_2023-12-03T17-07-49.588193.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": 0.332827899924185,
"acc_stderr": 0.012979892496598278
},
"harness|gsm8k|5": {
"acc": 0.332827899924185,
"acc_stderr": 0.012979892496598278
}
}
```
### 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] | [
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|
KAUE2006/AdamSandlerAlexandreMoreno | KAUE2006 | 2023-12-03T17:12:11Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T17:08:08Z | ---
license: openrail
---
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|
gaizerick/samira | gaizerick | 2023-12-03T17:12:15Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T17:10:49Z | ---
license: openrail
---
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|
open-llm-leaderboard/details_NEU-HAI__mental-alpaca | open-llm-leaderboard | 2023-12-03T17:17:41Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:17:37Z | ---
pretty_name: Evaluation run of NEU-HAI/mental-alpaca
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NEU-HAI/mental-alpaca](https://huggingface.co/NEU-HAI/mental-alpaca) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_NEU-HAI__mental-alpaca\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:17:34.237922](https://huggingface.co/datasets/open-llm-leaderboard/details_NEU-HAI__mental-alpaca/blob/main/results_2023-12-03T17-17-34.237922.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\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/NEU-HAI/mental-alpaca
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_17_34.237922
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-17-34.237922.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-17-34.237922.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_17_34.237922
path:
- results_2023-12-03T17-17-34.237922.parquet
- split: latest
path:
- results_2023-12-03T17-17-34.237922.parquet
---
# Dataset Card for Evaluation run of NEU-HAI/mental-alpaca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NEU-HAI/mental-alpaca
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [NEU-HAI/mental-alpaca](https://huggingface.co/NEU-HAI/mental-alpaca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_NEU-HAI__mental-alpaca",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:17:34.237922](https://huggingface.co/datasets/open-llm-leaderboard/details_NEU-HAI__mental-alpaca/blob/main/results_2023-12-03T17-17-34.237922.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": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_OrionStarAI__OrionStar-Yi-34B-Chat-Llama | open-llm-leaderboard | 2023-12-03T18:22:09Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:19:22Z | ---
pretty_name: Evaluation run of OrionStarAI/OrionStar-Yi-34B-Chat-Llama
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OrionStarAI/OrionStar-Yi-34B-Chat-Llama](https://huggingface.co/OrionStarAI/OrionStar-Yi-34B-Chat-Llama)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 4 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_OrionStarAI__OrionStar-Yi-34B-Chat-Llama\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:22:03.358595](https://huggingface.co/datasets/open-llm-leaderboard/details_OrionStarAI__OrionStar-Yi-34B-Chat-Llama/blob/main/results_2023-12-03T18-22-03.358595.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\": 0.5390447308567097,\n\
\ \"acc_stderr\": 0.013730428449116344\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5390447308567097,\n \"acc_stderr\": 0.013730428449116344\n\
\ }\n}\n```"
repo_url: https://huggingface.co/OrionStarAI/OrionStar-Yi-34B-Chat-Llama
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_19_19.971847
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-19-19.971847.parquet'
- split: 2023_12_03T17_20_20.086635
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-20-20.086635.parquet'
- split: 2023_12_03T18_21_56.763818
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-21-56.763818.parquet'
- split: 2023_12_03T18_22_03.358595
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-22-03.358595.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-22-03.358595.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_19_19.971847
path:
- results_2023-12-03T17-19-19.971847.parquet
- split: 2023_12_03T17_20_20.086635
path:
- results_2023-12-03T17-20-20.086635.parquet
- split: 2023_12_03T18_21_56.763818
path:
- results_2023-12-03T18-21-56.763818.parquet
- split: 2023_12_03T18_22_03.358595
path:
- results_2023-12-03T18-22-03.358595.parquet
- split: latest
path:
- results_2023-12-03T18-22-03.358595.parquet
---
# Dataset Card for Evaluation run of OrionStarAI/OrionStar-Yi-34B-Chat-Llama
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OrionStarAI/OrionStar-Yi-34B-Chat-Llama
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [OrionStarAI/OrionStar-Yi-34B-Chat-Llama](https://huggingface.co/OrionStarAI/OrionStar-Yi-34B-Chat-Llama) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 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_OrionStarAI__OrionStar-Yi-34B-Chat-Llama",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:22:03.358595](https://huggingface.co/datasets/open-llm-leaderboard/details_OrionStarAI__OrionStar-Yi-34B-Chat-Llama/blob/main/results_2023-12-03T18-22-03.358595.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": 0.5390447308567097,
"acc_stderr": 0.013730428449116344
},
"harness|gsm8k|5": {
"acc": 0.5390447308567097,
"acc_stderr": 0.013730428449116344
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_HiTZ__GoLLIE-7B | open-llm-leaderboard | 2023-12-03T17:27:43Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:27:38Z | ---
pretty_name: Evaluation run of HiTZ/GoLLIE-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [HiTZ/GoLLIE-7B](https://huggingface.co/HiTZ/GoLLIE-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_HiTZ__GoLLIE-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:27:35.795751](https://huggingface.co/datasets/open-llm-leaderboard/details_HiTZ__GoLLIE-7B/blob/main/results_2023-12-03T17-27-35.795751.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\": 0.032600454890068235,\n\
\ \"acc_stderr\": 0.004891669021939591\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.032600454890068235,\n \"acc_stderr\": 0.004891669021939591\n\
\ }\n}\n```"
repo_url: https://huggingface.co/HiTZ/GoLLIE-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_27_35.795751
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-27-35.795751.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-27-35.795751.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_27_35.795751
path:
- results_2023-12-03T17-27-35.795751.parquet
- split: latest
path:
- results_2023-12-03T17-27-35.795751.parquet
---
# Dataset Card for Evaluation run of HiTZ/GoLLIE-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HiTZ/GoLLIE-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [HiTZ/GoLLIE-7B](https://huggingface.co/HiTZ/GoLLIE-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_HiTZ__GoLLIE-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:27:35.795751](https://huggingface.co/datasets/open-llm-leaderboard/details_HiTZ__GoLLIE-7B/blob/main/results_2023-12-03T17-27-35.795751.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": 0.032600454890068235,
"acc_stderr": 0.004891669021939591
},
"harness|gsm8k|5": {
"acc": 0.032600454890068235,
"acc_stderr": 0.004891669021939591
}
}
```
### 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] | [
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|
orangetin/nectar-conversation | orangetin | 2023-12-03T17:42:41Z | 0 | 0 | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| [
"conversational",
"text-generation"
]
| 2023-12-03T17:30:23Z | ---
license: cc-by-4.0
task_categories:
- conversational
- text-generation
language:
- en
pretty_name: Nectar - Conversational
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
[berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) dataset reformatted for messages. Assistant response is the rank=1 response in the original dataset. | [
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|
open-llm-leaderboard/details_FPHam__Karen_TheEditor_V2_STRICT_Mistral_7B | open-llm-leaderboard | 2023-12-03T17:39:03Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:38:57Z | ---
pretty_name: Evaluation run of FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B](https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_FPHam__Karen_TheEditor_V2_STRICT_Mistral_7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:38:53.248093](https://huggingface.co/datasets/open-llm-leaderboard/details_FPHam__Karen_TheEditor_V2_STRICT_Mistral_7B/blob/main/results_2023-12-03T17-38-53.248093.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\": 0.3017437452615618,\n\
\ \"acc_stderr\": 0.012643544762873351\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.3017437452615618,\n \"acc_stderr\": 0.012643544762873351\n\
\ }\n}\n```"
repo_url: https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_38_53.248093
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-38-53.248093.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-38-53.248093.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_38_53.248093
path:
- results_2023-12-03T17-38-53.248093.parquet
- split: latest
path:
- results_2023-12-03T17-38-53.248093.parquet
---
# Dataset Card for Evaluation run of FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B](https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_FPHam__Karen_TheEditor_V2_STRICT_Mistral_7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:38:53.248093](https://huggingface.co/datasets/open-llm-leaderboard/details_FPHam__Karen_TheEditor_V2_STRICT_Mistral_7B/blob/main/results_2023-12-03T17-38-53.248093.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": 0.3017437452615618,
"acc_stderr": 0.012643544762873351
},
"harness|gsm8k|5": {
"acc": 0.3017437452615618,
"acc_stderr": 0.012643544762873351
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_maywell__Synatra-RP-Orca-2-7b-v0.1 | open-llm-leaderboard | 2023-12-03T17:42:07Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:42:02Z | ---
pretty_name: Evaluation run of maywell/Synatra-RP-Orca-2-7b-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [maywell/Synatra-RP-Orca-2-7b-v0.1](https://huggingface.co/maywell/Synatra-RP-Orca-2-7b-v0.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_maywell__Synatra-RP-Orca-2-7b-v0.1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:42:00.048220](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-RP-Orca-2-7b-v0.1/blob/main/results_2023-12-03T17-42-00.048220.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\": 0.39651250947687644,\n\
\ \"acc_stderr\": 0.013474258584033354\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.39651250947687644,\n \"acc_stderr\": 0.013474258584033354\n\
\ }\n}\n```"
repo_url: https://huggingface.co/maywell/Synatra-RP-Orca-2-7b-v0.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_42_00.048220
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-42-00.048220.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-42-00.048220.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_42_00.048220
path:
- results_2023-12-03T17-42-00.048220.parquet
- split: latest
path:
- results_2023-12-03T17-42-00.048220.parquet
---
# Dataset Card for Evaluation run of maywell/Synatra-RP-Orca-2-7b-v0.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/Synatra-RP-Orca-2-7b-v0.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [maywell/Synatra-RP-Orca-2-7b-v0.1](https://huggingface.co/maywell/Synatra-RP-Orca-2-7b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_maywell__Synatra-RP-Orca-2-7b-v0.1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:42:00.048220](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-RP-Orca-2-7b-v0.1/blob/main/results_2023-12-03T17-42-00.048220.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": 0.39651250947687644,
"acc_stderr": 0.013474258584033354
},
"harness|gsm8k|5": {
"acc": 0.39651250947687644,
"acc_stderr": 0.013474258584033354
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_tianlinliu0121__zephyr-7b-dpo-full-beta-0.2 | open-llm-leaderboard | 2023-12-03T18:01:32Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T17:57:43Z | ---
pretty_name: Evaluation run of tianlinliu0121/zephyr-7b-dpo-full-beta-0.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tianlinliu0121/zephyr-7b-dpo-full-beta-0.2](https://huggingface.co/tianlinliu0121/zephyr-7b-dpo-full-beta-0.2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 4 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_tianlinliu0121__zephyr-7b-dpo-full-beta-0.2\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:01:25.798597](https://huggingface.co/datasets/open-llm-leaderboard/details_tianlinliu0121__zephyr-7b-dpo-full-beta-0.2/blob/main/results_2023-12-03T18-01-25.798597.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\": 0.30022744503411675,\n\
\ \"acc_stderr\": 0.012625423152283044\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.30022744503411675,\n \"acc_stderr\": 0.012625423152283044\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tianlinliu0121/zephyr-7b-dpo-full-beta-0.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T17_57_40.359102
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-57-40.359102.parquet'
- split: 2023_12_03T17_57_42.100432
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-57-42.100432.parquet'
- split: 2023_12_03T18_01_20.411431
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-01-20.411431.parquet'
- split: 2023_12_03T18_01_25.798597
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-01-25.798597.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-01-25.798597.parquet'
- config_name: results
data_files:
- split: 2023_12_03T17_57_40.359102
path:
- results_2023-12-03T17-57-40.359102.parquet
- split: 2023_12_03T17_57_42.100432
path:
- results_2023-12-03T17-57-42.100432.parquet
- split: 2023_12_03T18_01_20.411431
path:
- results_2023-12-03T18-01-20.411431.parquet
- split: 2023_12_03T18_01_25.798597
path:
- results_2023-12-03T18-01-25.798597.parquet
- split: latest
path:
- results_2023-12-03T18-01-25.798597.parquet
---
# Dataset Card for Evaluation run of tianlinliu0121/zephyr-7b-dpo-full-beta-0.2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tianlinliu0121/zephyr-7b-dpo-full-beta-0.2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [tianlinliu0121/zephyr-7b-dpo-full-beta-0.2](https://huggingface.co/tianlinliu0121/zephyr-7b-dpo-full-beta-0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 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_tianlinliu0121__zephyr-7b-dpo-full-beta-0.2",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:01:25.798597](https://huggingface.co/datasets/open-llm-leaderboard/details_tianlinliu0121__zephyr-7b-dpo-full-beta-0.2/blob/main/results_2023-12-03T18-01-25.798597.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": 0.30022744503411675,
"acc_stderr": 0.012625423152283044
},
"harness|gsm8k|5": {
"acc": 0.30022744503411675,
"acc_stderr": 0.012625423152283044
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_maywell__Mini_Synatra_SFT | open-llm-leaderboard | 2023-12-03T18:00:33Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:00:28Z | ---
pretty_name: Evaluation run of maywell/Mini_Synatra_SFT
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [maywell/Mini_Synatra_SFT](https://huggingface.co/maywell/Mini_Synatra_SFT) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_maywell__Mini_Synatra_SFT\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:00:26.162132](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Mini_Synatra_SFT/blob/main/results_2023-12-03T18-00-26.162132.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\": 0.4488248673237301,\n\
\ \"acc_stderr\": 0.013700157442788066\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.4488248673237301,\n \"acc_stderr\": 0.013700157442788066\n\
\ }\n}\n```"
repo_url: https://huggingface.co/maywell/Mini_Synatra_SFT
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T18_00_26.162132
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-00-26.162132.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-00-26.162132.parquet'
- config_name: results
data_files:
- split: 2023_12_03T18_00_26.162132
path:
- results_2023-12-03T18-00-26.162132.parquet
- split: latest
path:
- results_2023-12-03T18-00-26.162132.parquet
---
# Dataset Card for Evaluation run of maywell/Mini_Synatra_SFT
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/Mini_Synatra_SFT
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [maywell/Mini_Synatra_SFT](https://huggingface.co/maywell/Mini_Synatra_SFT) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_maywell__Mini_Synatra_SFT",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:00:26.162132](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Mini_Synatra_SFT/blob/main/results_2023-12-03T18-00-26.162132.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": 0.4488248673237301,
"acc_stderr": 0.013700157442788066
},
"harness|gsm8k|5": {
"acc": 0.4488248673237301,
"acc_stderr": 0.013700157442788066
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_maywell__PiVoT-0.1-early | open-llm-leaderboard | 2023-12-03T18:08:39Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:05:24Z | ---
pretty_name: Evaluation run of maywell/PiVoT-0.1-early
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [maywell/PiVoT-0.1-early](https://huggingface.co/maywell/PiVoT-0.1-early) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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 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_maywell__PiVoT-0.1-early\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:08:31.894525](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__PiVoT-0.1-early/blob/main/results_2023-12-03T18-08-31.894525.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\": 0.444275966641395,\n\
\ \"acc_stderr\": 0.013686685712261672\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.444275966641395,\n \"acc_stderr\": 0.013686685712261672\n\
\ }\n}\n```"
repo_url: https://huggingface.co/maywell/PiVoT-0.1-early
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T18_05_21.417194
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-05-21.417194.parquet'
- split: 2023_12_03T18_08_31.894525
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-08-31.894525.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-08-31.894525.parquet'
- config_name: results
data_files:
- split: 2023_12_03T18_05_21.417194
path:
- results_2023-12-03T18-05-21.417194.parquet
- split: 2023_12_03T18_08_31.894525
path:
- results_2023-12-03T18-08-31.894525.parquet
- split: latest
path:
- results_2023-12-03T18-08-31.894525.parquet
---
# Dataset Card for Evaluation run of maywell/PiVoT-0.1-early
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/PiVoT-0.1-early
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [maywell/PiVoT-0.1-early](https://huggingface.co/maywell/PiVoT-0.1-early) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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 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_maywell__PiVoT-0.1-early",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:08:31.894525](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__PiVoT-0.1-early/blob/main/results_2023-12-03T18-08-31.894525.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": 0.444275966641395,
"acc_stderr": 0.013686685712261672
},
"harness|gsm8k|5": {
"acc": 0.444275966641395,
"acc_stderr": 0.013686685712261672
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_maywell__PiVoT-0.1-Evil-a | open-llm-leaderboard | 2023-12-03T18:10:44Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:05:43Z | ---
pretty_name: Evaluation run of maywell/PiVoT-0.1-Evil-a
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [maywell/PiVoT-0.1-Evil-a](https://huggingface.co/maywell/PiVoT-0.1-Evil-a) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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 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_maywell__PiVoT-0.1-Evil-a\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:10:37.734166](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__PiVoT-0.1-Evil-a/blob/main/results_2023-12-03T18-10-37.734166.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\": 0.4040940106141016,\n\
\ \"acc_stderr\": 0.01351675297272172\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.4040940106141016,\n \"acc_stderr\": 0.01351675297272172\n\
\ }\n}\n```"
repo_url: https://huggingface.co/maywell/PiVoT-0.1-Evil-a
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T18_05_40.726563
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-05-40.726563.parquet'
- split: 2023_12_03T18_10_37.734166
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-10-37.734166.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-10-37.734166.parquet'
- config_name: results
data_files:
- split: 2023_12_03T18_05_40.726563
path:
- results_2023-12-03T18-05-40.726563.parquet
- split: 2023_12_03T18_10_37.734166
path:
- results_2023-12-03T18-10-37.734166.parquet
- split: latest
path:
- results_2023-12-03T18-10-37.734166.parquet
---
# Dataset Card for Evaluation run of maywell/PiVoT-0.1-Evil-a
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/PiVoT-0.1-Evil-a
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [maywell/PiVoT-0.1-Evil-a](https://huggingface.co/maywell/PiVoT-0.1-Evil-a) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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 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_maywell__PiVoT-0.1-Evil-a",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:10:37.734166](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__PiVoT-0.1-Evil-a/blob/main/results_2023-12-03T18-10-37.734166.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": 0.4040940106141016,
"acc_stderr": 0.01351675297272172
},
"harness|gsm8k|5": {
"acc": 0.4040940106141016,
"acc_stderr": 0.01351675297272172
}
}
```
### 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] | [
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|
agangal/baseball-12 | agangal | 2023-12-03T18:11:13Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:11:09Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: additional_feature
dtype: string
splits:
- name: train
num_bytes: 3790371.0
num_examples: 12
download_size: 3792311
dataset_size: 3790371.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
open-llm-leaderboard/details_chargoddard__loyal-piano-m7 | open-llm-leaderboard | 2023-12-03T18:24:36Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:24:32Z | ---
pretty_name: Evaluation run of chargoddard/loyal-piano-m7
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/loyal-piano-m7](https://huggingface.co/chargoddard/loyal-piano-m7)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_chargoddard__loyal-piano-m7\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:24:30.101089](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__loyal-piano-m7/blob/main/results_2023-12-03T18-24-30.101089.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\": 0.5670962850644428,\n\
\ \"acc_stderr\": 0.013647916362576054\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5670962850644428,\n \"acc_stderr\": 0.013647916362576054\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/loyal-piano-m7
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T18_24_30.101089
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-24-30.101089.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-24-30.101089.parquet'
- config_name: results
data_files:
- split: 2023_12_03T18_24_30.101089
path:
- results_2023-12-03T18-24-30.101089.parquet
- split: latest
path:
- results_2023-12-03T18-24-30.101089.parquet
---
# Dataset Card for Evaluation run of chargoddard/loyal-piano-m7
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/loyal-piano-m7
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/loyal-piano-m7](https://huggingface.co/chargoddard/loyal-piano-m7) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_chargoddard__loyal-piano-m7",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:24:30.101089](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__loyal-piano-m7/blob/main/results_2023-12-03T18-24-30.101089.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": 0.5670962850644428,
"acc_stderr": 0.013647916362576054
},
"harness|gsm8k|5": {
"acc": 0.5670962850644428,
"acc_stderr": 0.013647916362576054
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_chargoddard__llama-polyglot-13b | open-llm-leaderboard | 2023-12-03T18:35:56Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:35:52Z | ---
pretty_name: Evaluation run of chargoddard/llama-polyglot-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/llama-polyglot-13b](https://huggingface.co/chargoddard/llama-polyglot-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_chargoddard__llama-polyglot-13b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:35:49.174459](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-polyglot-13b/blob/main/results_2023-12-03T18-35-49.174459.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\": 0.2259287338893101,\n\
\ \"acc_stderr\": 0.011519098777279956\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.2259287338893101,\n \"acc_stderr\": 0.011519098777279956\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/llama-polyglot-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T18_35_49.174459
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-35-49.174459.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-35-49.174459.parquet'
- config_name: results
data_files:
- split: 2023_12_03T18_35_49.174459
path:
- results_2023-12-03T18-35-49.174459.parquet
- split: latest
path:
- results_2023-12-03T18-35-49.174459.parquet
---
# Dataset Card for Evaluation run of chargoddard/llama-polyglot-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/llama-polyglot-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/llama-polyglot-13b](https://huggingface.co/chargoddard/llama-polyglot-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_chargoddard__llama-polyglot-13b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:35:49.174459](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-polyglot-13b/blob/main/results_2023-12-03T18-35-49.174459.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": 0.2259287338893101,
"acc_stderr": 0.011519098777279956
},
"harness|gsm8k|5": {
"acc": 0.2259287338893101,
"acc_stderr": 0.011519098777279956
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_uukuguy__speechless-mistral-7b-dare-0.85 | open-llm-leaderboard | 2023-12-03T18:43:17Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:43:13Z | ---
pretty_name: Evaluation run of uukuguy/speechless-mistral-7b-dare-0.85
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-mistral-7b-dare-0.85](https://huggingface.co/uukuguy/speechless-mistral-7b-dare-0.85)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_uukuguy__speechless-mistral-7b-dare-0.85\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:43:10.266119](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-7b-dare-0.85/blob/main/results_2023-12-03T18-43-10.266119.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\": 0.45564821834723274,\n\
\ \"acc_stderr\": 0.013718194542485601\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.45564821834723274,\n \"acc_stderr\": 0.013718194542485601\n\
\ }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-mistral-7b-dare-0.85
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T18_43_10.266119
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-43-10.266119.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-43-10.266119.parquet'
- config_name: results
data_files:
- split: 2023_12_03T18_43_10.266119
path:
- results_2023-12-03T18-43-10.266119.parquet
- split: latest
path:
- results_2023-12-03T18-43-10.266119.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-mistral-7b-dare-0.85
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-mistral-7b-dare-0.85
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [uukuguy/speechless-mistral-7b-dare-0.85](https://huggingface.co/uukuguy/speechless-mistral-7b-dare-0.85) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_uukuguy__speechless-mistral-7b-dare-0.85",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:43:10.266119](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-7b-dare-0.85/blob/main/results_2023-12-03T18-43-10.266119.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": 0.45564821834723274,
"acc_stderr": 0.013718194542485601
},
"harness|gsm8k|5": {
"acc": 0.45564821834723274,
"acc_stderr": 0.013718194542485601
}
}
```
### 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] | [
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|
roa7n/patched_test_f_UCH_ps_50__v2023d | roa7n | 2023-12-03T18:52:13Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:52:10Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: sequence_str
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 48828729
num_examples: 110542
download_size: 4291765
dataset_size: 48828729
---
# Dataset Card for "patched_test_f_UCH_ps_50__v2023d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
SUSTech/harness-result | SUSTech | 2023-12-03T18:52:39Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:52:36Z | ---
dataset_info:
features:
- name: model
dtype: string
- name: winogrande
dtype: float64
- name: arc
dtype: float64
- name: hellaswag
dtype: float64
- name: truthfulqa
dtype: float64
splits:
- name: train
num_bytes: 288
num_examples: 6
download_size: 3077
dataset_size: 288
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
open-llm-leaderboard/details_Jiayi-Pan__Tiny-Vicuna-1B | open-llm-leaderboard | 2023-12-03T18:54:26Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:54:18Z | ---
pretty_name: Evaluation run of Jiayi-Pan/Tiny-Vicuna-1B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Jiayi-Pan/Tiny-Vicuna-1B](https://huggingface.co/Jiayi-Pan/Tiny-Vicuna-1B) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_Jiayi-Pan__Tiny-Vicuna-1B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:54:15.372610](https://huggingface.co/datasets/open-llm-leaderboard/details_Jiayi-Pan__Tiny-Vicuna-1B/blob/main/results_2023-12-03T18-54-15.372610.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\": 0.015163002274450341,\n\
\ \"acc_stderr\": 0.003366022949726332\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.015163002274450341,\n \"acc_stderr\": 0.003366022949726332\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Jiayi-Pan/Tiny-Vicuna-1B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T18_54_15.372610
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-54-15.372610.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-54-15.372610.parquet'
- config_name: results
data_files:
- split: 2023_12_03T18_54_15.372610
path:
- results_2023-12-03T18-54-15.372610.parquet
- split: latest
path:
- results_2023-12-03T18-54-15.372610.parquet
---
# Dataset Card for Evaluation run of Jiayi-Pan/Tiny-Vicuna-1B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Jiayi-Pan/Tiny-Vicuna-1B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Jiayi-Pan/Tiny-Vicuna-1B](https://huggingface.co/Jiayi-Pan/Tiny-Vicuna-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_Jiayi-Pan__Tiny-Vicuna-1B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:54:15.372610](https://huggingface.co/datasets/open-llm-leaderboard/details_Jiayi-Pan__Tiny-Vicuna-1B/blob/main/results_2023-12-03T18-54-15.372610.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": 0.015163002274450341,
"acc_stderr": 0.003366022949726332
},
"harness|gsm8k|5": {
"acc": 0.015163002274450341,
"acc_stderr": 0.003366022949726332
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_yeen214__llama2_7b_merge_orcafamily | open-llm-leaderboard | 2023-12-03T18:59:22Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T18:59:17Z | ---
pretty_name: Evaluation run of yeen214/llama2_7b_merge_orcafamily
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeen214/llama2_7b_merge_orcafamily](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_yeen214__llama2_7b_merge_orcafamily\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:59:14.919755](https://huggingface.co/datasets/open-llm-leaderboard/details_yeen214__llama2_7b_merge_orcafamily/blob/main/results_2023-12-03T18-59-14.919755.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\": 0.2312357846853677,\n\
\ \"acc_stderr\": 0.0116135875031666\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.2312357846853677,\n \"acc_stderr\": 0.0116135875031666\n\
\ }\n}\n```"
repo_url: https://huggingface.co/yeen214/llama2_7b_merge_orcafamily
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T18_59_14.919755
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-59-14.919755.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-59-14.919755.parquet'
- config_name: results
data_files:
- split: 2023_12_03T18_59_14.919755
path:
- results_2023-12-03T18-59-14.919755.parquet
- split: latest
path:
- results_2023-12-03T18-59-14.919755.parquet
---
# Dataset Card for Evaluation run of yeen214/llama2_7b_merge_orcafamily
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeen214/llama2_7b_merge_orcafamily
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [yeen214/llama2_7b_merge_orcafamily](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_yeen214__llama2_7b_merge_orcafamily",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:59:14.919755](https://huggingface.co/datasets/open-llm-leaderboard/details_yeen214__llama2_7b_merge_orcafamily/blob/main/results_2023-12-03T18-59-14.919755.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": 0.2312357846853677,
"acc_stderr": 0.0116135875031666
},
"harness|gsm8k|5": {
"acc": 0.2312357846853677,
"acc_stderr": 0.0116135875031666
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_prithivida__Asimov-7B-v2 | open-llm-leaderboard | 2023-12-03T19:03:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:02:44Z | ---
pretty_name: Evaluation run of prithivida/Asimov-7B-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [prithivida/Asimov-7B-v2](https://huggingface.co/prithivida/Asimov-7B-v2) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_prithivida__Asimov-7B-v2\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:02:27.334666](https://huggingface.co/datasets/open-llm-leaderboard/details_prithivida__Asimov-7B-v2/blob/main/results_2023-12-03T19-02-27.334666.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\": 0.10917361637604246,\n\
\ \"acc_stderr\": 0.008590089300511155\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.10917361637604246,\n \"acc_stderr\": 0.008590089300511155\n\
\ }\n}\n```"
repo_url: https://huggingface.co/prithivida/Asimov-7B-v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T19_02_27.334666
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-02-27.334666.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-02-27.334666.parquet'
- config_name: results
data_files:
- split: 2023_12_03T19_02_27.334666
path:
- results_2023-12-03T19-02-27.334666.parquet
- split: latest
path:
- results_2023-12-03T19-02-27.334666.parquet
---
# Dataset Card for Evaluation run of prithivida/Asimov-7B-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/prithivida/Asimov-7B-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [prithivida/Asimov-7B-v2](https://huggingface.co/prithivida/Asimov-7B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_prithivida__Asimov-7B-v2",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:02:27.334666](https://huggingface.co/datasets/open-llm-leaderboard/details_prithivida__Asimov-7B-v2/blob/main/results_2023-12-03T19-02-27.334666.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": 0.10917361637604246,
"acc_stderr": 0.008590089300511155
},
"harness|gsm8k|5": {
"acc": 0.10917361637604246,
"acc_stderr": 0.008590089300511155
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85 | open-llm-leaderboard | 2023-12-03T19:05:01Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:04:33Z | ---
pretty_name: Evaluation run of uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85](https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:04:28.043244](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85/blob/main/results_2023-12-03T19-04-28.043244.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\": 0.40333586050037906,\n\
\ \"acc_stderr\": 0.013512654781814695\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.40333586050037906,\n \"acc_stderr\": 0.013512654781814695\n\
\ }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T19_04_28.043244
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-04-28.043244.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-04-28.043244.parquet'
- config_name: results
data_files:
- split: 2023_12_03T19_04_28.043244
path:
- results_2023-12-03T19-04-28.043244.parquet
- split: latest
path:
- results_2023-12-03T19-04-28.043244.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85](https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:04:28.043244](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b-dare-0.85/blob/main/results_2023-12-03T19-04-28.043244.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": 0.40333586050037906,
"acc_stderr": 0.013512654781814695
},
"harness|gsm8k|5": {
"acc": 0.40333586050037906,
"acc_stderr": 0.013512654781814695
}
}
```
### 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] | [
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|
roa7n/patched_test_f_SPOUT_ps_50__v2023d | roa7n | 2023-12-03T19:06:21Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:06:18Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: sequence_str
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1947829
num_examples: 5816
download_size: 187258
dataset_size: 1947829
---
# Dataset Card for "patched_test_f_SPOUT_ps_50__v2023d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
open-llm-leaderboard/details_HuggingFaceH4__mistral-7b-sft-beta | open-llm-leaderboard | 2023-12-03T19:08:24Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:08:20Z | ---
pretty_name: Evaluation run of HuggingFaceH4/mistral-7b-sft-beta
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_HuggingFaceH4__mistral-7b-sft-beta\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:08:18.030621](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__mistral-7b-sft-beta/blob/main/results_2023-12-03T19-08-18.030621.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\": 0.3646702047005307,\n\
\ \"acc_stderr\": 0.013258428375662245\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.3646702047005307,\n \"acc_stderr\": 0.013258428375662245\n\
\ }\n}\n```"
repo_url: https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T19_08_18.030621
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-08-18.030621.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-08-18.030621.parquet'
- config_name: results
data_files:
- split: 2023_12_03T19_08_18.030621
path:
- results_2023-12-03T19-08-18.030621.parquet
- split: latest
path:
- results_2023-12-03T19-08-18.030621.parquet
---
# Dataset Card for Evaluation run of HuggingFaceH4/mistral-7b-sft-beta
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_HuggingFaceH4__mistral-7b-sft-beta",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:08:18.030621](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__mistral-7b-sft-beta/blob/main/results_2023-12-03T19-08-18.030621.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": 0.3646702047005307,
"acc_stderr": 0.013258428375662245
},
"harness|gsm8k|5": {
"acc": 0.3646702047005307,
"acc_stderr": 0.013258428375662245
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_Walmart-the-bag__MysticFusion-13B | open-llm-leaderboard | 2023-12-03T19:14:43Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:14:38Z | ---
pretty_name: Evaluation run of Walmart-the-bag/MysticFusion-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Walmart-the-bag/MysticFusion-13B](https://huggingface.co/Walmart-the-bag/MysticFusion-13B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_Walmart-the-bag__MysticFusion-13B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:14:35.382111](https://huggingface.co/datasets/open-llm-leaderboard/details_Walmart-the-bag__MysticFusion-13B/blob/main/results_2023-12-03T19-14-35.382111.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\": 0.24791508718726307,\n\
\ \"acc_stderr\": 0.011893980214826166\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.24791508718726307,\n \"acc_stderr\": 0.011893980214826166\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Walmart-the-bag/MysticFusion-13B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T19_14_35.382111
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-14-35.382111.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-14-35.382111.parquet'
- config_name: results
data_files:
- split: 2023_12_03T19_14_35.382111
path:
- results_2023-12-03T19-14-35.382111.parquet
- split: latest
path:
- results_2023-12-03T19-14-35.382111.parquet
---
# Dataset Card for Evaluation run of Walmart-the-bag/MysticFusion-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Walmart-the-bag/MysticFusion-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Walmart-the-bag/MysticFusion-13B](https://huggingface.co/Walmart-the-bag/MysticFusion-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_Walmart-the-bag__MysticFusion-13B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:14:35.382111](https://huggingface.co/datasets/open-llm-leaderboard/details_Walmart-the-bag__MysticFusion-13B/blob/main/results_2023-12-03T19-14-35.382111.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": 0.24791508718726307,
"acc_stderr": 0.011893980214826166
},
"harness|gsm8k|5": {
"acc": 0.24791508718726307,
"acc_stderr": 0.011893980214826166
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_xxyyy123__1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128 | open-llm-leaderboard | 2023-12-03T19:24:56Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:24:51Z | ---
pretty_name: Evaluation run of xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128](https://huggingface.co/xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_xxyyy123__1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:24:48.526699](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128/blob/main/results_2023-12-03T19-24-48.526699.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\": 0.40181956027293403,\n\
\ \"acc_stderr\": 0.013504357787494032\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.40181956027293403,\n \"acc_stderr\": 0.013504357787494032\n\
\ }\n}\n```"
repo_url: https://huggingface.co/xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T19_24_48.526699
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-24-48.526699.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-24-48.526699.parquet'
- config_name: results
data_files:
- split: 2023_12_03T19_24_48.526699
path:
- results_2023-12-03T19-24-48.526699.parquet
- split: latest
path:
- results_2023-12-03T19-24-48.526699.parquet
---
# Dataset Card for Evaluation run of xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128](https://huggingface.co/xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_xxyyy123__1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:24:48.526699](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128/blob/main/results_2023-12-03T19-24-48.526699.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": 0.40181956027293403,
"acc_stderr": 0.013504357787494032
},
"harness|gsm8k|5": {
"acc": 0.40181956027293403,
"acc_stderr": 0.013504357787494032
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_Sao10K__Stheno-v2-Delta-fp16 | open-llm-leaderboard | 2023-12-03T19:28:05Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:27:55Z | ---
pretty_name: Evaluation run of Sao10K/Stheno-V2-Delta-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Sao10K/Stheno-V2-Delta-fp16](https://huggingface.co/Sao10K/Stheno-V2-Delta-fp16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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 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_Sao10K__Stheno-V2-Delta-fp16\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:27:58.117537](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Stheno-V2-Delta-fp16/blob/main/results_2023-12-03T19-27-58.117537.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\": 0.1281273692191054,\n\
\ \"acc_stderr\": 0.009206398549980031\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.1281273692191054,\n \"acc_stderr\": 0.009206398549980031\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Sao10K/Stheno-V2-Delta-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T19_27_52.761303
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-27-52.761303.parquet'
- split: 2023_12_03T19_27_58.117537
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-27-58.117537.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-27-58.117537.parquet'
- config_name: results
data_files:
- split: 2023_12_03T19_27_52.761303
path:
- results_2023-12-03T19-27-52.761303.parquet
- split: 2023_12_03T19_27_58.117537
path:
- results_2023-12-03T19-27-58.117537.parquet
- split: latest
path:
- results_2023-12-03T19-27-58.117537.parquet
---
# Dataset Card for Evaluation run of Sao10K/Stheno-V2-Delta-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/Stheno-V2-Delta-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Sao10K/Stheno-V2-Delta-fp16](https://huggingface.co/Sao10K/Stheno-V2-Delta-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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 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_Sao10K__Stheno-V2-Delta-fp16",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:27:58.117537](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Stheno-V2-Delta-fp16/blob/main/results_2023-12-03T19-27-58.117537.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": 0.1281273692191054,
"acc_stderr": 0.009206398549980031
},
"harness|gsm8k|5": {
"acc": 0.1281273692191054,
"acc_stderr": 0.009206398549980031
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_TigerResearch__tigerbot-70b-chat-v2 | open-llm-leaderboard | 2023-12-03T19:49:35Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:40:07Z | ---
pretty_name: Evaluation run of TigerResearch/tigerbot-70b-chat-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TigerResearch/tigerbot-70b-chat-v2](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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 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_TigerResearch__tigerbot-70b-chat-v2\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:49:28.607011](https://huggingface.co/datasets/open-llm-leaderboard/details_TigerResearch__tigerbot-70b-chat-v2/blob/main/results_2023-12-03T19-49-28.607011.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\": 0.5435936315390447,\n\
\ \"acc_stderr\": 0.013720038270485332\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5435936315390447,\n \"acc_stderr\": 0.013720038270485332\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T19_40_05.038065
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-40-05.038065.parquet'
- split: 2023_12_03T19_49_28.607011
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-49-28.607011.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-49-28.607011.parquet'
- config_name: results
data_files:
- split: 2023_12_03T19_40_05.038065
path:
- results_2023-12-03T19-40-05.038065.parquet
- split: 2023_12_03T19_49_28.607011
path:
- results_2023-12-03T19-49-28.607011.parquet
- split: latest
path:
- results_2023-12-03T19-49-28.607011.parquet
---
# Dataset Card for Evaluation run of TigerResearch/tigerbot-70b-chat-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TigerResearch/tigerbot-70b-chat-v2](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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 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_TigerResearch__tigerbot-70b-chat-v2",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:49:28.607011](https://huggingface.co/datasets/open-llm-leaderboard/details_TigerResearch__tigerbot-70b-chat-v2/blob/main/results_2023-12-03T19-49-28.607011.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": 0.5435936315390447,
"acc_stderr": 0.013720038270485332
},
"harness|gsm8k|5": {
"acc": 0.5435936315390447,
"acc_stderr": 0.013720038270485332
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_chargoddard__Yi-34B-Llama | open-llm-leaderboard | 2023-12-03T19:49:27Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T19:49:22Z | ---
pretty_name: Evaluation run of chargoddard/Yi-34B-Llama
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/Yi-34B-Llama](https://huggingface.co/chargoddard/Yi-34B-Llama) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_chargoddard__Yi-34B-Llama\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:49:20.371477](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__Yi-34B-Llama/blob/main/results_2023-12-03T19-49-20.371477.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\": 0.6080363912054587,\n\
\ \"acc_stderr\": 0.013447140886023824\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.6080363912054587,\n \"acc_stderr\": 0.013447140886023824\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/Yi-34B-Llama
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T19_49_20.371477
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-49-20.371477.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-49-20.371477.parquet'
- config_name: results
data_files:
- split: 2023_12_03T19_49_20.371477
path:
- results_2023-12-03T19-49-20.371477.parquet
- split: latest
path:
- results_2023-12-03T19-49-20.371477.parquet
---
# Dataset Card for Evaluation run of chargoddard/Yi-34B-Llama
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/Yi-34B-Llama
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/Yi-34B-Llama](https://huggingface.co/chargoddard/Yi-34B-Llama) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_chargoddard__Yi-34B-Llama",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:49:20.371477](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__Yi-34B-Llama/blob/main/results_2023-12-03T19-49-20.371477.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": 0.6080363912054587,
"acc_stderr": 0.013447140886023824
},
"harness|gsm8k|5": {
"acc": 0.6080363912054587,
"acc_stderr": 0.013447140886023824
}
}
```
### 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] | [
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|
Carlosgg14/HISSOKA | Carlosgg14 | 2023-12-03T20:00:27Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T20:00:07Z | ---
license: openrail
---
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|
open-llm-leaderboard/details_Walmart-the-bag__Misted-7B | open-llm-leaderboard | 2023-12-03T20:09:09Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T20:09:04Z | ---
pretty_name: Evaluation run of Walmart-the-bag/Misted-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Walmart-the-bag/Misted-7B](https://huggingface.co/Walmart-the-bag/Misted-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_Walmart-the-bag__Misted-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T20:09:02.176077](https://huggingface.co/datasets/open-llm-leaderboard/details_Walmart-the-bag__Misted-7B/blob/main/results_2023-12-03T20-09-02.176077.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\": 0.5959059893858984,\n\
\ \"acc_stderr\": 0.01351675297272172\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.5959059893858984,\n \"acc_stderr\": 0.01351675297272172\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Walmart-the-bag/Misted-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T20_09_02.176077
path:
- '**/details_harness|gsm8k|5_2023-12-03T20-09-02.176077.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T20-09-02.176077.parquet'
- config_name: results
data_files:
- split: 2023_12_03T20_09_02.176077
path:
- results_2023-12-03T20-09-02.176077.parquet
- split: latest
path:
- results_2023-12-03T20-09-02.176077.parquet
---
# Dataset Card for Evaluation run of Walmart-the-bag/Misted-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Walmart-the-bag/Misted-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Walmart-the-bag/Misted-7B](https://huggingface.co/Walmart-the-bag/Misted-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_Walmart-the-bag__Misted-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T20:09:02.176077](https://huggingface.co/datasets/open-llm-leaderboard/details_Walmart-the-bag__Misted-7B/blob/main/results_2023-12-03T20-09-02.176077.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": 0.5959059893858984,
"acc_stderr": 0.01351675297272172
},
"harness|gsm8k|5": {
"acc": 0.5959059893858984,
"acc_stderr": 0.01351675297272172
}
}
```
### 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] | [
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|
NomeIncrivel/Mufasa | NomeIncrivel | 2023-12-03T20:23:31Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T20:23:10Z | ---
license: openrail
---
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|
Carlosgg14/HISSOKANEW | Carlosgg14 | 2023-12-03T20:30:09Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T20:29:51Z | ---
license: openrail
---
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|
NomeIncrivel/Robin | NomeIncrivel | 2023-12-03T20:32:41Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T20:32:13Z | ---
license: openrail
---
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|
NomeIncrivel/Timao | NomeIncrivel | 2023-12-03T20:37:03Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T20:36:36Z | ---
license: openrail
---
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|
NomeIncrivel/Laranja-Irritante | NomeIncrivel | 2023-12-03T20:40:44Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T20:40:20Z | ---
license: openrail
---
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|
sayannath/forest-data-with-caption | sayannath | 2023-12-03T20:51:13Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T20:51:12Z | ---
license: apache-2.0
---
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|
roa7n/patched_test_f_SPOUT_ps_50_preds_v2023d | roa7n | 2023-12-03T21:01:43Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T21:01:41Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: sequence_str
dtype: string
- name: label
dtype: int64
- name: m1_preds
dtype: float32
splits:
- name: train
num_bytes: 1971093
num_examples: 5816
download_size: 219220
dataset_size: 1971093
---
# Dataset Card for "patched_test_f_SPOUT_ps_50_preds_v2023d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
open-llm-leaderboard/details_bhenrym14__platypus-yi-34b | open-llm-leaderboard | 2023-12-03T21:40:15Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T21:36:59Z | ---
pretty_name: Evaluation run of bhenrym14/platypus-yi-34b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bhenrym14/platypus-yi-34b](https://huggingface.co/bhenrym14/platypus-yi-34b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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 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_bhenrym14__platypus-yi-34b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T21:40:07.043539](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__platypus-yi-34b/blob/main/results_2023-12-03T21-40-07.043539.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\": 0.6103108415466262,\n\
\ \"acc_stderr\": 0.01343312323611072\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.6103108415466262,\n \"acc_stderr\": 0.01343312323611072\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bhenrym14/platypus-yi-34b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T21_36_56.890995
path:
- '**/details_harness|gsm8k|5_2023-12-03T21-36-56.890995.parquet'
- split: 2023_12_03T21_40_07.043539
path:
- '**/details_harness|gsm8k|5_2023-12-03T21-40-07.043539.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T21-40-07.043539.parquet'
- config_name: results
data_files:
- split: 2023_12_03T21_36_56.890995
path:
- results_2023-12-03T21-36-56.890995.parquet
- split: 2023_12_03T21_40_07.043539
path:
- results_2023-12-03T21-40-07.043539.parquet
- split: latest
path:
- results_2023-12-03T21-40-07.043539.parquet
---
# Dataset Card for Evaluation run of bhenrym14/platypus-yi-34b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bhenrym14/platypus-yi-34b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bhenrym14/platypus-yi-34b](https://huggingface.co/bhenrym14/platypus-yi-34b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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 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_bhenrym14__platypus-yi-34b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T21:40:07.043539](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__platypus-yi-34b/blob/main/results_2023-12-03T21-40-07.043539.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": 0.6103108415466262,
"acc_stderr": 0.01343312323611072
},
"harness|gsm8k|5": {
"acc": 0.6103108415466262,
"acc_stderr": 0.01343312323611072
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_brucethemoose__Capybara-Tess-Yi-34B-200K | open-llm-leaderboard | 2023-12-03T21:47:35Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T21:41:09Z | ---
pretty_name: Evaluation run of brucethemoose/Capybara-Tess-Yi-34B-200K
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [brucethemoose/Capybara-Tess-Yi-34B-200K](https://huggingface.co/brucethemoose/Capybara-Tess-Yi-34B-200K)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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 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_brucethemoose__Capybara-Tess-Yi-34B-200K\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T21:47:28.833040](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__Capybara-Tess-Yi-34B-200K/blob/main/results_2023-12-03T21-47-28.833040.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\": 0.5739196360879454,\n\
\ \"acc_stderr\": 0.013621144396086712\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5739196360879454,\n \"acc_stderr\": 0.013621144396086712\n\
\ }\n}\n```"
repo_url: https://huggingface.co/brucethemoose/Capybara-Tess-Yi-34B-200K
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T21_41_07.092884
path:
- '**/details_harness|gsm8k|5_2023-12-03T21-41-07.092884.parquet'
- split: 2023_12_03T21_47_28.833040
path:
- '**/details_harness|gsm8k|5_2023-12-03T21-47-28.833040.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T21-47-28.833040.parquet'
- config_name: results
data_files:
- split: 2023_12_03T21_41_07.092884
path:
- results_2023-12-03T21-41-07.092884.parquet
- split: 2023_12_03T21_47_28.833040
path:
- results_2023-12-03T21-47-28.833040.parquet
- split: latest
path:
- results_2023-12-03T21-47-28.833040.parquet
---
# Dataset Card for Evaluation run of brucethemoose/Capybara-Tess-Yi-34B-200K
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/brucethemoose/Capybara-Tess-Yi-34B-200K
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [brucethemoose/Capybara-Tess-Yi-34B-200K](https://huggingface.co/brucethemoose/Capybara-Tess-Yi-34B-200K) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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 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_brucethemoose__Capybara-Tess-Yi-34B-200K",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T21:47:28.833040](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__Capybara-Tess-Yi-34B-200K/blob/main/results_2023-12-03T21-47-28.833040.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": 0.5739196360879454,
"acc_stderr": 0.013621144396086712
},
"harness|gsm8k|5": {
"acc": 0.5739196360879454,
"acc_stderr": 0.013621144396086712
}
}
```
### 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] | [
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|
ihaflix1/Drauziovirella | ihaflix1 | 2023-12-03T22:14:37Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T22:13:39Z | ---
license: openrail
---
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|
kkyo/Nagisin | kkyo | 2023-12-03T22:35:01Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T22:34:27Z | ---
license: openrail
---
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|
ihaflix1/drauzio | ihaflix1 | 2023-12-03T22:36:32Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T22:36:07Z | ---
license: openrail
---
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|
Thiago32/Drauziovirella | Thiago32 | 2023-12-03T23:14:33Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T23:14:33Z | ---
license: openrail
---
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|
dsmueller/ams_qa_pairs | dsmueller | 2023-12-04T00:07:04Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T23:54:07Z | ---
license: apache-2.0
---
This is where aerospace mechanisms symposia training question/answer pairs are stored.
See this repository for details: https://github.com/dsmueller3760/aerospace_chatbot/tree/main | [
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|
Thiago32/1drauziovirella | Thiago32 | 2023-12-04T00:11:24Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-04T00:08:23Z | ---
license: openrail
---
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|
roupenminassian/vehicle-dataset-v6 | roupenminassian | 2023-12-04T00:14:15Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T00:12:25Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: int64
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
struct:
- name: id
sequence: int64
- name: area
sequence: float64
- name: bbox
sequence:
sequence: float64
- name: category
sequence: int64
splits:
- name: train
num_bytes: 253291624.29
num_examples: 2222
download_size: 248180130
dataset_size: 253291624.29
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vehicle-dataset-v6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
terru3/tokenized_tinystories384 | terru3 | 2023-12-04T00:34:25Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T00:26:36Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 5998238715
num_examples: 2119719
- name: validation
num_bytes: 61703030
num_examples: 21990
download_size: 1771825341
dataset_size: 6059941745
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
xiaofujie/pr | xiaofujie | 2023-12-04T11:00:57Z | 0 | 0 | [
"license:bsl-1.0",
"region:us"
]
| null | 2023-12-04T00:31:27Z | ---
license: bsl-1.0
---
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|
inswave/AISqaure_Intergrated_v2 | inswave | 2023-12-04T00:34:57Z | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-12-04T00:33:20Z | ---
license: cc-by-nc-sa-4.0
---
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|
Chunt0/adminol-12-3 | Chunt0 | 2023-12-04T00:39:20Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T00:37:39Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 11968667.0
num_examples: 34
download_size: 11935418
dataset_size: 11968667.0
---
# Dataset Card for "adminol-12-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
agangal/baseball-full-captions | agangal | 2023-12-04T00:48:51Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T00:48:48Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: additional_feature
dtype: string
splits:
- name: train
num_bytes: 18244105.0
num_examples: 54
download_size: 18243329
dataset_size: 18244105.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Xokito/datasetzakev | Xokito | 2023-12-04T01:09:32Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-04T01:09:16Z | ---
license: openrail
---
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|
roupenminassian/vehicle-dataset-v7 | roupenminassian | 2023-12-04T01:15:34Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T01:13:31Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: int64
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
struct:
- name: id
sequence: int64
- name: area
sequence: float64
- name: bbox
sequence:
sequence: float64
- name: category
sequence: int64
splits:
- name: train
num_bytes: 281009635.7
num_examples: 2438
download_size: 275908173
dataset_size: 281009635.7
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vehicle-dataset-v7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
ntphu/datademo | ntphu | 2023-12-04T01:31:33Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-04T01:31:33Z | ---
license: mit
---
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|
sahityas/goodreads-llama-7b-negated | sahityas | 2023-12-04T01:49:29Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T01:49:28Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 29534
num_examples: 254
download_size: 16020
dataset_size: 29534
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Deni016/silvioluiz | Deni016 | 2023-12-04T01:58:01Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-04T01:57:31Z | ---
license: openrail
---
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|
PieDiscover/recordings_20230716_20230729 | PieDiscover | 2023-12-04T02:34:06Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-04T01:58:19Z | ---
license: mit
---
This dataset consists of real-world recordings placed in several locations in Hangzhou, China. Researchers may find it useful for fine-tuning Speech-to-Text models. For privacy reasons, the file is encrypted, and anyone interested in the dataset should contact the uploader for the access key. However, access will only be granted to those who can provide a reasonable explanation of their intended use of the data and agree to respect the privacy of the individuals in the recordings. | [
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|
inswave/AISqaure_Dpo_v1 | inswave | 2023-12-04T02:02:07Z | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-12-04T02:01:42Z | ---
license: cc-by-nc-sa-4.0
---
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|
Broomva/instruct-spa-pbb | Broomva | 2023-12-04T02:21:58Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T02:21:57Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1896051.1872492959
num_examples: 7483
download_size: 925517
dataset_size: 1896051.1872492959
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
shiviguptta/coco_canny | shiviguptta | 2023-12-04T02:33:49Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T02:33:46Z | ---
dataset_info:
features:
- name: file_name
dtype: string
- name: conditional_image_file
dtype: string
- name: text
dtype: string
- name: image
dtype: image
- name: conditional_image
dtype: image
splits:
- name: train
num_bytes: 15412639.0
num_examples: 306
download_size: 15400355
dataset_size: 15412639.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
sahityas/goodreads-llama-7b-negated-a | sahityas | 2023-12-04T02:43:26Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T02:43:25Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 29500
num_examples: 254
download_size: 15968
dataset_size: 29500
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Broomva/translation_pbb_spa | Broomva | 2023-12-04T02:46:09Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T02:46:03Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: int64
- name: translation
struct:
- name: pbb
dtype: string
- name: spa
dtype: string
splits:
- name: train
num_bytes: 109901
num_examples: 2428
- name: validation
num_bytes: 31457
num_examples: 607
- name: test
num_bytes: 33789
num_examples: 759
download_size: 137803
dataset_size: 175147
---
# Dataset Card for "translation_pbb_spa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
meta-math/GSM8K_zh | meta-math | 2023-12-04T04:02:01Z | 0 | 0 | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"language:zh",
"license:mit",
"math",
"math-qa",
"chinese-math-qa",
"region:us"
]
| [
"question-answering"
]
| 2023-12-04T03:08:44Z | ---
license: mit
task_categories:
- question-answering
language:
- en
- zh
tags:
- math
- math-qa
- chinese-math-qa
size_categories:
- n<1K
---
# Dataset
`GSM8K_zh` is a dataset for mathematical reasoning in Chinese, question-answer pairs are translated from GSM8K (https://github.com/openai/grade-school-math/tree/master) by `GPT-3.5-Turbo` with few-shot prompting.
The dataset consists of 7473 training samples and 1319 testing samples. The former is for **supervised fine-tuning**, while the latter is for **evaluation**.
for training samples, `question_zh` and `answer_zh` are question and answer keys, respectively;
for testing samples, only the translated questions are provided (`question_zh`).
# Citation
If you find the `GSM8K_zh` dataset useful for your projects/papers, please cite the following paper.
```bibtex
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
journal={arXiv preprint arXiv:2309.12284},
year={2023}
}
``` | [
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|
brch12/bahman | brch12 | 2023-12-04T03:11:09Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-04T03:11:09Z | ---
license: apache-2.0
---
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|
nlp-with-deeplearning/ko.databricks-dolly-15k | nlp-with-deeplearning | 2023-12-04T03:27:59Z | 0 | 0 | [
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:en",
"language:ko",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| [
"question-answering",
"summarization"
]
| 2023-12-04T03:12:08Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
- summarization
language:
- en
- ko
size_categories:
- 10K<n<100K
---
원본 데이터셋: [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) | [
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|
Parikshith/grow-1-monolingual-ha-en-1_1m | Parikshith | 2023-12-04T03:14:35Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T03:12:51Z | ---
dataset_info:
features:
- name: ha
dtype: string
- name: generated_text
dtype: string
splits:
- name: train
num_bytes: 265225402
num_examples: 1100000
download_size: 171871099
dataset_size: 265225402
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
meta-math/MetaMathQA_GSM8K_zh | meta-math | 2023-12-04T05:23:20Z | 0 | 0 | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"language:zh",
"license:mit",
"math",
"math-qa",
"region:us"
]
| [
"question-answering"
]
| 2023-12-04T03:13:47Z | ---
license: mit
task_categories:
- question-answering
language:
- en
- zh
tags:
- math
- math-qa
size_categories:
- n<1K
---
# Dataset
`MetaMathQA_GSM8K_zh` is a dataset for mathematical reasoning in Chinese,
question-answer pairs are translated from MetaMathQA (https://huggingface.co/datasets/meta-math/MetaMathQA) by `GPT-3.5-Turbo` with few-shot prompting.
The dataset consists of 231685 samples.
# Citation
If you find the `GSM8K_zh` dataset useful for your projects/papers, please cite the following paper.
```bibtex
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
journal={arXiv preprint arXiv:2309.12284},
year={2023}
}
``` | [
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|
nlp-with-deeplearning/ko.openhermes | nlp-with-deeplearning | 2023-12-04T03:26:54Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"language:ko",
"license:cc-by-nc-sa-4.0",
"distillation",
"synthetic data",
"gpt",
"region:us"
]
| [
"text-generation"
]
| 2023-12-04T03:14:12Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- en
- ko
tags:
- distillation
- synthetic data
- gpt
size_categories:
- 100K<n<1M
---
원본 데이터셋: [teknium/openhermes](https://huggingface.co/datasets/teknium/openhermes) | [
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|
nlp-with-deeplearning/Ko.HelpSteer | nlp-with-deeplearning | 2023-12-04T03:25:23Z | 0 | 0 | [
"size_categories:10K<n<100K",
"language:en",
"language:ko",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-12-04T03:18:07Z | ---
license: cc-by-nc-sa-4.0
size_categories:
- 10K<n<100K
language:
- en
- ko
---
원본 데이터셋: [nvidia/HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) | [
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|
sahityas/goodreads-llama-7b-a | sahityas | 2023-12-04T03:23:13Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T03:23:10Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 28396
num_examples: 253
download_size: 15865
dataset_size: 28396
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Seongill/nq_cbr_total | Seongill | 2023-12-04T03:34:48Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-04T03:34:14Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: case_question
dtype: string
- name: case_context
dtype: string
- name: case_answer
dtype: string
splits:
- name: train
num_bytes: 654471488
num_examples: 87925
- name: test
num_bytes: 26921812
num_examples: 3610
download_size: 386473537
dataset_size: 681393300
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
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