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open-llm-leaderboard/details_CausalLM__14B-DPO-alpha | open-llm-leaderboard | 2023-12-02T14:29:54Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:28:48Z | ---
pretty_name: Evaluation run of CausalLM/14B-DPO-alpha
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CausalLM/14B-DPO-alpha](https://huggingface.co/CausalLM/14B-DPO-alpha) 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_CausalLM__14B-DPO-alpha\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:29:47.856386](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__14B-DPO-alpha/blob/main/results_2023-12-02T14-29-47.856386.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\": 0.6269901440485216,\n\
\ \"acc_stderr\": 0.01332087660977722\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.6269901440485216,\n \"acc_stderr\": 0.01332087660977722\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CausalLM/14B-DPO-alpha
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_02T14_28_45.783629
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-28-45.783629.parquet'
- split: 2023_12_02T14_29_47.856386
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-29-47.856386.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-29-47.856386.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_28_45.783629
path:
- results_2023-12-02T14-28-45.783629.parquet
- split: 2023_12_02T14_29_47.856386
path:
- results_2023-12-02T14-29-47.856386.parquet
- split: latest
path:
- results_2023-12-02T14-29-47.856386.parquet
---
# Dataset Card for Evaluation run of CausalLM/14B-DPO-alpha
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CausalLM/14B-DPO-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 [CausalLM/14B-DPO-alpha](https://huggingface.co/CausalLM/14B-DPO-alpha) 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_CausalLM__14B-DPO-alpha",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:29:47.856386](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__14B-DPO-alpha/blob/main/results_2023-12-02T14-29-47.856386.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": 0.6269901440485216,
"acc_stderr": 0.01332087660977722
},
"harness|gsm8k|5": {
"acc": 0.6269901440485216,
"acc_stderr": 0.01332087660977722
}
}
```
### 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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|
sriramahesh2000/GenetarionDataset | sriramahesh2000 | 2023-12-02T14:35:53Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T14:35:29Z | ---
license: apache-2.0
--- | [
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|
open-llm-leaderboard/details_monology__openinstruct-mistral-7b | open-llm-leaderboard | 2023-12-02T14:37:52Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:37:47Z | ---
pretty_name: Evaluation run of monology/openinstruct-mistral-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [monology/openinstruct-mistral-7b](https://huggingface.co/monology/openinstruct-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_monology__openinstruct-mistral-7b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:37:45.308018](https://huggingface.co/datasets/open-llm-leaderboard/details_monology__openinstruct-mistral-7b/blob/main/results_2023-12-02T14-37-45.308018.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\": 0.5049279757391963,\n\
\ \"acc_stderr\": 0.013771815775470575\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5049279757391963,\n \"acc_stderr\": 0.013771815775470575\n\
\ }\n}\n```"
repo_url: https://huggingface.co/monology/openinstruct-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_02T14_37_45.308018
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-37-45.308018.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-37-45.308018.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_37_45.308018
path:
- results_2023-12-02T14-37-45.308018.parquet
- split: latest
path:
- results_2023-12-02T14-37-45.308018.parquet
---
# Dataset Card for Evaluation run of monology/openinstruct-mistral-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/monology/openinstruct-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 [monology/openinstruct-mistral-7b](https://huggingface.co/monology/openinstruct-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_monology__openinstruct-mistral-7b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:37:45.308018](https://huggingface.co/datasets/open-llm-leaderboard/details_monology__openinstruct-mistral-7b/blob/main/results_2023-12-02T14-37-45.308018.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": 0.5049279757391963,
"acc_stderr": 0.013771815775470575
},
"harness|gsm8k|5": {
"acc": 0.5049279757391963,
"acc_stderr": 0.013771815775470575
}
}
```
### 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_vihangd__smartyplats-7b-v2 | open-llm-leaderboard | 2023-12-02T14:38:05Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:38:00Z | ---
pretty_name: Evaluation run of vihangd/smartyplats-7b-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [vihangd/smartyplats-7b-v2](https://huggingface.co/vihangd/smartyplats-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_vihangd__smartyplats-7b-v2\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:37:57.896136](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__smartyplats-7b-v2/blob/main/results_2023-12-02T14-37-57.896136.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\": 0.38817285822592873,\n\
\ \"acc_stderr\": 0.013423607564002743\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.38817285822592873,\n \"acc_stderr\": 0.013423607564002743\n\
\ }\n}\n```"
repo_url: https://huggingface.co/vihangd/smartyplats-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_02T14_37_57.896136
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-37-57.896136.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-37-57.896136.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_37_57.896136
path:
- results_2023-12-02T14-37-57.896136.parquet
- split: latest
path:
- results_2023-12-02T14-37-57.896136.parquet
---
# Dataset Card for Evaluation run of vihangd/smartyplats-7b-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vihangd/smartyplats-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 [vihangd/smartyplats-7b-v2](https://huggingface.co/vihangd/smartyplats-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_vihangd__smartyplats-7b-v2",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:37:57.896136](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__smartyplats-7b-v2/blob/main/results_2023-12-02T14-37-57.896136.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": 0.38817285822592873,
"acc_stderr": 0.013423607564002743
},
"harness|gsm8k|5": {
"acc": 0.38817285822592873,
"acc_stderr": 0.013423607564002743
}
}
```
### 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_VAGOsolutions__SauerkrautLM-7b-HerO | open-llm-leaderboard | 2023-12-02T14:47:14Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:47:10Z | ---
pretty_name: Evaluation run of VAGOsolutions/SauerkrautLM-7b-HerO
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [VAGOsolutions/SauerkrautLM-7b-HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)\
\ 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_VAGOsolutions__SauerkrautLM-7b-HerO\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:47:07.323198](https://huggingface.co/datasets/open-llm-leaderboard/details_VAGOsolutions__SauerkrautLM-7b-HerO/blob/main/results_2023-12-02T14-47-07.323198.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\": 0.4927975739196361,\n\
\ \"acc_stderr\": 0.013771055751972868\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.4927975739196361,\n \"acc_stderr\": 0.013771055751972868\n\
\ }\n}\n```"
repo_url: https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO
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_02T14_47_07.323198
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-47-07.323198.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-47-07.323198.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_47_07.323198
path:
- results_2023-12-02T14-47-07.323198.parquet
- split: latest
path:
- results_2023-12-02T14-47-07.323198.parquet
---
# Dataset Card for Evaluation run of VAGOsolutions/SauerkrautLM-7b-HerO
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO
- **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 [VAGOsolutions/SauerkrautLM-7b-HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) 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_VAGOsolutions__SauerkrautLM-7b-HerO",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:47:07.323198](https://huggingface.co/datasets/open-llm-leaderboard/details_VAGOsolutions__SauerkrautLM-7b-HerO/blob/main/results_2023-12-02T14-47-07.323198.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": 0.4927975739196361,
"acc_stderr": 0.013771055751972868
},
"harness|gsm8k|5": {
"acc": 0.4927975739196361,
"acc_stderr": 0.013771055751972868
}
}
```
### 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_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1 | open-llm-leaderboard | 2023-12-02T14:49:17Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:49:12Z | ---
pretty_name: Evaluation run of habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1](https://huggingface.co/habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1)\
\ 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_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:49:10.140020](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1/blob/main/results_2023-12-02T14-49-10.140020.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\": 0.01592115238817286,\n\
\ \"acc_stderr\": 0.0034478192723889976\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.01592115238817286,\n \"acc_stderr\": 0.0034478192723889976\n\
\ }\n}\n```"
repo_url: https://huggingface.co/habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1
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_02T14_49_10.140020
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-49-10.140020.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-49-10.140020.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_49_10.140020
path:
- results_2023-12-02T14-49-10.140020.parquet
- split: latest
path:
- results_2023-12-02T14-49-10.140020.parquet
---
# Dataset Card for Evaluation run of habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1
- **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 [habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1](https://huggingface.co/habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1) 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_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:49:10.140020](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1/blob/main/results_2023-12-02T14-49-10.140020.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": 0.01592115238817286,
"acc_stderr": 0.0034478192723889976
},
"harness|gsm8k|5": {
"acc": 0.01592115238817286,
"acc_stderr": 0.0034478192723889976
}
}
```
### 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_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1 | open-llm-leaderboard | 2023-12-02T14:49:21Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:49:16Z | ---
pretty_name: Evaluation run of habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1](https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1)\
\ 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_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:49:13.784877](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1/blob/main/results_2023-12-02T14-49-13.784877.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\": 0.013646702047005308,\n\
\ \"acc_stderr\": 0.0031957470754807814\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.013646702047005308,\n \"acc_stderr\": 0.0031957470754807814\n\
\ }\n}\n```"
repo_url: https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
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_02T14_49_13.784877
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-49-13.784877.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-49-13.784877.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_49_13.784877
path:
- results_2023-12-02T14-49-13.784877.parquet
- split: latest
path:
- results_2023-12-02T14-49-13.784877.parquet
---
# Dataset Card for Evaluation run of habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
- **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 [habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1](https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1) 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_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:49:13.784877](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1/blob/main/results_2023-12-02T14-49-13.784877.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": 0.013646702047005308,
"acc_stderr": 0.0031957470754807814
},
"harness|gsm8k|5": {
"acc": 0.013646702047005308,
"acc_stderr": 0.0031957470754807814
}
}
```
### 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_habanoz__TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 | open-llm-leaderboard | 2023-12-02T14:51:17Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:51:12Z | ---
pretty_name: Evaluation run of habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1](https://huggingface.co/habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1)\
\ 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_habanoz__TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:51:09.635098](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1/blob/main/results_2023-12-02T14-51-09.635098.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/habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1
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_02T14_51_09.635098
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-51-09.635098.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-51-09.635098.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_51_09.635098
path:
- results_2023-12-02T14-51-09.635098.parquet
- split: latest
path:
- results_2023-12-02T14-51-09.635098.parquet
---
# Dataset Card for Evaluation run of habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1
- **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 [habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1](https://huggingface.co/habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1) 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_habanoz__TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:51:09.635098](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1/blob/main/results_2023-12-02T14-51-09.635098.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_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1 | open-llm-leaderboard | 2023-12-02T14:51:34Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:51:29Z | ---
pretty_name: Evaluation run of habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1](https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1)\
\ 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_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:51:26.830602](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1/blob/main/results_2023-12-02T14-51-26.830602.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\": 0.01288855193328279,\n\
\ \"acc_stderr\": 0.003106901266499671\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.01288855193328279,\n \"acc_stderr\": 0.003106901266499671\n\
\ }\n}\n```"
repo_url: https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1
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_02T14_51_26.830602
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-51-26.830602.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-51-26.830602.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_51_26.830602
path:
- results_2023-12-02T14-51-26.830602.parquet
- split: latest
path:
- results_2023-12-02T14-51-26.830602.parquet
---
# Dataset Card for Evaluation run of habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1
- **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 [habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1](https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1) 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_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:51:26.830602](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-4epochs-oasst1-top1-instruct-V1/blob/main/results_2023-12-02T14-51-26.830602.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": 0.01288855193328279,
"acc_stderr": 0.003106901266499671
},
"harness|gsm8k|5": {
"acc": 0.01288855193328279,
"acc_stderr": 0.003106901266499671
}
}
```
### 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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|
Danieldlima21/ralux | Danieldlima21 | 2023-12-02T15:00:33Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T14:57:57Z | ---
license: openrail
---
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|
Rayan2023/dataset2music | Rayan2023 | 2023-12-02T15:01:49Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T15:01:28Z | ---
license: openrail
---
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|
open-llm-leaderboard/details_openaccess-ai-collective__openhermes-2_5-dpo-no-robots-v2 | open-llm-leaderboard | 2023-12-02T15:09:14Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:03:52Z | ---
pretty_name: Evaluation run of openaccess-ai-collective/openhermes-2_5-dpo-no-robots-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [openaccess-ai-collective/openhermes-2_5-dpo-no-robots-v2](https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots-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_openaccess-ai-collective__openhermes-2_5-dpo-no-robots-v2\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:09:06.633053](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__openhermes-2_5-dpo-no-robots-v2/blob/main/results_2023-12-02T15-09-06.633053.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\": 0.55420773313116,\n\
\ \"acc_stderr\": 0.013691305174506691\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.55420773313116,\n \"acc_stderr\": 0.013691305174506691\n\
\ }\n}\n```"
repo_url: https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots-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_02T15_03_49.963342
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-03-49.963342.parquet'
- split: 2023_12_02T15_09_06.633053
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-09-06.633053.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-09-06.633053.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_03_49.963342
path:
- results_2023-12-02T15-03-49.963342.parquet
- split: 2023_12_02T15_09_06.633053
path:
- results_2023-12-02T15-09-06.633053.parquet
- split: latest
path:
- results_2023-12-02T15-09-06.633053.parquet
---
# Dataset Card for Evaluation run of openaccess-ai-collective/openhermes-2_5-dpo-no-robots-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots-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 [openaccess-ai-collective/openhermes-2_5-dpo-no-robots-v2](https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots-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_openaccess-ai-collective__openhermes-2_5-dpo-no-robots-v2",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:09:06.633053](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__openhermes-2_5-dpo-no-robots-v2/blob/main/results_2023-12-02T15-09-06.633053.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": 0.55420773313116,
"acc_stderr": 0.013691305174506691
},
"harness|gsm8k|5": {
"acc": 0.55420773313116,
"acc_stderr": 0.013691305174506691
}
}
```
### 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_openaccess-ai-collective__openhermes-2_5-dpo-no-robots | open-llm-leaderboard | 2023-12-02T15:08:33Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:08:28Z | ---
pretty_name: Evaluation run of openaccess-ai-collective/openhermes-2_5-dpo-no-robots
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [openaccess-ai-collective/openhermes-2_5-dpo-no-robots](https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots)\
\ 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_openaccess-ai-collective__openhermes-2_5-dpo-no-robots\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:08:25.673533](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__openhermes-2_5-dpo-no-robots/blob/main/results_2023-12-02T15-08-25.673533.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\": 0.5526914329037149,\n\
\ \"acc_stderr\": 0.013695795709089898\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5526914329037149,\n \"acc_stderr\": 0.013695795709089898\n\
\ }\n}\n```"
repo_url: https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots
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_02T15_08_25.673533
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-08-25.673533.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-08-25.673533.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_08_25.673533
path:
- results_2023-12-02T15-08-25.673533.parquet
- split: latest
path:
- results_2023-12-02T15-08-25.673533.parquet
---
# Dataset Card for Evaluation run of openaccess-ai-collective/openhermes-2_5-dpo-no-robots
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots
- **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 [openaccess-ai-collective/openhermes-2_5-dpo-no-robots](https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots) 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_openaccess-ai-collective__openhermes-2_5-dpo-no-robots",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:08:25.673533](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__openhermes-2_5-dpo-no-robots/blob/main/results_2023-12-02T15-08-25.673533.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": 0.5526914329037149,
"acc_stderr": 0.013695795709089898
},
"harness|gsm8k|5": {
"acc": 0.5526914329037149,
"acc_stderr": 0.013695795709089898
}
}
```
### 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_rinna__youri-7b-instruction | open-llm-leaderboard | 2023-12-02T15:10:10Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:10:04Z | ---
pretty_name: Evaluation run of rinna/youri-7b-instruction
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rinna/youri-7b-instruction](https://huggingface.co/rinna/youri-7b-instruction)\
\ 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_rinna__youri-7b-instruction\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:10:01.872073](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__youri-7b-instruction/blob/main/results_2023-12-02T15-10-01.872073.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/rinna/youri-7b-instruction
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_02T15_10_01.872073
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-10-01.872073.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-10-01.872073.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_10_01.872073
path:
- results_2023-12-02T15-10-01.872073.parquet
- split: latest
path:
- results_2023-12-02T15-10-01.872073.parquet
---
# Dataset Card for Evaluation run of rinna/youri-7b-instruction
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rinna/youri-7b-instruction
- **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 [rinna/youri-7b-instruction](https://huggingface.co/rinna/youri-7b-instruction) 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_rinna__youri-7b-instruction",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:10:01.872073](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__youri-7b-instruction/blob/main/results_2023-12-02T15-10-01.872073.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_rinna__youri-7b-chat | open-llm-leaderboard | 2023-12-02T15:12:29Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:11:39Z | ---
pretty_name: Evaluation run of rinna/youri-7b-chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rinna/youri-7b-chat](https://huggingface.co/rinna/youri-7b-chat) 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_rinna__youri-7b-chat\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:12:23.080545](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__youri-7b-chat/blob/main/results_2023-12-02T15-12-23.080545.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\": 0.013646702047005308,\n\
\ \"acc_stderr\": 0.0031957470754808235\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.013646702047005308,\n \"acc_stderr\": 0.0031957470754808235\n\
\ }\n}\n```"
repo_url: https://huggingface.co/rinna/youri-7b-chat
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_02T15_11_37.192628
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-11-37.192628.parquet'
- split: 2023_12_02T15_11_43.336973
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-11-43.336973.parquet'
- split: 2023_12_02T15_11_58.617219
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-11-58.617219.parquet'
- split: 2023_12_02T15_12_23.080545
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-12-23.080545.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-12-23.080545.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_11_37.192628
path:
- results_2023-12-02T15-11-37.192628.parquet
- split: 2023_12_02T15_11_43.336973
path:
- results_2023-12-02T15-11-43.336973.parquet
- split: 2023_12_02T15_11_58.617219
path:
- results_2023-12-02T15-11-58.617219.parquet
- split: 2023_12_02T15_12_23.080545
path:
- results_2023-12-02T15-12-23.080545.parquet
- split: latest
path:
- results_2023-12-02T15-12-23.080545.parquet
---
# Dataset Card for Evaluation run of rinna/youri-7b-chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rinna/youri-7b-chat
- **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 [rinna/youri-7b-chat](https://huggingface.co/rinna/youri-7b-chat) 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_rinna__youri-7b-chat",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:12:23.080545](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__youri-7b-chat/blob/main/results_2023-12-02T15-12-23.080545.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": 0.013646702047005308,
"acc_stderr": 0.0031957470754808235
},
"harness|gsm8k|5": {
"acc": 0.013646702047005308,
"acc_stderr": 0.0031957470754808235
}
}
```
### 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_iGenius-AI-Team__LLAMA-13B-test-finetuning | open-llm-leaderboard | 2023-12-02T15:13:15Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:13:10Z | ---
pretty_name: Evaluation run of iGenius-AI-Team/LLAMA-13B-test-finetuning
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [iGenius-AI-Team/LLAMA-13B-test-finetuning](https://huggingface.co/iGenius-AI-Team/LLAMA-13B-test-finetuning)\
\ 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_iGenius-AI-Team__LLAMA-13B-test-finetuning\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:13:07.767154](https://huggingface.co/datasets/open-llm-leaderboard/details_iGenius-AI-Team__LLAMA-13B-test-finetuning/blob/main/results_2023-12-02T15-13-07.767154.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\": 0.22517058377558757,\n\
\ \"acc_stderr\": 0.011505385424294625\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.22517058377558757,\n \"acc_stderr\": 0.011505385424294625\n\
\ }\n}\n```"
repo_url: https://huggingface.co/iGenius-AI-Team/LLAMA-13B-test-finetuning
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_02T15_13_07.767154
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-13-07.767154.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-13-07.767154.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_13_07.767154
path:
- results_2023-12-02T15-13-07.767154.parquet
- split: latest
path:
- results_2023-12-02T15-13-07.767154.parquet
---
# Dataset Card for Evaluation run of iGenius-AI-Team/LLAMA-13B-test-finetuning
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/iGenius-AI-Team/LLAMA-13B-test-finetuning
- **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 [iGenius-AI-Team/LLAMA-13B-test-finetuning](https://huggingface.co/iGenius-AI-Team/LLAMA-13B-test-finetuning) 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_iGenius-AI-Team__LLAMA-13B-test-finetuning",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:13:07.767154](https://huggingface.co/datasets/open-llm-leaderboard/details_iGenius-AI-Team__LLAMA-13B-test-finetuning/blob/main/results_2023-12-02T15-13-07.767154.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": 0.22517058377558757,
"acc_stderr": 0.011505385424294625
},
"harness|gsm8k|5": {
"acc": 0.22517058377558757,
"acc_stderr": 0.011505385424294625
}
}
```
### 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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|
Schandkroete/SLCSentiment1 | Schandkroete | 2023-12-02T16:24:48Z | 0 | 0 | [
"size_categories:n<1K",
"language:en",
"region:us"
]
| null | 2023-12-02T15:15:05Z | ---
language:
- en
size_categories:
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|
mesolitica/chatgpt4-commonsense-qa | mesolitica | 2023-12-03T05:16:18Z | 0 | 0 | [
"task_categories:question-answering",
"language:ms",
"region:us"
]
| [
"question-answering"
]
| 2023-12-02T15:21:55Z | ---
task_categories:
- question-answering
language:
- ms
---
# Synthetic CommonSense
Generated using ChatGPT4, originally from https://huggingface.co/datasets/commonsense_qa
Notebook at https://github.com/mesolitica/malaysian-dataset/tree/master/question-answer/chatgpt4-commonsense | [
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|
open-llm-leaderboard/details_rinna__youri-7b | open-llm-leaderboard | 2023-12-02T15:23:49Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:23:35Z | ---
pretty_name: Evaluation run of rinna/youri-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rinna/youri-7b](https://huggingface.co/rinna/youri-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_rinna__youri-7b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:23:30.420258](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__youri-7b/blob/main/results_2023-12-02T15-23-30.420258.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\": 0.08642911296436695,\n\
\ \"acc_stderr\": 0.007740044337103775\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.08642911296436695,\n \"acc_stderr\": 0.007740044337103775\n\
\ }\n}\n```"
repo_url: https://huggingface.co/rinna/youri-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_02T15_23_30.420258
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-23-30.420258.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-23-30.420258.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_23_30.420258
path:
- results_2023-12-02T15-23-30.420258.parquet
- split: latest
path:
- results_2023-12-02T15-23-30.420258.parquet
---
# Dataset Card for Evaluation run of rinna/youri-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rinna/youri-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 [rinna/youri-7b](https://huggingface.co/rinna/youri-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_rinna__youri-7b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:23:30.420258](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__youri-7b/blob/main/results_2023-12-02T15-23-30.420258.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": 0.08642911296436695,
"acc_stderr": 0.007740044337103775
},
"harness|gsm8k|5": {
"acc": 0.08642911296436695,
"acc_stderr": 0.007740044337103775
}
}
```
### 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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|
Rayan2023/dataset2teste | Rayan2023 | 2023-12-04T00:25:48Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T15:25:27Z | ---
license: openrail
---
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|
open-llm-leaderboard/details_ajibawa-2023__SlimOrca-13B | open-llm-leaderboard | 2023-12-02T15:27:29Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:27:22Z | ---
pretty_name: Evaluation run of ajibawa-2023/SlimOrca-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ajibawa-2023/SlimOrca-13B](https://huggingface.co/ajibawa-2023/SlimOrca-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_ajibawa-2023__SlimOrca-13B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:27:16.441316](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__SlimOrca-13B/blob/main/results_2023-12-02T15-27-16.441316.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\": 0.3995451099317665,\n\
\ \"acc_stderr\": 0.013491660298815988\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.3995451099317665,\n \"acc_stderr\": 0.013491660298815988\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ajibawa-2023/SlimOrca-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_02T15_27_16.441316
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-27-16.441316.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-27-16.441316.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_27_16.441316
path:
- results_2023-12-02T15-27-16.441316.parquet
- split: latest
path:
- results_2023-12-02T15-27-16.441316.parquet
---
# Dataset Card for Evaluation run of ajibawa-2023/SlimOrca-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/SlimOrca-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 [ajibawa-2023/SlimOrca-13B](https://huggingface.co/ajibawa-2023/SlimOrca-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_ajibawa-2023__SlimOrca-13B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:27:16.441316](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__SlimOrca-13B/blob/main/results_2023-12-02T15-27-16.441316.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": 0.3995451099317665,
"acc_stderr": 0.013491660298815988
},
"harness|gsm8k|5": {
"acc": 0.3995451099317665,
"acc_stderr": 0.013491660298815988
}
}
```
### 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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|
murilor9/piteco | murilor9 | 2023-12-02T15:32:59Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T15:30:44Z | ---
license: openrail
---
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|
mikehemberger/inat_2021_train_mini_plantae | mikehemberger | 2023-12-03T02:36:42Z | 0 | 1 | [
"region:us"
]
| null | 2023-12-02T15:33:44Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 05729_Plantae_Bryophyta_Bryopsida_Bryales_Bryaceae_Bryum_argenteum
'1': 05730_Plantae_Bryophyta_Bryopsida_Bryales_Bryaceae_Rhodobryum_ontariense
'2': 05731_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Leucolepis_acanthoneura
'3': 05732_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Plagiomnium_cuspidatum
'4': 05733_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Plagiomnium_insigne
'5': 05734_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Rhizomnium_glabrescens
'6': 05735_Plantae_Bryophyta_Bryopsida_Dicranales_Dicranaceae_Dicranum_scoparium
'7': 05736_Plantae_Bryophyta_Bryopsida_Dicranales_Ditrichaceae_Ceratodon_purpureus
'8': 05737_Plantae_Bryophyta_Bryopsida_Dicranales_Leucobryaceae_Leucobryum_glaucum
'9': 05738_Plantae_Bryophyta_Bryopsida_Funariales_Funariaceae_Funaria_hygrometrica
'10': 05739_Plantae_Bryophyta_Bryopsida_Funariales_Funariaceae_Physcomitrium_pyriforme
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'1857': 07586_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_verrucosus
'1858': 07587_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_fimbriata
'1859': 07588_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_glauca
'1860': 07589_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_palustris
'1861': 07590_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Paxistima_myrsinites
'1862': 07591_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Pterocelastrus_tricuspidatus
'1863': 07592_Plantae_Tracheophyta_Magnoliopsida_Ceratophyllales_Ceratophyllaceae_Ceratophyllum_demersum
'1864': 07593_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_alba
'1865': 07594_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_alternifolia
'1866': 07595_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_amomum
'1867': 07596_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_canadensis
'1868': 07597_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_drummondii
'1869': 07598_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_florida
'1870': 07599_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_mas
'1871': 07600_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_nuttallii
'1872': 07601_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_racemosa
'1873': 07602_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_rugosa
'1874': 07603_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_sanguinea
'1875': 07604_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_sericea
'1876': 07605_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_suecica
'1877': 07606_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_unalaschkensis
'1878': 07607_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_arborescens
'1879': 07608_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_macrophylla
'1880': 07609_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_quercifolia
'1881': 07610_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Philadelphus_lewisii
'1882': 07611_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Cevallia_sinuata
'1883': 07612_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Eucnide_urens
'1884': 07613_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_hispida
'1885': 07614_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_involucrata
'1886': 07615_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_laevicaulis
'1887': 07616_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Petalonyx_thurberi
'1888': 07617_Plantae_Tracheophyta_Magnoliopsida_Cornales_Nyssaceae_Nyssa_sylvatica
'1889': 07618_Plantae_Tracheophyta_Magnoliopsida_Crossosomatales_Staphyleaceae_Staphylea_trifolia
'1890': 07619_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Coriariaceae_Coriaria_arborea
'1891': 07620_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Corynocarpaceae_Corynocarpus_laevigatus
'1892': 07621_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Brandegea_bigelovii
'1893': 07622_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Bryonia_cretica
'1894': 07623_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Cucurbita_foetidissima
'1895': 07624_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Cucurbita_palmata
'1896': 07625_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Ecballium_elaterium
'1897': 07626_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Echinocystis_lobata
'1898': 07627_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Ibervillea_lindheimeri
'1899': 07628_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_fabacea
'1900': 07629_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_macrocarpa
'1901': 07630_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_oregana
'1902': 07631_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Melothria_pendula
'1903': 07632_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Momordica_charantia
'1904': 07633_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Sicyos_angulatus
'1905': 07634_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Centranthus_ruber
'1906': 07635_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Diervilla_lonicera
'1907': 07636_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_fullonum
'1908': 07637_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_laciniatus
'1909': 07638_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_sativus
'1910': 07639_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Knautia_arvensis
'1911': 07640_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Leycesteria_formosa
'1912': 07641_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Linnaea_borealis
'1913': 07642_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_albiflora
'1914': 07643_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_canadensis
'1915': 07644_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_ciliosa
'1916': 07645_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_dioica
'1917': 07646_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_hispidula
'1918': 07647_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_involucrata
'1919': 07648_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_japonica
'1920': 07649_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_maackii
'1921': 07650_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_morrowii
'1922': 07651_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_periclymenum
'1923': 07652_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_sempervirens
'1924': 07653_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_subspicata
'1925': 07654_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_tatarica
'1926': 07655_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_xylosteum
'1927': 07656_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Plectritis_congesta
'1928': 07657_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Scabiosa_columbaria
'1929': 07658_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Scabiosa_ochroleuca
'1930': 07659_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Sixalix_atropurpurea
'1931': 07660_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Succisa_pratensis
'1932': 07661_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_albus
'1933': 07662_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_mollis
'1934': 07663_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_occidentalis
'1935': 07664_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_orbiculatus
'1936': 07665_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_rotundifolius
'1937': 07666_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Triosteum_aurantiacum
'1938': 07667_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valeriana_officinalis
'1939': 07668_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valeriana_sitchensis
'1940': 07669_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valerianella_locusta
'1941': 07670_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valerianella_radiata
'1942': 07671_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Adoxa_moschatellina
'1943': 07672_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_canadensis
'1944': 07673_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_cerulea
'1945': 07674_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_ebulus
'1946': 07675_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_javanica
'1947': 07676_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_nigra
'1948': 07677_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_racemosa
'1949': 07678_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_acerifolium
'1950': 07679_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_dentatum
'1951': 07680_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_edule
'1952': 07681_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lantana
'1953': 07682_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lantanoides
'1954': 07683_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lentago
'1955': 07684_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_nudum
'1956': 07685_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_opulus
'1957': 07686_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_prunifolium
'1958': 07687_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rafinesquianum
'1959': 07688_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rhytidophyllum
'1960': 07689_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rufidulum
'1961': 07690_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_tinus
'1962': 07691_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_balfourii
'1963': 07692_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_capensis
'1964': 07693_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_glandulifera
'1965': 07694_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_noli-tangere
'1966': 07695_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_pallida
'1967': 07696_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_parviflora
'1968': 07697_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_walleriana
'1969': 07698_Plantae_Tracheophyta_Magnoliopsida_Ericales_Clethraceae_Clethra_alnifolia
'1970': 07699_Plantae_Tracheophyta_Magnoliopsida_Ericales_Cyrillaceae_Cyrilla_racemiflora
'1971': 07700_Plantae_Tracheophyta_Magnoliopsida_Ericales_Diapensiaceae_Diapensia_lapponica
'1972': 07701_Plantae_Tracheophyta_Magnoliopsida_Ericales_Diapensiaceae_Galax_urceolata
'1973': 07702_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ebenaceae_Diospyros_texana
'1974': 07703_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ebenaceae_Diospyros_virginiana
'1975': 07704_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Allotropa_virgata
'1976': 07705_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Andromeda_polifolia
'1977': 07706_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_menziesii
'1978': 07707_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_unedo
'1979': 07708_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_xalapensis
'1980': 07709_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_glauca
'1981': 07710_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_nevadensis
'1982': 07711_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_patula
'1983': 07712_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_pungens
'1984': 07713_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_uva-ursi
'1985': 07714_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctous_alpina
'1986': 07715_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Calluna_vulgaris
'1987': 07716_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Cassiope_mertensiana
'1988': 07717_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Cassiope_tetragona
'1989': 07718_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Ceratiola_ericoides
'1990': 07719_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chamaedaphne_calyculata
'1991': 07720_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_maculata
'1992': 07721_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_menziesii
'1993': 07722_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_umbellata
'1994': 07723_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Dracophyllum_longifolium
'1995': 07724_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Elliottia_pyroliflora
'1996': 07725_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Empetrum_nigrum
'1997': 07726_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Epacris_impressa
'1998': 07727_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Epigaea_repens
'1999': 07728_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_abietina
'2000': 07729_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_arborea
'2001': 07730_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_cerinthoides
'2002': 07731_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_cinerea
'2003': 07732_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_coccinea
'2004': 07733_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_corifolia
'2005': 07734_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_discolor
'2006': 07735_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_imbricata
'2007': 07736_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_lusitanica
'2008': 07737_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_plukenetii
'2009': 07738_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_tetralix
'2010': 07739_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_viscaria
'2011': 07740_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaultheria_antipoda
'2012': 07741_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaultheria_depressa
'2013': 07742_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaultheria_hispidula
'2014': 07743_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaultheria_procumbens
'2015': 07744_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaultheria_shallon
'2016': 07745_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaylussacia_baccata
'2017': 07746_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_angustifolia
'2018': 07747_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_latifolia
'2019': 07748_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_microphylla
'2020': 07749_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_polifolia
'2021': 07750_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_procumbens
'2022': 07751_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leptecophylla_juniperina
'2023': 07752_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leptecophylla_tameiameiae
'2024': 07753_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leucopogon_fasciculatus
'2025': 07754_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leucopogon_fraseri
'2026': 07755_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leucothoe_fontanesiana
'2027': 07756_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Lyonia_lucida
'2028': 07757_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Moneses_uniflora
'2029': 07758_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Monotropa_hypopitys
'2030': 07759_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Monotropa_uniflora
'2031': 07760_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Orthilia_secunda
'2032': 07761_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Oxydendrum_arboreum
'2033': 07762_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pentachondra_pumila
'2034': 07763_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Phyllodoce_breweri
'2035': 07764_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Phyllodoce_empetriformis
'2036': 07765_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pterospora_andromedea
'2037': 07766_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pyrola_americana
'2038': 07767_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pyrola_asarifolia
'2039': 07768_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pyrola_chlorantha
'2040': 07769_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pyrola_elliptica
'2041': 07770_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pyrola_minor
'2042': 07771_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pyrola_picta
'2043': 07772_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Pyrola_rotundifolia
'2044': 07773_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_albiflorum
'2045': 07774_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_calendulaceum
'2046': 07775_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_canadense
'2047': 07776_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_canescens
'2048': 07777_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_columbianum
'2049': 07778_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_ferrugineum
'2050': 07779_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_groenlandicum
'2051': 07780_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_lapponicum
'2052': 07781_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_macrophyllum
'2053': 07782_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_maximum
'2054': 07783_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_menziesii
'2055': 07784_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_occidentale
'2056': 07785_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_periclymenoides
'2057': 07786_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Rhododendron_tomentosum
'2058': 07787_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Sarcodes_sanguinea
'2059': 07788_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_angustifolium
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'2724': 08453_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonurus_cardiaca
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'2727': 08456_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lycopus_americanus
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'2746': 08475_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Origanum_vulgare
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'2871': 08600_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Odontites_vulgaris
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'2873': 08602_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Orobanche_minor
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'2876': 08605_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_canadensis
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'2878': 08607_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_groenlandica
'2879': 08608_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_labradorica
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'2885': 08614_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Rhinanthus_alectorolophus
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'3093': 08822_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Stillingia_sylvatica
'3094': 08823_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Stillingia_texana
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'3102': 08831_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_kalmianum
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'3113': 08842_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_caerulea
'3114': 08843_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_edulis
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'3118': 08847_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_suberosa
'3119': 08848_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_tetrandra
'3120': 08849_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_tripartita
'3121': 08850_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_vitifolia
'3122': 08851_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Turnera_ulmifolia
'3123': 08852_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Bischofia_javanica
'3124': 08853_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Phyllanthus_tenellus
'3125': 08854_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Phyllanthus_urinaria
'3126': 08855_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Rhizophoraceae_Rhizophora_mangle
'3127': 08856_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_alba
'3128': 08857_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_balsamifera
'3129': 08858_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_deltoides
'3130': 08859_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_fremontii
'3131': 08860_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_grandidentata
'3132': 08861_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_nigra
'3133': 08862_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_tremula
'3134': 08863_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_tremuloides
'3135': 08864_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_trichocarpa
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'3137': 08866_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_babylonica
'3138': 08867_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_bebbiana
'3139': 08868_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_caprea
'3140': 08869_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_cinerea
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'3142': 08871_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_eriocephala
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'3145': 08874_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_interior
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'3148': 08877_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_myrsinifolia
'3149': 08878_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_nigra
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'3151': 08880_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_triandra
'3152': 08881_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_viminalis
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'3155': 08884_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_adunca
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'3158': 08887_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_biflora
'3159': 08888_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_blanda
'3160': 08889_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_canadensis
'3161': 08890_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_canina
'3162': 08891_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_glabella
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'3164': 08893_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_hirta
'3165': 08894_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_labradorica
'3166': 08895_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_lanceolata
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'3169': 08898_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_mirabilis
'3170': 08899_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_nuttallii
'3171': 08900_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_ocellata
'3172': 08901_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_odorata
'3173': 08902_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_palmata
'3174': 08903_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pedata
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'3176': 08905_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pedunculata
'3177': 08906_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_primulifolia
'3178': 08907_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pubescens
'3179': 08908_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_purpurea
'3180': 08909_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_reichenbachiana
'3181': 08910_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_riviniana
'3182': 08911_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_rostrata
'3183': 08912_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_rotundifolia
'3184': 08913_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sagittata
'3185': 08914_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sempervirens
'3186': 08915_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sororia
'3187': 08916_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_striata
'3188': 08917_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_tricolor
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'3190': 08919_Plantae_Tracheophyta_Magnoliopsida_Malvales_Bixaceae_Cochlospermum_vitifolium
'3191': 08920_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_albidus
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'3194': 08923_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_monspeliensis
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'3196': 08925_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Crocanthemum_canadense
'3197': 08926_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Crocanthemum_scoparium
'3198': 08927_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Helianthemum_nummularium
'3199': 08928_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Hudsonia_tomentosa
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'3202': 08931_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Adansonia_digitata
'3203': 08932_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Alcea_rosea
'3204': 08933_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Allowissadula_holosericea
'3205': 08934_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Anoda_cristata
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'3208': 08937_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Ceiba_pentandra
'3209': 08938_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Entelea_arborescens
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'3211': 08940_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Fremontodendron_californicum
'3212': 08941_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Gossypium_hirsutum
'3213': 08942_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Guazuma_ulmifolia
'3214': 08943_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Herissantia_crispa
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'3217': 08946_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hibiscus_moscheutos
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'3219': 08948_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hibiscus_syriacus
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'3221': 08950_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hibiscus_trionum
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'3223': 08952_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Horsfordia_newberryi
'3224': 08953_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Kosteletzkya_pentacarpos
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'3226': 08955_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_arborea
'3227': 08956_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_assurgentiflora
'3228': 08957_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_moschata
'3229': 08958_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_neglecta
'3230': 08959_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_parviflora
'3231': 08960_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_sylvestris
'3232': 08961_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_thuringiaca
'3233': 08962_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvastrum_coromandelianum
'3234': 08963_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvaviscus_arboreus
'3235': 08964_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvella_leprosa
'3236': 08965_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Melochia_pyramidata
'3237': 08966_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Melochia_tomentosa
'3238': 08967_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Modiola_caroliniana
'3239': 08968_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Pachira_aquatica
'3240': 08969_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Pavonia_lasiopetala
'3241': 08970_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Plagianthus_divaricatus
'3242': 08971_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Plagianthus_regius
'3243': 08972_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sida_abutifolia
'3244': 08973_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sida_ciliaris
'3245': 08974_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sida_rhombifolia
'3246': 08975_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sidalcea_malviflora
'3247': 08976_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sidalcea_oregana
'3248': 08977_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sidalcea_sparsifolia
'3249': 08978_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sphaeralcea_ambigua
'3250': 08979_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sphaeralcea_angustifolia
'3251': 08980_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Sphaeralcea_coccinea
'3252': 08981_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Theobroma_cacao
'3253': 08982_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Thespesia_populnea
'3254': 08983_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Tilia_americana
'3255': 08984_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Tilia_cordata
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'3257': 08986_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Waltheria_indica
'3258': 08987_Plantae_Tracheophyta_Magnoliopsida_Malvales_Muntingiaceae_Muntingia_calabura
'3259': 08988_Plantae_Tracheophyta_Magnoliopsida_Malvales_Thymelaeaceae_Daphne_gnidium
'3260': 08989_Plantae_Tracheophyta_Magnoliopsida_Malvales_Thymelaeaceae_Daphne_laureola
'3261': 08990_Plantae_Tracheophyta_Magnoliopsida_Malvales_Thymelaeaceae_Daphne_mezereum
'3262': 08991_Plantae_Tracheophyta_Magnoliopsida_Malvales_Thymelaeaceae_Dirca_palustris
'3263': 08992_Plantae_Tracheophyta_Magnoliopsida_Malvales_Thymelaeaceae_Pimelea_villosa
'3264': 08993_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Combretaceae_Conocarpus_erectus
'3265': 08994_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Combretaceae_Laguncularia_racemosa
'3266': 08995_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Combretaceae_Terminalia_catappa
'3267': 08996_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Ammannia_coccinea
'3268': 08997_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Cuphea_carthagenensis
'3269': 08998_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Decodon_verticillatus
'3270': 08999_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Lagerstroemia_indica
'3271': 09000_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Lythrum_alatum
'3272': 09001_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Lythrum_hyssopifolia
'3273': 09002_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Lythrum_salicaria
'3274': 09003_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Punica_granatum
'3275': 09004_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Trapa_natans
'3276': 09005_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Clidemia_hirta
'3277': 09006_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Melastoma_malabathricum
'3278': 09007_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_alifanus
'3279': 09008_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_mariana
'3280': 09009_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_virginica
'3281': 09010_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Eucalyptus_camaldulensis
'3282': 09011_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Eucalyptus_globulus
'3283': 09012_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Leptospermum_laevigatum
'3284': 09013_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Leptospermum_scoparium
'3285': 09014_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Lophomyrtus_bullata
'3286': 09015_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Metrosideros_diffusa
'3287': 09016_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Metrosideros_excelsa
'3288': 09017_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Metrosideros_fulgens
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splits:
- name: train
num_bytes: 25180455748.65
num_examples: 213550
download_size: 7778993107
dataset_size: 25180455748.65
---
# Dataset Card for "inat_2021_train_mini_plantae"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
EagleConsortium/MrEagle-2126 | EagleConsortium | 2023-12-03T04:01:08Z | 0 | 0 | [
"license:wtfpl",
"region:us"
]
| null | 2023-12-02T15:41:41Z | ---
license: wtfpl
---
A dataset of 2,126 1-turn conversations artificially generated using GPT-4, designed to fit the tone of the Discord bot Mr. Eagle.
This dataset was used to train MrEagle-LoRA.
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|
open-llm-leaderboard/details_mrm8488__mistral-7b-ft-h4-no_robots_instructions | open-llm-leaderboard | 2023-12-02T15:43:29Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:42:33Z | ---
pretty_name: Evaluation run of mrm8488/mistral-7b-ft-h4-no_robots_instructions
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [mrm8488/mistral-7b-ft-h4-no_robots_instructions](https://huggingface.co/mrm8488/mistral-7b-ft-h4-no_robots_instructions)\
\ 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_mrm8488__mistral-7b-ft-h4-no_robots_instructions\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:43:14.595425](https://huggingface.co/datasets/open-llm-leaderboard/details_mrm8488__mistral-7b-ft-h4-no_robots_instructions/blob/main/results_2023-12-02T15-43-14.595425.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\": 0.36694465504169826,\n\
\ \"acc_stderr\": 0.013275883047712211\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.36694465504169826,\n \"acc_stderr\": 0.013275883047712211\n\
\ }\n}\n```"
repo_url: https://huggingface.co/mrm8488/mistral-7b-ft-h4-no_robots_instructions
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_02T15_42_28.726427
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-42-28.726427.parquet'
- split: 2023_12_02T15_42_53.272777
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-42-53.272777.parquet'
- split: 2023_12_02T15_43_07.243379
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-43-07.243379.parquet'
- split: 2023_12_02T15_43_14.595425
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-43-14.595425.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-43-14.595425.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_42_28.726427
path:
- results_2023-12-02T15-42-28.726427.parquet
- split: 2023_12_02T15_42_53.272777
path:
- results_2023-12-02T15-42-53.272777.parquet
- split: 2023_12_02T15_43_07.243379
path:
- results_2023-12-02T15-43-07.243379.parquet
- split: 2023_12_02T15_43_14.595425
path:
- results_2023-12-02T15-43-14.595425.parquet
- split: latest
path:
- results_2023-12-02T15-43-14.595425.parquet
---
# Dataset Card for Evaluation run of mrm8488/mistral-7b-ft-h4-no_robots_instructions
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mrm8488/mistral-7b-ft-h4-no_robots_instructions
- **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 [mrm8488/mistral-7b-ft-h4-no_robots_instructions](https://huggingface.co/mrm8488/mistral-7b-ft-h4-no_robots_instructions) 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_mrm8488__mistral-7b-ft-h4-no_robots_instructions",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:43:14.595425](https://huggingface.co/datasets/open-llm-leaderboard/details_mrm8488__mistral-7b-ft-h4-no_robots_instructions/blob/main/results_2023-12-02T15-43-14.595425.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": 0.36694465504169826,
"acc_stderr": 0.013275883047712211
},
"harness|gsm8k|5": {
"acc": 0.36694465504169826,
"acc_stderr": 0.013275883047712211
}
}
```
### 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_pszemraj__distilgpt2-HC3 | open-llm-leaderboard | 2023-12-02T15:43:04Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:42:52Z | ---
pretty_name: Evaluation run of pszemraj/distilgpt2-HC3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [pszemraj/distilgpt2-HC3](https://huggingface.co/pszemraj/distilgpt2-HC3) 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_pszemraj__distilgpt2-HC3\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:42:48.762697](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__distilgpt2-HC3/blob/main/results_2023-12-02T15-42-48.762697.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/pszemraj/distilgpt2-HC3
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_02T15_42_48.762697
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-42-48.762697.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-42-48.762697.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_42_48.762697
path:
- results_2023-12-02T15-42-48.762697.parquet
- split: latest
path:
- results_2023-12-02T15-42-48.762697.parquet
---
# Dataset Card for Evaluation run of pszemraj/distilgpt2-HC3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/pszemraj/distilgpt2-HC3
- **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 [pszemraj/distilgpt2-HC3](https://huggingface.co/pszemraj/distilgpt2-HC3) 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_pszemraj__distilgpt2-HC3",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:42:48.762697](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__distilgpt2-HC3/blob/main/results_2023-12-02T15-42-48.762697.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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|
RenatoBC/markfinley | RenatoBC | 2023-12-02T15:45:15Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T15:44:05Z | ---
license: openrail
---
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|
open-llm-leaderboard/details_KevinNi__mistral-class-bio-tutor | open-llm-leaderboard | 2023-12-02T15:48:44Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:48:35Z | ---
pretty_name: Evaluation run of KevinNi/mistral-class-bio-tutor
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KevinNi/mistral-class-bio-tutor](https://huggingface.co/KevinNi/mistral-class-bio-tutor)\
\ 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_KevinNi__mistral-class-bio-tutor\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:48:30.567817](https://huggingface.co/datasets/open-llm-leaderboard/details_KevinNi__mistral-class-bio-tutor/blob/main/results_2023-12-02T15-48-30.567817.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/KevinNi/mistral-class-bio-tutor
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_02T15_48_30.567817
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-48-30.567817.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-48-30.567817.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_48_30.567817
path:
- results_2023-12-02T15-48-30.567817.parquet
- split: latest
path:
- results_2023-12-02T15-48-30.567817.parquet
---
# Dataset Card for Evaluation run of KevinNi/mistral-class-bio-tutor
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KevinNi/mistral-class-bio-tutor
- **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 [KevinNi/mistral-class-bio-tutor](https://huggingface.co/KevinNi/mistral-class-bio-tutor) 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_KevinNi__mistral-class-bio-tutor",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:48:30.567817](https://huggingface.co/datasets/open-llm-leaderboard/details_KevinNi__mistral-class-bio-tutor/blob/main/results_2023-12-02T15-48-30.567817.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_hakurei__mommygpt-3B | open-llm-leaderboard | 2023-12-02T15:58:39Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T15:58:30Z | ---
pretty_name: Evaluation run of hakurei/mommygpt-3B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [hakurei/mommygpt-3B](https://huggingface.co/hakurei/mommygpt-3B) 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_hakurei__mommygpt-3B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:58:26.242540](https://huggingface.co/datasets/open-llm-leaderboard/details_hakurei__mommygpt-3B/blob/main/results_2023-12-02T15-58-26.242540.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\": 0.02122820318423048,\n\
\ \"acc_stderr\": 0.003970449129848636\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.02122820318423048,\n \"acc_stderr\": 0.003970449129848636\n\
\ }\n}\n```"
repo_url: https://huggingface.co/hakurei/mommygpt-3B
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_02T15_58_26.242540
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-58-26.242540.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-58-26.242540.parquet'
- config_name: results
data_files:
- split: 2023_12_02T15_58_26.242540
path:
- results_2023-12-02T15-58-26.242540.parquet
- split: latest
path:
- results_2023-12-02T15-58-26.242540.parquet
---
# Dataset Card for Evaluation run of hakurei/mommygpt-3B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/hakurei/mommygpt-3B
- **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 [hakurei/mommygpt-3B](https://huggingface.co/hakurei/mommygpt-3B) 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_hakurei__mommygpt-3B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:58:26.242540](https://huggingface.co/datasets/open-llm-leaderboard/details_hakurei__mommygpt-3B/blob/main/results_2023-12-02T15-58-26.242540.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": 0.02122820318423048,
"acc_stderr": 0.003970449129848636
},
"harness|gsm8k|5": {
"acc": 0.02122820318423048,
"acc_stderr": 0.003970449129848636
}
}
```
### 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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|
JinglesDados/Peri | JinglesDados | 2023-12-02T16:14:08Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T16:13:28Z | ---
license: openrail
---
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|
nextpy/CodeExercise-Python-27k-EVOL | nextpy | 2023-12-02T16:21:32Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T16:15:26Z | ---
license: apache-2.0
---
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|
open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v1 | open-llm-leaderboard | 2023-12-02T16:26:37Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T16:17:13Z | ---
pretty_name: Evaluation run of adonlee/LLaMA_2_13B_SFT_v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [adonlee/LLaMA_2_13B_SFT_v1](https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v1)\
\ 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_adonlee__LLaMA_2_13B_SFT_v1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T16:26:28.271568](https://huggingface.co/datasets/open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v1/blob/main/results_2023-12-02T16-26-28.271568.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\": 0.39272175890826383,\n\
\ \"acc_stderr\": 0.013451745349586569\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.39272175890826383,\n \"acc_stderr\": 0.013451745349586569\n\
\ }\n}\n```"
repo_url: https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v1
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_02T16_17_05.792628
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-17-05.792628.parquet'
- split: 2023_12_02T16_26_28.271568
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-26-28.271568.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-26-28.271568.parquet'
- config_name: results
data_files:
- split: 2023_12_02T16_17_05.792628
path:
- results_2023-12-02T16-17-05.792628.parquet
- split: 2023_12_02T16_26_28.271568
path:
- results_2023-12-02T16-26-28.271568.parquet
- split: latest
path:
- results_2023-12-02T16-26-28.271568.parquet
---
# Dataset Card for Evaluation run of adonlee/LLaMA_2_13B_SFT_v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v1
- **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 [adonlee/LLaMA_2_13B_SFT_v1](https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v1) 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_adonlee__LLaMA_2_13B_SFT_v1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T16:26:28.271568](https://huggingface.co/datasets/open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v1/blob/main/results_2023-12-02T16-26-28.271568.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": 0.39272175890826383,
"acc_stderr": 0.013451745349586569
},
"harness|gsm8k|5": {
"acc": 0.39272175890826383,
"acc_stderr": 0.013451745349586569
}
}
```
### 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_fblgit__juanako-7b-UNA | open-llm-leaderboard | 2023-12-02T16:19:34Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T16:19:23Z | ---
pretty_name: Evaluation run of fblgit/juanako-7b-UNA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA) 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_fblgit__juanako-7b-UNA\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T16:19:18.907702](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__juanako-7b-UNA/blob/main/results_2023-12-02T16-19-18.907702.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\": 0.44806671721000757,\n\
\ \"acc_stderr\": 0.013697992668274525\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.44806671721000757,\n \"acc_stderr\": 0.013697992668274525\n\
\ }\n}\n```"
repo_url: https://huggingface.co/fblgit/juanako-7b-UNA
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_02T16_19_18.907702
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-19-18.907702.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-19-18.907702.parquet'
- config_name: results
data_files:
- split: 2023_12_02T16_19_18.907702
path:
- results_2023-12-02T16-19-18.907702.parquet
- split: latest
path:
- results_2023-12-02T16-19-18.907702.parquet
---
# Dataset Card for Evaluation run of fblgit/juanako-7b-UNA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/fblgit/juanako-7b-UNA
- **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 [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA) 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_fblgit__juanako-7b-UNA",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T16:19:18.907702](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__juanako-7b-UNA/blob/main/results_2023-12-02T16-19-18.907702.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": 0.44806671721000757,
"acc_stderr": 0.013697992668274525
},
"harness|gsm8k|5": {
"acc": 0.44806671721000757,
"acc_stderr": 0.013697992668274525
}
}
```
### 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_adonlee__LLaMA_2_13B_SFT_v1.5 | open-llm-leaderboard | 2023-12-02T16:28:53Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T16:28:48Z | ---
pretty_name: Evaluation run of adonlee/LLaMA_2_13B_SFT_v1.5
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [adonlee/LLaMA_2_13B_SFT_v1.5](https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v1.5)\
\ 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_adonlee__LLaMA_2_13B_SFT_v1.5\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T16:28:44.620973](https://huggingface.co/datasets/open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v1.5/blob/main/results_2023-12-02T16-28-44.620973.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\": 0.35405610310841545,\n\
\ \"acc_stderr\": 0.013172728385222557\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.35405610310841545,\n \"acc_stderr\": 0.013172728385222557\n\
\ }\n}\n```"
repo_url: https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v1.5
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_02T16_28_44.620973
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-28-44.620973.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-28-44.620973.parquet'
- config_name: results
data_files:
- split: 2023_12_02T16_28_44.620973
path:
- results_2023-12-02T16-28-44.620973.parquet
- split: latest
path:
- results_2023-12-02T16-28-44.620973.parquet
---
# Dataset Card for Evaluation run of adonlee/LLaMA_2_13B_SFT_v1.5
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v1.5
- **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 [adonlee/LLaMA_2_13B_SFT_v1.5](https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v1.5) 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_adonlee__LLaMA_2_13B_SFT_v1.5",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T16:28:44.620973](https://huggingface.co/datasets/open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v1.5/blob/main/results_2023-12-02T16-28-44.620973.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": 0.35405610310841545,
"acc_stderr": 0.013172728385222557
},
"harness|gsm8k|5": {
"acc": 0.35405610310841545,
"acc_stderr": 0.013172728385222557
}
}
```
### 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_abhishek__ccy0-2g7e-wqsa-0 | open-llm-leaderboard | 2023-12-02T16:46:45Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T16:33:06Z | ---
pretty_name: Evaluation run of abhishek/ccy0-2g7e-wqsa-0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [abhishek/ccy0-2g7e-wqsa-0](https://huggingface.co/abhishek/ccy0-2g7e-wqsa-0)\
\ 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_abhishek__ccy0-2g7e-wqsa-0\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T16:46:35.234385](https://huggingface.co/datasets/open-llm-leaderboard/details_abhishek__ccy0-2g7e-wqsa-0/blob/main/results_2023-12-02T16-46-35.234385.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\": 0.32221379833206976,\n\
\ \"acc_stderr\": 0.01287243548118878\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.32221379833206976,\n \"acc_stderr\": 0.01287243548118878\n\
\ }\n}\n```"
repo_url: https://huggingface.co/abhishek/ccy0-2g7e-wqsa-0
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_02T16_33_02.439769
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-33-02.439769.parquet'
- split: 2023_12_02T16_46_35.234385
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-46-35.234385.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-46-35.234385.parquet'
- config_name: results
data_files:
- split: 2023_12_02T16_33_02.439769
path:
- results_2023-12-02T16-33-02.439769.parquet
- split: 2023_12_02T16_46_35.234385
path:
- results_2023-12-02T16-46-35.234385.parquet
- split: latest
path:
- results_2023-12-02T16-46-35.234385.parquet
---
# Dataset Card for Evaluation run of abhishek/ccy0-2g7e-wqsa-0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/abhishek/ccy0-2g7e-wqsa-0
- **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 [abhishek/ccy0-2g7e-wqsa-0](https://huggingface.co/abhishek/ccy0-2g7e-wqsa-0) 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_abhishek__ccy0-2g7e-wqsa-0",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T16:46:35.234385](https://huggingface.co/datasets/open-llm-leaderboard/details_abhishek__ccy0-2g7e-wqsa-0/blob/main/results_2023-12-02T16-46-35.234385.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": 0.32221379833206976,
"acc_stderr": 0.01287243548118878
},
"harness|gsm8k|5": {
"acc": 0.32221379833206976,
"acc_stderr": 0.01287243548118878
}
}
```
### 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_kaist-ai__prometheus-13b-v1.0 | open-llm-leaderboard | 2023-12-02T16:48:17Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T16:48:06Z | ---
pretty_name: Evaluation run of kaist-ai/prometheus-13b-v1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [kaist-ai/prometheus-13b-v1.0](https://huggingface.co/kaist-ai/prometheus-13b-v1.0)\
\ 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_kaist-ai__prometheus-13b-v1.0\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T16:47:58.854671](https://huggingface.co/datasets/open-llm-leaderboard/details_kaist-ai__prometheus-13b-v1.0/blob/main/results_2023-12-02T16-47-58.854671.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\": 0.3169067475360121,\n\
\ \"acc_stderr\": 0.012815868296721386\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.3169067475360121,\n \"acc_stderr\": 0.012815868296721386\n\
\ }\n}\n```"
repo_url: https://huggingface.co/kaist-ai/prometheus-13b-v1.0
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_02T16_47_58.854671
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-47-58.854671.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-47-58.854671.parquet'
- config_name: results
data_files:
- split: 2023_12_02T16_47_58.854671
path:
- results_2023-12-02T16-47-58.854671.parquet
- split: latest
path:
- results_2023-12-02T16-47-58.854671.parquet
---
# Dataset Card for Evaluation run of kaist-ai/prometheus-13b-v1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/kaist-ai/prometheus-13b-v1.0
- **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 [kaist-ai/prometheus-13b-v1.0](https://huggingface.co/kaist-ai/prometheus-13b-v1.0) 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_kaist-ai__prometheus-13b-v1.0",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T16:47:58.854671](https://huggingface.co/datasets/open-llm-leaderboard/details_kaist-ai__prometheus-13b-v1.0/blob/main/results_2023-12-02T16-47-58.854671.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": 0.3169067475360121,
"acc_stderr": 0.012815868296721386
},
"harness|gsm8k|5": {
"acc": 0.3169067475360121,
"acc_stderr": 0.012815868296721386
}
}
```
### 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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|
JST4/Experiment | JST4 | 2023-12-04T06:07:46Z | 0 | 1 | [
"license:mit",
"region:us"
]
| null | 2023-12-02T16:52:30Z | ---
license: mit
dataset_info:
features:
- name: data
dtype: string
- name: activation
dtype: string
- name: loss_function
dtype: string
- name: optimizer
dtype: string
- name: batch_size
dtype: int64
- name: epochs
dtype: int64
- name: learning_rate
dtype: float64
- name: lr_scheduler_step
dtype: int64
- name: train_loss
dtype: float64
- name: train_acc
dtype: float64
- name: test_loss
dtype: float64
- name: test_acc
dtype: float64
- name: best_at_epoch
dtype: int64
- name: score
dtype: float64
- name: res_url
dtype: string
- name: fi_url
dtype: string
splits:
- name: result
num_bytes: 235913
num_examples: 383
download_size: 44161
dataset_size: 235913
configs:
- config_name: default
data_files:
- split: result
path: data/result-*
---
## Jaringan Syaraf Tiruan Kelompok 4 | [
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|
Citizen75/training_presets | Citizen75 | 2023-12-02T18:20:49Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T17:21:38Z | ---
license: apache-2.0
---
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|
jahb57/gpt2_token_embeddings | jahb57 | 2023-12-03T00:18:34Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T17:33:54Z | ---
dataset_info:
features:
- name: sentences
dtype: string
splits:
- name: train
num_bytes: 351
num_examples: 4
download_size: 1620
dataset_size: 351
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
jahb57/bert_token_embeddings | jahb57 | 2023-12-02T17:34:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T17:34:13Z | ---
dataset_info:
features:
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 196864
num_examples: 64
download_size: 296018
dataset_size: 196864
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
biadrivex/yong | biadrivex | 2023-12-02T17:57:42Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T17:56:51Z | ---
license: openrail
---
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|
Vinnyyw/Anahivoices | Vinnyyw | 2023-12-02T18:21:21Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T18:19:33Z | ---
license: openrail
---
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|
Yhyu13/ToolBench_toolllama_G123_dfs | Yhyu13 | 2023-12-02T19:11:07Z | 0 | 2 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T18:45:58Z | ---
license: apache-2.0
---
Dataset mentioned for ToolBench project https://github.com/OpenBMB/ToolBench
They were in the google drive data.zip https://drive.google.com/drive/folders/1yBUQ732mPu-KclJnuQELEhtKakdXFc3J
These two json are already processed by the original author. Just plugin into the ToolBnech repo deepseed arguments.
```
--data_path ./toolllama_G123_dfs_train.json \
--eval_data_path ./toolllama_G123_dfs_eval.json \
```
My objective is to tailer the training data to 1/100 size and used them for the LLaMA-Factory project. https://github.com/hiyouga/LLaMA-Factory
So that more open source models could benifit from function calling dataset. | [
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|
Pedro1230987/MarceloGarcia | Pedro1230987 | 2023-12-02T18:51:27Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T18:50:06Z | ---
license: openrail
---
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|
dapooni/sorsolingo_bsl_en | dapooni | 2023-12-02T20:09:22Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T20:06:21Z | ---
dataset_info:
features:
- name: bsl
dtype: string
- name: en
dtype: string
splits:
- name: train
num_bytes: 163869
num_examples: 3303
download_size: 118001
dataset_size: 163869
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
gaizerick/falasvayne | gaizerick | 2023-12-02T20:09:24Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T20:07:44Z | ---
license: openrail
---
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|
Samidu/alfa | Samidu | 2023-12-02T20:20:50Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-02T20:19:16Z | ---
license: mit
---
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|
TioRob/LauraMoon | TioRob | 2023-12-02T20:29:14Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T20:28:14Z | ---
license: openrail
---
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|
Pankaj202133/Pankaj | Pankaj202133 | 2023-12-02T20:31:39Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
]
| null | 2023-12-02T20:29:43Z | ---
license: cc-by-nc-4.0
---
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|
gilsonk12/THIAGOSCHUTZ | gilsonk12 | 2023-12-02T20:33:13Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T20:32:36Z | ---
license: openrail
---
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|
open-llm-leaderboard/details_qblocks__falcon_7b_norobots | open-llm-leaderboard | 2023-12-02T22:03:13Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T22:03:05Z | ---
pretty_name: Evaluation run of qblocks/falcon_7b_norobots
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [qblocks/falcon_7b_norobots](https://huggingface.co/qblocks/falcon_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 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_qblocks__falcon_7b_norobots\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T22:03:06.046351](https://huggingface.co/datasets/open-llm-leaderboard/details_qblocks__falcon_7b_norobots/blob/main/results_2023-12-02T22-03-06.046351.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\": 0.04473085670962851,\n\
\ \"acc_stderr\": 0.005693886131407044\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.04473085670962851,\n \"acc_stderr\": 0.005693886131407044\n\
\ }\n}\n```"
repo_url: https://huggingface.co/qblocks/falcon_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_02T22_03_02.492484
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-03-02.492484.parquet'
- split: 2023_12_02T22_03_06.046351
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-03-06.046351.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-03-06.046351.parquet'
- config_name: results
data_files:
- split: 2023_12_02T22_03_02.492484
path:
- results_2023-12-02T22-03-02.492484.parquet
- split: 2023_12_02T22_03_06.046351
path:
- results_2023-12-02T22-03-06.046351.parquet
- split: latest
path:
- results_2023-12-02T22-03-06.046351.parquet
---
# Dataset Card for Evaluation run of qblocks/falcon_7b_norobots
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/qblocks/falcon_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/falcon_7b_norobots](https://huggingface.co/qblocks/falcon_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 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_qblocks__falcon_7b_norobots",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T22:03:06.046351](https://huggingface.co/datasets/open-llm-leaderboard/details_qblocks__falcon_7b_norobots/blob/main/results_2023-12-02T22-03-06.046351.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": 0.04473085670962851,
"acc_stderr": 0.005693886131407044
},
"harness|gsm8k|5": {
"acc": 0.04473085670962851,
"acc_stderr": 0.005693886131407044
}
}
```
### 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_abhishek__zephyr-beta-math | open-llm-leaderboard | 2023-12-02T22:04:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T22:04:11Z | ---
pretty_name: Evaluation run of abhishek/zephyr-beta-math
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [abhishek/zephyr-beta-math](https://huggingface.co/abhishek/zephyr-beta-math)\
\ 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_abhishek__zephyr-beta-math\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T22:04:08.967316](https://huggingface.co/datasets/open-llm-leaderboard/details_abhishek__zephyr-beta-math/blob/main/results_2023-12-02T22-04-08.967316.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\": 0.5640636846095527,\n\
\ \"acc_stderr\": 0.013658968058849159\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5640636846095527,\n \"acc_stderr\": 0.013658968058849159\n\
\ }\n}\n```"
repo_url: https://huggingface.co/abhishek/zephyr-beta-math
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_02T22_04_08.967316
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-04-08.967316.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-04-08.967316.parquet'
- config_name: results
data_files:
- split: 2023_12_02T22_04_08.967316
path:
- results_2023-12-02T22-04-08.967316.parquet
- split: latest
path:
- results_2023-12-02T22-04-08.967316.parquet
---
# Dataset Card for Evaluation run of abhishek/zephyr-beta-math
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/abhishek/zephyr-beta-math
- **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 [abhishek/zephyr-beta-math](https://huggingface.co/abhishek/zephyr-beta-math) 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_abhishek__zephyr-beta-math",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T22:04:08.967316](https://huggingface.co/datasets/open-llm-leaderboard/details_abhishek__zephyr-beta-math/blob/main/results_2023-12-02T22-04-08.967316.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": 0.5640636846095527,
"acc_stderr": 0.013658968058849159
},
"harness|gsm8k|5": {
"acc": 0.5640636846095527,
"acc_stderr": 0.013658968058849159
}
}
```
### 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_klyang__MentaLLaMA-chat-7B | open-llm-leaderboard | 2023-12-02T22:04:59Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T22:04:54Z | ---
pretty_name: Evaluation run of klyang/MentaLLaMA-chat-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [klyang/MentaLLaMA-chat-7B](https://huggingface.co/klyang/MentaLLaMA-chat-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_klyang__MentaLLaMA-chat-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T22:04:51.845916](https://huggingface.co/datasets/open-llm-leaderboard/details_klyang__MentaLLaMA-chat-7B/blob/main/results_2023-12-02T22-04-51.845916.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\": 0.16148597422289612,\n\
\ \"acc_stderr\": 0.010135959452134306\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.16148597422289612,\n \"acc_stderr\": 0.010135959452134306\n\
\ }\n}\n```"
repo_url: https://huggingface.co/klyang/MentaLLaMA-chat-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_02T22_04_51.845916
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-04-51.845916.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-04-51.845916.parquet'
- config_name: results
data_files:
- split: 2023_12_02T22_04_51.845916
path:
- results_2023-12-02T22-04-51.845916.parquet
- split: latest
path:
- results_2023-12-02T22-04-51.845916.parquet
---
# Dataset Card for Evaluation run of klyang/MentaLLaMA-chat-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/klyang/MentaLLaMA-chat-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 [klyang/MentaLLaMA-chat-7B](https://huggingface.co/klyang/MentaLLaMA-chat-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_klyang__MentaLLaMA-chat-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T22:04:51.845916](https://huggingface.co/datasets/open-llm-leaderboard/details_klyang__MentaLLaMA-chat-7B/blob/main/results_2023-12-02T22-04-51.845916.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": 0.16148597422289612,
"acc_stderr": 0.010135959452134306
},
"harness|gsm8k|5": {
"acc": 0.16148597422289612,
"acc_stderr": 0.010135959452134306
}
}
```
### 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_qblocks__zephyr_7b_norobots | open-llm-leaderboard | 2023-12-02T22:10:58Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T22:10:54Z | ---
pretty_name: Evaluation run of qblocks/zephyr_7b_norobots
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [qblocks/zephyr_7b_norobots](https://huggingface.co/qblocks/zephyr_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__zephyr_7b_norobots\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T22:10:51.334218](https://huggingface.co/datasets/open-llm-leaderboard/details_qblocks__zephyr_7b_norobots/blob/main/results_2023-12-02T22-10-51.334218.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\": 0.20621683093252463,\n\
\ \"acc_stderr\": 0.011144364089781441\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.20621683093252463,\n \"acc_stderr\": 0.011144364089781441\n\
\ }\n}\n```"
repo_url: https://huggingface.co/qblocks/zephyr_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_02T22_10_51.334218
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-10-51.334218.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-10-51.334218.parquet'
- config_name: results
data_files:
- split: 2023_12_02T22_10_51.334218
path:
- results_2023-12-02T22-10-51.334218.parquet
- split: latest
path:
- results_2023-12-02T22-10-51.334218.parquet
---
# Dataset Card for Evaluation run of qblocks/zephyr_7b_norobots
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/qblocks/zephyr_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/zephyr_7b_norobots](https://huggingface.co/qblocks/zephyr_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__zephyr_7b_norobots",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T22:10:51.334218](https://huggingface.co/datasets/open-llm-leaderboard/details_qblocks__zephyr_7b_norobots/blob/main/results_2023-12-02T22-10-51.334218.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": 0.20621683093252463,
"acc_stderr": 0.011144364089781441
},
"harness|gsm8k|5": {
"acc": 0.20621683093252463,
"acc_stderr": 0.011144364089781441
}
}
```
### 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_harborwater__open-llama-3b-claude-30k | open-llm-leaderboard | 2023-12-02T22:19:58Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T22:19:53Z | ---
pretty_name: Evaluation run of harborwater/open-llama-3b-claude-30k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [harborwater/open-llama-3b-claude-30k](https://huggingface.co/harborwater/open-llama-3b-claude-30k)\
\ 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_harborwater__open-llama-3b-claude-30k\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T22:19:50.317589](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__open-llama-3b-claude-30k/blob/main/results_2023-12-02T22-19-50.317589.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\": 0.021986353297952996,\n\
\ \"acc_stderr\": 0.004039162758110046\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.021986353297952996,\n \"acc_stderr\": 0.004039162758110046\n\
\ }\n}\n```"
repo_url: https://huggingface.co/harborwater/open-llama-3b-claude-30k
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_02T22_19_50.317589
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-19-50.317589.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-19-50.317589.parquet'
- config_name: results
data_files:
- split: 2023_12_02T22_19_50.317589
path:
- results_2023-12-02T22-19-50.317589.parquet
- split: latest
path:
- results_2023-12-02T22-19-50.317589.parquet
---
# Dataset Card for Evaluation run of harborwater/open-llama-3b-claude-30k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/harborwater/open-llama-3b-claude-30k
- **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 [harborwater/open-llama-3b-claude-30k](https://huggingface.co/harborwater/open-llama-3b-claude-30k) 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_harborwater__open-llama-3b-claude-30k",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T22:19:50.317589](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__open-llama-3b-claude-30k/blob/main/results_2023-12-02T22-19-50.317589.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": 0.021986353297952996,
"acc_stderr": 0.004039162758110046
},
"harness|gsm8k|5": {
"acc": 0.021986353297952996,
"acc_stderr": 0.004039162758110046
}
}
```
### 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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|
tiagoblima/qg_squad_v1_pt | tiagoblima | 2023-12-02T23:29:39Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T23:01:06Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: paragraph
dtype: string
- name: answer
dtype: string
- name: paragraph_id
dtype: string
- name: paragraph_question
dtype: string
splits:
- name: train
num_bytes: 156957713
num_examples: 87599
- name: validation
num_bytes: 19224127
num_examples: 10570
download_size: 17481361
dataset_size: 176181840
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
ContinuumLabs/Genie | ContinuumLabs | 2023-12-04T01:15:43Z | 0 | 1 | [
"region:us"
]
| null | 2023-12-02T23:19:10Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: instruction
dtype: string
- name: domain
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 453516
num_examples: 1000
download_size: 242147
dataset_size: 453516
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
open-llm-leaderboard/details_migtissera__Tess-M-v1.1 | open-llm-leaderboard | 2023-12-03T00:02:07Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T00:02:03Z | ---
pretty_name: Evaluation run of migtissera/Tess-M-v1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [migtissera/Tess-M-v1.1](https://huggingface.co/migtissera/Tess-M-v1.1) 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_migtissera__Tess-M-v1.1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T00:02:00.527557](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-v1.1/blob/main/results_2023-12-03T00-02-00.527557.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\": 0.5466262319939348,\n\
\ \"acc_stderr\": 0.013712471049515444\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5466262319939348,\n \"acc_stderr\": 0.013712471049515444\n\
\ }\n}\n```"
repo_url: https://huggingface.co/migtissera/Tess-M-v1.1
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_03T00_02_00.527557
path:
- '**/details_harness|gsm8k|5_2023-12-03T00-02-00.527557.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T00-02-00.527557.parquet'
- config_name: results
data_files:
- split: 2023_12_03T00_02_00.527557
path:
- results_2023-12-03T00-02-00.527557.parquet
- split: latest
path:
- results_2023-12-03T00-02-00.527557.parquet
---
# Dataset Card for Evaluation run of migtissera/Tess-M-v1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/migtissera/Tess-M-v1.1
- **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 [migtissera/Tess-M-v1.1](https://huggingface.co/migtissera/Tess-M-v1.1) 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_migtissera__Tess-M-v1.1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T00:02:00.527557](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-v1.1/blob/main/results_2023-12-03T00-02-00.527557.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": 0.5466262319939348,
"acc_stderr": 0.013712471049515444
},
"harness|gsm8k|5": {
"acc": 0.5466262319939348,
"acc_stderr": 0.013712471049515444
}
}
```
### 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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|
gilsonk12/Rastreando | gilsonk12 | 2023-12-03T00:12:03Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T00:11:19Z | ---
license: openrail
---
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|
Rayan2023/rafaeldataset | Rayan2023 | 2023-12-03T00:18:10Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T00:17:52Z | ---
license: openrail
---
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|
Rayan2023/gabrielteste1 | Rayan2023 | 2023-12-03T00:50:59Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T00:50:43Z | ---
license: openrail
---
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|
lum-ai/metal-python-synthetic-explanations-gpt4-raw | lum-ai | 2023-12-03T01:30:25Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T01:29:16Z | ---
dataset_info:
features:
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struct:
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splits:
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num_examples: 18272
- name: test
num_bytes: 324461765.3020142
num_examples: 35131
download_size: 75623364
dataset_size: 3263444573.351795
---
# Dataset Card for "metal-python-synthetic-explanations-gpt4-raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
RenatoBC/markfinley2 | RenatoBC | 2023-12-03T01:32:59Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T01:32:24Z | ---
license: openrail
---
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|
icaro23/icaro5 | icaro23 | 2023-12-03T02:11:35Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T02:07:28Z | ---
license: apache-2.0
---
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|
icaro23/icaro4 | icaro23 | 2023-12-03T02:38:13Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T02:26:09Z | ---
license: apache-2.0
---
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|
multiset/recordings_20230702_20230715 | multiset | 2023-12-03T03:22:45Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-03T02:33:15Z | ---
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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|
syp1229/Y_normal_add | syp1229 | 2023-12-03T02:52:18Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T02:49:08Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sample_rate
dtype: int64
- name: text
dtype: string
- name: scriptId
dtype: int64
- name: fileNm
dtype: string
- name: recrdTime
dtype: float64
- name: recrdQuality
dtype: int64
- name: recrdDt
dtype: string
- name: scriptSetNo
dtype: string
- name: recrdEnvrn
dtype: string
- name: colctUnitCode
dtype: string
- name: cityCode
dtype: string
- name: recrdUnit
dtype: string
- name: convrsThema
dtype: string
- name: gender
dtype: string
- name: recorderId
dtype: string
- name: age
dtype: int64
splits:
- name: train
num_bytes: 5348401048
num_examples: 7001
- name: test
num_bytes: 3224765
num_examples: 5
download_size: 1168516332
dataset_size: 5351625813
---
# Dataset Card for "Y_normal_add"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
jvicts22/mjacksondeep | jvicts22 | 2023-12-03T03:25:07Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T03:24:31Z | ---
license: openrail
---
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|
Teera/RelationExtraction-NLG-Thai | Teera | 2023-12-03T03:56:13Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T03:51:49Z | ---
license: apache-2.0
---
This is translate dataset NLG for data extraction in english language to thai language.
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|
Danieldlima21/mcmelody | Danieldlima21 | 2023-12-03T04:26:58Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T04:26:08Z | ---
license: openrail
---
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|
Carlosgg14/killua | Carlosgg14 | 2023-12-03T04:36:05Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T04:35:32Z | ---
license: openrail
---
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|
Vinnyyw/AnahiSA | Vinnyyw | 2023-12-03T04:49:22Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T04:47:43Z | ---
license: openrail
---
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|
dapooni/sorsolingo_bsl_tl | dapooni | 2023-12-03T05:11:40Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T05:10:07Z | ---
dataset_info:
features:
- name: bsl
dtype: string
- name: tl
dtype: string
splits:
- name: train
num_bytes: 172777
num_examples: 3303
download_size: 120569
dataset_size: 172777
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
zicsx/Hindi-Gutenberg | zicsx | 2023-12-03T05:50:57Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T05:46:29Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9668856603
num_examples: 11159
download_size: 3798213011
dataset_size: 9668856603
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Hindi-Gutenberg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
automated-research-group/phi-boolq-results_playing | automated-research-group | 2023-12-03T06:24:29Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T06:24:28Z | ---
dataset_info:
config_name: '{''do_sample''=False, ''beams''=1}'
features:
- name: id
dtype: string
- name: prediction
dtype: string
- name: bool_accuracy
dtype: bool
splits:
- name: train
num_bytes: 93279
num_examples: 3270
download_size: 68581
dataset_size: 93279
configs:
- config_name: '{''do_sample''=False, ''beams''=1}'
data_files:
- split: train
path: '{''do_sample''=False, ''beams''=1}/train-*'
---
# Dataset Card for "phi-boolq-results_playing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
mesolitica/chatgpt-malaysian-qa-choice | mesolitica | 2023-12-03T07:59:59Z | 0 | 0 | [
"task_categories:question-answering",
"language:ms",
"region:us"
]
| [
"question-answering"
]
| 2023-12-03T06:27:27Z | ---
task_categories:
- question-answering
language:
- ms
pretty_name: malaysian-qa-choice
---
# Synthetic QA Choice
Generated using ChatGPT3.5,
1. https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/dewanbahasa-jdbp.jsonl
2. https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/majalahsains.jsonl
3. https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/wikipedia-2023-10-01.jsonl
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/question-answer/chatgpt3.5-qa-choice | [
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|
Falah/anime_arabic_style_prompts | Falah | 2023-12-03T13:25:52Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T07:03:53Z | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 623328012
num_examples: 1000000
download_size: 112334528
dataset_size: 623328012
---
# Dataset Card for "anime_arabic_style_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
stoptalk344/vacuum1333 | stoptalk344 | 2023-12-03T07:16:16Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T07:06:55Z | ---
license: apache-2.0
---
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|
Asimok/KGLQA-KeySentenceSelect-QuALITY | Asimok | 2023-12-03T10:38:25Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T07:14:30Z | ---
configs:
- config_name: normal
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-QuALITY/train.jsonl"
- split: dev
path:
- "KGLQA-KeySentenceSelect-QuALITY/dev.jsonl"
- split: test
path:
- "KGLQA-KeySentenceSelect-QuALITY/test.jsonl"
- config_name: instruct
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-QuALITY-instruct/train.jsonl"
- split: dev
path:
- "KGLQA-KeySentenceSelect-QuALITY-instruct/dev.jsonl"
- config_name: raw
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-QuALITY-raw/*.train"
- split: dev
path:
- "KGLQA-KeySentenceSelect-QuALITY-raw/*.dev.jsonl"
- split: test
path:
- "KGLQA-KeySentenceSelect-QuALITY-raw/*.test.jsonl"
---
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|
AshanGimhana/Mydata | AshanGimhana | 2023-12-03T07:18:43Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-03T07:18:43Z | ---
license: mit
---
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|
toshi456/LLaVA-CC3M-Pretrain-595K-JA | toshi456 | 2023-12-03T07:45:54Z | 0 | 0 | [
"task_categories:visual-question-answering",
"size_categories:100K<n<1M",
"language:ja",
"license:other",
"region:us"
]
| [
"visual-question-answering"
]
| 2023-12-03T07:26:55Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 146361663
num_examples: 595375
download_size: 45579837
dataset_size: 146361663
license: other
task_categories:
- visual-question-answering
language:
- ja
pretty_name: 'Japanese LLaVA CC3M Pretrain 595K '
size_categories:
- 100K<n<1M
---
# Dataset Card for "LLaVA-CC3M-Pretrain-595K-JA"
## Dataset Details
**Dataset Type:**
Japanese LLaVA CC3M Pretrain 595K is a localized version of the original LLaVA Visual Instruct CC3M 595K dataset. This version is translated into Japanese using [cyberagent/calm2-7b-chat](https://huggingface.co/cyberagent/calm2-7b-chat) and is aimed at serving similar purposes in the context of Japanese language.
**Resources for More Information:**
For information on the original dataset: [liuhaotian/LLaVA-CC3M-Pretrain-595K](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K)
**License:**
Must comply with license of [CC-3M](https://github.com/google-research-datasets/conceptual-captions/blob/master/LICENSE), [BLIP](https://github.com/salesforce/BLIP/blob/main/LICENSE.txt) (if you use their synthetic caption).
CC-3M The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
**Questions or Comments:**
For questions or comments about the original model, you can go to [LLaVA GitHub Issues](https://github.com/haotian-liu/LLaVA/issues).
## Intended use
**Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. | [
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|
longhoang06/GSM8K-ViMetaMath | longhoang06 | 2023-12-03T07:34:56Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T07:34:50Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 213748056
num_examples: 240000
download_size: 95107081
dataset_size: 213748056
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Asimok/KGLQA-KnowledgeBank-QuALITY | Asimok | 2023-12-03T08:28:45Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T08:04:01Z | ---
configs:
- config_name: caption
data_files:
- split: train
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption/test.jsonl"
- config_name: selected
data_files:
- split: train
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption-Selected/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption-Selected/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption-Selected/test.jsonl"
- config_name: instruct
data_files:
- split: train
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption-Selected-instruct/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption-Selected-instruct/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBank-QuALITY-Caption-Selected-instruct/test.jsonl"
---
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|
Danieldlima21/Bocoyoutuber | Danieldlima21 | 2023-12-03T08:26:56Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-03T08:26:05Z | ---
license: openrail
---
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|
zicsx/Hindi-HackerNews | zicsx | 2023-12-03T10:23:45Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T08:34:52Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9055034477
num_examples: 787509
download_size: 0
dataset_size: 9055034477
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Hindi-HackerNews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Asimok/KGLQA-LangChain-QuALITY | Asimok | 2023-12-03T08:56:47Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T08:43:14Z | ---
license: apache-2.0
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|
Asimok/KGLQA-LangChain-RACE | Asimok | 2023-12-03T09:02:40Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T08:55:04Z | ---
license: apache-2.0
---
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|
Asimok/KGLQA-KeySentenceSelect-RACE | Asimok | 2023-12-03T08:59:13Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T08:57:51Z | ---
license: apache-2.0
---
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|
Asimok/KGLQA-KnowledgeBank-RACE | Asimok | 2023-12-03T09:05:59Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T09:00:41Z | ---
configs:
- config_name: caption
data_files:
- split: train
path:
- "KGLQA-KnowledgeBank-RACE-Caption/*_train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBank-RACE-Caption/*dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBank-RACE-Caption/*test.jsonl"
- config_name: selected
data_files:
- split: train
path:
- "KGLQA-KnowledgeBank-RACE-Caption-Selected/*_train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBank-RACE-Caption-Selected/*dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBank-RACE-Caption-Selected/*test.jsonl"
- config_name: instruct
data_files:
- split: train
path:
- "KGLQA-KnowledgeBank-RACE-Caption-Selected-instruct/*_train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBank-RACE-Caption-Selected-instruct/*dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBank-RACE-Caption-Selected-instruct/*test.jsonl"
---
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|
fadeke/clavis_studio_dataset | fadeke | 2023-12-03T09:15:52Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T09:10:27Z | ---
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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|
Jacques7103/AI | Jacques7103 | 2023-12-03T10:51:14Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T09:43:56Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': apple_pie
'1': baby_back_ribs
'2': baklava
'3': beef_carpaccio
'4': beef_tartare
'5': beet_salad
'6': beignets
'7': bibimbap
'8': bread_pudding
'9': breakfast_burrito
'10': bruschetta
'11': caesar_salad
'12': cannoli
'13': caprese_salad
'14': carrot_cake
'15': ceviche
'16': cheese_plate
'17': cheesecake
'18': chicken_curry
'19': chicken_quesadilla
'20': chicken_wings
'21': chocolate_cake
'22': chocolate_mousse
'23': churros
'24': clam_chowder
'25': club_sandwich
'26': crab_cakes
'27': creme_brulee
'28': croque_madame
'29': cup_cakes
'30': deviled_eggs
'31': donuts
'32': dumplings
'33': edamame
'34': eggs_benedict
'35': escargots
'36': falafel
'37': filet_mignon
'38': fish_and_chips
'39': foie_gras
'40': french_fries
'41': french_onion_soup
'42': french_toast
'43': fried_calamari
'44': fried_rice
'45': frozen_yogurt
'46': garlic_bread
'47': gnocchi
'48': greek_salad
'49': grilled_cheese_sandwich
'50': grilled_salmon
'51': guacamole
'52': gyoza
'53': hamburger
'54': hot_and_sour_soup
'55': hot_dog
'56': huevos_rancheros
'57': hummus
'58': ice_cream
'59': lasagna
'60': lobster_bisque
'61': lobster_roll_sandwich
'62': macaroni_and_cheese
'63': macarons
'64': miso_soup
'65': mussels
'66': nachos
'67': omelette
'68': onion_rings
'69': oysters
'70': pad_thai
'71': paella
'72': pancakes
'73': panna_cotta
'74': peking_duck
'75': pho
'76': pizza
'77': pork_chop
'78': poutine
'79': prime_rib
'80': pulled_pork_sandwich
'81': ramen
'82': ravioli
'83': red_velvet_cake
'84': risotto
'85': samosa
'86': sashimi
'87': scallops
'88': seaweed_salad
'89': shrimp_and_grits
'90': spaghetti_bolognese
'91': spaghetti_carbonara
'92': spring_rolls
'93': steak
'94': strawberry_shortcake
'95': sushi
'96': tacos
'97': takoyaki
'98': tiramisu
'99': tuna_tartare
'100': waffles
splits:
- name: train
num_bytes: 4356950902.35
num_examples: 85850
- name: test
num_bytes: 762032024.4
num_examples: 15150
download_size: 5060231219
dataset_size: 5118982926.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
Asimok/KGLQA-KeySentenceSelect-NCR | Asimok | 2023-12-03T10:47:14Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T09:55:57Z | ---
configs:
- config_name: normal
data_files:
- split: train
path:
- KGLQA-KeySentenceSelect-NCR/train.jsonl
- split: dev
path:
- KGLQA-KeySentenceSelect-NCR/dev.jsonl
- split: test
path:
- KGLQA-KeySentenceSelect-NCR/test.jsonl
- config_name: instruct
data_files:
- split: train
path:
- KGLQA-KeySentenceSelect-NCR-instruct/train.jsonl
- split: dev
path:
- KGLQA-KeySentenceSelect-NCR-instruct/dev.jsonl
- split: test
path:
- KGLQA-KeySentenceSelect-NCR-instruct/test.jsonl
- config_name: raw
data_files:
- split: train
path:
- KGLQA-KeySentenceSelect-NCR-raw/train.json
- split: dev
path:
- KGLQA-KeySentenceSelect-NCR-raw/dev.json
- split: test
path:
- KGLQA-KeySentenceSelect-NCR-raw/test.json
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|
Asimok/KGLQA-KnowledgeBant-NCR | Asimok | 2023-12-03T10:05:23Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T10:03:55Z | ---
configs:
- config_name: caption
data_files:
- split: train
path:
- "KGLQA-KnowledgeBant-NCR-Caption/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBant-NCR-Caption/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBant-NCR-Caption/test.jsonl"
- config_name: selected
data_files:
- split: train
path:
- "KGLQA-KnowledgeBant-NCR-Caption-Selected/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBant-NCR-Caption-Selected/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBant-NCR-Caption-Selected/test.jsonl"
- config_name: instruct
data_files:
- split: train
path:
- "KGLQA-KnowledgeBant-NCR-Caption-Selected-instruct/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBant-NCR-Caption-Selected-instruct/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBant-NCR-Caption-Selected-instruct/test.jsonl"
---
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|
Poreya/azil | Poreya | 2023-12-03T10:05:03Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-03T10:05:03Z | ---
license: mit
---
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|
Asimok/KGLQA-LangChain-NCR | Asimok | 2023-12-03T10:07:10Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T10:06:29Z | ---
license: apache-2.0
---
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|
Asimok/KGLQA-LangChain-CCLUE-MRC | Asimok | 2023-12-03T10:11:51Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-03T10:11:39Z | ---
license: apache-2.0
---
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|
Asimok/KGLQA-KeySentenceSelect-CCLUE-MRC | Asimok | 2023-12-03T10:16:28Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T10:15:51Z | ---
configs:
- config_name: raw
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC-raw/train.jsonl"
- split: dev
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC-raw/dev.jsonl"
- split: test
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC-raw/test.jsonl"
- config_name: normal
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC/train.jsonl"
- split: dev
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC/dev.jsonl"
- split: test
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC/test.jsonl"
- config_name: instruct
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC-instruct/train.jsonl"
- split: dev
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC-instruct/dev.jsonl"
- split: test
path:
- "KGLQA-KeySentenceSelect-CCLUE-MRC-instruct/test.jsonl"
---
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|
Asimok/KGLQA-KnowledgeBant-CCLUE-MRC | Asimok | 2023-12-03T10:18:03Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T10:17:08Z | ---
configs:
- config_name: caption
data_files:
- split: train
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption/test.jsonl"
- config_name: selected
data_files:
- split: train
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption-Selected/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption-Selected/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption-Selected/test.jsonl"
- config_name: instruct
data_files:
- split: train
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption-Selected-instruct/train.jsonl"
- split: dev
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption-Selected-instruct/dev.jsonl"
- split: test
path:
- "KGLQA-KnowledgeBant-CCLUE-MRC-Caption-Selected-instruct/test.jsonl"
---
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|
zicsx/Hindi-PhilPapers | zicsx | 2023-12-03T10:33:23Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-03T10:22:56Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2367530789
num_examples: 24942
download_size: 797028187
dataset_size: 2367530789
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Hindi-PhilPapers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Crystal427/EroCrystyWriter | Crystal427 | 2023-12-03T10:25:01Z | 0 | 0 | [
"license:gpl-3.0",
"region:us"
]
| null | 2023-12-03T10:25:01Z | ---
license: gpl-3.0
---
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|
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