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open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca
|
open-llm-leaderboard
| 2023-10-13T02:31:53Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-13T02:31:44Z |
---
pretty_name: Evaluation run of TFLai/bloom-560m-4bit-alpaca
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TFLai/bloom-560m-4bit-alpaca](https://huggingface.co/TFLai/bloom-560m-4bit-alpaca)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-13T02:31:40.775341](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca/blob/main/results_2023-10-13T02-31-40.775341.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001153523489932886,\n\
\ \"em_stderr\": 0.00034761798968570957,\n \"f1\": 0.028393456375839028,\n\
\ \"f1_stderr\": 0.0009648156202587861,\n \"acc\": 0.25213936558333583,\n\
\ \"acc_stderr\": 0.007562025280082852\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001153523489932886,\n \"em_stderr\": 0.00034761798968570957,\n\
\ \"f1\": 0.028393456375839028,\n \"f1_stderr\": 0.0009648156202587861\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \
\ \"acc_stderr\": 0.001071779348549266\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5027624309392266,\n \"acc_stderr\": 0.014052271211616438\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TFLai/bloom-560m-4bit-alpaca
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T02_31_40.775341
path:
- '**/details_harness|drop|3_2023-10-13T02-31-40.775341.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T02-31-40.775341.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T02_31_40.775341
path:
- '**/details_harness|gsm8k|5_2023-10-13T02-31-40.775341.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T02-31-40.775341.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T02_31_40.775341
path:
- '**/details_harness|winogrande|5_2023-10-13T02-31-40.775341.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T02-31-40.775341.parquet'
- config_name: results
data_files:
- split: 2023_10_13T02_31_40.775341
path:
- results_2023-10-13T02-31-40.775341.parquet
- split: latest
path:
- results_2023-10-13T02-31-40.775341.parquet
---
# Dataset Card for Evaluation run of TFLai/bloom-560m-4bit-alpaca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/bloom-560m-4bit-alpaca
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TFLai/bloom-560m-4bit-alpaca](https://huggingface.co/TFLai/bloom-560m-4bit-alpaca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T02:31:40.775341](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca/blob/main/results_2023-10-13T02-31-40.775341.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968570957,
"f1": 0.028393456375839028,
"f1_stderr": 0.0009648156202587861,
"acc": 0.25213936558333583,
"acc_stderr": 0.007562025280082852
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968570957,
"f1": 0.028393456375839028,
"f1_stderr": 0.0009648156202587861
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.001071779348549266
},
"harness|winogrande|5": {
"acc": 0.5027624309392266,
"acc_stderr": 0.014052271211616438
}
}
```
### 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_cerebras__Cerebras-GPT-590M
|
open-llm-leaderboard
| 2023-10-14T22:11:19Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-14T22:11:11Z |
---
pretty_name: Evaluation run of cerebras/Cerebras-GPT-590M
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cerebras/Cerebras-GPT-590M](https://huggingface.co/cerebras/Cerebras-GPT-590M)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cerebras__Cerebras-GPT-590M\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-14T22:11:07.408754](https://huggingface.co/datasets/open-llm-leaderboard/details_cerebras__Cerebras-GPT-590M/blob/main/results_2023-10-14T22-11-07.408754.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001153523489932886,\n\
\ \"em_stderr\": 0.00034761798968571054,\n \"f1\": 0.039916107382550345,\n\
\ \"f1_stderr\": 0.001153929680724628,\n \"acc\": 0.24300057504519282,\n\
\ \"acc_stderr\": 0.007948184376446\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001153523489932886,\n \"em_stderr\": 0.00034761798968571054,\n\
\ \"f1\": 0.039916107382550345,\n \"f1_stderr\": 0.001153929680724628\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.004548900682335102,\n \
\ \"acc_stderr\": 0.0018535550440036204\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.48145224940805054,\n \"acc_stderr\": 0.014042813708888378\n\
\ }\n}\n```"
repo_url: https://huggingface.co/cerebras/Cerebras-GPT-590M
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_14T22_11_07.408754
path:
- '**/details_harness|drop|3_2023-10-14T22-11-07.408754.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-14T22-11-07.408754.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_14T22_11_07.408754
path:
- '**/details_harness|gsm8k|5_2023-10-14T22-11-07.408754.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-14T22-11-07.408754.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_14T22_11_07.408754
path:
- '**/details_harness|winogrande|5_2023-10-14T22-11-07.408754.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-14T22-11-07.408754.parquet'
- config_name: results
data_files:
- split: 2023_10_14T22_11_07.408754
path:
- results_2023-10-14T22-11-07.408754.parquet
- split: latest
path:
- results_2023-10-14T22-11-07.408754.parquet
---
# Dataset Card for Evaluation run of cerebras/Cerebras-GPT-590M
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/cerebras/Cerebras-GPT-590M
- **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 [cerebras/Cerebras-GPT-590M](https://huggingface.co/cerebras/Cerebras-GPT-590M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_cerebras__Cerebras-GPT-590M",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-14T22:11:07.408754](https://huggingface.co/datasets/open-llm-leaderboard/details_cerebras__Cerebras-GPT-590M/blob/main/results_2023-10-14T22-11-07.408754.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968571054,
"f1": 0.039916107382550345,
"f1_stderr": 0.001153929680724628,
"acc": 0.24300057504519282,
"acc_stderr": 0.007948184376446
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968571054,
"f1": 0.039916107382550345,
"f1_stderr": 0.001153929680724628
},
"harness|gsm8k|5": {
"acc": 0.004548900682335102,
"acc_stderr": 0.0018535550440036204
},
"harness|winogrande|5": {
"acc": 0.48145224940805054,
"acc_stderr": 0.014042813708888378
}
}
```
### 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_edor__Platypus2-mini-7B
|
open-llm-leaderboard
| 2023-10-15T00:02:29Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-15T00:02:21Z |
---
pretty_name: Evaluation run of edor/Platypus2-mini-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [edor/Platypus2-mini-7B](https://huggingface.co/edor/Platypus2-mini-7B) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_edor__Platypus2-mini-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T00:02:17.687247](https://huggingface.co/datasets/open-llm-leaderboard/details_edor__Platypus2-mini-7B/blob/main/results_2023-10-15T00-02-17.687247.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.037856543624161076,\n\
\ \"em_stderr\": 0.0019544746699158705,\n \"f1\": 0.09621854026845604,\n\
\ \"f1_stderr\": 0.00226406080927082,\n \"acc\": 0.4067747623974298,\n\
\ \"acc_stderr\": 0.0093991751789674\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.037856543624161076,\n \"em_stderr\": 0.0019544746699158705,\n\
\ \"f1\": 0.09621854026845604,\n \"f1_stderr\": 0.00226406080927082\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0621683093252464,\n \
\ \"acc_stderr\": 0.006651035644531692\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7513812154696132,\n \"acc_stderr\": 0.012147314713403108\n\
\ }\n}\n```"
repo_url: https://huggingface.co/edor/Platypus2-mini-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T00_02_17.687247
path:
- '**/details_harness|drop|3_2023-10-15T00-02-17.687247.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T00-02-17.687247.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T00_02_17.687247
path:
- '**/details_harness|gsm8k|5_2023-10-15T00-02-17.687247.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T00-02-17.687247.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T00_02_17.687247
path:
- '**/details_harness|winogrande|5_2023-10-15T00-02-17.687247.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T00-02-17.687247.parquet'
- config_name: results
data_files:
- split: 2023_10_15T00_02_17.687247
path:
- results_2023-10-15T00-02-17.687247.parquet
- split: latest
path:
- results_2023-10-15T00-02-17.687247.parquet
---
# Dataset Card for Evaluation run of edor/Platypus2-mini-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/edor/Platypus2-mini-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 [edor/Platypus2-mini-7B](https://huggingface.co/edor/Platypus2-mini-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_edor__Platypus2-mini-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T00:02:17.687247](https://huggingface.co/datasets/open-llm-leaderboard/details_edor__Platypus2-mini-7B/blob/main/results_2023-10-15T00-02-17.687247.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.037856543624161076,
"em_stderr": 0.0019544746699158705,
"f1": 0.09621854026845604,
"f1_stderr": 0.00226406080927082,
"acc": 0.4067747623974298,
"acc_stderr": 0.0093991751789674
},
"harness|drop|3": {
"em": 0.037856543624161076,
"em_stderr": 0.0019544746699158705,
"f1": 0.09621854026845604,
"f1_stderr": 0.00226406080927082
},
"harness|gsm8k|5": {
"acc": 0.0621683093252464,
"acc_stderr": 0.006651035644531692
},
"harness|winogrande|5": {
"acc": 0.7513812154696132,
"acc_stderr": 0.012147314713403108
}
}
```
### 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_beaugogh__Llama2-7b-openorca-mc-v1
|
open-llm-leaderboard
| 2023-10-15T05:51:42Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-15T05:51:34Z |
---
pretty_name: Evaluation run of beaugogh/Llama2-7b-openorca-mc-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [beaugogh/Llama2-7b-openorca-mc-v1](https://huggingface.co/beaugogh/Llama2-7b-openorca-mc-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_beaugogh__Llama2-7b-openorca-mc-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T05:51:30.480988](https://huggingface.co/datasets/open-llm-leaderboard/details_beaugogh__Llama2-7b-openorca-mc-v1/blob/main/results_2023-10-15T05-51-30.480988.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\
\ \"em_stderr\": 0.00036305608931189984,\n \"f1\": 0.054172609060402964,\n\
\ \"f1_stderr\": 0.0013304749578777586,\n \"acc\": 0.38787336798763505,\n\
\ \"acc_stderr\": 0.0089323131312436\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931189984,\n\
\ \"f1\": 0.054172609060402964,\n \"f1_stderr\": 0.0013304749578777586\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04094010614101592,\n \
\ \"acc_stderr\": 0.0054580767962943404\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7348066298342542,\n \"acc_stderr\": 0.01240654946619286\n\
\ }\n}\n```"
repo_url: https://huggingface.co/beaugogh/Llama2-7b-openorca-mc-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T05_51_30.480988
path:
- '**/details_harness|drop|3_2023-10-15T05-51-30.480988.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T05-51-30.480988.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T05_51_30.480988
path:
- '**/details_harness|gsm8k|5_2023-10-15T05-51-30.480988.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T05-51-30.480988.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T05_51_30.480988
path:
- '**/details_harness|winogrande|5_2023-10-15T05-51-30.480988.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T05-51-30.480988.parquet'
- config_name: results
data_files:
- split: 2023_10_15T05_51_30.480988
path:
- results_2023-10-15T05-51-30.480988.parquet
- split: latest
path:
- results_2023-10-15T05-51-30.480988.parquet
---
# Dataset Card for Evaluation run of beaugogh/Llama2-7b-openorca-mc-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beaugogh/Llama2-7b-openorca-mc-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 [beaugogh/Llama2-7b-openorca-mc-v1](https://huggingface.co/beaugogh/Llama2-7b-openorca-mc-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_beaugogh__Llama2-7b-openorca-mc-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T05:51:30.480988](https://huggingface.co/datasets/open-llm-leaderboard/details_beaugogh__Llama2-7b-openorca-mc-v1/blob/main/results_2023-10-15T05-51-30.480988.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931189984,
"f1": 0.054172609060402964,
"f1_stderr": 0.0013304749578777586,
"acc": 0.38787336798763505,
"acc_stderr": 0.0089323131312436
},
"harness|drop|3": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931189984,
"f1": 0.054172609060402964,
"f1_stderr": 0.0013304749578777586
},
"harness|gsm8k|5": {
"acc": 0.04094010614101592,
"acc_stderr": 0.0054580767962943404
},
"harness|winogrande|5": {
"acc": 0.7348066298342542,
"acc_stderr": 0.01240654946619286
}
}
```
### 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_beaugogh__Llama2-13b-sharegpt4
|
open-llm-leaderboard
| 2023-10-15T09:30:50Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-15T09:30:41Z |
---
pretty_name: Evaluation run of beaugogh/Llama2-13b-sharegpt4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [beaugogh/Llama2-13b-sharegpt4](https://huggingface.co/beaugogh/Llama2-13b-sharegpt4)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_beaugogh__Llama2-13b-sharegpt4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T09:30:37.851108](https://huggingface.co/datasets/open-llm-leaderboard/details_beaugogh__Llama2-13b-sharegpt4/blob/main/results_2023-10-15T09-30-37.851108.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001153523489932886,\n\
\ \"em_stderr\": 0.00034761798968571027,\n \"f1\": 0.05843015939597327,\n\
\ \"f1_stderr\": 0.0013137444686186492,\n \"acc\": 0.4200579473220307,\n\
\ \"acc_stderr\": 0.009967774108676528\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001153523489932886,\n \"em_stderr\": 0.00034761798968571027,\n\
\ \"f1\": 0.05843015939597327,\n \"f1_stderr\": 0.0013137444686186492\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08794541319181198,\n \
\ \"acc_stderr\": 0.007801162197487711\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.012134386019865348\n\
\ }\n}\n```"
repo_url: https://huggingface.co/beaugogh/Llama2-13b-sharegpt4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T09_30_37.851108
path:
- '**/details_harness|drop|3_2023-10-15T09-30-37.851108.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T09-30-37.851108.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T09_30_37.851108
path:
- '**/details_harness|gsm8k|5_2023-10-15T09-30-37.851108.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T09-30-37.851108.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T09_30_37.851108
path:
- '**/details_harness|winogrande|5_2023-10-15T09-30-37.851108.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T09-30-37.851108.parquet'
- config_name: results
data_files:
- split: 2023_10_15T09_30_37.851108
path:
- results_2023-10-15T09-30-37.851108.parquet
- split: latest
path:
- results_2023-10-15T09-30-37.851108.parquet
---
# Dataset Card for Evaluation run of beaugogh/Llama2-13b-sharegpt4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beaugogh/Llama2-13b-sharegpt4
- **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 [beaugogh/Llama2-13b-sharegpt4](https://huggingface.co/beaugogh/Llama2-13b-sharegpt4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_beaugogh__Llama2-13b-sharegpt4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T09:30:37.851108](https://huggingface.co/datasets/open-llm-leaderboard/details_beaugogh__Llama2-13b-sharegpt4/blob/main/results_2023-10-15T09-30-37.851108.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968571027,
"f1": 0.05843015939597327,
"f1_stderr": 0.0013137444686186492,
"acc": 0.4200579473220307,
"acc_stderr": 0.009967774108676528
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968571027,
"f1": 0.05843015939597327,
"f1_stderr": 0.0013137444686186492
},
"harness|gsm8k|5": {
"acc": 0.08794541319181198,
"acc_stderr": 0.007801162197487711
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.012134386019865348
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_chargoddard__llama2-22b
|
open-llm-leaderboard
| 2023-10-15T19:23:20Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-15T19:23:11Z |
---
pretty_name: Evaluation run of chargoddard/llama2-22b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/llama2-22b](https://huggingface.co/chargoddard/llama2-22b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__llama2-22b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T19:23:07.867810](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama2-22b/blob/main/results_2023-10-15T19-23-07.867810.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0020973154362416107,\n\
\ \"em_stderr\": 0.00046850650303682974,\n \"f1\": 0.06078334731543612,\n\
\ \"f1_stderr\": 0.0013790362682380892,\n \"acc\": 0.4312689350534026,\n\
\ \"acc_stderr\": 0.010092981888945675\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.00046850650303682974,\n\
\ \"f1\": 0.06078334731543612,\n \"f1_stderr\": 0.0013790362682380892\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09931766489764973,\n \
\ \"acc_stderr\": 0.008238371412683961\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7632202052091555,\n \"acc_stderr\": 0.011947592365207389\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/llama2-22b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T19_23_07.867810
path:
- '**/details_harness|drop|3_2023-10-15T19-23-07.867810.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T19-23-07.867810.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T19_23_07.867810
path:
- '**/details_harness|gsm8k|5_2023-10-15T19-23-07.867810.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T19-23-07.867810.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T19_23_07.867810
path:
- '**/details_harness|winogrande|5_2023-10-15T19-23-07.867810.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T19-23-07.867810.parquet'
- config_name: results
data_files:
- split: 2023_10_15T19_23_07.867810
path:
- results_2023-10-15T19-23-07.867810.parquet
- split: latest
path:
- results_2023-10-15T19-23-07.867810.parquet
---
# Dataset Card for Evaluation run of chargoddard/llama2-22b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/llama2-22b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/llama2-22b](https://huggingface.co/chargoddard/llama2-22b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chargoddard__llama2-22b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T19:23:07.867810](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama2-22b/blob/main/results_2023-10-15T19-23-07.867810.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0020973154362416107,
"em_stderr": 0.00046850650303682974,
"f1": 0.06078334731543612,
"f1_stderr": 0.0013790362682380892,
"acc": 0.4312689350534026,
"acc_stderr": 0.010092981888945675
},
"harness|drop|3": {
"em": 0.0020973154362416107,
"em_stderr": 0.00046850650303682974,
"f1": 0.06078334731543612,
"f1_stderr": 0.0013790362682380892
},
"harness|gsm8k|5": {
"acc": 0.09931766489764973,
"acc_stderr": 0.008238371412683961
},
"harness|winogrande|5": {
"acc": 0.7632202052091555,
"acc_stderr": 0.011947592365207389
}
}
```
### 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_Corianas__590m
|
open-llm-leaderboard
| 2023-10-15T22:43:40Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-15T22:43:32Z |
---
pretty_name: Evaluation run of Corianas/590m
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Corianas/590m](https://huggingface.co/Corianas/590m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Corianas__590m\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T22:43:28.791779](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__590m/blob/main/results_2023-10-15T22-43-28.791779.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.010276845637583893,\n\
\ \"em_stderr\": 0.0010328242665282278,\n \"f1\": 0.0602705536912752,\n\
\ \"f1_stderr\": 0.0016432009705513089,\n \"acc\": 0.24228909873484075,\n\
\ \"acc_stderr\": 0.0074016381223505675\n },\n \"harness|drop|3\":\
\ {\n \"em\": 0.010276845637583893,\n \"em_stderr\": 0.0010328242665282278,\n\
\ \"f1\": 0.0602705536912752,\n \"f1_stderr\": 0.0016432009705513089\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225333\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.48382004735595896,\n \"acc_stderr\": 0.014045126130978601\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Corianas/590m
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T22_43_28.791779
path:
- '**/details_harness|drop|3_2023-10-15T22-43-28.791779.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T22-43-28.791779.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T22_43_28.791779
path:
- '**/details_harness|gsm8k|5_2023-10-15T22-43-28.791779.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T22-43-28.791779.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T22_43_28.791779
path:
- '**/details_harness|winogrande|5_2023-10-15T22-43-28.791779.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T22-43-28.791779.parquet'
- config_name: results
data_files:
- split: 2023_10_15T22_43_28.791779
path:
- results_2023-10-15T22-43-28.791779.parquet
- split: latest
path:
- results_2023-10-15T22-43-28.791779.parquet
---
# Dataset Card for Evaluation run of Corianas/590m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Corianas/590m
- **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 [Corianas/590m](https://huggingface.co/Corianas/590m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Corianas__590m",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T22:43:28.791779](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__590m/blob/main/results_2023-10-15T22-43-28.791779.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.010276845637583893,
"em_stderr": 0.0010328242665282278,
"f1": 0.0602705536912752,
"f1_stderr": 0.0016432009705513089,
"acc": 0.24228909873484075,
"acc_stderr": 0.0074016381223505675
},
"harness|drop|3": {
"em": 0.010276845637583893,
"em_stderr": 0.0010328242665282278,
"f1": 0.0602705536912752,
"f1_stderr": 0.0016432009705513089
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225333
},
"harness|winogrande|5": {
"acc": 0.48382004735595896,
"acc_stderr": 0.014045126130978601
}
}
```
### 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_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload
|
open-llm-leaderboard
| 2023-10-16T09:37:36Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-16T09:37:28Z |
---
pretty_name: Evaluation run of Lajonbot/WizardLM-13B-V1.2-PL-lora_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Lajonbot/WizardLM-13B-V1.2-PL-lora_unload](https://huggingface.co/Lajonbot/WizardLM-13B-V1.2-PL-lora_unload)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T09:37:24.771314](https://huggingface.co/datasets/open-llm-leaderboard/details_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload/blob/main/results_2023-10-16T09-37-24.771314.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003984899328859061,\n\
\ \"em_stderr\": 0.0006451805848102423,\n \"f1\": 0.06672923657718131,\n\
\ \"f1_stderr\": 0.0015525464124355034,\n \"acc\": 0.41089372554487175,\n\
\ \"acc_stderr\": 0.010708286080716344\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003984899328859061,\n \"em_stderr\": 0.0006451805848102423,\n\
\ \"f1\": 0.06672923657718131,\n \"f1_stderr\": 0.0015525464124355034\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11144806671721001,\n \
\ \"acc_stderr\": 0.008668021353794427\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7103393843725335,\n \"acc_stderr\": 0.012748550807638263\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Lajonbot/WizardLM-13B-V1.2-PL-lora_unload
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T09_37_24.771314
path:
- '**/details_harness|drop|3_2023-10-16T09-37-24.771314.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T09-37-24.771314.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T09_37_24.771314
path:
- '**/details_harness|gsm8k|5_2023-10-16T09-37-24.771314.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T09-37-24.771314.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T09_37_24.771314
path:
- '**/details_harness|winogrande|5_2023-10-16T09-37-24.771314.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T09-37-24.771314.parquet'
- config_name: results
data_files:
- split: 2023_10_16T09_37_24.771314
path:
- results_2023-10-16T09-37-24.771314.parquet
- split: latest
path:
- results_2023-10-16T09-37-24.771314.parquet
---
# Dataset Card for Evaluation run of Lajonbot/WizardLM-13B-V1.2-PL-lora_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Lajonbot/WizardLM-13B-V1.2-PL-lora_unload
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Lajonbot/WizardLM-13B-V1.2-PL-lora_unload](https://huggingface.co/Lajonbot/WizardLM-13B-V1.2-PL-lora_unload) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T09:37:24.771314](https://huggingface.co/datasets/open-llm-leaderboard/details_Lajonbot__WizardLM-13B-V1.2-PL-lora_unload/blob/main/results_2023-10-16T09-37-24.771314.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.003984899328859061,
"em_stderr": 0.0006451805848102423,
"f1": 0.06672923657718131,
"f1_stderr": 0.0015525464124355034,
"acc": 0.41089372554487175,
"acc_stderr": 0.010708286080716344
},
"harness|drop|3": {
"em": 0.003984899328859061,
"em_stderr": 0.0006451805848102423,
"f1": 0.06672923657718131,
"f1_stderr": 0.0015525464124355034
},
"harness|gsm8k|5": {
"acc": 0.11144806671721001,
"acc_stderr": 0.008668021353794427
},
"harness|winogrande|5": {
"acc": 0.7103393843725335,
"acc_stderr": 0.012748550807638263
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_microsoft__CodeGPT-small-py
|
open-llm-leaderboard
| 2023-10-16T10:55:35Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-16T10:55:24Z |
---
pretty_name: Evaluation run of microsoft/CodeGPT-small-py
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [microsoft/CodeGPT-small-py](https://huggingface.co/microsoft/CodeGPT-small-py)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_microsoft__CodeGPT-small-py\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T10:55:21.745604](https://huggingface.co/datasets/open-llm-leaderboard/details_microsoft__CodeGPT-small-py/blob/main/results_2023-10-16T10-55-21.745604.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.00388003355704698,\n\
\ \"em_stderr\": 0.0006366682825519956,\n \"f1\": 0.016416736577181235,\n\
\ \"f1_stderr\": 0.0008900949322041355,\n \"acc\": 0.24388318863456984,\n\
\ \"acc_stderr\": 0.007024139410202808\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.00388003355704698,\n \"em_stderr\": 0.0006366682825519956,\n\
\ \"f1\": 0.016416736577181235,\n \"f1_stderr\": 0.0008900949322041355\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.4877663772691397,\n\
\ \"acc_stderr\": 0.014048278820405616\n }\n}\n```"
repo_url: https://huggingface.co/microsoft/CodeGPT-small-py
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T10_55_21.745604
path:
- '**/details_harness|drop|3_2023-10-16T10-55-21.745604.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T10-55-21.745604.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T10_55_21.745604
path:
- '**/details_harness|gsm8k|5_2023-10-16T10-55-21.745604.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T10-55-21.745604.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T10_55_21.745604
path:
- '**/details_harness|winogrande|5_2023-10-16T10-55-21.745604.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T10-55-21.745604.parquet'
- config_name: results
data_files:
- split: 2023_10_16T10_55_21.745604
path:
- results_2023-10-16T10-55-21.745604.parquet
- split: latest
path:
- results_2023-10-16T10-55-21.745604.parquet
---
# Dataset Card for Evaluation run of microsoft/CodeGPT-small-py
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/microsoft/CodeGPT-small-py
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [microsoft/CodeGPT-small-py](https://huggingface.co/microsoft/CodeGPT-small-py) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_microsoft__CodeGPT-small-py",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T10:55:21.745604](https://huggingface.co/datasets/open-llm-leaderboard/details_microsoft__CodeGPT-small-py/blob/main/results_2023-10-16T10-55-21.745604.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.00388003355704698,
"em_stderr": 0.0006366682825519956,
"f1": 0.016416736577181235,
"f1_stderr": 0.0008900949322041355,
"acc": 0.24388318863456984,
"acc_stderr": 0.007024139410202808
},
"harness|drop|3": {
"em": 0.00388003355704698,
"em_stderr": 0.0006366682825519956,
"f1": 0.016416736577181235,
"f1_stderr": 0.0008900949322041355
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.4877663772691397,
"acc_stderr": 0.014048278820405616
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_lmsys__longchat-7b-v1.5-32k
|
open-llm-leaderboard
| 2023-10-16T16:20:45Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-16T16:20:37Z |
---
pretty_name: Evaluation run of lmsys/longchat-7b-v1.5-32k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lmsys/longchat-7b-v1.5-32k](https://huggingface.co/lmsys/longchat-7b-v1.5-32k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lmsys__longchat-7b-v1.5-32k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T16:20:33.188247](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__longchat-7b-v1.5-32k/blob/main/results_2023-10-16T16-20-33.188247.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.08252936241610738,\n\
\ \"em_stderr\": 0.0028179934761829416,\n \"f1\": 0.1372829278523486,\n\
\ \"f1_stderr\": 0.0030245592633561815,\n \"acc\": 0.3672124310289838,\n\
\ \"acc_stderr\": 0.009455449816488642\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08252936241610738,\n \"em_stderr\": 0.0028179934761829416,\n\
\ \"f1\": 0.1372829278523486,\n \"f1_stderr\": 0.0030245592633561815\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.047763457164518575,\n \
\ \"acc_stderr\": 0.005874387536229305\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6866614048934491,\n \"acc_stderr\": 0.01303651209674798\n\
\ }\n}\n```"
repo_url: https://huggingface.co/lmsys/longchat-7b-v1.5-32k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T16_20_33.188247
path:
- '**/details_harness|drop|3_2023-10-16T16-20-33.188247.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T16-20-33.188247.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T16_20_33.188247
path:
- '**/details_harness|gsm8k|5_2023-10-16T16-20-33.188247.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T16-20-33.188247.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T16_20_33.188247
path:
- '**/details_harness|winogrande|5_2023-10-16T16-20-33.188247.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T16-20-33.188247.parquet'
- config_name: results
data_files:
- split: 2023_10_16T16_20_33.188247
path:
- results_2023-10-16T16-20-33.188247.parquet
- split: latest
path:
- results_2023-10-16T16-20-33.188247.parquet
---
# Dataset Card for Evaluation run of lmsys/longchat-7b-v1.5-32k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lmsys/longchat-7b-v1.5-32k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [lmsys/longchat-7b-v1.5-32k](https://huggingface.co/lmsys/longchat-7b-v1.5-32k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_lmsys__longchat-7b-v1.5-32k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T16:20:33.188247](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__longchat-7b-v1.5-32k/blob/main/results_2023-10-16T16-20-33.188247.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.08252936241610738,
"em_stderr": 0.0028179934761829416,
"f1": 0.1372829278523486,
"f1_stderr": 0.0030245592633561815,
"acc": 0.3672124310289838,
"acc_stderr": 0.009455449816488642
},
"harness|drop|3": {
"em": 0.08252936241610738,
"em_stderr": 0.0028179934761829416,
"f1": 0.1372829278523486,
"f1_stderr": 0.0030245592633561815
},
"harness|gsm8k|5": {
"acc": 0.047763457164518575,
"acc_stderr": 0.005874387536229305
},
"harness|winogrande|5": {
"acc": 0.6866614048934491,
"acc_stderr": 0.01303651209674798
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_bigscience__bloom-1b7
|
open-llm-leaderboard
| 2023-12-04T13:06:19Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-16T16:35:31Z |
---
pretty_name: Evaluation run of bigscience/bloom-1b7
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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_bigscience__bloom-1b7\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-04T13:06:13.491181](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloom-1b7/blob/main/results_2023-12-04T13-06-13.491181.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\": 0.008339651250947688,\n\
\ \"acc_stderr\": 0.0025049422268605148\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.008339651250947688,\n \"acc_stderr\": 0.0025049422268605148\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bigscience/bloom-1b7
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T16_35_28.358737
path:
- '**/details_harness|drop|3_2023-10-16T16-35-28.358737.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T16-35-28.358737.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T16_35_28.358737
path:
- '**/details_harness|gsm8k|5_2023-10-16T16-35-28.358737.parquet'
- split: 2023_12_03T16_04_08.979472
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-04-08.979472.parquet'
- split: 2023_12_04T09_54_54.675804
path:
- '**/details_harness|gsm8k|5_2023-12-04T09-54-54.675804.parquet'
- split: 2023_12_04T13_06_13.491181
path:
- '**/details_harness|gsm8k|5_2023-12-04T13-06-13.491181.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T13-06-13.491181.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T16_35_28.358737
path:
- '**/details_harness|winogrande|5_2023-10-16T16-35-28.358737.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T16-35-28.358737.parquet'
- config_name: results
data_files:
- split: 2023_10_16T16_35_28.358737
path:
- results_2023-10-16T16-35-28.358737.parquet
- split: 2023_12_03T16_04_08.979472
path:
- results_2023-12-03T16-04-08.979472.parquet
- split: 2023_12_04T09_54_54.675804
path:
- results_2023-12-04T09-54-54.675804.parquet
- split: 2023_12_04T13_06_13.491181
path:
- results_2023-12-04T13-06-13.491181.parquet
- split: latest
path:
- results_2023-12-04T13-06-13.491181.parquet
---
# Dataset Card for Evaluation run of bigscience/bloom-1b7
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigscience/bloom-1b7
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_bigscience__bloom-1b7",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T13:06:13.491181](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloom-1b7/blob/main/results_2023-12-04T13-06-13.491181.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": 0.008339651250947688,
"acc_stderr": 0.0025049422268605148
},
"harness|gsm8k|5": {
"acc": 0.008339651250947688,
"acc_stderr": 0.0025049422268605148
}
}
```
### 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_dvruette__oasst-llama-13b-2-epochs
|
open-llm-leaderboard
| 2023-10-16T19:36:03Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-16T19:35:55Z |
---
pretty_name: Evaluation run of dvruette/oasst-llama-13b-2-epochs
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dvruette/oasst-llama-13b-2-epochs](https://huggingface.co/dvruette/oasst-llama-13b-2-epochs)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dvruette__oasst-llama-13b-2-epochs\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T19:35:51.495118](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-llama-13b-2-epochs/blob/main/results_2023-10-16T19-35-51.495118.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.04299496644295302,\n\
\ \"em_stderr\": 0.002077330365557692,\n \"f1\": 0.10806732382550314,\n\
\ \"f1_stderr\": 0.0024113571936826787,\n \"acc\": 0.42569171474168144,\n\
\ \"acc_stderr\": 0.00971706467249931\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.04299496644295302,\n \"em_stderr\": 0.002077330365557692,\n\
\ \"f1\": 0.10806732382550314,\n \"f1_stderr\": 0.0024113571936826787\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08263836239575435,\n \
\ \"acc_stderr\": 0.007584089220148114\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dvruette/oasst-llama-13b-2-epochs
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T19_35_51.495118
path:
- '**/details_harness|drop|3_2023-10-16T19-35-51.495118.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T19-35-51.495118.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T19_35_51.495118
path:
- '**/details_harness|gsm8k|5_2023-10-16T19-35-51.495118.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T19-35-51.495118.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T19_35_51.495118
path:
- '**/details_harness|winogrande|5_2023-10-16T19-35-51.495118.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T19-35-51.495118.parquet'
- config_name: results
data_files:
- split: 2023_10_16T19_35_51.495118
path:
- results_2023-10-16T19-35-51.495118.parquet
- split: latest
path:
- results_2023-10-16T19-35-51.495118.parquet
---
# Dataset Card for Evaluation run of dvruette/oasst-llama-13b-2-epochs
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dvruette/oasst-llama-13b-2-epochs
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [dvruette/oasst-llama-13b-2-epochs](https://huggingface.co/dvruette/oasst-llama-13b-2-epochs) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dvruette__oasst-llama-13b-2-epochs",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T19:35:51.495118](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-llama-13b-2-epochs/blob/main/results_2023-10-16T19-35-51.495118.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.04299496644295302,
"em_stderr": 0.002077330365557692,
"f1": 0.10806732382550314,
"f1_stderr": 0.0024113571936826787,
"acc": 0.42569171474168144,
"acc_stderr": 0.00971706467249931
},
"harness|drop|3": {
"em": 0.04299496644295302,
"em_stderr": 0.002077330365557692,
"f1": 0.10806732382550314,
"f1_stderr": 0.0024113571936826787
},
"harness|gsm8k|5": {
"acc": 0.08263836239575435,
"acc_stderr": 0.007584089220148114
},
"harness|winogrande|5": {
"acc": 0.7687450670876085,
"acc_stderr": 0.01185004012485051
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_ajibawa-2023__scarlett-33b
|
open-llm-leaderboard
| 2023-10-16T22:35:46Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-16T22:35:37Z |
---
pretty_name: Evaluation run of ajibawa-2023/scarlett-33b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ajibawa-2023/scarlett-33b](https://huggingface.co/ajibawa-2023/scarlett-33b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ajibawa-2023__scarlett-33b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T22:35:33.432949](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__scarlett-33b/blob/main/results_2023-10-16T22-35-33.432949.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3665058724832215,\n\
\ \"em_stderr\": 0.004934593891762348,\n \"f1\": 0.43883598993288797,\n\
\ \"f1_stderr\": 0.004751167980569885,\n \"acc\": 0.39800367765635275,\n\
\ \"acc_stderr\": 0.008206189612832142\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3665058724832215,\n \"em_stderr\": 0.004934593891762348,\n\
\ \"f1\": 0.43883598993288797,\n \"f1_stderr\": 0.004751167980569885\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.028051554207733132,\n \
\ \"acc_stderr\": 0.00454822953383635\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827933\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ajibawa-2023/scarlett-33b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T22_35_33.432949
path:
- '**/details_harness|drop|3_2023-10-16T22-35-33.432949.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T22-35-33.432949.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T22_35_33.432949
path:
- '**/details_harness|gsm8k|5_2023-10-16T22-35-33.432949.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T22-35-33.432949.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T22_35_33.432949
path:
- '**/details_harness|winogrande|5_2023-10-16T22-35-33.432949.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T22-35-33.432949.parquet'
- config_name: results
data_files:
- split: 2023_10_16T22_35_33.432949
path:
- results_2023-10-16T22-35-33.432949.parquet
- split: latest
path:
- results_2023-10-16T22-35-33.432949.parquet
---
# Dataset Card for Evaluation run of ajibawa-2023/scarlett-33b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/scarlett-33b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ajibawa-2023/scarlett-33b](https://huggingface.co/ajibawa-2023/scarlett-33b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ajibawa-2023__scarlett-33b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T22:35:33.432949](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__scarlett-33b/blob/main/results_2023-10-16T22-35-33.432949.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.3665058724832215,
"em_stderr": 0.004934593891762348,
"f1": 0.43883598993288797,
"f1_stderr": 0.004751167980569885,
"acc": 0.39800367765635275,
"acc_stderr": 0.008206189612832142
},
"harness|drop|3": {
"em": 0.3665058724832215,
"em_stderr": 0.004934593891762348,
"f1": 0.43883598993288797,
"f1_stderr": 0.004751167980569885
},
"harness|gsm8k|5": {
"acc": 0.028051554207733132,
"acc_stderr": 0.00454822953383635
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827933
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b
|
open-llm-leaderboard
| 2023-10-16T23:19:31Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-16T23:19:21Z |
---
pretty_name: Evaluation run of Gryphe/MythoMax-L2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T23:19:17.622542](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b/blob/main/results_2023-10-16T23-19-17.622542.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.13433305369127516,\n\
\ \"em_stderr\": 0.00349225954139751,\n \"f1\": 0.20734689597315364,\n\
\ \"f1_stderr\": 0.003631918882586114,\n \"acc\": 0.42119517249261446,\n\
\ \"acc_stderr\": 0.010012961564157645\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.13433305369127516,\n \"em_stderr\": 0.00349225954139751,\n\
\ \"f1\": 0.20734689597315364,\n \"f1_stderr\": 0.003631918882586114\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09021986353297953,\n \
\ \"acc_stderr\": 0.00789153710844994\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.01213438601986535\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Gryphe/MythoMax-L2-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T23_19_17.622542
path:
- '**/details_harness|drop|3_2023-10-16T23-19-17.622542.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T23-19-17.622542.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T23_19_17.622542
path:
- '**/details_harness|gsm8k|5_2023-10-16T23-19-17.622542.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T23-19-17.622542.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T23_19_17.622542
path:
- '**/details_harness|winogrande|5_2023-10-16T23-19-17.622542.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T23-19-17.622542.parquet'
- config_name: results
data_files:
- split: 2023_10_16T23_19_17.622542
path:
- results_2023-10-16T23-19-17.622542.parquet
- split: latest
path:
- results_2023-10-16T23-19-17.622542.parquet
---
# Dataset Card for Evaluation run of Gryphe/MythoMax-L2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Gryphe/MythoMax-L2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T23:19:17.622542](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoMax-L2-13b/blob/main/results_2023-10-16T23-19-17.622542.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.13433305369127516,
"em_stderr": 0.00349225954139751,
"f1": 0.20734689597315364,
"f1_stderr": 0.003631918882586114,
"acc": 0.42119517249261446,
"acc_stderr": 0.010012961564157645
},
"harness|drop|3": {
"em": 0.13433305369127516,
"em_stderr": 0.00349225954139751,
"f1": 0.20734689597315364,
"f1_stderr": 0.003631918882586114
},
"harness|gsm8k|5": {
"acc": 0.09021986353297953,
"acc_stderr": 0.00789153710844994
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.01213438601986535
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_MBZUAI__LaMini-GPT-774M
|
open-llm-leaderboard
| 2023-10-17T01:05:35Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-17T01:05:26Z |
---
pretty_name: Evaluation run of MBZUAI/LaMini-GPT-774M
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [MBZUAI/LaMini-GPT-774M](https://huggingface.co/MBZUAI/LaMini-GPT-774M) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MBZUAI__LaMini-GPT-774M\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T01:05:23.378180](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__LaMini-GPT-774M/blob/main/results_2023-10-17T01-05-23.378180.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.03544463087248322,\n\
\ \"em_stderr\": 0.0018935573437954087,\n \"f1\": 0.12509857382550346,\n\
\ \"f1_stderr\": 0.0025549304231766066,\n \"acc\": 0.2829518547750592,\n\
\ \"acc_stderr\": 0.006964941277847027\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.03544463087248322,\n \"em_stderr\": 0.0018935573437954087,\n\
\ \"f1\": 0.12509857382550346,\n \"f1_stderr\": 0.0025549304231766066\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5659037095501184,\n\
\ \"acc_stderr\": 0.013929882555694054\n }\n}\n```"
repo_url: https://huggingface.co/MBZUAI/LaMini-GPT-774M
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T01_05_23.378180
path:
- '**/details_harness|drop|3_2023-10-17T01-05-23.378180.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T01-05-23.378180.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T01_05_23.378180
path:
- '**/details_harness|gsm8k|5_2023-10-17T01-05-23.378180.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T01-05-23.378180.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T01_05_23.378180
path:
- '**/details_harness|winogrande|5_2023-10-17T01-05-23.378180.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T01-05-23.378180.parquet'
- config_name: results
data_files:
- split: 2023_10_17T01_05_23.378180
path:
- results_2023-10-17T01-05-23.378180.parquet
- split: latest
path:
- results_2023-10-17T01-05-23.378180.parquet
---
# Dataset Card for Evaluation run of MBZUAI/LaMini-GPT-774M
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/MBZUAI/LaMini-GPT-774M
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [MBZUAI/LaMini-GPT-774M](https://huggingface.co/MBZUAI/LaMini-GPT-774M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_MBZUAI__LaMini-GPT-774M",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T01:05:23.378180](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__LaMini-GPT-774M/blob/main/results_2023-10-17T01-05-23.378180.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.03544463087248322,
"em_stderr": 0.0018935573437954087,
"f1": 0.12509857382550346,
"f1_stderr": 0.0025549304231766066,
"acc": 0.2829518547750592,
"acc_stderr": 0.006964941277847027
},
"harness|drop|3": {
"em": 0.03544463087248322,
"em_stderr": 0.0018935573437954087,
"f1": 0.12509857382550346,
"f1_stderr": 0.0025549304231766066
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5659037095501184,
"acc_stderr": 0.013929882555694054
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_bofenghuang__vigogne-33b-instruct
|
open-llm-leaderboard
| 2023-10-17T06:48:29Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-17T06:48:21Z |
---
pretty_name: Evaluation run of bofenghuang/vigogne-33b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bofenghuang/vigogne-33b-instruct](https://huggingface.co/bofenghuang/vigogne-33b-instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bofenghuang__vigogne-33b-instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T06:48:17.282592](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-33b-instruct/blob/main/results_2023-10-17T06-48-17.282592.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.4092911073825503,\n\
\ \"em_stderr\": 0.005035499534676373,\n \"f1\": 0.47988779362416334,\n\
\ \"f1_stderr\": 0.004806379711128169,\n \"acc\": 0.4499623916853611,\n\
\ \"acc_stderr\": 0.010072884519008809\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.4092911073825503,\n \"em_stderr\": 0.005035499534676373,\n\
\ \"f1\": 0.47988779362416334,\n \"f1_stderr\": 0.004806379711128169\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11144806671721001,\n \
\ \"acc_stderr\": 0.008668021353794433\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7884767166535123,\n \"acc_stderr\": 0.011477747684223187\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bofenghuang/vigogne-33b-instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T06_48_17.282592
path:
- '**/details_harness|drop|3_2023-10-17T06-48-17.282592.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T06-48-17.282592.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T06_48_17.282592
path:
- '**/details_harness|gsm8k|5_2023-10-17T06-48-17.282592.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T06-48-17.282592.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T06_48_17.282592
path:
- '**/details_harness|winogrande|5_2023-10-17T06-48-17.282592.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T06-48-17.282592.parquet'
- config_name: results
data_files:
- split: 2023_10_17T06_48_17.282592
path:
- results_2023-10-17T06-48-17.282592.parquet
- split: latest
path:
- results_2023-10-17T06-48-17.282592.parquet
---
# Dataset Card for Evaluation run of bofenghuang/vigogne-33b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bofenghuang/vigogne-33b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bofenghuang/vigogne-33b-instruct](https://huggingface.co/bofenghuang/vigogne-33b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_bofenghuang__vigogne-33b-instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T06:48:17.282592](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-33b-instruct/blob/main/results_2023-10-17T06-48-17.282592.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.4092911073825503,
"em_stderr": 0.005035499534676373,
"f1": 0.47988779362416334,
"f1_stderr": 0.004806379711128169,
"acc": 0.4499623916853611,
"acc_stderr": 0.010072884519008809
},
"harness|drop|3": {
"em": 0.4092911073825503,
"em_stderr": 0.005035499534676373,
"f1": 0.47988779362416334,
"f1_stderr": 0.004806379711128169
},
"harness|gsm8k|5": {
"acc": 0.11144806671721001,
"acc_stderr": 0.008668021353794433
},
"harness|winogrande|5": {
"acc": 0.7884767166535123,
"acc_stderr": 0.011477747684223187
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_Aspik101__30B-Lazarus-instruct-PL-lora_unload
|
open-llm-leaderboard
| 2023-10-17T08:13:36Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-17T08:13:28Z |
---
pretty_name: Evaluation run of Aspik101/30B-Lazarus-instruct-PL-lora_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aspik101/30B-Lazarus-instruct-PL-lora_unload](https://huggingface.co/Aspik101/30B-Lazarus-instruct-PL-lora_unload)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aspik101__30B-Lazarus-instruct-PL-lora_unload\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T08:13:24.195120](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__30B-Lazarus-instruct-PL-lora_unload/blob/main/results_2023-10-17T08-13-24.195120.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.01164010067114094,\n\
\ \"em_stderr\": 0.0010984380734032925,\n \"f1\": 0.07800545302013438,\n\
\ \"f1_stderr\": 0.0017935902090569574,\n \"acc\": 0.4522835158298991,\n\
\ \"acc_stderr\": 0.010087630088457804\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01164010067114094,\n \"em_stderr\": 0.0010984380734032925,\n\
\ \"f1\": 0.07800545302013438,\n \"f1_stderr\": 0.0017935902090569574\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11372251705837756,\n \
\ \"acc_stderr\": 0.008744810131034036\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7908445146014207,\n \"acc_stderr\": 0.01143045004588157\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aspik101/30B-Lazarus-instruct-PL-lora_unload
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T08_13_24.195120
path:
- '**/details_harness|drop|3_2023-10-17T08-13-24.195120.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T08-13-24.195120.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T08_13_24.195120
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-13-24.195120.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-13-24.195120.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T08_13_24.195120
path:
- '**/details_harness|winogrande|5_2023-10-17T08-13-24.195120.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T08-13-24.195120.parquet'
- config_name: results
data_files:
- split: 2023_10_17T08_13_24.195120
path:
- results_2023-10-17T08-13-24.195120.parquet
- split: latest
path:
- results_2023-10-17T08-13-24.195120.parquet
---
# Dataset Card for Evaluation run of Aspik101/30B-Lazarus-instruct-PL-lora_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aspik101/30B-Lazarus-instruct-PL-lora_unload
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Aspik101/30B-Lazarus-instruct-PL-lora_unload](https://huggingface.co/Aspik101/30B-Lazarus-instruct-PL-lora_unload) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Aspik101__30B-Lazarus-instruct-PL-lora_unload",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T08:13:24.195120](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__30B-Lazarus-instruct-PL-lora_unload/blob/main/results_2023-10-17T08-13-24.195120.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.01164010067114094,
"em_stderr": 0.0010984380734032925,
"f1": 0.07800545302013438,
"f1_stderr": 0.0017935902090569574,
"acc": 0.4522835158298991,
"acc_stderr": 0.010087630088457804
},
"harness|drop|3": {
"em": 0.01164010067114094,
"em_stderr": 0.0010984380734032925,
"f1": 0.07800545302013438,
"f1_stderr": 0.0017935902090569574
},
"harness|gsm8k|5": {
"acc": 0.11372251705837756,
"acc_stderr": 0.008744810131034036
},
"harness|winogrande|5": {
"acc": 0.7908445146014207,
"acc_stderr": 0.01143045004588157
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w
|
open-llm-leaderboard
| 2023-10-23T05:19:39Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-23T05:19:33Z |
---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE1_17w](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T05:19:29.097153](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w/blob/main/results_2023-10-23T05-19-29.097153.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.36398909395973156,\n\
\ \"em_stderr\": 0.00492738070289941,\n \"f1\": 0.4025178271812084,\n\
\ \"f1_stderr\": 0.004834808125344133,\n \"acc\": 0.45268383345111485,\n\
\ \"acc_stderr\": 0.010561270649266652\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.36398909395973156,\n \"em_stderr\": 0.00492738070289941,\n\
\ \"f1\": 0.4025178271812084,\n \"f1_stderr\": 0.004834808125344133\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1326762699014405,\n \
\ \"acc_stderr\": 0.009343929131442216\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7726913970007893,\n \"acc_stderr\": 0.011778612167091087\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T05_19_29.097153
path:
- '**/details_harness|drop|3_2023-10-23T05-19-29.097153.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T05-19-29.097153.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T05_19_29.097153
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-19-29.097153.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-19-29.097153.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T05_19_29.097153
path:
- '**/details_harness|winogrande|5_2023-10-23T05-19-29.097153.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T05-19-29.097153.parquet'
- config_name: results
data_files:
- split: 2023_10_23T05_19_29.097153
path:
- results_2023-10-23T05-19-29.097153.parquet
- split: latest
path:
- results_2023-10-23T05-19-29.097153.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w
- **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 [CHIH-HUNG/llama-2-13b-FINETUNE1_17w](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:19:29.097153](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w/blob/main/results_2023-10-23T05-19-29.097153.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.36398909395973156,
"em_stderr": 0.00492738070289941,
"f1": 0.4025178271812084,
"f1_stderr": 0.004834808125344133,
"acc": 0.45268383345111485,
"acc_stderr": 0.010561270649266652
},
"harness|drop|3": {
"em": 0.36398909395973156,
"em_stderr": 0.00492738070289941,
"f1": 0.4025178271812084,
"f1_stderr": 0.004834808125344133
},
"harness|gsm8k|5": {
"acc": 0.1326762699014405,
"acc_stderr": 0.009343929131442216
},
"harness|winogrande|5": {
"acc": 0.7726913970007893,
"acc_stderr": 0.011778612167091087
}
}
```
### 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_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
|
open-llm-leaderboard
| 2023-10-23T05:27:24Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-23T05:27:17Z |
---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T05:27:14.109658](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj/blob/main/results_2023-10-23T05-27-14.109658.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3719588926174497,\n\
\ \"em_stderr\": 0.004949726013193945,\n \"f1\": 0.4084679110738261,\n\
\ \"f1_stderr\": 0.004843145937750956,\n \"acc\": 0.4480415851620389,\n\
\ \"acc_stderr\": 0.010535274120903989\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3719588926174497,\n \"em_stderr\": 0.004949726013193945,\n\
\ \"f1\": 0.4084679110738261,\n \"f1_stderr\": 0.004843145937750956\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1281273692191054,\n \
\ \"acc_stderr\": 0.009206398549980031\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827948\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T05_27_14.109658
path:
- '**/details_harness|drop|3_2023-10-23T05-27-14.109658.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T05-27-14.109658.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T05_27_14.109658
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-27-14.109658.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-27-14.109658.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T05_27_14.109658
path:
- '**/details_harness|winogrande|5_2023-10-23T05-27-14.109658.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T05-27-14.109658.parquet'
- config_name: results
data_files:
- split: 2023_10_23T05_27_14.109658
path:
- results_2023-10-23T05-27-14.109658.parquet
- split: latest
path:
- results_2023-10-23T05-27-14.109658.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
- **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 [CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:27:14.109658](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj/blob/main/results_2023-10-23T05-27-14.109658.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.3719588926174497,
"em_stderr": 0.004949726013193945,
"f1": 0.4084679110738261,
"f1_stderr": 0.004843145937750956,
"acc": 0.4480415851620389,
"acc_stderr": 0.010535274120903989
},
"harness|drop|3": {
"em": 0.3719588926174497,
"em_stderr": 0.004949726013193945,
"f1": 0.4084679110738261,
"f1_stderr": 0.004843145937750956
},
"harness|gsm8k|5": {
"acc": 0.1281273692191054,
"acc_stderr": 0.009206398549980031
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827948
}
}
```
### 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_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj
|
open-llm-leaderboard
| 2023-10-23T05:42:03Z | 77 | 0 |
[
"region:us"
] | null | 2023-10-23T05:41:56Z |
---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T05:41:52.177937](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj/blob/main/results_2023-10-23T05-41-52.177937.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.08137583892617449,\n\
\ \"em_stderr\": 0.0027999889835206245,\n \"f1\": 0.13315016778523447,\n\
\ \"f1_stderr\": 0.0029419319985989354,\n \"acc\": 0.4454347335673805,\n\
\ \"acc_stderr\": 0.010395126943573653\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08137583892617449,\n \"em_stderr\": 0.0027999889835206245,\n\
\ \"f1\": 0.13315016778523447,\n \"f1_stderr\": 0.0029419319985989354\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12054586808188021,\n \
\ \"acc_stderr\": 0.008968608285309067\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838238\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T05_41_52.177937
path:
- '**/details_harness|drop|3_2023-10-23T05-41-52.177937.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T05-41-52.177937.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T05_41_52.177937
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-41-52.177937.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-41-52.177937.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T05_41_52.177937
path:
- '**/details_harness|winogrande|5_2023-10-23T05-41-52.177937.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T05-41-52.177937.parquet'
- config_name: results
data_files:
- split: 2023_10_23T05_41_52.177937
path:
- results_2023-10-23T05-41-52.177937.parquet
- split: latest
path:
- results_2023-10-23T05-41-52.177937.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj
- **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 [CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:41:52.177937](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj/blob/main/results_2023-10-23T05-41-52.177937.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.08137583892617449,
"em_stderr": 0.0027999889835206245,
"f1": 0.13315016778523447,
"f1_stderr": 0.0029419319985989354,
"acc": 0.4454347335673805,
"acc_stderr": 0.010395126943573653
},
"harness|drop|3": {
"em": 0.08137583892617449,
"em_stderr": 0.0027999889835206245,
"f1": 0.13315016778523447,
"f1_stderr": 0.0029419319985989354
},
"harness|gsm8k|5": {
"acc": 0.12054586808188021,
"acc_stderr": 0.008968608285309067
},
"harness|winogrande|5": {
"acc": 0.7703235990528808,
"acc_stderr": 0.011821645601838238
}
}
```
### 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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] |
Miaosen/openai-humaneval-sky-shadow
|
Miaosen
| 2023-11-12T09:44:37Z | 77 | 0 |
[
"task_categories:text-classification",
"size_categories:n<1K",
"license:apache-2.0",
"code",
"arxiv:2310.19341",
"region:us"
] |
[
"text-classification"
] | 2023-11-10T09:40:36Z |
---
license: apache-2.0
task_categories:
- text-classification
tags:
- code
size_categories:
- n<1K
---
# Shadow Humaneval dataset
This dataset is generated by GPT-4 to mimic [openai-humaneval](https://huggingface.co/datasets/openai_humaneval) dataset. Each problem of HumanEval has a corresponding shadow problem in this dataset.
The usage of this dataset is to check Whether a code generation model has data leakage during its training progress. You can refer to [Skywork](https://arxiv.org/pdf/2310.19341.pdf) for further details.
|
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] |
bys2058/sd1111
|
bys2058
| 2023-11-14T13:52:27Z | 77 | 0 |
[
"region:us"
] | null | 2023-11-11T01:15:43Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: hair_mask
dtype: image
- name: result_image
dtype: image
- name: image_caption
dtype: string
splits:
- name: train
num_bytes: 93744509413.828
num_examples: 54062
download_size: 91885409225
dataset_size: 93744509413.828
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sd1111"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jxie/pascal-voc-2012
|
jxie
| 2023-11-16T06:41:31Z | 77 | 0 |
[
"region:us"
] | null | 2023-11-15T00:45:48Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 1115392514.736
num_examples: 10582
- name: val
num_bytes: 164042133.619
num_examples: 1449
download_size: 1405650165
dataset_size: 1279434648.355
---
# Dataset Card for "pascal-voc-2012"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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hyperinfer/justizscrap_verkehrsunfall
|
hyperinfer
| 2023-11-21T14:16:07Z | 77 | 0 |
[
"region:us"
] | null | 2023-11-21T14:15:21Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 200301130.7274635
num_examples: 8386
- name: test
num_bytes: 50087225.27253649
num_examples: 2097
download_size: 127470636
dataset_size: 250388356.0
---
# Dataset Card for "justizscrap_verkehrsunfall"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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-0.4186035692691803,
-0.050963908433914185,
-0.3351818323135376,
-0.2730180025100708
] |
BeIR/quora-qrels
|
BeIR
| 2022-10-23T06:07:21Z | 76 | 0 |
[
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-05T17:27:09Z |
---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
|
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] |
shjwudp/chinese-c4
|
shjwudp
| 2023-06-20T11:40:06Z | 76 | 13 |
[
"language:zh",
"license:cc-by-4.0",
"region:us"
] | null | 2022-11-15T01:27:26Z |
---
license: cc-by-4.0
language:
- zh
---
## Introduction
Chinese-C4 is a clean Chinese internet dataset based on Common Crawl. The dataset is 46.29GB and has undergone multiple cleaning strategies, including Chinese filtering, heuristic cleaning based on punctuation, line-based hashing for deduplication, and repetition removal.
The dataset is open source and free for commercial use, and you are welcome to use the data and the cleaning strategies provided and contribute your cleaning strategies.
You can find the cleaning script for the dataset on GitHub [c4-dataset-script](https://github.com/shjwudp/c4-dataset-script).
|
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] |
Den4ikAI/russian_instructions
|
Den4ikAI
| 2023-03-12T04:59:14Z | 76 | 12 |
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:ru",
"license:mit",
"region:us"
] |
[
"conversational"
] | 2023-03-06T17:06:32Z |
---
license: mit
task_categories:
- conversational
language:
- ru
size_categories:
- 10K<n<100K
---
Новая версия: https://huggingface.co/datasets/Den4ikAI/russian_instructions_2
Русский датасет инструкций и QA.
Структура датасета:
```
{
"dialogue":[
"Как я могу улучшить свою связь между телом и разумом?",
"Начните с разработки регулярной практики осознанности. 2. Обязательно практикуйте баланс на нескольких уровнях: физическом, эмоциональном, умственном и духовном. 3. Свяжитесь с природой, когда это возможно - идите на прогулки или бегайте на улице, или просто сидите в парке и наблюдайте природу вокруг вас. 4. Развивайте позитивные утверждения, которые вы можете использовать, чтобы помочь поддерживать оптимизм и сильные отношения с вашим телом.",
"Больше пожалуйста",
"Конечно, вот еще несколько: 5. Включите здоровую диету цельных продуктов и много воды, которая питает ваш ум и тело. 6. Научитесь быть в гармонии с вашим телом, замечая изменения в том, как вы себя чувствуете, и уделите время, чтобы оценить и решить любые возникающие потребности."
],
"rounds":[
1,
2
],
"uuid":"53e8d08b-1f45-4ef4-b6d8-3a04758cafd8"
}
```
### Citation:
```
@MISC{russian_instructions,
author = {Denis Petrov},
title = {Russian instructions dataset for conversational agents},
url = {https://huggingface.co/datasets/Den4ikAI/russian_instructions},
year = 2023
}
```
|
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climatebert/climate_detection
|
climatebert
| 2023-04-18T14:39:49Z | 76 | 2 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-classification"
] | 2023-04-11T13:06:20Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: ClimateTalkDetection
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
splits:
- name: train
num_bytes: 638487
num_examples: 1300
- name: test
num_bytes: 222330
num_examples: 400
download_size: 492038
dataset_size: 860817
---
# Dataset Card for climate_detection
## Dataset Description
- **Homepage:** [climatebert.ai](https://climatebert.ai)
- **Repository:**
- **Paper:** [papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435)
- **Leaderboard:**
- **Point of Contact:** [Nicolas Webersinke](mailto:[email protected])
### Dataset Summary
We introduce an expert-annotated dataset for detecting climate-related paragraphs in corporate disclosures.
### Supported Tasks and Leaderboards
The dataset supports a binary classification task of whether a given paragraph is climate-related or not.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
```
{
'text': '− Scope 3: Optional scope that includes indirect emissions associated with the goods and services supply chain produced outside the organization. Included are emissions from the transport of products from our logistics centres to stores (downstream) performed by external logistics operators (air, land and sea transport) as well as the emissions associated with electricity consumption in franchise stores.',
'label': 1
}
```
### Data Fields
- text: a paragraph extracted from corporate annual reports and sustainability reports
- label: the label (0 -> not climate-related, 1 -> climate-related)
### Data Splits
The dataset is split into:
- train: 1,300
- test: 400
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Our dataset contains climate-related paragraphs extracted from financial disclosures by firms. We collect text from corporate annual reports and sustainability reports.
For more information regarding our sample selection, please refer to the Appendix of our paper (see [citation](#citation-information)).
#### Who are the source language producers?
Mainly large listed companies.
### Annotations
#### Annotation process
For more information on our annotation process and annotation guidelines, please refer to the Appendix of our paper (see [citation](#citation-information)).
#### Who are the annotators?
The authors and students at Universität Zürich and Friedrich-Alexander-Universität Erlangen-Nürnberg with majors in finance and sustainable finance.
### Personal and Sensitive Information
Since our text sources contain public information, no personal and sensitive information should be included.
## 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
- Julia Anna Bingler
- Mathias Kraus
- Markus Leippold
- Nicolas Webersinke
### Licensing Information
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
If you are interested in commercial use of the dataset, please contact [[email protected]](mailto:[email protected]).
### Citation Information
```bibtex
@techreport{bingler2023cheaptalk,
title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
type={Working paper},
institution={Available at SSRN 3998435},
year={2023}
}
```
### Contributions
Thanks to [@webersni](https://github.com/webersni) for adding this dataset.
|
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sam-mosaic/dolly_chatml
|
sam-mosaic
| 2023-07-18T00:23:37Z | 76 | 0 |
[
"language:en",
"region:us"
] | null | 2023-04-14T17:51:26Z |
---
language: en
dataset_info:
features:
- name: text
dtype: string
- name: cat
dtype: string
splits:
- name: train
num_bytes: 11767434
num_examples: 8497
download_size: 5401759
dataset_size: 11767434
---
# Dataset Card for "dolly_chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
polytechXhf/onepiece-dataset
|
polytechXhf
| 2023-05-05T15:17:56Z | 76 | 0 |
[
"region:us"
] | null | 2023-05-02T15:59:45Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: char_name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 120488910.0
num_examples: 922
download_size: 120447392
dataset_size: 120488910.0
---
# Dataset Card for "onepiece-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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andreped/IBDColEpi
|
andreped
| 2023-11-08T22:02:54Z | 76 | 0 |
[
"task_categories:image-segmentation",
"size_categories:1B<n<10B",
"language:en",
"license:mit",
"medical",
"region:us"
] |
[
"image-segmentation"
] | 2023-05-29T15:32:48Z |
---
license: mit
task_categories:
- image-segmentation
language:
- en
tags:
- medical
pretty_name: IBDColEpi
size_categories:
- 1B<n<10B
---
# IBDColEpi: 140 HE and 111 CD3-stained colon biopsies of active and inactivate inflammatory bowel disease with epithelium annotated
To access and work with the data in Python, you can do so through the Python API with datasets. See this Jupyter Notebook on how to get started:
https://github.com/andreped/NoCodeSeg/blob/main/notebooks/IBDColEpi-load-dataset-example.ipynb
Note that it is also possible to download the data through the web interface at Hugging Face, but also through [this google drive](https://drive.google.com/drive/u/0/folders/1eUVs1DA1UYayUYjr8_aY3O5xDgV1uLvH)
and [this dataverseNO](https://dataverse.no/dataset.xhtml?persistentId=doi:10.18710/TLA01U) link.
--------------------
GENERAL INFORMATION
--------------------
1. Title of Dataset: 140 HE and 111 CD3-stained colon biopsies of active and inactivate inflammatory bowel disease with epithelium annotated: the IBDColEpi dataset
2. DOI: https://doi.org/10.18710/TLA01U
3. Contact Information
Name: André Pedersen
Institution: NTNU Norwegian University of Science and Technology
Email: [email protected]
ORCID: https://orcid.org/0000-0002-3637-953X
4. Contributors: See metadata field Contributor.
5. Kind of data: See metadata field Kind of Data.
6. Date of data collection/generation: See metadata field Date of Collection.
7. Geographic location: See metadata section Geographic Coverage.
8. Funding sources: See metadata section Grant Information.
9. Description of dataset:
General description and ethics approvals: The dataset contains 140 HE and 111 CD3 stained, formalin fixed paraffin embedded (FFPE) biopsies of colonic mucosa. The biopsies were extracted from the NTNU/St. Olavs hospital, Trondheim University Hospital (Norway) biobank of patients with confirmed inflammatory bowel disease or healthy controls with gastrointestinal symptoms but no macroscopic- or microscopic disease. Inclusion and colonoscopies were performed at the Department of Gastroenterology and Hepatology at St. Olavs hospital, Trondheim University Hospital from 2007 to 2018. All patients gave written informed consent and ethical approvals were obtained from the Central Norway Regional Committee for Medical and Health Research Ethics (reference number 2013/212/REKMidt). Consent to publish the anonymized whole slide image (WSI) dataset was given by REKMidt in 2021. Each database ID number used in this study was changed to new anonymized IDs only containing the information “active” or “inactive” disease and whether the WSI has haematoxylin-eosin (HE) staining or CD3 immunostaining. The biopsies included in the biobank are sampled such that one biopsy from an unaffected/inactive area and one from an area affected/active area were included from each patient and given a separate ID number. Hence, two biopsies with different ID numbers can be from the same patient. "Active" is defined as the presence of intraepithelial granulocytes in one or more location in the biopsies. Still, the changes may be focal, hence majority of the epithelium may still lack intraepithelial granulocytes or other signs of active disease (crypt abscesses, granulation tissue, etc.).
---------------------------
SHARING/ACCESS INFORMATION
---------------------------
(See metadata record for dataset.)
1. Licenses/Restrictions: See Terms section.
2. Links to publications that cite or use the data: See metadata field Related Publication.
3. Links/relationships to related data sets: See metadata field Related Datasets.
4. Data sources: See metadata field Data Sources.
5. Recommended citation: See citation generated by repository.
---------------------
DATA & FILE OVERVIEW
---------------------
1. File List:
00_README.txt
trained-models.zip
patch-dataset-CD3.zip
patch-dataset-HE.zip
qupath-project-annotations.zip
TIFF-annotations.zip
WSI_part_01.zip
WSI_part_02.zip
WSI_part_03.zip
WSI_part_04.zip
WSI_part_05.zip
WSI_part_06.zip
WSI_part_07.zip
WSI_part_08.zip
WSI_part_09.zip
WSI_part_10.zip
2. Relationship between files, if important:
- trained-models.zip: the best performing trained models (for both HE and CD3) on the images from WSI_part_*.zip using the manual delineations from TIFF-annotations.zip.
- WSI_path_*.zip: the colon biopsies described in the metadata (1-10). For each ID, the active/inactive label Y is stored in the filename, with the format: "ID-X_Y.ndpi".
- TIFF-annotations.zip: the corresponding annotations to the WSIs. The filenames of the annotations are in the same structure as the corresponding WSIs, with the format: "ID-X_Y.tiff".
- patch-dataset-*.zip: the corresponding patch images and labels, split into train/validation/test sets, relevant for the evaluation of the design in the publication. Both for HE and CD3
- qupath-project-annotations.zip: the qupath project file, also containing the annotations of all WSIs, but can be directly read in QuPath (after renaming of WSI paths).
|
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] |
clarin-knext/msmarco-pl
|
clarin-knext
| 2023-06-07T08:22:03Z | 76 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:02:28Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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tum-nlp/sexism-socialmedia-balanced
|
tum-nlp
| 2023-06-08T11:56:54Z | 76 | 1 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-06-08T11:56:02Z |
---
license: cc-by-sa-4.0
---
|
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open-llm-leaderboard/details_ziqingyang__chinese-llama-2-7b
|
open-llm-leaderboard
| 2023-10-17T16:11:22Z | 76 | 0 |
[
"region:us"
] | null | 2023-08-18T11:30:29Z |
---
pretty_name: Evaluation run of ziqingyang/chinese-llama-2-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ziqingyang/chinese-llama-2-7b](https://huggingface.co/ziqingyang/chinese-llama-2-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ziqingyang__chinese-llama-2-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T16:11:09.467879](https://huggingface.co/datasets/open-llm-leaderboard/details_ziqingyang__chinese-llama-2-7b/blob/main/results_2023-10-17T16-11-09.467879.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.008703859060402684,\n\
\ \"em_stderr\": 0.0009512557261398741,\n \"f1\": 0.08773280201342261,\n\
\ \"f1_stderr\": 0.0016822920336997918,\n \"acc\": 0.35211166049236076,\n\
\ \"acc_stderr\": 0.008141255030998417\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.008703859060402684,\n \"em_stderr\": 0.0009512557261398741,\n\
\ \"f1\": 0.08773280201342261,\n \"f1_stderr\": 0.0016822920336997918\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.014404852160727824,\n \
\ \"acc_stderr\": 0.0032820559171369344\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6898184688239937,\n \"acc_stderr\": 0.0130004541448599\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ziqingyang/chinese-llama-2-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|arc:challenge|25_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T16_11_09.467879
path:
- '**/details_harness|drop|3_2023-10-17T16-11-09.467879.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T16-11-09.467879.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T16_11_09.467879
path:
- '**/details_harness|gsm8k|5_2023-10-17T16-11-09.467879.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T16-11-09.467879.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hellaswag|10_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:36:32.525773.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_human_sexuality_5
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path:
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path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_management_5
data_files:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:36:32.525773.parquet'
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path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
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- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
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- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:36:32.525773.parquet'
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:36:32.525773.parquet'
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path:
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- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:36:32.525773.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T11:36:32.525773.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T16_11_09.467879
path:
- '**/details_harness|winogrande|5_2023-10-17T16-11-09.467879.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T16-11-09.467879.parquet'
- config_name: results
data_files:
- split: 2023_08_09T11_36_32.525773
path:
- results_2023-08-09T11:36:32.525773.parquet
- split: 2023_10_17T16_11_09.467879
path:
- results_2023-10-17T16-11-09.467879.parquet
- split: latest
path:
- results_2023-10-17T16-11-09.467879.parquet
---
# Dataset Card for Evaluation run of ziqingyang/chinese-llama-2-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ziqingyang/chinese-llama-2-7b
- **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 [ziqingyang/chinese-llama-2-7b](https://huggingface.co/ziqingyang/chinese-llama-2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ziqingyang__chinese-llama-2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T16:11:09.467879](https://huggingface.co/datasets/open-llm-leaderboard/details_ziqingyang__chinese-llama-2-7b/blob/main/results_2023-10-17T16-11-09.467879.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.008703859060402684,
"em_stderr": 0.0009512557261398741,
"f1": 0.08773280201342261,
"f1_stderr": 0.0016822920336997918,
"acc": 0.35211166049236076,
"acc_stderr": 0.008141255030998417
},
"harness|drop|3": {
"em": 0.008703859060402684,
"em_stderr": 0.0009512557261398741,
"f1": 0.08773280201342261,
"f1_stderr": 0.0016822920336997918
},
"harness|gsm8k|5": {
"acc": 0.014404852160727824,
"acc_stderr": 0.0032820559171369344
},
"harness|winogrande|5": {
"acc": 0.6898184688239937,
"acc_stderr": 0.0130004541448599
}
}
```
### 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_Fredithefish__ReasonixPajama-3B-HF
|
open-llm-leaderboard
| 2023-10-17T20:47:55Z | 76 | 0 |
[
"region:us"
] | null | 2023-08-18T18:47:11Z |
---
pretty_name: Evaluation run of Fredithefish/ReasonixPajama-3B-HF
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Fredithefish/ReasonixPajama-3B-HF](https://huggingface.co/Fredithefish/ReasonixPajama-3B-HF)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Fredithefish__ReasonixPajama-3B-HF\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T20:47:42.602044](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__ReasonixPajama-3B-HF/blob/main/results_2023-10-17T20-47-42.602044.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.005557885906040268,\n\
\ \"em_stderr\": 0.0007613497667018498,\n \"f1\": 0.08515520134228192,\n\
\ \"f1_stderr\": 0.001865179611495464,\n \"acc\": 0.3211223493917147,\n\
\ \"acc_stderr\": 0.007758248793713638\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.005557885906040268,\n \"em_stderr\": 0.0007613497667018498,\n\
\ \"f1\": 0.08515520134228192,\n \"f1_stderr\": 0.001865179611495464\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \
\ \"acc_stderr\": 0.002001305720948056\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6369376479873717,\n \"acc_stderr\": 0.01351519186647922\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Fredithefish/ReasonixPajama-3B-HF
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|arc:challenge|25_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T20_47_42.602044
path:
- '**/details_harness|drop|3_2023-10-17T20-47-42.602044.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T20-47-42.602044.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T20_47_42.602044
path:
- '**/details_harness|gsm8k|5_2023-10-17T20-47-42.602044.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T20-47-42.602044.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hellaswag|10_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T15:18:48.992858.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T15:18:48.992858.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T15:18:48.992858.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T20_47_42.602044
path:
- '**/details_harness|winogrande|5_2023-10-17T20-47-42.602044.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T20-47-42.602044.parquet'
- config_name: results
data_files:
- split: 2023_08_17T15_18_48.992858
path:
- results_2023-08-17T15:18:48.992858.parquet
- split: 2023_10_17T20_47_42.602044
path:
- results_2023-10-17T20-47-42.602044.parquet
- split: latest
path:
- results_2023-10-17T20-47-42.602044.parquet
---
# Dataset Card for Evaluation run of Fredithefish/ReasonixPajama-3B-HF
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Fredithefish/ReasonixPajama-3B-HF
- **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 [Fredithefish/ReasonixPajama-3B-HF](https://huggingface.co/Fredithefish/ReasonixPajama-3B-HF) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Fredithefish__ReasonixPajama-3B-HF",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T20:47:42.602044](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__ReasonixPajama-3B-HF/blob/main/results_2023-10-17T20-47-42.602044.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.005557885906040268,
"em_stderr": 0.0007613497667018498,
"f1": 0.08515520134228192,
"f1_stderr": 0.001865179611495464,
"acc": 0.3211223493917147,
"acc_stderr": 0.007758248793713638
},
"harness|drop|3": {
"em": 0.005557885906040268,
"em_stderr": 0.0007613497667018498,
"f1": 0.08515520134228192,
"f1_stderr": 0.001865179611495464
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.002001305720948056
},
"harness|winogrande|5": {
"acc": 0.6369376479873717,
"acc_stderr": 0.01351519186647922
}
}
```
### 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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mohamedemam/Arabic-samsum-dialogsum
|
mohamedemam
| 2023-09-11T14:35:29Z | 76 | 1 |
[
"task_categories:summarization",
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:ar",
"license:cc-by-nc-2.0",
"arxiv:1911.12237",
"region:us"
] |
[
"summarization",
"conversational"
] | 2023-09-11T12:48:44Z |
---
dataset_info:
features:
- name: index
dtype: int64
- name: id
dtype: string
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
splits:
- name: train
num_bytes: 27913254
num_examples: 24813
download_size: 13968520
dataset_size: 27913254
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-nc-2.0
task_categories:
- summarization
- conversational
language:
- ar
pretty_name: ar messum
size_categories:
- 10K<n<100K
---
# Dataset Card for "Arabic-samsum-dialogsum"
this dataset is comption between samsum and dialogsum dataset translated in arabic
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://arxiv.org/abs/1911.12237v2
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/1911.12237v2
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger convesations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person.
The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Arabic
## Dataset Structure
t
### Data Instances
The created dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people
The first instance in the training set:
{'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: unique id of an example.
### Data Splits
- train: 24732
## Dataset Creation
### Curation Rationale
In paper:
> In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typ- ically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assis- tant and a client buying petrol.
As a consequence, we decided to create a chat dialogue dataset by constructing such conversa- tions that would epitomize the style of a messenger app.
### Source Data
#### Initial Data Collection and Normalization
In paper:
> We asked linguists to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. It includes chit-chats, gossiping about friends, arranging meetings, discussing politics, consulting university assignments with colleagues, etc. Therefore, this dataset does not contain any sensitive data or fragments of other corpora.
#### Who are the source language producers?
linguists
### Annotations
#### Annotation process
In paper:
> Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one ref- erence summary.
#### Who are the annotators?
language experts
### Personal and Sensitive Information
None, see above: Initial Data Collection and Normalization
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
non-commercial licence: CC BY-NC-ND 4.0
### Citation Information
```
@inproceedings{gliwa-etal-2019-samsum,
title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
author = "Gliwa, Bogdan and
Mochol, Iwona and
Biesek, Maciej and
Wawer, Aleksander",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5409",
doi = "10.18653/v1/D19-5409",
pages = "70--79"
}
```
### Contributions
Thanks to [@cccntu](https://github.com/cccntu) for adding this dataset.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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notrichardren/HaluEval
|
notrichardren
| 2023-09-11T21:09:44Z | 76 | 0 |
[
"region:us"
] | null | 2023-09-11T21:09:34Z |
---
dataset_info:
- config_name: dialogue
features:
- name: knowledge
dtype: string
- name: dialogue_history
dtype: string
- name: right_response
dtype: string
- name: hallucinated_response
dtype: string
- name: task_type
dtype: string
splits:
- name: train
num_bytes: 6332598
num_examples: 10000
download_size: 3451421
dataset_size: 6332598
- config_name: general
features:
- name: user_query
dtype: string
- name: chatgpt_response
dtype: string
- name: hallucination_label
dtype: string
- name: task_type
dtype: string
splits:
- name: train
num_bytes: 3010941
num_examples: 5000
download_size: 1849332
dataset_size: 3010941
- config_name: qa
features:
- name: knowledge
dtype: string
- name: question
dtype: string
- name: right_answer
dtype: string
- name: hallucinated_answer
dtype: string
- name: task_type
dtype: string
splits:
- name: train
num_bytes: 5546422
num_examples: 10000
download_size: 3753464
dataset_size: 5546422
- config_name: summarization
features:
- name: document
dtype: string
- name: right_summary
dtype: string
- name: hallucinated_summary
dtype: string
- name: task_type
dtype: string
splits:
- name: train
num_bytes: 46578787
num_examples: 10000
download_size: 27986765
dataset_size: 46578787
configs:
- config_name: dialogue
data_files:
- split: train
path: dialogue/train-*
- config_name: general
data_files:
- split: train
path: general/train-*
- config_name: qa
data_files:
- split: train
path: qa/train-*
- config_name: summarization
data_files:
- split: train
path: summarization/train-*
---
# Dataset Card for "HaluEval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
HaltiaAI/Her-The-Movie-Samantha-and-Theodore-Dataset
|
HaltiaAI
| 2023-09-15T13:28:07Z | 76 | 2 |
[
"license:other",
"Movie Dialog",
"Her The Movie",
"Dialogs from the Her Movie (2013)",
"region:us"
] | null | 2023-09-15T11:37:12Z |
---
license: other
tags:
- Movie Dialog
- Her The Movie
- Dialogs from the Her Movie (2013)
---
|
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open-llm-leaderboard/details_tiiuae__falcon-40b-instruct
|
open-llm-leaderboard
| 2023-12-03T19:30:11Z | 76 | 0 |
[
"region:us"
] | null | 2023-09-23T13:36:24Z |
---
pretty_name: Evaluation run of tiiuae/falcon-40b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 5 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_tiiuae__falcon-40b-instruct\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:30:05.245215](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-40b-instruct/blob/main/results_2023-12-03T19-30-05.245215.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\": 0.3434420015163002,\n\
\ \"acc_stderr\": 0.01307993381180031\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.3434420015163002,\n \"acc_stderr\": 0.01307993381180031\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tiiuae/falcon-40b-instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T13_36_20.116121
path:
- '**/details_harness|drop|3_2023-09-23T13-36-20.116121.parquet'
- split: 2023_10_15T21_54_31.012081
path:
- '**/details_harness|drop|3_2023-10-15T21-54-31.012081.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T21-54-31.012081.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T13_36_20.116121
path:
- '**/details_harness|gsm8k|5_2023-09-23T13-36-20.116121.parquet'
- split: 2023_10_15T21_54_31.012081
path:
- '**/details_harness|gsm8k|5_2023-10-15T21-54-31.012081.parquet'
- split: 2023_12_03T19_26_50.556964
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-26-50.556964.parquet'
- split: 2023_12_03T19_27_45.725319
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-27-45.725319.parquet'
- split: 2023_12_03T19_30_05.245215
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-30-05.245215.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-30-05.245215.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T13_36_20.116121
path:
- '**/details_harness|winogrande|5_2023-09-23T13-36-20.116121.parquet'
- split: 2023_10_15T21_54_31.012081
path:
- '**/details_harness|winogrande|5_2023-10-15T21-54-31.012081.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T21-54-31.012081.parquet'
- config_name: results
data_files:
- split: 2023_09_23T13_36_20.116121
path:
- results_2023-09-23T13-36-20.116121.parquet
- split: 2023_10_15T21_54_31.012081
path:
- results_2023-10-15T21-54-31.012081.parquet
- split: 2023_12_03T19_26_50.556964
path:
- results_2023-12-03T19-26-50.556964.parquet
- split: 2023_12_03T19_27_45.725319
path:
- results_2023-12-03T19-27-45.725319.parquet
- split: 2023_12_03T19_30_05.245215
path:
- results_2023-12-03T19-30-05.245215.parquet
- split: latest
path:
- results_2023-12-03T19-30-05.245215.parquet
---
# Dataset Card for Evaluation run of tiiuae/falcon-40b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-40b-instruct
- **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 [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 5 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_tiiuae__falcon-40b-instruct",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:30:05.245215](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-40b-instruct/blob/main/results_2023-12-03T19-30-05.245215.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": 0.3434420015163002,
"acc_stderr": 0.01307993381180031
},
"harness|gsm8k|5": {
"acc": 0.3434420015163002,
"acc_stderr": 0.01307993381180031
}
}
```
### 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_MayaPH__opt-flan-iml-6.7b
|
open-llm-leaderboard
| 2023-10-13T03:06:44Z | 76 | 0 |
[
"region:us"
] | null | 2023-10-13T03:06:36Z |
---
pretty_name: Evaluation run of MayaPH/opt-flan-iml-6.7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [MayaPH/opt-flan-iml-6.7b](https://huggingface.co/MayaPH/opt-flan-iml-6.7b) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-13T03:06:32.697788](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b/blob/main/results_2023-10-13T03-06-32.697788.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.07518875838926174,\n\
\ \"em_stderr\": 0.002700490526265294,\n \"f1\": 0.10838401845637569,\n\
\ \"f1_stderr\": 0.0028760995167941457,\n \"acc\": 0.3212312549329124,\n\
\ \"acc_stderr\": 0.006735003721960345\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.07518875838926174,\n \"em_stderr\": 0.002700490526265294,\n\
\ \"f1\": 0.10838401845637569,\n \"f1_stderr\": 0.0028760995167941457\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6424625098658248,\n\
\ \"acc_stderr\": 0.01347000744392069\n }\n}\n```"
repo_url: https://huggingface.co/MayaPH/opt-flan-iml-6.7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T03_06_32.697788
path:
- '**/details_harness|drop|3_2023-10-13T03-06-32.697788.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T03-06-32.697788.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T03_06_32.697788
path:
- '**/details_harness|gsm8k|5_2023-10-13T03-06-32.697788.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T03-06-32.697788.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T03_06_32.697788
path:
- '**/details_harness|winogrande|5_2023-10-13T03-06-32.697788.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T03-06-32.697788.parquet'
- config_name: results
data_files:
- split: 2023_10_13T03_06_32.697788
path:
- results_2023-10-13T03-06-32.697788.parquet
- split: latest
path:
- results_2023-10-13T03-06-32.697788.parquet
---
# Dataset Card for Evaluation run of MayaPH/opt-flan-iml-6.7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/MayaPH/opt-flan-iml-6.7b
- **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 [MayaPH/opt-flan-iml-6.7b](https://huggingface.co/MayaPH/opt-flan-iml-6.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T03:06:32.697788](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b/blob/main/results_2023-10-13T03-06-32.697788.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.07518875838926174,
"em_stderr": 0.002700490526265294,
"f1": 0.10838401845637569,
"f1_stderr": 0.0028760995167941457,
"acc": 0.3212312549329124,
"acc_stderr": 0.006735003721960345
},
"harness|drop|3": {
"em": 0.07518875838926174,
"em_stderr": 0.002700490526265294,
"f1": 0.10838401845637569,
"f1_stderr": 0.0028760995167941457
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6424625098658248,
"acc_stderr": 0.01347000744392069
}
}
```
### 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_YeungNLP__firefly-bloom-2b6-v2
|
open-llm-leaderboard
| 2023-10-13T11:51:54Z | 76 | 0 |
[
"region:us"
] | null | 2023-10-13T11:51:46Z |
---
pretty_name: Evaluation run of YeungNLP/firefly-bloom-2b6-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [YeungNLP/firefly-bloom-2b6-v2](https://huggingface.co/YeungNLP/firefly-bloom-2b6-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_YeungNLP__firefly-bloom-2b6-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-13T11:51:41.999066](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-bloom-2b6-v2/blob/main/results_2023-10-13T11-51-41.999066.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.08630453020134228,\n\
\ \"em_stderr\": 0.002875790094905939,\n \"f1\": 0.1275723573825503,\n\
\ \"f1_stderr\": 0.00310355978869451,\n \"acc\": 0.2825940222825524,\n\
\ \"acc_stderr\": 0.008796871542302145\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08630453020134228,\n \"em_stderr\": 0.002875790094905939,\n\
\ \"f1\": 0.1275723573825503,\n \"f1_stderr\": 0.00310355978869451\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.017437452615617893,\n \
\ \"acc_stderr\": 0.003605486867998265\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5477505919494869,\n \"acc_stderr\": 0.013988256216606024\n\
\ }\n}\n```"
repo_url: https://huggingface.co/YeungNLP/firefly-bloom-2b6-v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T11_51_41.999066
path:
- '**/details_harness|drop|3_2023-10-13T11-51-41.999066.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T11-51-41.999066.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T11_51_41.999066
path:
- '**/details_harness|gsm8k|5_2023-10-13T11-51-41.999066.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T11-51-41.999066.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T11_51_41.999066
path:
- '**/details_harness|winogrande|5_2023-10-13T11-51-41.999066.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T11-51-41.999066.parquet'
- config_name: results
data_files:
- split: 2023_10_13T11_51_41.999066
path:
- results_2023-10-13T11-51-41.999066.parquet
- split: latest
path:
- results_2023-10-13T11-51-41.999066.parquet
---
# Dataset Card for Evaluation run of YeungNLP/firefly-bloom-2b6-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/YeungNLP/firefly-bloom-2b6-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 [YeungNLP/firefly-bloom-2b6-v2](https://huggingface.co/YeungNLP/firefly-bloom-2b6-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_YeungNLP__firefly-bloom-2b6-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T11:51:41.999066](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-bloom-2b6-v2/blob/main/results_2023-10-13T11-51-41.999066.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.08630453020134228,
"em_stderr": 0.002875790094905939,
"f1": 0.1275723573825503,
"f1_stderr": 0.00310355978869451,
"acc": 0.2825940222825524,
"acc_stderr": 0.008796871542302145
},
"harness|drop|3": {
"em": 0.08630453020134228,
"em_stderr": 0.002875790094905939,
"f1": 0.1275723573825503,
"f1_stderr": 0.00310355978869451
},
"harness|gsm8k|5": {
"acc": 0.017437452615617893,
"acc_stderr": 0.003605486867998265
},
"harness|winogrande|5": {
"acc": 0.5477505919494869,
"acc_stderr": 0.013988256216606024
}
}
```
### 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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FrsECM/CelebAHQ_mask
|
FrsECM
| 2023-11-02T13:22:21Z | 76 | 0 |
[
"task_categories:image-segmentation",
"task_categories:image-to-image",
"size_categories:10K<n<100K",
"region:us"
] |
[
"image-segmentation",
"image-to-image"
] | 2023-10-18T13:59:25Z |
---
size_categories:
- 10K<n<100K
task_categories:
- image-segmentation
- image-to-image
pretty_name: CelebAHQ Mask Dataset
dataset_info:
features:
- name: image_id
dtype: string
- name: image
dtype: image
- name: annotation
dtype: image
splits:
- name: train
num_bytes: 2829644617.0
num_examples: 28500
- name: test
num_bytes: 150219016.0
num_examples: 1500
download_size: 2993732687
dataset_size: 2979863633.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj
|
open-llm-leaderboard
| 2023-10-23T06:02:33Z | 76 | 0 |
[
"region:us"
] | null | 2023-10-23T06:02:26Z |
---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T06:02:22.448569](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj/blob/main/results_2023-10-23T06-02-22.448569.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.10098573825503356,\n\
\ \"em_stderr\": 0.0030856947694457384,\n \"f1\": 0.15267407718120746,\n\
\ \"f1_stderr\": 0.0031959753495490175,\n \"acc\": 0.4472367612449459,\n\
\ \"acc_stderr\": 0.010567855433819127\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.10098573825503356,\n \"em_stderr\": 0.0030856947694457384,\n\
\ \"f1\": 0.15267407718120746,\n \"f1_stderr\": 0.0031959753495490175\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1288855193328279,\n \
\ \"acc_stderr\": 0.009229580761400269\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237985\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T06_02_22.448569
path:
- '**/details_harness|drop|3_2023-10-23T06-02-22.448569.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T06-02-22.448569.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T06_02_22.448569
path:
- '**/details_harness|gsm8k|5_2023-10-23T06-02-22.448569.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T06-02-22.448569.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T06_02_22.448569
path:
- '**/details_harness|winogrande|5_2023-10-23T06-02-22.448569.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T06-02-22.448569.parquet'
- config_name: results
data_files:
- split: 2023_10_23T06_02_22.448569
path:
- results_2023-10-23T06-02-22.448569.parquet
- split: latest
path:
- results_2023-10-23T06-02-22.448569.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
- **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 [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T06:02:22.448569](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj/blob/main/results_2023-10-23T06-02-22.448569.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.10098573825503356,
"em_stderr": 0.0030856947694457384,
"f1": 0.15267407718120746,
"f1_stderr": 0.0031959753495490175,
"acc": 0.4472367612449459,
"acc_stderr": 0.010567855433819127
},
"harness|drop|3": {
"em": 0.10098573825503356,
"em_stderr": 0.0030856947694457384,
"f1": 0.15267407718120746,
"f1_stderr": 0.0031959753495490175
},
"harness|gsm8k|5": {
"acc": 0.1288855193328279,
"acc_stderr": 0.009229580761400269
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237985
}
}
```
### 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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nateraw/rap-lyrics-v1
|
nateraw
| 2023-11-06T09:07:02Z | 76 | 0 |
[
"region:us"
] | null | 2023-11-06T09:07:00Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: artist
dtype: string
- name: title
dtype: string
- name: full_title
dtype: string
- name: lyrics
dtype: string
splits:
- name: train
num_bytes: 7948557
num_examples: 2350
download_size: 4158696
dataset_size: 7948557
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rap-lyrics-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
celsowm/adoro_cinema_filmes
|
celsowm
| 2023-11-11T11:08:24Z | 76 | 0 |
[
"region:us"
] | null | 2023-11-06T21:27:31Z |
---
dataset_info:
features:
- name: titulo
dtype: string
- name: sinopse
dtype: string
- name: generos
sequence: string
- name: link
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 23369140
num_examples: 42918
download_size: 13807632
dataset_size: 23369140
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "adoro_cinema_filmes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
SalomonMetre13/nnd_fr_26k
|
SalomonMetre13
| 2023-11-20T09:08:05Z | 76 | 0 |
[
"task_categories:translation",
"size_categories:10K<n<100K",
"language:nnd",
"license:mit",
"region:us"
] |
[
"translation"
] | 2023-11-12T15:40:13Z |
---
license: mit
language:
- nnd
task_categories:
- translation
size_categories:
- 10K<n<100K
---
This <span style="color:teal;">parallel corpus </span> contains <span style="color:teal;">26240</span> aligned sentence pairs <span style="color:teal;">Nande-French</span> in a <span style="color:teal;">90:10</span> split for the train and the test sets. It has been mainly used to fine-tune the <span style="color:teal;"> t5-base </span> pretrained model for the development of <a href="https://huggingface.co/SalomonMetre13/nnd_fr_mt_v3" style="color:green;">this translation model </a>
|
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] |
higgsfield/hacker_news_top_comment
|
higgsfield
| 2023-11-14T02:01:02Z | 76 | 1 |
[
"region:us"
] | null | 2023-11-14T01:34:14Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 77485794
num_examples: 118779
download_size: 52065753
dataset_size: 77485794
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hacker_news_top_comment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nguyenphuthien/vietnamese_ultrafeedback_binarized
|
nguyenphuthien
| 2023-11-16T08:49:01Z | 76 | 0 |
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:vi",
"license:mit",
"region:us"
] |
[
"conversational",
"text-generation"
] | 2023-11-15T09:16:44Z |
---
license: mit
task_categories:
- conversational
- text-generation
language:
- vi
pretty_name: Vietnamese UltraFeedback Binarized
size_categories:
- 10K<n<100K
---
|
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atmallen/qm_alice_easy_2_mixture_1.0e
|
atmallen
| 2023-11-16T18:18:09Z | 76 | 0 |
[
"region:us"
] | null | 2023-11-16T03:33:25Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 12520368.5
num_examples: 117117
- name: validation
num_bytes: 1221097.5
num_examples: 11279
- name: test
num_bytes: 1205746.0
num_examples: 11186
download_size: 3708154
dataset_size: 14947212.0
---
# Dataset Card for "qm_alice_easy_2_mixture_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Sijuade/cats_dogs_birds_latents
|
Sijuade
| 2023-11-18T22:50:46Z | 76 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-18T22:49:49Z |
---
license: mit
dataset_info:
features:
- name: latent
sequence:
sequence:
sequence: float32
- name: noised_latents
sequence:
sequence:
sequence: float32
- name: noise
sequence:
sequence:
sequence:
sequence: float32
- name: timesteps
dtype: float64
- name: label
dtype: int64
splits:
- name: train
num_bytes: 677448192
num_examples: 13344
download_size: 683560149
dataset_size: 677448192
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
jmbrito/b3-historical-quotes
|
jmbrito
| 2023-11-20T15:07:52Z | 76 | 0 |
[
"size_categories:1M<n<10M",
"license:mit",
"finance",
"b3",
"quotes",
"historical",
"region:us"
] | null | 2023-11-20T07:00:53Z |
---
license: mit
tags:
- finance
- b3
- quotes
- historical
pretty_name: B3 Historical Quotes
size_categories:
- 1M<n<10M
---
# B3 Historical Quotes
<!-- Provide a quick summary of the dataset. -->
This dataset is a collection of historical quotes from the brazilian stock market(B3).
It contains historical quotes from all stocks in the country from Jan/2015 until Out/2023.
## Dataset Details
All the data was retrieved as is from [B3 Historical Data](https://www.b3.com.br/en_us/market-data-and-indices/data-services/market-data/historical-data/equities/historical-quotes/)
and parsed to a csv. The columns are the same as the ones from the original content.
If you need more informations about the columns, it can be found in the [official b3 documentation](https://www.b3.com.br/en_us/market-data-and-indices/data-services/market-data/historical-data/equities/historical-quote-data/).
|
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] |
chiayewken/saycan
|
chiayewken
| 2023-11-23T04:21:11Z | 76 | 0 |
[
"region:us"
] | null | 2023-11-23T04:17:30Z |
---
dataset_info:
features:
- name: INPUT
dtype: string
- name: OUTPUT
dtype: string
splits:
- name: test
num_bytes: 14865
num_examples: 99
download_size: 4765
dataset_size: 14865
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# SayCan
This repo contains the data for ["Do As I Can, Not As I Say:
Grounding Language in Robotic Affordances"](https://say-can.github.io).
The original data link is here: https://raw.githubusercontent.com/say-can/say-can.github.io/main/data/saycan_plan_v0_l.tsv
This dataset is distributed with the CC BY 4.0 license.
|
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joelniklaus/MultiLegalPileWikipediaFiltered
|
joelniklaus
| 2023-03-28T19:23:38Z | 75 | 2 |
[
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"region:us"
] |
[
"fill-mask"
] | 2023-01-31T21:51:25Z |
---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "MultiLegalPileWikipediaFiltered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles."
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
---
# Dataset Card for MultiLegalPileWikipediaFiltered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
The Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.
It spans over 24 languages and four legal text types.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask.
### Languages
The following languages are supported:
bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
It is structured in the following format: {language}_{text_type}_{shard}.jsonl.xz
text_type is one of the following:
- caselaw
- contracts
- legislation
- other
- wikipedia
Use the dataset like this:
```python
from datasets import load_dataset
config = 'en_contracts' # {language}_{text_type}
dataset = load_dataset('joelito/Multi_Legal_Pile', config, split='train', streaming=True)
```
'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.
To load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '
all_legislation').
### Data Instances
The file format is jsonl.xz and there is a `train` and `validation` split available.
Since some configurations are very small or non-existent, they might not contain a train split or not be present at all.
The complete dataset consists of five large subsets:
- [Native Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile)
- [Eurlex Resources](https://huggingface.co/datasets/joelito/eurlex_resources)
- [MC4 Legal](https://huggingface.co/datasets/joelito/mc4_legal)
- [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law)
- [EU Wikipedias](https://huggingface.co/datasets/joelito/EU_Wikipedias)
| Language | Source | Size (MB) | Words | Documents | Words/Document |
|:-----------|:------------|-----------------:|------------:|------------:|-----------------:|
| all | all | 1.29761e+06 | 81214262514 | 57305071 | 1417 |
| all | caselaw | 695837 | 44372248995 | 30085886 | 1474 |
| all | contracts | 122599 | 7964531030 | 1785686 | 4460 |
| all | legislation | 189135 | 10879386581 | 3601518 | 3020 |
| all | other | 126570 | 8780080882 | 3358073 | 2614 |
| all | wikipedia | 163468 | 9218015026 | 18473908 | 498 |
| bg | all | 14028 | 535256525 | 355650 | 1505 |
| bg | caselaw | 2897 | 109634090 | 52648 | 2082 |
| bg | contracts | 748 | 31292877 | 7107 | 4403 |
| bg | legislation | 8015 | 308946116 | 82777 | 3732 |
| bg | other | 0 | 0 | 0 | 0 |
| bg | wikipedia | 2368 | 85383442 | 213118 | 400 |
| cs | all | 21818 | 1123000335 | 839914 | 1337 |
| cs | caselaw | 11151 | 574336489 | 296652 | 1936 |
| cs | contracts | 492 | 28106428 | 7383 | 3806 |
| cs | legislation | 6288 | 333850509 | 88731 | 3762 |
| cs | other | 0 | 0 | 0 | 0 |
| cs | wikipedia | 3887 | 186706909 | 447148 | 417 |
| da | all | 16024 | 970954498 | 576256 | 1684 |
| da | caselaw | 3469 | 210730560 | 89702 | 2349 |
| da | contracts | 559 | 35592407 | 10827 | 3287 |
| da | legislation | 10736 | 653153146 | 265868 | 2456 |
| da | other | 0 | 0 | 0 | 0 |
| da | wikipedia | 1259 | 71478385 | 209859 | 340 |
| de | all | 63887 | 3512253170 | 3216030 | 1092 |
| de | caselaw | 31527 | 1785439383 | 596800 | 2991 |
| de | contracts | 614 | 36786772 | 11041 | 3331 |
| de | legislation | 8934 | 512840663 | 276034 | 1857 |
| de | other | 0 | 0 | 0 | 0 |
| de | wikipedia | 22812 | 1177186352 | 2332155 | 504 |
| el | all | 23167 | 800722723 | 457553 | 1750 |
| el | caselaw | 6007 | 203770918 | 85496 | 2383 |
| el | contracts | 1050 | 38963772 | 10266 | 3795 |
| el | legislation | 12906 | 455240770 | 171356 | 2656 |
| el | other | 0 | 0 | 0 | 0 |
| el | wikipedia | 3204 | 102747263 | 190435 | 539 |
| en | all | 712173 | 47279626514 | 21112650 | 2239 |
| en | caselaw | 380976 | 25561971376 | 10240724 | 2496 |
| en | contracts | 71360 | 7260323438 | 1594942 | 4552 |
| en | legislation | 36587 | 2537696894 | 657805 | 3857 |
| en | other | 126570 | 8780080882 | 3358073 | 2614 |
| en | wikipedia | 51053 | 3139553924 | 5261106 | 596 |
| es | all | 23657 | 1515689548 | 1567527 | 966 |
| es | caselaw | 3299 | 220506573 | 83872 | 2629 |
| es | contracts | 594 | 41840328 | 10048 | 4164 |
| es | legislation | 6837 | 462661276 | 149368 | 3097 |
| es | other | 0 | 0 | 0 | 0 |
| es | wikipedia | 12928 | 790681371 | 1324239 | 597 |
| et | all | 7446 | 372896353 | 261641 | 1425 |
| et | caselaw | 1835 | 92951578 | 58736 | 1582 |
| et | contracts | 433 | 24017402 | 7371 | 3258 |
| et | legislation | 4200 | 210952455 | 63922 | 3300 |
| et | other | 0 | 0 | 0 | 0 |
| et | wikipedia | 978 | 44974918 | 131612 | 341 |
| fi | all | 11501 | 513990484 | 592986 | 866 |
| fi | caselaw | 2854 | 126368889 | 77882 | 1622 |
| fi | contracts | 504 | 25386705 | 8894 | 2854 |
| fi | legislation | 5532 | 252344531 | 103907 | 2428 |
| fi | other | 0 | 0 | 0 | 0 |
| fi | wikipedia | 2610 | 109890359 | 402303 | 273 |
| fr | all | 47186 | 2936056985 | 2734954 | 1073 |
| fr | caselaw | 18313 | 1170335690 | 435569 | 2686 |
| fr | contracts | 633 | 41983091 | 11071 | 3792 |
| fr | legislation | 9297 | 600170792 | 243313 | 2466 |
| fr | other | 0 | 0 | 0 | 0 |
| fr | wikipedia | 18942 | 1123567412 | 2045001 | 549 |
| ga | all | 1209 | 72041312 | 30064 | 2396 |
| ga | caselaw | 11 | 676795 | 835 | 810 |
| ga | contracts | 29 | 1820765 | 365 | 4988 |
| ga | legislation | 1048 | 62513018 | 5983 | 10448 |
| ga | other | 0 | 0 | 0 | 0 |
| ga | wikipedia | 122 | 7030734 | 22881 | 307 |
| hr | all | 5377 | 315295665 | 211151 | 1493 |
| hr | caselaw | 1026 | 62358456 | 31322 | 1990 |
| hr | contracts | 395 | 24957774 | 6552 | 3809 |
| hr | legislation | 2906 | 171415656 | 36365 | 4713 |
| hr | other | 0 | 0 | 0 | 0 |
| hr | wikipedia | 1050 | 56563779 | 136912 | 413 |
| hu | all | 12351 | 564082537 | 495822 | 1137 |
| hu | caselaw | 2376 | 110034426 | 59074 | 1862 |
| hu | contracts | 534 | 27258352 | 7385 | 3691 |
| hu | legislation | 5744 | 264572303 | 86862 | 3045 |
| hu | other | 0 | 0 | 0 | 0 |
| hu | wikipedia | 3697 | 162217456 | 342501 | 473 |
| it | all | 26744 | 1658638775 | 1615301 | 1026 |
| it | caselaw | 6483 | 406520336 | 156630 | 2595 |
| it | contracts | 597 | 40131223 | 10985 | 3653 |
| it | legislation | 8332 | 542579039 | 227968 | 2380 |
| it | other | 0 | 0 | 0 | 0 |
| it | wikipedia | 11332 | 669408177 | 1219718 | 548 |
| lt | all | 7772 | 399310081 | 264537 | 1509 |
| lt | caselaw | 1992 | 101672069 | 59485 | 1709 |
| lt | contracts | 475 | 27009922 | 7473 | 3614 |
| lt | legislation | 4550 | 235543873 | 64106 | 3674 |
| lt | other | 0 | 0 | 0 | 0 |
| lt | wikipedia | 755 | 35084217 | 133473 | 262 |
| lv | all | 7701 | 386833125 | 211244 | 1831 |
| lv | caselaw | 2082 | 103311512 | 58992 | 1751 |
| lv | contracts | 481 | 26692972 | 7429 | 3593 |
| lv | legislation | 4621 | 233088284 | 64087 | 3637 |
| lv | other | 0 | 0 | 0 | 0 |
| lv | wikipedia | 518 | 23740357 | 80736 | 294 |
| mt | all | 7180 | 370558634 | 122056 | 3035 |
| mt | caselaw | 2016 | 100309542 | 52942 | 1894 |
| mt | contracts | 486 | 27701852 | 6937 | 3993 |
| mt | legislation | 4620 | 239708644 | 57979 | 4134 |
| mt | other | 0 | 0 | 0 | 0 |
| mt | wikipedia | 58 | 2838596 | 4198 | 676 |
| nl | all | 17674 | 1112460059 | 1200534 | 926 |
| nl | caselaw | 3227 | 206147113 | 87170 | 2364 |
| nl | contracts | 604 | 40245662 | 11027 | 3649 |
| nl | legislation | 8484 | 550788527 | 232204 | 2372 |
| nl | other | 0 | 0 | 0 | 0 |
| nl | wikipedia | 5360 | 315278757 | 870133 | 362 |
| pl | all | 14762 | 773692198 | 1160849 | 666 |
| pl | caselaw | 2141 | 115695709 | 59649 | 1939 |
| pl | contracts | 489 | 28543526 | 7478 | 3817 |
| pl | legislation | 5459 | 299334705 | 89264 | 3353 |
| pl | other | 0 | 0 | 0 | 0 |
| pl | wikipedia | 6672 | 330118258 | 1004458 | 328 |
| pt | all | 210656 | 13466463586 | 18173061 | 741 |
| pt | caselaw | 196919 | 12611760973 | 17251236 | 731 |
| pt | contracts | 571 | 37997495 | 9897 | 3839 |
| pt | legislation | 6853 | 439066783 | 148176 | 2963 |
| pt | other | 0 | 0 | 0 | 0 |
| pt | wikipedia | 6313 | 377638335 | 763752 | 494 |
| ro | all | 14794 | 808799454 | 481763 | 1678 |
| ro | caselaw | 1960 | 114665535 | 53092 | 2159 |
| ro | contracts | 495 | 31496978 | 7202 | 4373 |
| ro | legislation | 10464 | 559092153 | 215694 | 2592 |
| ro | other | 0 | 0 | 0 | 0 |
| ro | wikipedia | 1874 | 103544788 | 205775 | 503 |
| sk | all | 8700 | 463447112 | 262638 | 1764 |
| sk | caselaw | 2072 | 109996398 | 59383 | 1852 |
| sk | contracts | 489 | 28298113 | 7470 | 3788 |
| sk | legislation | 5208 | 280182047 | 76760 | 3650 |
| sk | other | 0 | 0 | 0 | 0 |
| sk | wikipedia | 931 | 44970554 | 119025 | 377 |
| sl | all | 9345 | 561775614 | 277497 | 2024 |
| sl | caselaw | 1816 | 111097741 | 59193 | 1876 |
| sl | contracts | 432 | 28238938 | 7475 | 3777 |
| sl | legislation | 6057 | 365513763 | 88651 | 4123 |
| sl | other | 0 | 0 | 0 | 0 |
| sl | wikipedia | 1041 | 56925172 | 122178 | 465 |
| sv | all | 12457 | 700417227 | 1083393 | 646 |
| sv | caselaw | 2806 | 161956844 | 78802 | 2055 |
| sv | contracts | 491 | 29844238 | 9061 | 3293 |
| sv | legislation | 5456 | 308130634 | 104338 | 2953 |
| sv | other | 0 | 0 | 0 | 0 |
| sv | wikipedia | 3704 | 200485511 | 891192 | 224 |
### Data Fields
[More Information Needed]
### Data Splits
There are two splits: train and validation. The validation split contains 1000 examples and the training split contains the rest of the data.
#### Data Size
```bash
$ xz --list data/*.xz
Strms Blocks Compressed Uncompressed Ratio Check Filename
1 1 167.6 MiB 3’276.3 MiB 0.051 CRC64 data/bg_caselaw_train.0.jsonl.xz
1 1 502.3 KiB 9’398.0 KiB 0.053 CRC64 data/bg_caselaw_validation.0.jsonl.xz
1 1 33.4 MiB 700.3 MiB 0.048 CRC64 data/bg_contracts_train.0.jsonl.xz
1 1 5’989.6 KiB 123.0 MiB 0.048 CRC64 data/bg_contracts_validation.0.jsonl.xz
1 1 418.5 MiB 8’931.0 MiB 0.047 CRC64 data/bg_legislation_train.0.jsonl.xz
1 1 5’029.4 KiB 103.1 MiB 0.048 CRC64 data/bg_legislation_validation.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/bg_other_validation.0.jsonl.xz
1 1 192.2 MiB 2’488.6 MiB 0.077 CRC64 data/bg_wikipedia_train.0.jsonl.xz
1 1 1’757.8 KiB 22.9 MiB 0.075 CRC64 data/bg_wikipedia_validation.0.jsonl.xz
1 1 476.9 MiB 4’126.1 MiB 0.116 CRC64 data/cs_caselaw_train.0.jsonl.xz
1 1 259.8 MiB 2’556.9 MiB 0.102 CRC64 data/cs_caselaw_train.1.jsonl.xz
1 1 420.1 KiB 3’370.3 KiB 0.125 CRC64 data/cs_caselaw_validation.0.jsonl.xz
1 1 24.9 MiB 237.9 MiB 0.105 CRC64 data/cs_contracts_train.0.jsonl.xz
1 1 4’412.1 KiB 41.7 MiB 0.103 CRC64 data/cs_contracts_validation.0.jsonl.xz
1 1 361.2 MiB 3’488.9 MiB 0.104 CRC64 data/cs_legislation_train.0.jsonl.xz
1 1 10.3 MiB 91.6 MiB 0.112 CRC64 data/cs_legislation_validation.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/cs_other_validation.0.jsonl.xz
1 1 390.6 MiB 1’939.4 MiB 0.201 CRC64 data/cs_wikipedia_train.0.jsonl.xz
1 1 2’604.7 KiB 12.2 MiB 0.209 CRC64 data/cs_wikipedia_validation.0.jsonl.xz
1 1 252.5 MiB 1’529.7 MiB 0.165 CRC64 data/da_caselaw_train.0.jsonl.xz
1 1 555.9 KiB 3’227.1 KiB 0.172 CRC64 data/da_caselaw_validation.0.jsonl.xz
1 1 30.1 MiB 233.9 MiB 0.129 CRC64 data/da_contracts_train.0.jsonl.xz
1 1 2’897.6 KiB 23.6 MiB 0.120 CRC64 data/da_contracts_validation.0.jsonl.xz
1 1 476.9 MiB 3’325.8 MiB 0.143 CRC64 data/da_legislation_train.0.jsonl.xz
1 1 237.3 MiB 1’444.5 MiB 0.164 CRC64 data/da_legislation_train.1.jsonl.xz
1 1 3’232.5 KiB 60.6 MiB 0.052 CRC64 data/da_legislation_validation.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/da_other_validation.0.jsonl.xz
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1 0 32 B 0 B --- CRC64 data/sl_other_validation.0.jsonl.xz
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-------------------------------------------------------------------------------
374 351 90.1 GiB 579.9 GiB 0.155 CRC64 374 files
```
## Dataset Creation
This dataset has been created by combining the following datasets:
Native Multi Legal Pile, Eurlex Resources, MC4 Legal, Pile of Law, EU Wikipedias.
It has been filtered to remove short documents (less than 64 whitespace-separated tokens) and
documents with more than 30% punctuation or numbers (see prepare_legal_data.py for more details).
### 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
```
TODO add citation
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
|
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] |
mstz/seeds
|
mstz
| 2023-04-16T17:58:19Z | 75 | 0 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"seeds",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-13T10:55:57Z |
---
language:
- en
tags:
- seeds
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Page Blocks
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- seeds
- seeds_binary
license: cc
---
# Post Operative
The [Seeds dataset](https://archive-beta.ics.uci.edu/dataset/236/seeds) from the [UCI repository](https://archive-beta.ics.uci.edu/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-----------------------|---------------------------|-------------------------|
| seeds | Multiclass classification.| |
| seeds_0 | Binary classification. | Is the seed of class 0? |
| seeds_1 | Binary classification. | Is the seed of class 1? |
| seeds_2 | Binary classification. | Is the seed of class 2? |
|
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] |
rcp-meetings/rudialogsum_v2
|
rcp-meetings
| 2023-05-12T14:35:48Z | 75 | 0 |
[
"task_categories:text2text-generation",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ru",
"license:mit",
"region:us"
] |
[
"text2text-generation",
"summarization"
] | 2023-05-12T14:30:27Z |
---
license: mit
task_categories:
- text2text-generation
- summarization
language:
- ru
size_categories:
- 10K<n<100K
---
Датасет dialogsum переведенный на русский язык. Глюки перевода устранены автоматической чисткой
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janPiljan/SaGIS
|
janPiljan
| 2023-07-15T03:19:03Z | 75 | 1 |
[
"task_categories:table-question-answering",
"size_categories:n<1K",
"language:en",
"license:mit",
"chemistry",
"biology",
"medical",
"general",
"region:us"
] |
[
"table-question-answering"
] | 2023-07-06T23:18:48Z |
---
license: mit
task_categories:
- table-question-answering
language:
- en
tags:
- chemistry
- biology
- medical
- general
pretty_name: The Scientific and General Information (Data)Set
size_categories:
- n<1K
---
SaGIS: The Scientific and General Information (Data)Set.
The information stored in the dataset is information from OpenAI GPT 3.5-Turbo, Google PaLM, and Anthropic Claude (2). The information may not be entirely factual.
|
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] |
DynamicSuperb/SpeechTextMatching_LJSpeech
|
DynamicSuperb
| 2023-07-12T11:11:34Z | 75 | 0 |
[
"region:us"
] | null | 2023-07-09T13:17:45Z |
---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: text
dtype: string
- name: instruction
dtype: string
- name: label
dtype: string
- name: transcription
dtype: string
splits:
- name: test
num_bytes: 3802579053.0
num_examples: 13100
download_size: 3786166384
dataset_size: 3802579053.0
---
# Dataset Card for "speechTextMatching_LJSpeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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M-A-D/Mixed-Arabic-Datasets-Repo
|
M-A-D
| 2023-10-16T21:25:35Z | 75 | 12 |
[
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:translation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:fill-mask",
"size_categories:1B<n<10B",
"language:ar",
"region:us"
] |
[
"text-classification",
"question-answering",
"translation",
"summarization",
"conversational",
"text-generation",
"text2text-generation",
"fill-mask"
] | 2023-08-27T01:19:21Z |
---
language:
- ar
size_categories:
- 1B<n<10B
task_categories:
- text-classification
- question-answering
- translation
- summarization
- conversational
- text-generation
- text2text-generation
- fill-mask
pretty_name: Mixed Arabic Datasets (MAD) Corpus
dataset_info:
- config_name: Ara--Ali-C137--Hindawi-Books-dataset
features:
- name: BookLink
dtype: string
- name: BookName
dtype: string
- name: AuthorName
dtype: string
- name: AboutBook
dtype: string
- name: ChapterLink
dtype: string
- name: ChapterName
dtype: string
- name: ChapterText
dtype: string
- name: AboutAuthor
dtype: string
splits:
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num_bytes: 1364854259
num_examples: 49821
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dataset_size: 1364854259
- config_name: Ara--Goud--Goud-sum
features:
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dtype: string
- name: headline
dtype: string
- name: categories
dtype: string
splits:
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num_examples: 139288
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dataset_size: 288296544
- config_name: Ara--J-Mourad--MNAD.v1
features:
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dtype: string
- name: Body
dtype: string
- name: Category
dtype: string
splits:
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dataset_size: 1101921980
- config_name: Ara--JihadZa--IADD
features:
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dtype: string
- name: Region
dtype: string
- name: DataSource
dtype: string
- name: Country
dtype: string
splits:
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num_bytes: 19167070
num_examples: 135804
download_size: 8644491
dataset_size: 19167070
- config_name: Ara--LeMGarouani--MAC-corpus
features:
- name: tweets
dtype: string
- name: type
dtype: string
- name: class
dtype: string
splits:
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num_bytes: 1945646
num_examples: 18087
download_size: 866198
dataset_size: 1945646
- config_name: Ara--MBZUAI--Bactrian-X
features:
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dtype: string
- name: id
dtype: string
- name: output
dtype: string
splits:
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num_bytes: 66093524
num_examples: 67017
download_size: 33063779
dataset_size: 66093524
- config_name: Ara--OpenAssistant--oasst1
features:
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dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_count
dtype: int32
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: float64
- name: synthetic
dtype: bool
- name: model_name
dtype: 'null'
- name: detoxify
dtype: 'null'
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
struct:
- name: count
sequence: int32
- name: name
sequence: string
- name: labels
struct:
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sequence: int32
- name: name
sequence: string
- name: value
sequence: float64
- name: __index_level_0__
dtype: int64
splits:
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num_examples: 56
download_size: 30984
dataset_size: 58168
- config_name: Ara--Wikipedia
features:
- name: id
dtype: string
- name: url
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splits:
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- config_name: Ara--bigscience--xP3
features:
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dtype: string
- name: targets
dtype: string
splits:
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num_bytes: 4727881680
num_examples: 2148955
download_size: 2805060725
dataset_size: 4727881680
- config_name: Ara--cardiffnlp--tweet_sentiment_multilingual
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
splits:
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num_bytes: 306108
num_examples: 1839
- name: validation
num_bytes: 53276
num_examples: 324
- name: test
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num_examples: 870
download_size: 279900
dataset_size: 500920
- config_name: Ara--miracl--miracl
features:
- name: query_id
dtype: string
- name: query
dtype: string
- name: positive_passages
list:
- name: docid
dtype: string
- name: text
dtype: string
- name: title
dtype: string
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list:
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dtype: string
- name: text
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dtype: string
splits:
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num_bytes: 32012083
num_examples: 3495
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dataset_size: 32012083
- config_name: Ara--mustapha--QuranExe
features:
- name: text
dtype: string
- name: resource_name
dtype: string
- name: verses_keys
dtype: string
splits:
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num_bytes: 133108687
num_examples: 49888
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dataset_size: 133108687
- config_name: Ara--pain--Arabic-Tweets
features:
- name: text
dtype: string
splits:
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num_bytes: 41639770853
num_examples: 202700438
download_size: 22561651700
dataset_size: 41639770853
- config_name: Ara--saudinewsnet
features:
- name: source
dtype: string
- name: url
dtype: string
- name: date_extracted
dtype: string
- name: title
dtype: string
- name: author
dtype: string
- name: content
dtype: string
splits:
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num_bytes: 103654009
num_examples: 31030
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dataset_size: 103654009
- config_name: Ary--AbderrahmanSkiredj1--Darija-Wikipedia
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 8104410
num_examples: 4862
download_size: 3229966
dataset_size: 8104410
- config_name: Ary--Ali-C137--Darija-Stories-Dataset
features:
- name: ChapterName
dtype: string
- name: ChapterLink
dtype: string
- name: Author
dtype: string
- name: Text
dtype: string
- name: Tags
dtype: int64
splits:
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download_size: 241528641
dataset_size: 476926644
- config_name: Ary--Wikipedia
features:
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dtype: string
splits:
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num_bytes: 10007364
num_examples: 6703
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dataset_size: 10007364
- config_name: Arz--Wikipedia
features:
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dtype: string
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dtype: string
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dtype: string
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dtype: string
splits:
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num_bytes: 1364641408
num_examples: 1617770
download_size: 306420318
dataset_size: 1364641408
configs:
- config_name: Ara--Ali-C137--Hindawi-Books-dataset
data_files:
- split: train
path: Ara--Ali-C137--Hindawi-Books-dataset/train-*
- config_name: Ara--Goud--Goud-sum
data_files:
- split: train
path: Ara--Goud--Goud-sum/train-*
- config_name: Ara--J-Mourad--MNAD.v1
data_files:
- split: train
path: Ara--J-Mourad--MNAD.v1/train-*
- config_name: Ara--JihadZa--IADD
data_files:
- split: train
path: Ara--JihadZa--IADD/train-*
- config_name: Ara--LeMGarouani--MAC-corpus
data_files:
- split: train
path: Ara--LeMGarouani--MAC-corpus/train-*
- config_name: Ara--MBZUAI--Bactrian-X
data_files:
- split: train
path: Ara--MBZUAI--Bactrian-X/train-*
- config_name: Ara--OpenAssistant--oasst1
data_files:
- split: train
path: Ara--OpenAssistant--oasst1/train-*
- config_name: Ara--Wikipedia
data_files:
- split: train
path: Ara--Wikipedia/train-*
- config_name: Ara--bigscience--xP3
data_files:
- split: train
path: Ara--bigscience--xP3/train-*
- config_name: Ara--cardiffnlp--tweet_sentiment_multilingual
data_files:
- split: train
path: Ara--cardiffnlp--tweet_sentiment_multilingual/train-*
- split: validation
path: Ara--cardiffnlp--tweet_sentiment_multilingual/validation-*
- split: test
path: Ara--cardiffnlp--tweet_sentiment_multilingual/test-*
- config_name: Ara--miracl--miracl
data_files:
- split: train
path: Ara--miracl--miracl/train-*
- config_name: Ara--mustapha--QuranExe
data_files:
- split: train
path: Ara--mustapha--QuranExe/train-*
- config_name: Ara--pain--Arabic-Tweets
data_files:
- split: train
path: Ara--pain--Arabic-Tweets/train-*
- config_name: Ara--saudinewsnet
data_files:
- split: train
path: Ara--saudinewsnet/train-*
- config_name: Ary--AbderrahmanSkiredj1--Darija-Wikipedia
data_files:
- split: train
path: Ary--AbderrahmanSkiredj1--Darija-Wikipedia/train-*
- config_name: Ary--Ali-C137--Darija-Stories-Dataset
data_files:
- split: train
path: Ary--Ali-C137--Darija-Stories-Dataset/train-*
- config_name: Ary--Wikipedia
data_files:
- split: train
path: Ary--Wikipedia/train-*
- config_name: Arz--Wikipedia
data_files:
- split: train
path: Arz--Wikipedia/train-*
---
# Dataset Card for "Mixed Arabic Datasets (MAD) Corpus"
**The Mixed Arabic Datasets Corpus : A Community-Driven Collection of Diverse Arabic Texts**
## Dataset Description
The Mixed Arabic Datasets (MAD) presents a dynamic compilation of diverse Arabic texts sourced from various online platforms and datasets. It addresses a critical challenge faced by researchers, linguists, and language enthusiasts: the fragmentation of Arabic language datasets across the Internet. With MAD, we are trying to centralize these dispersed resources into a single, comprehensive repository.
Encompassing a wide spectrum of content, ranging from social media conversations to literary masterpieces, MAD captures the rich tapestry of Arabic communication, including both standard Arabic and regional dialects.
This corpus offers comprehensive insights into the linguistic diversity and cultural nuances of Arabic expression.
## Usage
If you want to use this dataset you pick one among the available configs:
`Ara--MBZUAI--Bactrian-X` | `Ara--OpenAssistant--oasst1` | `Ary--AbderrahmanSkiredj1--Darija-Wikipedia`
`Ara--Wikipedia` | `Ary--Wikipedia` | `Arz--Wikipedia`
`Ary--Ali-C137--Darija-Stories-Dataset` | `Ara--Ali-C137--Hindawi-Books-dataset` | ``
Example of usage:
```python
dataset = load_dataset('M-A-D/Mixed-Arabic-Datasets-Repo', 'Ara--MBZUAI--Bactrian-X')
```
If you loaded multiple datasets and wanted to merge them together then you can simply laverage `concatenate_datasets()` from `datasets`
```pyhton
dataset3 = concatenate_datasets([dataset1['train'], dataset2['train']])
```
Note : proccess the datasets before merging in order to make sure you have a new dataset that is consistent
## Dataset Size
The Mixed Arabic Datasets (MAD) is a dynamic and evolving collection, with its size fluctuating as new datasets are added or removed. As MAD continuously expands, it becomes a living resource that adapts to the ever-changing landscape of Arabic language datasets.
**Dataset List**
MAD draws from a diverse array of sources, each contributing to its richness and breadth. While the collection is constantly evolving, some of the datasets that are poised to join MAD in the near future include:
- [✔] OpenAssistant/oasst1 (ar portion) : [Dataset Link](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [✔] MBZUAI/Bactrian-X (ar portion) : [Dataset Link](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ar/train)
- [✔] AbderrahmanSkiredj1/Darija-Wikipedia : [Dataset Link](https://huggingface.co/datasets/AbderrahmanSkiredj1/moroccan_darija_wikipedia_dataset)
- [✔] Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Moroccan Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Egyptian Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Darija Stories Dataset : [Dataset Link](https://huggingface.co/datasets/Ali-C137/Darija-Stories-Dataset)
- [✔] Hindawi Books Dataset : [Dataset Link](https://huggingface.co/datasets/Ali-C137/Hindawi-Books-dataset)
- [] uonlp/CulturaX - ar : [Dataset Link](https://huggingface.co/datasets/uonlp/CulturaX/viewer/ar/train)
- [✔] Pain/ArabicTweets : [Dataset Link](https://huggingface.co/datasets/pain/Arabic-Tweets)
- [] Abu-El-Khair Corpus : [Dataset Link](https://huggingface.co/datasets/arabic_billion_words)
- [✔] QuranExe : [Dataset Link](https://huggingface.co/datasets/mustapha/QuranExe)
- [✔] MNAD : [Dataset Link](https://huggingface.co/datasets/J-Mourad/MNAD.v1)
- [✔] IADD : [Dataset Link](https://raw.githubusercontent.com/JihadZa/IADD/main/IADD.json)
- [] OSIAN : [Dataset Link](https://wortschatz.uni-leipzig.de/en/download/Arabic#ara-tn_newscrawl-OSIAN_2018)
- [✔] MAC corpus : [Dataset Link](https://raw.githubusercontent.com/LeMGarouani/MAC/main/MAC%20corpus.csv)
- [✔] Goud.ma-Sum : [Dataset Link](https://huggingface.co/datasets/Goud/Goud-sum)
- [✔] SaudiNewsNet : [Dataset Link](https://huggingface.co/datasets/saudinewsnet)
- [✔] Miracl : [Dataset Link](https://huggingface.co/datasets/miracl/miracl)
- [✔] CardiffNLP/TweetSentimentMulti : [Dataset Link](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)
- [] OSCAR-2301 : [Dataset Link](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301/viewer/ar/train)
- [] mc4 : [Dataset Link](https://huggingface.co/datasets/mc4/viewer/ar/train)
- [✔] bigscience/xP3 : [Dataset Link](https://huggingface.co/datasets/bigscience/xP3/viewer/ar/train)
- [] Muennighoff/xP3x : [Dataset Link](https://huggingface.co/datasets/Muennighoff/xP3x)
- [] Ai_Society : [Dataset Link](https://huggingface.co/datasets/camel-ai/ai_society_translated)
## Potential Use Cases
The Mixed Arabic Datasets (MAD) holds the potential to catalyze a multitude of groundbreaking applications:
- **Linguistic Analysis:** Employ MAD to conduct in-depth linguistic studies, exploring dialectal variances, language evolution, and grammatical structures.
- **Topic Modeling:** Dive into diverse themes and subjects through the extensive collection, revealing insights into emerging trends and prevalent topics.
- **Sentiment Understanding:** Decode sentiments spanning Arabic dialects, revealing cultural nuances and emotional dynamics.
- **Sociocultural Research:** Embark on a sociolinguistic journey, unraveling the intricate connection between language, culture, and societal shifts.
## Dataset Access
MAD's access mechanism is unique: while it doesn't carry a general license itself, each constituent dataset within the corpus retains its individual license. By accessing the dataset details through the provided links in the "Dataset List" section above, users can understand the specific licensing terms for each dataset.
### Join Us on Discord
For discussions, contributions, and community interactions, join us on Discord! [](https://discord.gg/2NpJ9JGm)
### How to Contribute
Want to contribute to the Mixed Arabic Datasets project? Follow our comprehensive guide on Google Colab for step-by-step instructions: [Contribution Guide](https://colab.research.google.com/drive/1kOIRoicgCOV8TPvASAI_2uMY7rpXnqzJ?usp=sharing).
**Note**: If you'd like to test a contribution before submitting it, feel free to do so on the [MAD Test Dataset](https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Dataset-test).
## Citation
```
@dataset{
title = {Mixed Arabic Datasets (MAD)},
author = {MAD Community},
howpublished = {Dataset},
url = {https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Datasets-Repo},
year = {2023},
}
```
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zelros/insurance-fr
|
zelros
| 2023-10-21T13:19:38Z | 75 | 0 |
[
"insurance",
"region:us"
] | null | 2023-09-01T07:16:26Z |
---
tags:
- insurance
---
This dataset contains question/answer pairs from a French home insurance (MRH: Multi-Risk Home Insurance).
It comes from structuring the following open sources:
- https://www.mma.fr/assurance-habitation.html
- https://cap.mma.fr/files/live/sites/mmafr/files/documents-cg/cg410/Habitation_MMA_410p.pdf
The objective of this dataset is to contribute to open source research projects aiming to, for instance:
* fine-tune LLMs on high-quality datasets, specializing them in the insurance domain
* develop new question/answer applications using Retrieval Augmented Generation (RAG) for insurance contracts
* assess the knowledge of language models in the insurance field
* more generally, apply LLMs to the insurance domain for better understanding and increased transparency of this industry.
Other datasets of the same kind (but on other types of insurance, other languages, or from different sources) are also available - or will be available soon - and are part of this research effort.
|
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hippocrates/DDI2013_test
|
hippocrates
| 2023-10-12T19:21:33Z | 75 | 0 |
[
"region:us"
] | null | 2023-10-08T22:20:53Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 20658927
num_examples: 18779
- name: valid
num_bytes: 8739656
num_examples: 7244
- name: test
num_bytes: 6455758
num_examples: 5761
download_size: 3113073
dataset_size: 35854341
---
# Dataset Card for "DDI2013_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
happylkx/InstructCoder
|
happylkx
| 2023-11-09T08:59:57Z | 75 | 4 |
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"code",
"arxiv:2310.20329",
"region:us"
] |
[
"text-generation"
] | 2023-10-09T11:21:14Z |
---
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: instruct_coder
size_categories:
- 100K<n<1M
---
<div align="center">
<img src="https://github.com/Happylkx/InstructCoder/raw/main/docs/logo.png">
</div>
<div align="center">
<a href="https://github.com/qishenghu/CodeInstruct/blob/main/CodeInstruct.pdf">Paper</a> |
<a href="https://github.com/qishenghu/CodeInstruct">Code</a> |
<a href="https://happylkx.github.io/InstructCoder/">Blog</a>
<!-- <a href="https://blog.nus.edu.sg/kaixinli/2023/05/23/codeinstruct/">Blog</a> -->
</div>
<!-- | [Checkpoints](link_to_checkpoints) -->
# InstructCoder (CodeInstruct): Empowering Language Models to Edit Code
## Updates
- May 23, 2023: Paper, code and data released.
## Overview
InstructCoder is the first dataset designed to adapt LLMs for general code editing. It consists of 114,239 instruction-input-output triplets and covers multiple distinct code editing scenarios, generated by ChatGPT. LLaMA-33B finetuned on InstructCoder performs on par with ChatGPT on a real-world test set derived from GitHub commits.
In the ever-evolving world of software development, efficient and effective code editing plays a pivotal role. As the demands for faster development cycles and increased productivity continue to rise, harnessing the power of Large Language Models in code editing has emerged as a game-changer. This project aims to revolutionize code editing, empowering developers with intelligent tools and techniques.
Though the community has studied extensively on coding with LLMs, our experiments demonstrate that current LLMs still struggle for code editing. To this end, we curate a code editing dataset, dubbed InstructCoder, for improving and evaluating code editing abilities of LLMs. InstructCoder is an instructional dataset containing diverse code-editing tasks, covering 20 scenarios where code editing may be performed. [The dataset is released here.](https://github.com/Happylkx/InstructCoder) Our empirical experiments reveal that open-source LLMs display notable gains in code editing abilities after finetuning on InstructCoder. Some qualitative examples are provided.
## Data Collection
InstructCoder is systematically collected through an iterative process. Initial seed tasks are selected from GitHub commits. Inspired by Self-Instruct, we prompt ChatGPT to bootstrap new instructions. Then, plausible scenarios where the filtered instructions may be used are generated. Finally, corresponding code input and output are obtained conditioned on both the instruction and scenario. High-quality samples are manually selected and recurrently added to the task pool for further generation.
## Results
The largest model fine-tuned, LLaMA-33B, performs on par with ChatGPT, with 79.3% accuracy on the test set. Some qualitative examples generated by our model are shown here.
## Release
We are planning to release the following assets:
- [x] Full dataset
- [x] Source Code
<!-- The LoRA checkpoints are available [here](link_to_checkpoints). -->
## Qualitative Examples
In this section, we showcase three qualitative examples generated by the LLaMA-33B model.
1. **Instruction:** Transform the code into a class method called ‘calculate_distance’ that calculates the distance between two points in 3D space.
**Input code:**
```python
import math
def distance(point1, point2):
x1, y1, z1 = point1
x2, y2, z2 = point2
dx = x2 - x1
dy = y2 - y1
dz = z2 - z1
return math.sqrt(dx**2 + dy**2 + dz**2)
point1 = (0, 0, 0)
point2 = (1, 1, 1)
print(distance(point1, point2))
```
Output code:
```python
import math
class Point:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def calculate_distance(self, other_point):
dx = other_point.x - self.x
dy = other_point.y - self.y
dz = other_point.z - self.z
return math.sqrt(dx**2 + dy**2 + dz**2)
point1 = Point(0, 0, 0)
point2 = Point(1, 1, 1)
print(point1.calculate_distance(point2))
```
2. Instruction: Integrate error handling into the code that performs batch processing of large datasets, including job failures or incomplete runs.
Input code:
```python
import pandas as pd
def process_orders(orders_file, shipping_file, inventory_file):
orders = pd.read_csv(orders_file)
shipping = pd.read_csv(shipping_file)
inventory = pd.read_csv(inventory_file)
# Perform data processing and analysis here...
results = {"success": True, "message": "Batch processing completed successfully."}
return results
orders_file = "orders.csv"
shipping_file = "shipping.csv"
inventory_file = "inventory.csv"
results = process_orders(orders_file, shipping_file, inventory_file)
print(results)
```
Output code:
```python
import pandas as pd
import logging
logging.basicConfig(filename='batch_processing.log', level=logging.ERROR)
def process_orders(orders_file, shipping_file, inventory_file):
try:
orders = pd.read_csv(orders_file)
shipping = pd.read_csv(shipping_file)
inventory = pd.read_csv(inventory_file)
# Perform data processing and analysis here...
results = {"success": True, "message": "Batch processing completed successfully."}
return results
except Exception as e:
logging.error(f"Error processing batch: {e}")
results = {"success": False, "message": "Batch processing failed. Please check the input files for any issues."}
return results
orders_file = "orders.csv"
shipping_file = "shipping.csv"
inventory_file = "inventory.csv"
results = process_orders(orders_file, shipping_file, inventory_file)
print(results)
```
3. Instruction: Refactor any code that uses ‘exec’ or ‘eval’ for security reasons.
Input code:
```python
Copy code
game_data = {
"score": 0,
"level": 1
}
def execute_script(script):
exec(script)
script = """
game_data["score"] += 10
if game_data["score"] >= 100:
game_data["level"] += 1
"""
execute_script(script)
print(game_data)
```
Output code:
```python
game_data = {
"score": 0,
"level": 1
}
def execute_script(script):
compiled_script = compile(script, "", "exec")
exec(compiled_script, game_data)
script = """
game_data["score"] += 10
if game_data["score"] >= 100:
game_data["level"] += 1
"""
execute_script(script)
print(game_data)
```
Now the script is not able to access the data in game_data.
## Limitations and Safety Concerns
By recognizing and addressing limitations and safety concerns, we hope developers can foster responsible AI deployment and use, promoting a more ethical, equitable, and secure future for large language models in code production.
1. **License:** You must ensure compliance with the corresponding licenses of the LLMs. It is important to thoroughly review the licenses and make sure that your usage is in accordance with their terms. For instance, you are not allowed to use LLaMA commercially, for it is released under a noncommercial license.
2. **Incomplete or Imperfect Knowledge:** LLMs are trained on vast amounts of data, which may not always be up-to-date or entirely accurate. For example, the APIs of a library may change over time. Consequently, the information provided by the models could be outdated, inaccurate, or even misleading in some instances.
3. **Overuse and Dependency:** Users might incorrectly interpret or rely too heavily on the outputs generated by large language models. It is crucial to provide proper guidance and promote an understanding of the model’s limitations, encouraging users to critically assess and verify the information or suggestions provided. Please make sure to check the generation of the models before using them.
Overreliance on large language models could lead to complacency, potentially causing users to undervalue human intelligence, such as creativity and critical thinking. We encourage users to use AI as a tool to supplement, rather than replace, human input and judgment.
4. **Malicious Use:** There is a risk that malicious actors might use the tools for nefarious purposes, such as generating malicious software. It is important to monitor the use and deployment of these models, track and report abuse, and develop countermeasures to address potential malicious activity.
5. **Bias and Discrimination:** Language models can inherit societal biases present in their training data, possibly leading to discriminatory or biased generations. Though our dataset is not likely to contain such toxic data, they may appear in the responses because of the base LLMs.
## Citation
Feel free to cite our work if you find it interesting or use the data:
```plain
@misc{2023instructcoder,
title={InstructCoder: Empowering Language Models for Code Editing},
author={Qisheng Hu and Kaixin Li and Xu Zhao and Yuxi Xie and Tiedong Liu and Hui Chen and Qizhe Xie and Junxian He},
year={2023},
eprint={2310.20329},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Conclusion
The integration of AI into code editing represents a significant milestone in the evolution of software development. By leveraging AI’s capabilities in understanding code semantics, patterns, and best practices, developers can unlock new levels of productivity, code quality, and efficiency. This project we’ve explored demonstrates the immense potential of intelligent code editing tools. As the software development landscape continues to evolve, embracing AI is poised to become a standard practice, and sets the stage for a future where developers can focus more on creativity and problem-solving, while AI handles the mundane aspects of coding.
|
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open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT
|
open-llm-leaderboard
| 2023-10-13T04:33:40Z | 75 | 0 |
[
"region:us"
] | null | 2023-10-13T04:33:32Z |
---
pretty_name: Evaluation run of RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT](https://huggingface.co/RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-13T04:33:28.538192](https://huggingface.co/datasets/open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT/blob/main/results_2023-10-13T04-33-28.538192.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\
\ \"em_stderr\": 0.00036305608931188796,\n \"f1\": 0.052196937919463185,\n\
\ \"f1_stderr\": 0.0012732861194066877,\n \"acc\": 0.4008241516587451,\n\
\ \"acc_stderr\": 0.009542578755221624\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931188796,\n\
\ \"f1\": 0.052196937919463185,\n \"f1_stderr\": 0.0012732861194066877\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06368460955269144,\n \
\ \"acc_stderr\": 0.006726213078805692\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.012358944431637557\n\
\ }\n}\n```"
repo_url: https://huggingface.co/RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T04_33_28.538192
path:
- '**/details_harness|drop|3_2023-10-13T04-33-28.538192.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T04-33-28.538192.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T04_33_28.538192
path:
- '**/details_harness|gsm8k|5_2023-10-13T04-33-28.538192.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T04-33-28.538192.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T04_33_28.538192
path:
- '**/details_harness|winogrande|5_2023-10-13T04-33-28.538192.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T04-33-28.538192.parquet'
- config_name: results
data_files:
- split: 2023_10_13T04_33_28.538192
path:
- results_2023-10-13T04-33-28.538192.parquet
- split: latest
path:
- results_2023-10-13T04-33-28.538192.parquet
---
# Dataset Card for Evaluation run of RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT](https://huggingface.co/RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T04:33:28.538192](https://huggingface.co/datasets/open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT/blob/main/results_2023-10-13T04-33-28.538192.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931188796,
"f1": 0.052196937919463185,
"f1_stderr": 0.0012732861194066877,
"acc": 0.4008241516587451,
"acc_stderr": 0.009542578755221624
},
"harness|drop|3": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931188796,
"f1": 0.052196937919463185,
"f1_stderr": 0.0012732861194066877
},
"harness|gsm8k|5": {
"acc": 0.06368460955269144,
"acc_stderr": 0.006726213078805692
},
"harness|winogrande|5": {
"acc": 0.7379636937647988,
"acc_stderr": 0.012358944431637557
}
}
```
### 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_meta-llama__Llama-2-13b-chat-hf
|
open-llm-leaderboard
| 2023-10-14T19:39:38Z | 75 | 0 |
[
"region:us"
] | null | 2023-10-14T19:39:30Z |
---
pretty_name: Evaluation run of meta-llama/Llama-2-13b-chat-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_meta-llama__Llama-2-13b-chat-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-14T19:39:26.636545](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-13b-chat-hf/blob/main/results_2023-10-14T19-39-26.636545.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.1782718120805369,\n\
\ \"em_stderr\": 0.003919630092588375,\n \"f1\": 0.2387195889261742,\n\
\ \"f1_stderr\": 0.003944947017182046,\n \"acc\": 0.448727630233375,\n\
\ \"acc_stderr\": 0.011074189612085313\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.1782718120805369,\n \"em_stderr\": 0.003919630092588375,\n\
\ \"f1\": 0.2387195889261742,\n \"f1_stderr\": 0.003944947017182046\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15238817285822592,\n \
\ \"acc_stderr\": 0.009899572254794204\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n\
\ }\n}\n```"
repo_url: https://huggingface.co/meta-llama/Llama-2-13b-chat-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_14T19_39_26.636545
path:
- '**/details_harness|drop|3_2023-10-14T19-39-26.636545.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-14T19-39-26.636545.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_14T19_39_26.636545
path:
- '**/details_harness|gsm8k|5_2023-10-14T19-39-26.636545.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-14T19-39-26.636545.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_14T19_39_26.636545
path:
- '**/details_harness|winogrande|5_2023-10-14T19-39-26.636545.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-14T19-39-26.636545.parquet'
- config_name: results
data_files:
- split: 2023_10_14T19_39_26.636545
path:
- results_2023-10-14T19-39-26.636545.parquet
- split: latest
path:
- results_2023-10-14T19-39-26.636545.parquet
---
# Dataset Card for Evaluation run of meta-llama/Llama-2-13b-chat-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/meta-llama/Llama-2-13b-chat-hf
- **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 [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_meta-llama__Llama-2-13b-chat-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-14T19:39:26.636545](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-13b-chat-hf/blob/main/results_2023-10-14T19-39-26.636545.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.1782718120805369,
"em_stderr": 0.003919630092588375,
"f1": 0.2387195889261742,
"f1_stderr": 0.003944947017182046,
"acc": 0.448727630233375,
"acc_stderr": 0.011074189612085313
},
"harness|drop|3": {
"em": 0.1782718120805369,
"em_stderr": 0.003919630092588375,
"f1": 0.2387195889261742,
"f1_stderr": 0.003944947017182046
},
"harness|gsm8k|5": {
"acc": 0.15238817285822592,
"acc_stderr": 0.009899572254794204
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### 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_jondurbin__airoboros-l2-7b-gpt4-2.0
|
open-llm-leaderboard
| 2023-10-22T19:06:46Z | 75 | 0 |
[
"region:us"
] | null | 2023-10-22T19:06:38Z |
---
pretty_name: Evaluation run of jondurbin/airoboros-l2-7b-gpt4-2.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/airoboros-l2-7b-gpt4-2.0](https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-2.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-2.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T19:06:34.610591](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-2.0/blob/main/results_2023-10-22T19-06-34.610591.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2790478187919463,\n\
\ \"em_stderr\": 0.004593378120842089,\n \"f1\": 0.3568791946308748,\n\
\ \"f1_stderr\": 0.004541856353836489,\n \"acc\": 0.3714854775657577,\n\
\ \"acc_stderr\": 0.008787294914639698\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2790478187919463,\n \"em_stderr\": 0.004593378120842089,\n\
\ \"f1\": 0.3568791946308748,\n \"f1_stderr\": 0.004541856353836489\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.03184230477634572,\n \
\ \"acc_stderr\": 0.004836348558260957\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7111286503551697,\n \"acc_stderr\": 0.01273824127101844\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-2.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_22T19_06_34.610591
path:
- '**/details_harness|drop|3_2023-10-22T19-06-34.610591.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T19-06-34.610591.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T19_06_34.610591
path:
- '**/details_harness|gsm8k|5_2023-10-22T19-06-34.610591.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T19-06-34.610591.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T19_06_34.610591
path:
- '**/details_harness|winogrande|5_2023-10-22T19-06-34.610591.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T19-06-34.610591.parquet'
- config_name: results
data_files:
- split: 2023_10_22T19_06_34.610591
path:
- results_2023-10-22T19-06-34.610591.parquet
- split: latest
path:
- results_2023-10-22T19-06-34.610591.parquet
---
# Dataset Card for Evaluation run of jondurbin/airoboros-l2-7b-gpt4-2.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-2.0
- **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 [jondurbin/airoboros-l2-7b-gpt4-2.0](https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-2.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-2.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T19:06:34.610591](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-2.0/blob/main/results_2023-10-22T19-06-34.610591.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.2790478187919463,
"em_stderr": 0.004593378120842089,
"f1": 0.3568791946308748,
"f1_stderr": 0.004541856353836489,
"acc": 0.3714854775657577,
"acc_stderr": 0.008787294914639698
},
"harness|drop|3": {
"em": 0.2790478187919463,
"em_stderr": 0.004593378120842089,
"f1": 0.3568791946308748,
"f1_stderr": 0.004541856353836489
},
"harness|gsm8k|5": {
"acc": 0.03184230477634572,
"acc_stderr": 0.004836348558260957
},
"harness|winogrande|5": {
"acc": 0.7111286503551697,
"acc_stderr": 0.01273824127101844
}
}
```
### 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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] |
ariesta/forensic-timeline3
|
ariesta
| 2023-11-08T20:44:00Z | 75 | 0 |
[
"region:us"
] | null | 2023-11-05T10:51:37Z |
---
configs:
- config_name: casper-rw
data_files: casper-rw.csv
- config_name: dfrws-2009-jhuisi
data_files: dfrws-2009-jhuisi.csv
- config_name: dfrws-2009-nssal
data_files: dfrws-2009-nssal.csv
- config_name: honeynet-challenge7
data_files: honeynet-challenge7.csv
- config_name: honeynet-challenge5
data_files: honeynet-challenge5.csv
---
|
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] |
atmallen/qm_alice_easy_2_grader_last_1.0e
|
atmallen
| 2023-11-16T18:22:43Z | 75 | 0 |
[
"region:us"
] | null | 2023-11-16T03:25:43Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 8603063.0
num_examples: 117117
- name: validation
num_bytes: 831417.0
num_examples: 11279
- name: test
num_bytes: 825258.0
num_examples: 11186
download_size: 2481199
dataset_size: 10259738.0
---
# Dataset Card for "qm_alice_easy_2_grader_last_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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hotal/emergency_classification_prompt
|
hotal
| 2023-11-16T15:43:46Z | 75 | 1 |
[
"region:us"
] | null | 2023-11-16T14:51:47Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 27222546
num_examples: 25989
download_size: 4744670
dataset_size: 27222546
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emergency_classification_prompt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Santp98/sentences_triplets_secop2_splits
|
Santp98
| 2023-11-18T17:14:05Z | 75 | 0 |
[
"region:us"
] | null | 2023-11-18T17:13:17Z |
---
dataset_info:
features:
- name: segment_code_pos
dtype: string
- name: segment_code_neg
dtype: string
- name: anchor_sent
dtype: string
- name: positive_sent
dtype: string
- name: negative_sent
dtype: string
splits:
- name: train
num_bytes: 389514845.59367234
num_examples: 552087
- name: test
num_bytes: 83467920.46898298
num_examples: 118305
- name: validation
num_bytes: 83467214.93734469
num_examples: 118304
download_size: 313920558
dataset_size: 556449981.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
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] |
mideind/icelandic-error-corpus-IceEC
|
mideind
| 2022-10-25T09:51:04Z | 74 | 1 |
[
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:is",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language:
- is
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
pretty_name: Icelandic Error Corpus
---
# Icelandic Error Corpus
Refer to [https://github.com/antonkarl/iceErrorCorpus](https://github.com/antonkarl/iceErrorCorpus) for a description of the dataset.
Please cite the dataset as follows if you use it.
```
Anton Karl Ingason, Lilja Björk Stefánsdóttir, Þórunn Arnardóttir, and Xindan Xu. 2021. The Icelandic Error Corpus (IceEC). Version 1.1. (https://github.com/antonkarl/iceErrorCorpus)
```
|
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huggan/anime-faces
|
huggan
| 2022-03-22T10:01:22Z | 74 | 7 |
[
"license:cc0-1.0",
"region:us"
] | null | 2022-03-03T13:15:34Z |
---
license: cc0-1.0
---
# Dataset Card for anime-faces
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://www.kaggle.com/soumikrakshit/anime-faces
- **Repository:** https://www.kaggle.com/soumikrakshit/anime-faces
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** https://github.com/Mckinsey666
### Dataset Summary
This is a dataset consisting of 21551 anime faces scraped from www.getchu.com, which are then cropped using the anime face detection algorithm in https://github.com/nagadomi/lbpcascade_animeface. All images are resized to 64 * 64 for the sake of convenience. Please also cite the two sources when using this dataset.
Some outliers are still present in the dataset:
Bad cropping results
Some non-human faces.
Feel free to contribute to this dataset by adding images of similar quality or adding image labels.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
Has a data folder with png files inside.
### Data Splits
Only training set
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
---
annotations_creators:
- found
language_creators:
- found
languages:
- unknown
licenses:
- unknown
multilinguality:
- unknown
pretty_name: anime-faces
size_categories:
- unknown
source_datasets:
- original
task_categories:
- image-classification
task_ids: []
---
|
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okite97/news-data
|
okite97
| 2022-08-25T10:36:01Z | 74 | 2 |
[
"task_categories:text-classification",
"task_ids:topic-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"region:us"
] |
[
"text-classification"
] | 2022-07-28T09:10:22Z |
---
annotations_creators:
- other
language:
- 'en'
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: News Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- text-classification
task_ids:
- topic-classification
- multi-class-classification
---
# Dataset Card for news-data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Dataset Curators](#dataset-curators)
### Dataset Summary
The News Dataset is an English-language dataset containing just over 4k unique news articles scrapped from AriseTv- One of the most popular news television in Nigeria.
### Supported Tasks and Leaderboards
It supports news article classification into different categories.
### Languages
English
## Dataset Structure
### Data Instances
'''
{'Title': 'Nigeria: APC Yet to Zone Party Positions Ahead of Convention'
'Excerpt': 'The leadership of the All Progressives Congress (APC), has denied reports that it had zoned some party positions ahead of'
'Category': 'politics'
'labels': 2}
'''
### Data Fields
* Title: a string containing the title of a news title as shown
* Excerpt: a string containing a short extract from the body of the news
* Category: a string that tells the category of an example (string label)
* labels: integer telling the class of an example (label)
### Data Splits
| Dataset Split | Number of instances in split |
| ----------- | ----------- |
| Train | 4,594 |
| Paragraph | 811 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The code for the dataset creation at *https://github.com/chimaobi-okite/NLP-Projects-Competitions/blob/main/NewsCategorization/Data/NewsDataScraping.ipynb*. The examples were scrapped from
<https://www.arise.tv/>
### Annotations
#### Annotation process
The annotation is based on the news category in the [arisetv](https://www.arise.tv) website
#### Who are the annotators?
Journalists at arisetv
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that can classify news articles into categories.
This task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.
### Discussion of Biases
This data is biased towards news happenings in Nigeria but the model built using it can as well classify news from other parts of the world
with a slight degradation in performance.
### Dataset Curators
The dataset is created by people at arise but was scrapped by [@github-chimaobi-okite](https://github.com/chimaobi-okite/)
|
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cjvt/sentinews
|
cjvt
| 2022-08-17T06:28:13Z | 74 | 1 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:sl",
"license:cc-by-sa-4.0",
"slovenian sentiment",
"news articles",
"region:us"
] |
[
"text-classification"
] | 2022-08-15T08:32:30Z |
---
annotations_creators:
- crowdsourced
language:
- sl
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: SentiNews
size_categories: []
source_datasets:
- original
tags:
- slovenian sentiment
- news articles
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for SentiNews
## Dataset Description
- **Homepage:** https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene
- **Paper:** Bučar, J., Žnidaršič, M. & Povh, J. Annotated news corpora and a lexicon for sentiment analysis in Slovene. Lang Resources & Evaluation 52, 895–919 (2018). https://doi.org/10.1007/s10579-018-9413-3
### Dataset Summary
SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their sentiment by between two and six annotators.
It is annotated at three granularities:
- document-level (config `document_level`, 10 427 documents),
- paragraph-level (config `paragraph_level`, 89 999 paragraphs), and
- sentence-level (config `sentence_level`, 168 899 sentences).
### Supported Tasks and Leaderboards
Sentiment classification, three classes (negative, neutral, positive).
### Languages
Slovenian.
## Dataset Structure
### Data Instances
A sample instance from the sentence-level config:
```
{
'nid': 2,
'content': 'Vilo Prešeren je na dražbi ministrstva za obrambo kupilo nepremičninsko podjetje Condor Real s sedežem v Lescah.',
'sentiment': 'neutral',
'pid': 1,
'sid': 1
}
```
### Data Fields
The data fields are similar among all three configs, with the only difference being the IDs.
- `nid`: a uint16 containing a unique ID of the news article (document).
- `content`: a string containing the body of the news article
- `sentiment`: the sentiment of the instance
- `pid`: a uint8 containing the consecutive number of the paragraph inside the current news article, **not unique** (present in the configs `paragraph_level` and `sentence_level`)
- `sid`: a uint8 containing the consecutive number of the sentence inside the current paragraph, **not unique** (present in the config `sentence_level`)
## Additional Information
### Dataset Curators
Jože Bučar, Martin Žnidaršič, Janez Povh.
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@article{buvcar2018annotated,
title={Annotated news corpora and a lexicon for sentiment analysis in Slovene},
author={Bu{\v{c}}ar, Jo{\v{z}}e and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and Povh, Janez},
journal={Language Resources and Evaluation},
volume={52},
number={3},
pages={895--919},
year={2018},
publisher={Springer}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
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0.01613370142877102,
-0.2146121859550476,
-0.3783029615879059,
0.14966918528079987,
-0.25041714310646057,
-0.26099953055381775
] |
society-ethics/lila_camera_traps
|
society-ethics
| 2023-03-07T20:14:40Z | 74 | 5 |
[
"task_categories:image-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:other",
"biodiversity",
"camera trap data",
"wildlife monitoring",
"region:us"
] |
[
"image-classification"
] | 2022-10-30T22:34:29Z |
---
annotations_creators:
- expert-generated
license:
- other
language_creators:
- expert-generated
language:
- en
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-classification
tags:
- biodiversity
- camera trap data
- wildlife monitoring
pretty_name: LILA Camera Traps
---
# Dataset Card for LILA
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Tutorial](#tutorial)
- [Working with Taxonomies](#working-with-taxonomies)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://lila.science/
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** [[email protected]]([email protected])
### Dataset Summary
LILA Camera Traps is an aggregate data set of images taken by camera traps, which are devices that automatically (e.g. via motion detection) capture images of wild animals to help ecological research.
This data set is the first time when disparate camera trap data sets have been aggregated into a single training environment with a single [taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).
This data set consists of only camera trap image data sets, whereas the broader [LILA](lila.science/) website also has other data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for this topic.
See below for information about each specific dataset that LILA contains:
<details>
<summary> Caltech Camera Traps </summary>
This data set contains 243,100 images from 140 camera locations in the Southwestern United States, with labels for 21 animal categories (plus empty), primarily at the species level (for example, the most common labels are opossum, raccoon, and coyote), and approximately 66,000 bounding box annotations. Approximately 70% of images are labeled as empty.
More information about this data set is available [here](https://beerys.github.io/CaltechCameraTraps/).
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
For questions about this data set, contact [email protected].
If you use this data set, please cite the associated manuscript:
```bibtex
@inproceedings{DBLP:conf/eccv/BeeryHP18,
author = {Sara Beery and
Grant Van Horn and
Pietro Perona},
title = {Recognition in Terra Incognita},
booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich,
Germany, September 8-14, 2018, Proceedings, Part {XVI}},
pages = {472--489},
year = {2018},
crossref = {DBLP:conf/eccv/2018-16},
url = {https://doi.org/10.1007/978-3-030-01270-0\_28},
doi = {10.1007/978-3-030-01270-0\_28},
timestamp = {Mon, 08 Oct 2018 17:08:07 +0200},
biburl = {https://dblp.org/rec/bib/conf/eccv/BeeryHP18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
</details>
<details>
<summary> ENA24 </summary>
This data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image. The most common classes are “American Crow”, “American Black Bear”, and “Dog”.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
Please cite this manuscript if you use this data set:
```bibtex
@article{yousif2019dynamic,
title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild},
author={Yousif, Hayder and Kays, Roland and He, Zhihai},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2019},
publisher={IEEE}
}
```
For questions about this data set, contact [Hayder Yousif]([email protected]).
</details>
<details>
<summary> Missouri Camera Traps </summary>
This data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
If you use this data set, please cite the associated manuscript:
```bibtex
@article{zhang2016animal,
title={Animal detection from highly cluttered natural scenes using spatiotemporal object region proposals and patch verification},
author={Zhang, Zhi and He, Zhihai and Cao, Guitao and Cao, Wenming},
journal={IEEE Transactions on Multimedia},
volume={18},
number={10},
pages={2079--2092},
year={2016},
publisher={IEEE}
}
```
For questions about this data set, contact [Hayder Yousif]([email protected]) and [Zhi Zhang]([email protected]).
</details>
<details>
<summary> North American Camera Trap Images (NACTI) </summary>
This data set contains 3.7M camera trap images from five locations across the United States, with labels for 28 animal categories, primarily at the species level (for example, the most common labels are cattle, boar, and red deer). Approximately 12% of images are labeled as empty. We have also added bounding box annotations to 8892 images (mostly vehicles and birds).
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
Please cite this manuscript if you use this data set:
```bibtex
@article{tabak2019machine,
title={Machine learning to classify animal species in camera trap images: Applications in ecology},
author={Tabak, Michael A and Norouzzadeh, Mohammad S and Wolfson, David W and Sweeney, Steven J and VerCauteren, Kurt C and Snow, Nathan P and Halseth, Joseph M and Di Salvo, Paul A and Lewis, Jesse S and White, Michael D and others},
journal={Methods in Ecology and Evolution},
volume={10},
number={4},
pages={585--590},
year={2019},
publisher={Wiley Online Library}
}
```
For questions about this data set, contact [[email protected]]([email protected]).
</details>
<details>
<summary> WCS Camera Traps </summary>
This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the [Wildlife Conservation Society](https://www.wcs.org/). The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.
Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files. You can find more information about the data set [on the LILA website](https://lila.science/datasets/wcscameratraps).
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Wellington Camera Traps </summary>
This data set contains 270,450 images from 187 camera locations in Wellington, New Zealand. The cameras (Bushnell 119537, 119476, and 119436) recorded sequences of three images when triggered. Each sequence was labelled by citizen scientists and/or professional ecologists from Victoria University of Wellington into 17 classes: 15 animal categories (for example, the most common labels are bird, cat, and hedgehog), empty, and unclassifiable. Approximately 17% of images are labeled as empty. Images within each sequence share the same species label (even though the animal may not have been recorded in all three images).
If you use this data set, please cite the associated manuscript:
```bibtex
@article{anton2018monitoring,
title={Monitoring the mammalian fauna of urban areas using remote cameras and citizen science},
author={Anton, Victor and Hartley, Stephen and Geldenhuis, Andre and Wittmer, Heiko U},
journal={Journal of Urban Ecology},
volume={4},
number={1},
pages={juy002},
year={2018},
publisher={Oxford University Press}
}
```
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
For questions about this data set, contact [Victor Anton]([email protected]).
</details>
<details>
<summary> Island Conservation Camera Traps </summary>
This data set contains approximately 123,000 camera trap images from 123 camera locations from 7 islands in 6 countries. Data were provided by Island Conservation during projects conducted to prevent the extinction of threatened species on islands.
The most common classes are rabbit, rat, petrel, iguana, cat, goat, and pig, with both rat and cat represented between multiple island sites representing significantly different ecosystems (tropical forest, dry forest, and temperate forests). Additionally, this data set represents data from locations and ecosystems that, to our knowledge, are not well represented in publicly available datasets including >1,000 images each of iguanas, petrels, and shearwaters. A complete list of classes and associated image counts is available here. Approximately 60% of the images are empty. We have also included approximately 65,000 bounding box annotations for about 50,000 images.
In general cameras were dispersed across each project site to detect the presence of invasive vertebrate species that threaten native island species. Cameras were set to capture bursts of photos for each motion detection event (between three and eight photos) with a set delay between events (10 to 30 seconds) to minimize the number of photos. Images containing humans are referred to in metadata, but are not included in the data files.
For questions about this data set, contact [David Will]([email protected]) at Island Conservation.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.
</details>
<details>
<summary> Channel Islands Camera Traps </summary>
This data set contains 246,529 camera trap images from 73 camera locations in the Channel Islands, California. All animals are annotated with bounding boxes. Data were provided by The Nature Conservancy. Animals are classified as rodent1 (82914), fox (48150), bird (11099), skunk (1071), or other (159). 114,949 images (47%) are empty. All images of rats were taken on islands already known to have rat populations.
If you use these data in a publication or report, please use the following citation:
The Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset.
For questions about this data set, contact [Nathaniel Rindlaub]([email protected]) at The Nature Conservancy.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata.
</details>
<details>
<summary> Idaho Camera Traps </summary>
This data set contains approximately 1.5 million camera trap images from Idaho. Labels are provided for 62 categories, most of which are animal classes (“deer”, “elk”, and “cattle” are the most common animal classes), but labels also include some state indicators (e.g. “snow on lens”, “foggy lens”). Approximately 70.5% of images are labeled as empty. Annotations were assigned to image sequences, rather than individual images, so annotations are meaningful only at the sequence level.
The metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs).
Images were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication.
</details>
<details>
<summary> Snapshot Serengeti </summary>
This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the [Snapshot Serengeti project](https://snapshotserengeti.org/) -- the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem.
Labels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2.1.species_list.csv). We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images.
The images and species-level labels are described in more detail in the associated manuscript:
```bibtex
@misc{dryad_5pt92,
title = {Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna},
author = {Swanson, AB and Kosmala, M and Lintott, CJ and Simpson, RJ and Smith, A and Packer, C},
year = {2015},
journal = {Scientific Data},
URL = {https://doi.org/10.5061/dryad.5pt92},
doi = {doi:10.5061/dryad.5pt92},
publisher = {Dryad Digital Repository}
}
```
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Karoo </summary>
This data set contains 14889 sequences of camera trap images, totaling 38074 images, from the [Snapshot Karoo](https://www.zooniverse.org/projects/shuebner729/snapshot-karoo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Karoo National Park, located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species.
Labels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Kgalagadi </summary>
This data set contains 3611 sequences of camera trap images, totaling 10222 images, from the [Snapshot Kgalagadi](https://www.zooniverse.org/projects/shuebner729/snapshot-kgalagadi/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. The Kgalagadi Transfrontier Park stretches from the Namibian border across South Africa and into Botswana, covering a landscape commonly referred to as the Kalahari – an arid savanna. This region is of great interest to help us understand how animals cope with extreme temperatures at both ends of the scale.
Labels are provided for 31 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, birdother, and ostrich). Approximately 76.14% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Enonkishu </summary>
This data set contains 13301 sequences of camera trap images, totaling 28544 images, from the [Snapshot Enonkishu](https://www.zooniverse.org/projects/aguthmann/snapshot-enonkishu) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Enonkishu Conservancy is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies.
Labels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra). Approximately 64.76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Camdeboo </summary>
This data set contains 12132 sequences of camera trap images, totaling 30227 images, from the [Snapshot Camdeboo](https://www.zooniverse.org/projects/shuebner729/snapshot-camdeboo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Camdeboo National Park, South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species.
Labels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich). Approximately 43.74% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Mountain Zebra </summary>
This data set contains 71688 sequences of camera trap images, totaling 73034 images, from the [Snapshot Mountain Zebra](https://www.zooniverse.org/projects/meredithspalmer/snapshot-mountain-zebra/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Mountain Zebra National Park is located in the Eastern Cape of South Africa in a transitional area between several distinct biomes, which means it is home to many endemic species. As the name suggests, this park contains the largest remnant population of Cape Mountain zebras, ~700 as of 2019 and increasing steadily every year.
Labels are provided for 54 categories, primarily at the species level (for example, the most common labels are zebramountain, kudu, and springbok). Approximately 91.23% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Kruger </summary>
This data set contains 4747 sequences of camera trap images, totaling 10072 images, from the [Snapshot Kruger](https://www.zooniverse.org/projects/shuebner729/snapshot-kruger) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Kruger National Park, South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation.
Labels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> SWG Camera Traps </summary>
This data set contains 436,617 sequences of camera trap images from 982 locations in Vietnam and Lao, totaling 2,039,657 images. Labels are provided for 120 categories, primarily at the species level (for example, the most common labels are “Eurasian Wild Pig”, “Large-antlered Muntjac”, and “Unidentified Murid”). Approximately 12.98% of images are labeled as empty. A full list of species and associated image counts is available here. 101,659 bounding boxes are provided on 88,135 images.
This data set is provided by the Saola Working Group; providers include:
- IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group (SWG)
- Asian Arks
- Wildlife Conservation Society (Lao)
- WWF Lao
- Integrated Conservation of Biodiversity and Forests project, Lao (ICBF)
- Center for Environment and Rural Development, Vinh University, Vietnam
If you use these data in a publication or report, please use the following citation:
SWG (2021): Northern and Central Annamites Camera Traps 2.0. IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group. Dataset.
For questions about this data set, contact [email protected].
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Orinoquia Camera Traps </summary>
This data set contains 104,782 images collected from a 50-camera-trap array deployed from January to July 2020 within the private natural reserves El Rey Zamuro (31 km2) and Las Unamas (40 km2), located in the Meta department in the Orinoquía region in central Colombia. We deployed cameras using a stratified random sampling design across forest core area strata. Cameras were spaced 1 km apart from one another, located facing wildlife trails, and deployed with no bait. Images were stored and reviewed by experts using the Wildlife Insights platform.
This data set contains 51 classes, predominantly mammals such as the collared peccary, black agouti, spotted paca, white-lipped peccary, lowland tapir, and giant anteater. Approximately 20% of images are empty.
The main purpose of the study is to understand how humans, wildlife, and domestic animals interact in multi-functional landscapes (e.g., agricultural livestock areas with native forest remnants). However, this data set was also used to review model performance of AI-powered platforms – Wildlife Insights (WI), MegaDetector (MD), and Machine Learning for Wildlife Image Classification (MLWIC2). We provide a demonstration of the use of WI, MD, and MLWIC2 and R code for evaluating model performance of these platforms in the accompanying [GitHub repository](https://github.com/julianavelez1/Processing-Camera-Trap-Data-Using-AI).
If you use these data in a publication or report, please use the following citation:
```bibtex
@article{velez2022choosing,
title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence},
author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John},
journal={arXiv preprint arXiv:2202.02283},
year={2022}
}
```
For questions about this data set, contact [Juliana Velez Gomez]([email protected]).
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
### Supported Tasks and Leaderboards
No leaderboards exist for LILA.
### Languages
The [LILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/) is provided in English.
## Dataset Structure
### Data Instances
The data annotations are provided in [COCO Camera Traps](https://github.com/Microsoft/CameraTraps/blob/master/data_management/README.md#coco-cameratraps-format) format.
All of the datasets share a common category taxonomy, which is defined on the [LILA website](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).
### Data Fields
Different datasets may have slightly varying fields, which include:
`file_name`: the file name \
`width` and `height`: the dimensions of the image \
`study`: which research study the image was collected as part of \
`location` : the name of the location at which the image was taken \
`annotations`: information about image annotation, which includes the taxonomy information, bounding box/boxes (`bbox`/`bboxes`) if any, as well as any other annotation information. \
`image` : the `path` to download the image and any other information that is available, e.g. its size in `bytes`.
### Data Splits
This dataset does not have a predefined train/test split.
## Dataset Creation
### Curation Rationale
The datasets that constitute LILA have been provided by the organizations, projects and researchers who collected them.
### Source Data
#### Initial data collection and normalization
N/A
#### Who are the source language producers?
N/A
### Annotations
#### Annotation process
Each dataset has been annotated by the members of the project/organization that provided it.
#### Who are the annotators?
The annotations have been provided by domain experts in fields such as biology and ecology.
### Personal and Sensitive Information
Some of the original data sets included a “human” class label; for privacy reasons, these images were removed. Those labels are still present in the metadata. If those images are important to your work, contact the [LILA maintainers](mailto:[email protected]), since in some cases it will be possible to release those images under an alternative license.
## Considerations for Using the Data
### Social Impact of Dataset
Machine learning depends on labeled data, but accessing such data in biology and conservation is a challenge. Consequently, everyone benefits when labeled data is made available. Biologists and conservation scientists benefit by having data to train on, and free hosting allows teams to multiply the impact of their data (we suggest listing this benefit in grant proposals that fund data collection). ML researchers benefit by having data to experiment with.
### Discussion of Biases
These datasets do not represent global diversity, but are examples of local ecosystems and animals.
### Other Known Limitations
N/A
## Additional Information
### Tutorial
The [tutorial in this Google Colab notebook](https://colab.research.google.com/drive/17gPOIK-ksxPyX6yP9TaKIimlwf9DYe2R?usp=sharing) demonstrates how to work with this dataset, including filtering by species, collating configurations, and downloading images.
### Working with Taxonomies
All the taxonomy categories are saved as ClassLabels, which can be converted to strings as needed. Strings can likewise be converted to integers as needed, to filter the dataset. In the example below we filter the "Caltech Camera Traps" dataset to find all the entries with a "felis catus" as the species for the first annotation.
```python
dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train")
taxonomy = dataset.features["annotations"].feature["taxonomy"]
# Filters to show only cats
cats = dataset.filter(lambda x: x["annotations"]["taxonomy"][0]["species"] == taxonomy["species"].str2int("felis catus"))
```
The original common names have been saved with their taxonomy mappings in this repository in `common_names_to_tax.json`. These can be used, for example, to map from a taxonomy combination to a common name to help make queries more legible. Note, however, that there is a small number of duplicate common names with different taxonomy values which you will need to disambiguate.
The following example loads the first "sea turtle" in the "Island Conservation Camera Traps" dataset.
```python
LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name")
dataset = load_dataset("society-ethics/lila_camera_traps", "Island Conservation Camera Traps", split="train")
taxonomy = dataset.features["annotations"].feature["taxonomy"]
sea_turtle = LILA_COMMON_NAMES_TO_TAXONOMY.loc["sea turtle"].to_dict()
sea_turtle = {k: taxonomy[k].str2int(v) if v is not None else v for k, v in sea_turtle.items()} # Map to ClassLabel integers
sea_turtle_dataset = ds.filter(lambda x: x["annotations"]["taxonomy"][0] == sea_turtle)
```
The example below selects a random item from the dataset, and then maps from the taxonomy to a common name:
```python
LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name")
dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train")
taxonomy = dataset.features["annotations"].feature["taxonomy"]
random_entry = dataset.shuffle()[0]
filter_taxonomy = random_entry["annotations"]["taxonomy"][0]
filter_keys = list(map(lambda x: (x[0], taxonomy[x[0]].int2str(x[1])), filter(lambda x: x[1] is not None, list(filter_taxonomy.items()))))
if len(filter_keys) > 0:
print(LILA_COMMON_NAMES_TO_TAXONOMY[np.logical_and.reduce([
LILA_COMMON_NAMES_TO_TAXONOMY[k] == v for k,v in filter_keys
])])
else:
print("No common name found for the item.")
```
### Dataset Curators
LILA BC is maintained by a working group that includes representatives from Ecologize, Zooniverse, the Evolving AI Lab, Snapshot Safari, and Microsoft AI for Earth. Hosting on Microsoft Azure is provided by Microsoft AI for Earth.
### Licensing Information
Many, but not all, LILA data sets were released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Check the details of the specific dataset you are using in its section above.
### Citation Information
Citations for each dataset (if they exist) are provided in its section above.
### Contributions
Thanks to [@NimaBoscarino](https://github.com/NimaBoscarino/) for adding this dataset.
|
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piuba-bigdata/contextualized_hate_speech
|
piuba-bigdata
| 2023-04-29T14:19:58Z | 74 | 5 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:es",
"hate_speech",
"arxiv:2210.00465",
"region:us"
] |
[
"text-classification"
] | 2022-11-28T22:12:44Z |
---
language:
- es
pretty_name: contextualized_hate_speech
task_categories:
- text-classification
tags:
- hate_speech
size_categories:
- 10K<n<100K
---
# Contextualized Hate Speech: A dataset of comments in news outlets on Twitter
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper**: ["Assessing the impact of contextual information in hate speech detection"](https://arxiv.org/abs/2210.00465), Juan Manuel Pérez, Franco Luque, Demian Zayat, Martín Kondratzky, Agustín Moro, Pablo Serrati, Joaquín Zajac, Paula Miguel, Natalia Debandi, Agustín Gravano, Viviana Cotik
- **Point of Contact**: jmperez (at) dc uba ar
### Dataset Summary
This dataset is a collection of tweets that were posted in response to news articles from five specific Argentinean news outlets: Clarín, Infobae, La Nación, Perfil and Crónica, during the COVID-19 pandemic. The comments were analyzed for hate speech across eight different characteristics: against women, racist content, class hatred, against LGBTQ+ individuals, against physical appearance, against people with disabilities, against criminals, and for political reasons. All the data is in Spanish.
Each comments is labeled with the following variables
| Label | Description |
| :--------- | :---------------------------------------------------------------------- |
| HATEFUL | Contains hate speech (HS)? |
| CALLS | If it is hateful, is this message calling to (possibly violent) action? |
| WOMEN | Is this against women? |
| LGBTI | Is this against LGBTI people? |
| RACISM | Is this a racist message? |
| CLASS | Is this a classist message? |
| POLITICS | Is this HS due to political ideology? |
| DISABLED | Is this HS against disabled people? |
| APPEARANCE | Is this HS against people due to their appearance? (e.g. fatshaming) |
| CRIMINAL | Is this HS against criminals or people in conflict with law? |
There is an extra label `CALLS`, which represents whether a comment is a call to violent action or not.
### Citation Information
```bibtex
@article{perez2022contextual,
author = {Pérez, Juan Manuel and Luque, Franco M. and Zayat, Demian and Kondratzky, Martín and Moro, Agustín and Serrati, Pablo Santiago and Zajac, Joaquín and Miguel, Paula and Debandi, Natalia and Gravano, Agustín and Cotik, Viviana},
journal = {IEEE Access},
title = {Assessing the Impact of Contextual Information in Hate Speech Detection},
year = {2023},
volume = {11},
number = {},
pages = {30575-30590},
doi = {10.1109/ACCESS.2023.3258973}
}
```
### Contributions
[More Information Needed]
|
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Kaludi/data-food-classification
|
Kaludi
| 2023-01-29T19:34:43Z | 74 | 1 |
[
"task_categories:image-classification",
"region:us"
] |
[
"image-classification"
] | 2023-01-29T06:49:56Z |
---
task_categories:
- image-classification
---
# Dataset for project: food-classification
## Dataset Description
This dataset has been processed for project food-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<308x512 RGB PIL image>",
"target": 0
},
{
"image": "<512x512 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['apple_pie', 'falafel', 'french_toast', 'ice_cream', 'ramen', 'sushi', 'tiramisu'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1050 |
| valid | 350 |
|
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] |
marriamaslova/toxic_dvach
|
marriamaslova
| 2023-04-30T11:08:42Z | 74 | 0 |
[
"task_categories:text-classification",
"language:ru",
"region:us"
] |
[
"text-classification"
] | 2023-04-30T11:00:31Z |
---
task_categories:
- text-classification
language:
- ru
---
|
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jainr3/diffusiondb-pixelart
|
jainr3
| 2023-05-11T18:59:45Z | 74 | 8 |
[
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n>1T",
"source_datasets:modified",
"language:en",
"license:cc0-1.0",
"stable diffusion",
"prompt engineering",
"prompts",
"arxiv:2210.14896",
"region:us"
] |
[
"text-to-image",
"image-to-text"
] | 2023-05-11T17:28:21Z |
---
layout: default
title: Home
nav_order: 1
has_children: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: DiffusionDB-Pixelart
size_categories:
- n>1T
source_datasets:
- modified
tags:
- stable diffusion
- prompt engineering
- prompts
task_categories:
- text-to-image
- image-to-text
task_ids:
- image-captioning
---
# DiffusionDB-Pixelart
## Table of Contents
- [DiffusionDB](#diffusiondb)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Subset](#subset)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Metadata](#dataset-metadata)
- [Metadata Schema](#metadata-schema)
- [Data Splits](#data-splits)
- [Loading Data Subsets](#loading-data-subsets)
- [Method 1: Using Hugging Face Datasets Loader](#method-1-using-hugging-face-datasets-loader)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [DiffusionDB homepage](https://poloclub.github.io/diffusiondb)
- **Repository:** [DiffusionDB repository](https://github.com/poloclub/diffusiondb)
- **Distribution:** [DiffusionDB Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb)
- **Paper:** [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://arxiv.org/abs/2210.14896)
### Dataset Summary
**This is a subset of the DiffusionDB 2M dataset which has been turned into pixel-style art.**
DiffusionDB is the first large-scale text-to-image prompt dataset. It contains **14 million** images generated by Stable Diffusion using prompts and hyperparameters specified by real users.
DiffusionDB is publicly available at [🤗 Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb).
### Supported Tasks and Leaderboards
The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models.
### Languages
The text in the dataset is mostly English. It also contains other languages such as Spanish, Chinese, and Russian.
### Subset
DiffusionDB provides two subsets (DiffusionDB 2M and DiffusionDB Large) to support different needs. The pixelated version of the data was taken from the DiffusionDB 2M and has 2000 examples only.
|Subset|Num of Images|Num of Unique Prompts|Size|Image Directory|Metadata Table|
|:--|--:|--:|--:|--:|--:|
|DiffusionDB-pixelart|2k|~1.5k|~1.6GB|`images/`|`metadata.parquet`|
Images in DiffusionDB-pixelart are stored in `png` format.
## Dataset Structure
We use a modularized file structure to distribute DiffusionDB. The 2k images in DiffusionDB-pixelart are split into folders, where each folder contains 1,000 images and a JSON file that links these 1,000 images to their prompts and hyperparameters.
```bash
# DiffusionDB 2k
./
├── images
│ ├── part-000001
│ │ ├── 3bfcd9cf-26ea-4303-bbe1-b095853f5360.png
│ │ ├── 5f47c66c-51d4-4f2c-a872-a68518f44adb.png
│ │ ├── 66b428b9-55dc-4907-b116-55aaa887de30.png
│ │ ├── [...]
│ │ └── part-000001.json
│ ├── part-000002
│ ├── part-000003
│ ├── [...]
│ └── part-002000
└── metadata.parquet
```
These sub-folders have names `part-0xxxxx`, and each image has a unique name generated by [UUID Version 4](https://en.wikipedia.org/wiki/Universally_unique_identifier). The JSON file in a sub-folder has the same name as the sub-folder. Each image is a `PNG` file (DiffusionDB-pixelart). The JSON file contains key-value pairs mapping image filenames to their prompts and hyperparameters.
### Data Instances
For example, below is the image of `ec9b5e2c-028e-48ac-8857-a52814fd2a06.png` and its key-value pair in `part-000001.json`.
<img width="300" src="https://datasets-server.huggingface.co/assets/jainr3/diffusiondb-pixelart/--/2k_all/train/0/image/image.png">
```json
{
"ec9b5e2c-028e-48ac-8857-a52814fd2a06.png": {
"p": "doom eternal, game concept art, veins and worms, muscular, crustacean exoskeleton, chiroptera head, chiroptera ears, mecha, ferocious, fierce, hyperrealism, fine details, artstation, cgsociety, zbrush, no background ",
"se": 3312523387,
"c": 7.0,
"st": 50,
"sa": "k_euler"
},
}
```
### Data Fields
- key: Unique image name
- `p`: Text
### Dataset Metadata
To help you easily access prompts and other attributes of images without downloading all the Zip files, we include a metadata table `metadata.parquet` for DiffusionDB-pixelart.
Two tables share the same schema, and each row represents an image. We store these tables in the Parquet format because Parquet is column-based: you can efficiently query individual columns (e.g., prompts) without reading the entire table.
Below are three random rows from `metadata.parquet`.
| image_name | prompt | part_id | seed | step | cfg | sampler | width | height | user_name | timestamp | image_nsfw | prompt_nsfw |
|:-----------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------:|-----------:|-------:|------:|----------:|--------:|---------:|:-----------------------------------------------------------------|:--------------------------|-------------:|--------------:|
| 0c46f719-1679-4c64-9ba9-f181e0eae811.png | a small liquid sculpture, corvette, viscous, reflective, digital art | 1050 | 2026845913 | 50 | 7 | 8 | 512 | 512 | c2f288a2ba9df65c38386ffaaf7749106fed29311835b63d578405db9dbcafdb | 2022-08-11 09:05:00+00:00 | 0.0845108 | 0.00383462 |
| a00bdeaa-14eb-4f6c-a303-97732177eae9.png | human sculpture of lanky tall alien on a romantic date at italian restaurant with smiling woman, nice restaurant, photography, bokeh | 905 | 1183522603 | 50 | 10 | 8 | 512 | 768 | df778e253e6d32168eb22279a9776b3cde107cc82da05517dd6d114724918651 | 2022-08-19 17:55:00+00:00 | 0.692934 | 0.109437 |
| 6e5024ce-65ed-47f3-b296-edb2813e3c5b.png | portrait of barbaric spanish conquistador, symmetrical, by yoichi hatakenaka, studio ghibli and dan mumford | 286 | 1713292358 | 50 | 7 | 8 | 512 | 640 | 1c2e93cfb1430adbd956be9c690705fe295cbee7d9ac12de1953ce5e76d89906 | 2022-08-12 03:26:00+00:00 | 0.0773138 | 0.0249675 |
#### Metadata Schema
`metadata.parquet` schema:
|Column|Type|Description|
|:---|:---|:---|
|`image_name`|`string`|Image UUID filename.|
|`text`|`string`|The text prompt used to generate this image.|
> **Warning**
> Although the Stable Diffusion model has an NSFW filter that automatically blurs user-generated NSFW images, this NSFW filter is not perfect—DiffusionDB still contains some NSFW images. Therefore, we compute and provide the NSFW scores for images and prompts using the state-of-the-art models. The distribution of these scores is shown below. Please decide an appropriate NSFW score threshold to filter out NSFW images before using DiffusionDB in your projects.
<img src="https://i.imgur.com/1RiGAXL.png" width="100%">
### Data Splits
For DiffusionDB-pixelart, we split 2k images into folders where each folder contains 1,000 images and a JSON file.
### Loading Data Subsets
DiffusionDB is large! However, with our modularized file structure, you can easily load a desirable number of images and their prompts and hyperparameters. In the [`example-loading.ipynb`](https://github.com/poloclub/diffusiondb/blob/main/notebooks/example-loading.ipynb) notebook, we demonstrate three methods to load a subset of DiffusionDB. Below is a short summary.
#### Method 1: Using Hugging Face Datasets Loader
You can use the Hugging Face [`Datasets`](https://huggingface.co/docs/datasets/quickstart) library to easily load prompts and images from DiffusionDB. We pre-defined 16 DiffusionDB subsets (configurations) based on the number of instances. You can see all subsets in the [Dataset Preview](https://huggingface.co/datasets/poloclub/diffusiondb/viewer/all/train).
```python
import numpy as np
from datasets import load_dataset
# Load the dataset with the `2k_random_1k` subset
dataset = load_dataset('jainr3/diffusiondb-pixelart', '2k_random_1k')
```
## Dataset Creation
### Curation Rationale
Recent diffusion models have gained immense popularity by enabling high-quality and controllable image generation based on text prompts written in natural language. Since the release of these models, people from different domains have quickly applied them to create award-winning artworks, synthetic radiology images, and even hyper-realistic videos.
However, generating images with desired details is difficult, as it requires users to write proper prompts specifying the exact expected results. Developing such prompts requires trial and error, and can often feel random and unprincipled. Simon Willison analogizes writing prompts to wizards learning “magical spells”: users do not understand why some prompts work, but they will add these prompts to their “spell book.” For example, to generate highly-detailed images, it has become a common practice to add special keywords such as “trending on artstation” and “unreal engine” in the prompt.
Prompt engineering has become a field of study in the context of text-to-text generation, where researchers systematically investigate how to construct prompts to effectively solve different down-stream tasks. As large text-to-image models are relatively new, there is a pressing need to understand how these models react to prompts, how to write effective prompts, and how to design tools to help users generate images.
To help researchers tackle these critical challenges, we create DiffusionDB, the first large-scale prompt dataset with 14 million real prompt-image pairs.
### Source Data
#### Initial Data Collection and Normalization
We construct DiffusionDB by scraping user-generated images on the official Stable Diffusion Discord server. We choose Stable Diffusion because it is currently the only open-source large text-to-image generative model, and all generated images have a CC0 1.0 Universal Public Domain Dedication license that waives all copyright and allows uses for any purpose. We choose the official [Stable Diffusion Discord server](https://discord.gg/stablediffusion) because it is public, and it has strict rules against generating and sharing illegal, hateful, or NSFW (not suitable for work, such as sexual and violent content) images. The server also disallows users to write or share prompts with personal information.
#### Who are the source language producers?
The language producers are users of the official [Stable Diffusion Discord server](https://discord.gg/stablediffusion).
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The authors removed the discord usernames from the dataset.
We decide to anonymize the dataset because some prompts might include sensitive information: explicitly linking them to their creators can cause harm to creators.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better understanding of large text-to-image generative models.
The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models.
It should note that we collect images and their prompts from the Stable Diffusion Discord server. The Discord server has rules against users generating or sharing harmful or NSFW (not suitable for work, such as sexual and violent content) images. The Stable Diffusion model used in the server also has an NSFW filter that blurs the generated images if it detects NSFW content. However, it is still possible that some users had generated harmful images that were not detected by the NSFW filter or removed by the server moderators. Therefore, DiffusionDB can potentially contain these images. To mitigate the potential harm, we provide a [Google Form](https://forms.gle/GbYaSpRNYqxCafMZ9) on the [DiffusionDB website](https://poloclub.github.io/diffusiondb/) where users can report harmful or inappropriate images and prompts. We will closely monitor this form and remove reported images and prompts from DiffusionDB.
### Discussion of Biases
The 14 million images in DiffusionDB have diverse styles and categories. However, Discord can be a biased data source. Our images come from channels where early users could use a bot to use Stable Diffusion before release. As these users had started using Stable Diffusion before the model was public, we hypothesize that they are AI art enthusiasts and are likely to have experience with other text-to-image generative models. Therefore, the prompting style in DiffusionDB might not represent novice users. Similarly, the prompts in DiffusionDB might not generalize to domains that require specific knowledge, such as medical images.
### Other Known Limitations
**Generalizability.** Previous research has shown a prompt that works well on one generative model might not give the optimal result when used in other models.
Therefore, different models can need users to write different prompts. For example, many Stable Diffusion prompts use commas to separate keywords, while this pattern is less seen in prompts for DALL-E 2 or Midjourney. Thus, we caution researchers that some research findings from DiffusionDB might not be generalizable to other text-to-image generative models.
## Additional Information
### Dataset Curators
DiffusionDB is created by [Jay Wang](https://zijie.wang), [Evan Montoya](https://www.linkedin.com/in/evan-montoya-b252391b4/), [David Munechika](https://www.linkedin.com/in/dmunechika/), [Alex Yang](https://alexanderyang.me), [Ben Hoover](https://www.bhoov.com), [Polo Chau](https://faculty.cc.gatech.edu/~dchau/).
### Licensing Information
The DiffusionDB dataset is available under the [CC0 1.0 License](https://creativecommons.org/publicdomain/zero/1.0/).
The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE).
### Citation Information
```bibtex
@article{wangDiffusionDBLargescalePrompt2022,
title = {{{DiffusionDB}}: {{A}} Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models},
author = {Wang, Zijie J. and Montoya, Evan and Munechika, David and Yang, Haoyang and Hoover, Benjamin and Chau, Duen Horng},
year = {2022},
journal = {arXiv:2210.14896 [cs]},
url = {https://arxiv.org/abs/2210.14896}
}
```
### Contributions
If you have any questions, feel free to [open an issue](https://github.com/poloclub/diffusiondb/issues/new) or contact the original author [Jay Wang](https://zijie.wang).
|
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] |
Yuhthe/vietnews_word
|
Yuhthe
| 2023-07-26T03:00:48Z | 74 | 0 |
[
"task_categories:summarization",
"language:vi",
"region:us"
] |
[
"summarization"
] | 2023-07-25T10:14:56Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: guid
dtype: int64
- name: title
dtype: string
- name: abstract
dtype: string
- name: article
dtype: string
splits:
- name: train
num_bytes: 325418455
num_examples: 99134
- name: validation
num_bytes: 73397317
num_examples: 22184
- name: test
num_bytes: 74536959
num_examples: 22498
download_size: 246524133
dataset_size: 473352731
task_categories:
- summarization
language:
- vi
---
# Dataset Card for "vietnews_word"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
HydraLM/biology_dataset_alpaca
|
HydraLM
| 2023-07-27T18:43:14Z | 74 | 0 |
[
"region:us"
] | null | 2023-07-27T18:43:04Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 59941674
num_examples: 19999
download_size: 28644935
dataset_size: 59941674
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "biology_dataset_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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google/dreambooth
|
google
| 2023-08-15T16:46:24Z | 74 | 37 |
[
"license:cc-by-4.0",
"arxiv:2208.12242",
"region:us"
] | null | 2023-08-15T16:24:32Z |
---
configs:
- config_name: default
data_files:
- split: train
path: "dataset/backpack/*.jpg"
- config_name: backpack
data_files:
- split: train
path: "dataset/backpack/*.jpg"
- config_name: backpack_dog
data_files:
- split: train
path: "dataset/backpack_dog/*.jpg"
- config_name: bear_plushie
data_files:
- split: train
path: "dataset/bear_plushie/*.jpg"
- config_name: berry_bowl
data_files:
- split: train
path: "dataset/berry_bowl/*.jpg"
- config_name: can
data_files:
- split: train
path: "dataset/can/*.jpg"
- config_name: candle
data_files:
- split: train
path: "dataset/candle/*.jpg"
- config_name: cat
data_files:
- split: train
path: "dataset/cat/*.jpg"
- config_name: cat2
data_files:
- split: train
path: "dataset/cat2/*.jpg"
- config_name: clock
data_files:
- split: train
path: "dataset/clock/*.jpg"
- config_name: colorful_sneaker
data_files:
- split: train
path: "dataset/colorful_sneaker/*.jpg"
- config_name: dog
data_files:
- split: train
path: "dataset/dog/*.jpg"
- config_name: dog2
data_files:
- split: train
path: "dataset/dog2/*.jpg"
- config_name: dog3
data_files:
- split: train
path: "dataset/dog3/*.jpg"
- config_name: dog5
data_files:
- split: train
path: "dataset/dog5/*.jpg"
- config_name: dog6
data_files:
- split: train
path: "dataset/dog6/*.jpg"
- config_name: dog7
data_files:
- split: train
path: "dataset/dog7/*.jpg"
- config_name: dog8
data_files:
- split: train
path: "dataset/dog8/*.jpg"
- config_name: duck_toy
data_files:
- split: train
path: "dataset/duck_toy/*.jpg"
- config_name: fancy_boot
data_files:
- split: train
path: "dataset/fancy_boot/*.jpg"
- config_name: grey_sloth_plushie
data_files:
- split: train
path: "dataset/grey_sloth_plushie/*.jpg"
- config_name: monster_toy
data_files:
- split: train
path: "dataset/monster_toy/*.jpg"
- config_name: pink_sunglasses
data_files:
- split: train
path: "dataset/pink_sunglasses/*.jpg"
- config_name: poop_emoji
data_files:
- split: train
path: "dataset/poop_emoji/*.jpg"
- config_name: rc_car
data_files:
- split: train
path: "dataset/rc_car/*.jpg"
- config_name: red_cartoon
data_files:
- split: train
path: "dataset/red_cartoon/*.jpg"
- config_name: robot_toy
data_files:
- split: train
path: "dataset/robot_toy/*.jpg"
- config_name: shiny_sneaker
data_files:
- split: train
path: "dataset/shiny_sneaker/*.jpg"
- config_name: teapot
data_files:
- split: train
path: "dataset/teapot/*.jpg"
- config_name: vase
data_files:
- split: train
path: "dataset/vase/*.jpg"
- config_name: wolf_plushie
data_files:
- split: train
path: "dataset/wolf_plushie/*.jpg"
license: cc-by-4.0
---
# Dataset Card for "dreambooth"
## Dataset of the Google paper DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
The dataset includes 30 subjects of 15 different classes. 9 out of these subjects are live subjects (dogs and cats) and 21 are objects. The dataset contains a variable number of images per subject (4-6). Images of the subjects are usually captured in different conditions, environments and under different angles.
We include a file dataset/prompts\_and\_classes.txt which contains all of the prompts used in the paper for live subjects and objects, as well as the class name used for the subjects.
The images have either been captured by the paper authors, or sourced from www.unsplash.com
The dataset/references\_and\_licenses.txt file contains a list of all the reference links to the images in www.unsplash.com - and attribution to the photographer, along with the license of the image.
### [project page](https://dreambooth.github.io/) | [arxiv](https://arxiv.org/abs/2208.12242)
## Academic Citation
If you use this work please cite:
```
@inproceedings{ruiz2023dreambooth,
title={Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation},
author={Ruiz, Nataniel and Li, Yuanzhen and Jampani, Varun and Pritch, Yael and Rubinstein, Michael and Aberman, Kfir},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023}
}
```
## Disclaimer
This is not an officially supported Google product.
|
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hippocrates/medMCQA_test
|
hippocrates
| 2023-10-12T16:37:02Z | 74 | 0 |
[
"region:us"
] | null | 2023-10-03T12:29:51Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 75747167
num_examples: 182822
- name: valid
num_bytes: 1799482
num_examples: 4183
- name: test
num_bytes: 1799482
num_examples: 4183
download_size: 26861287
dataset_size: 79346131
---
# Dataset Card for "medMCQA_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ProlificAI/social-reasoning-rlhf
|
ProlificAI
| 2023-10-11T08:50:59Z | 74 | 5 |
[
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"human-feedback",
"rlhf",
"region:us"
] |
[
"text-generation"
] | 2023-10-10T23:45:21Z |
---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: Social Reasoning RLHF
size_categories:
- 1K<n<10K
tags:
- human-feedback
- rlhf
---
## Dataset Summary
This repository provides access to a social reasoning dataset that aims to provide signal to how humans navigate social situations, how they reason about them and how they understand each other. It contains questions probing people's thinking and understanding of various social situations.
This dataset was created by collating a set of questions within the following social reasoning tasks:
* understanding of emotions
* intent recognition
* social norms
* social responsibility
* reading of social cues
* perspective taking
* conflict resolution
* ethics
* moral judgement
* communication skills
* negotiation strategies
* understanding of empathy
* understanding of compassion
* understanding of trust
* understanding and use of humour
* showing kindness
* navigating diversity and cultural differences
* use of figurative language
* self-awareness
We asked a group of participants to provide their responses to the given questions, then we asked another group of participants to rate their responses in a pairwise comparison setting. The format of the dataset is as following:
```json
{
"question": "Question",
"chosen": "The chosen response",
"rejected": "The rejected response"
}
```
## Disclaimer
The guidelines encouraged participants to provide respectful, empathetic and inclusive responses, however the dataset may still contain responses that some may find offensive or upsetting.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("ProlificAI/social-reasoning-rlhf")
```
## About Prolific
Robust AI is built on high-quality human data. [Prolific](https://www.prolific.com/) makes it easy to get honest, accurate feedback on your models, from our balanced and vetted pool of taskers.
### Contact
Got any questions? Email [email protected]
|
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open-llm-leaderboard/details_huggingtweets__gladosystem
|
open-llm-leaderboard
| 2023-10-13T03:18:51Z | 74 | 0 |
[
"region:us"
] | null | 2023-10-13T03:18:43Z |
---
pretty_name: Evaluation run of huggingtweets/gladosystem
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [huggingtweets/gladosystem](https://huggingface.co/huggingtweets/gladosystem)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_huggingtweets__gladosystem\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-13T03:18:40.922910](https://huggingface.co/datasets/open-llm-leaderboard/details_huggingtweets__gladosystem/blob/main/results_2023-10-13T03-18-40.922910.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.010276845637583893,\n\
\ \"em_stderr\": 0.0010328242665282317,\n \"f1\": 0.014896182885906039,\n\
\ \"f1_stderr\": 0.0011273085873104653,\n \"acc\": 0.2533543804262036,\n\
\ \"acc_stderr\": 0.0070256103461651745\n },\n \"harness|drop|3\":\
\ {\n \"em\": 0.010276845637583893,\n \"em_stderr\": 0.0010328242665282317,\n\
\ \"f1\": 0.014896182885906039,\n \"f1_stderr\": 0.0011273085873104653\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5067087608524072,\n\
\ \"acc_stderr\": 0.014051220692330349\n }\n}\n```"
repo_url: https://huggingface.co/huggingtweets/gladosystem
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T03_18_40.922910
path:
- '**/details_harness|drop|3_2023-10-13T03-18-40.922910.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T03-18-40.922910.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T03_18_40.922910
path:
- '**/details_harness|gsm8k|5_2023-10-13T03-18-40.922910.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T03-18-40.922910.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T03_18_40.922910
path:
- '**/details_harness|winogrande|5_2023-10-13T03-18-40.922910.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T03-18-40.922910.parquet'
- config_name: results
data_files:
- split: 2023_10_13T03_18_40.922910
path:
- results_2023-10-13T03-18-40.922910.parquet
- split: latest
path:
- results_2023-10-13T03-18-40.922910.parquet
---
# Dataset Card for Evaluation run of huggingtweets/gladosystem
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/huggingtweets/gladosystem
- **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 [huggingtweets/gladosystem](https://huggingface.co/huggingtweets/gladosystem) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_huggingtweets__gladosystem",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T03:18:40.922910](https://huggingface.co/datasets/open-llm-leaderboard/details_huggingtweets__gladosystem/blob/main/results_2023-10-13T03-18-40.922910.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.010276845637583893,
"em_stderr": 0.0010328242665282317,
"f1": 0.014896182885906039,
"f1_stderr": 0.0011273085873104653,
"acc": 0.2533543804262036,
"acc_stderr": 0.0070256103461651745
},
"harness|drop|3": {
"em": 0.010276845637583893,
"em_stderr": 0.0010328242665282317,
"f1": 0.014896182885906039,
"f1_stderr": 0.0011273085873104653
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5067087608524072,
"acc_stderr": 0.014051220692330349
}
}
```
### 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_OpenBuddy__openbuddy-openllama-13b-v7-fp16
|
open-llm-leaderboard
| 2023-10-14T17:51:36Z | 74 | 0 |
[
"region:us"
] | null | 2023-10-13T09:46:56Z |
---
pretty_name: Evaluation run of OpenBuddy/openbuddy-openllama-13b-v7-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OpenBuddy/openbuddy-openllama-13b-v7-fp16](https://huggingface.co/OpenBuddy/openbuddy-openllama-13b-v7-fp16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_OpenBuddy__openbuddy-openllama-13b-v7-fp16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-14T17:51:28.265681](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-openllama-13b-v7-fp16/blob/main/results_2023-10-14T17-51-28.265681.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.13496224832214765,\n\
\ \"em_stderr\": 0.00349915623734624,\n \"f1\": 0.19493917785234854,\n\
\ \"f1_stderr\": 0.0036402036609824453,\n \"acc\": 0.39774068872582313,\n\
\ \"acc_stderr\": 0.010563523906790405\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.13496224832214765,\n \"em_stderr\": 0.00349915623734624,\n\
\ \"f1\": 0.19493917785234854,\n \"f1_stderr\": 0.0036402036609824453\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09855951478392722,\n \
\ \"acc_stderr\": 0.008210320350946331\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.696921862667719,\n \"acc_stderr\": 0.012916727462634477\n\
\ }\n}\n```"
repo_url: https://huggingface.co/OpenBuddy/openbuddy-openllama-13b-v7-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T09_46_52.076737
path:
- '**/details_harness|drop|3_2023-10-13T09-46-52.076737.parquet'
- split: 2023_10_14T17_51_28.265681
path:
- '**/details_harness|drop|3_2023-10-14T17-51-28.265681.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-14T17-51-28.265681.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T09_46_52.076737
path:
- '**/details_harness|gsm8k|5_2023-10-13T09-46-52.076737.parquet'
- split: 2023_10_14T17_51_28.265681
path:
- '**/details_harness|gsm8k|5_2023-10-14T17-51-28.265681.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-14T17-51-28.265681.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T09_46_52.076737
path:
- '**/details_harness|winogrande|5_2023-10-13T09-46-52.076737.parquet'
- split: 2023_10_14T17_51_28.265681
path:
- '**/details_harness|winogrande|5_2023-10-14T17-51-28.265681.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-14T17-51-28.265681.parquet'
- config_name: results
data_files:
- split: 2023_10_13T09_46_52.076737
path:
- results_2023-10-13T09-46-52.076737.parquet
- split: 2023_10_14T17_51_28.265681
path:
- results_2023-10-14T17-51-28.265681.parquet
- split: latest
path:
- results_2023-10-14T17-51-28.265681.parquet
---
# Dataset Card for Evaluation run of OpenBuddy/openbuddy-openllama-13b-v7-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OpenBuddy/openbuddy-openllama-13b-v7-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [OpenBuddy/openbuddy-openllama-13b-v7-fp16](https://huggingface.co/OpenBuddy/openbuddy-openllama-13b-v7-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_OpenBuddy__openbuddy-openllama-13b-v7-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-14T17:51:28.265681](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-openllama-13b-v7-fp16/blob/main/results_2023-10-14T17-51-28.265681.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.13496224832214765,
"em_stderr": 0.00349915623734624,
"f1": 0.19493917785234854,
"f1_stderr": 0.0036402036609824453,
"acc": 0.39774068872582313,
"acc_stderr": 0.010563523906790405
},
"harness|drop|3": {
"em": 0.13496224832214765,
"em_stderr": 0.00349915623734624,
"f1": 0.19493917785234854,
"f1_stderr": 0.0036402036609824453
},
"harness|gsm8k|5": {
"acc": 0.09855951478392722,
"acc_stderr": 0.008210320350946331
},
"harness|winogrande|5": {
"acc": 0.696921862667719,
"acc_stderr": 0.012916727462634477
}
}
```
### 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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hippocrates/PubmedSumm_test
|
hippocrates
| 2023-10-17T19:54:03Z | 74 | 0 |
[
"region:us"
] | null | 2023-10-13T10:51:14Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2293491121
num_examples: 119924
- name: valid
num_bytes: 129680450
num_examples: 6633
- name: test
num_bytes: 129463253
num_examples: 6658
download_size: 1172343963
dataset_size: 2552634824
---
# Dataset Card for "PubmedSumm_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B
|
open-llm-leaderboard
| 2023-10-13T11:52:07Z | 74 | 0 |
[
"region:us"
] | null | 2023-10-13T11:51:58Z |
---
pretty_name: Evaluation run of LLMs/Stable-Vicuna-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [LLMs/Stable-Vicuna-13B](https://huggingface.co/LLMs/Stable-Vicuna-13B) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-13T11:51:54.162285](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B/blob/main/results_2023-10-13T11-51-54.162285.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.012688758389261746,\n\
\ \"em_stderr\": 0.0011462418380586343,\n \"f1\": 0.06941170302013412,\n\
\ \"f1_stderr\": 0.0017195070383295536,\n \"acc\": 0.2849250197316496,\n\
\ \"acc_stderr\": 0.006957342547358349\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.012688758389261746,\n \"em_stderr\": 0.0011462418380586343,\n\
\ \"f1\": 0.06941170302013412,\n \"f1_stderr\": 0.0017195070383295536\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5698500394632992,\n\
\ \"acc_stderr\": 0.013914685094716698\n }\n}\n```"
repo_url: https://huggingface.co/LLMs/Stable-Vicuna-13B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T11_51_54.162285
path:
- '**/details_harness|drop|3_2023-10-13T11-51-54.162285.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T11-51-54.162285.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T11_51_54.162285
path:
- '**/details_harness|gsm8k|5_2023-10-13T11-51-54.162285.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T11-51-54.162285.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T11_51_54.162285
path:
- '**/details_harness|winogrande|5_2023-10-13T11-51-54.162285.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T11-51-54.162285.parquet'
- config_name: results
data_files:
- split: 2023_10_13T11_51_54.162285
path:
- results_2023-10-13T11-51-54.162285.parquet
- split: latest
path:
- results_2023-10-13T11-51-54.162285.parquet
---
# Dataset Card for Evaluation run of LLMs/Stable-Vicuna-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/LLMs/Stable-Vicuna-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 [LLMs/Stable-Vicuna-13B](https://huggingface.co/LLMs/Stable-Vicuna-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T11:51:54.162285](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B/blob/main/results_2023-10-13T11-51-54.162285.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.012688758389261746,
"em_stderr": 0.0011462418380586343,
"f1": 0.06941170302013412,
"f1_stderr": 0.0017195070383295536,
"acc": 0.2849250197316496,
"acc_stderr": 0.006957342547358349
},
"harness|drop|3": {
"em": 0.012688758389261746,
"em_stderr": 0.0011462418380586343,
"f1": 0.06941170302013412,
"f1_stderr": 0.0017195070383295536
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5698500394632992,
"acc_stderr": 0.013914685094716698
}
}
```
### 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_golaxy__gogpt-560m
|
open-llm-leaderboard
| 2023-10-14T16:13:40Z | 74 | 0 |
[
"region:us"
] | null | 2023-10-14T16:13:32Z |
---
pretty_name: Evaluation run of golaxy/gogpt-560m
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [golaxy/gogpt-560m](https://huggingface.co/golaxy/gogpt-560m) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_golaxy__gogpt-560m\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-14T16:13:28.692590](https://huggingface.co/datasets/open-llm-leaderboard/details_golaxy__gogpt-560m/blob/main/results_2023-10-14T16-13-28.692590.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0382760067114094,\n\
\ \"em_stderr\": 0.001964844510611307,\n \"f1\": 0.06699035234899327,\n\
\ \"f1_stderr\": 0.0021908023180713283,\n \"acc\": 0.2537490134175217,\n\
\ \"acc_stderr\": 0.00702545276061429\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0382760067114094,\n \"em_stderr\": 0.001964844510611307,\n\
\ \"f1\": 0.06699035234899327,\n \"f1_stderr\": 0.0021908023180713283\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5074980268350434,\n\
\ \"acc_stderr\": 0.01405090552122858\n }\n}\n```"
repo_url: https://huggingface.co/golaxy/gogpt-560m
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_14T16_13_28.692590
path:
- '**/details_harness|drop|3_2023-10-14T16-13-28.692590.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-14T16-13-28.692590.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_14T16_13_28.692590
path:
- '**/details_harness|gsm8k|5_2023-10-14T16-13-28.692590.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-14T16-13-28.692590.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_14T16_13_28.692590
path:
- '**/details_harness|winogrande|5_2023-10-14T16-13-28.692590.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-14T16-13-28.692590.parquet'
- config_name: results
data_files:
- split: 2023_10_14T16_13_28.692590
path:
- results_2023-10-14T16-13-28.692590.parquet
- split: latest
path:
- results_2023-10-14T16-13-28.692590.parquet
---
# Dataset Card for Evaluation run of golaxy/gogpt-560m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/golaxy/gogpt-560m
- **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 [golaxy/gogpt-560m](https://huggingface.co/golaxy/gogpt-560m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_golaxy__gogpt-560m",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-14T16:13:28.692590](https://huggingface.co/datasets/open-llm-leaderboard/details_golaxy__gogpt-560m/blob/main/results_2023-10-14T16-13-28.692590.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0382760067114094,
"em_stderr": 0.001964844510611307,
"f1": 0.06699035234899327,
"f1_stderr": 0.0021908023180713283,
"acc": 0.2537490134175217,
"acc_stderr": 0.00702545276061429
},
"harness|drop|3": {
"em": 0.0382760067114094,
"em_stderr": 0.001964844510611307,
"f1": 0.06699035234899327,
"f1_stderr": 0.0021908023180713283
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5074980268350434,
"acc_stderr": 0.01405090552122858
}
}
```
### 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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Ramitha/spanish-legal-data-lite
|
Ramitha
| 2023-11-03T15:47:07Z | 74 | 0 |
[
"region:us"
] | null | 2023-11-02T22:01:55Z |
---
dataset_info:
features:
- name: Data
dtype: string
splits:
- name: train
num_bytes: 122971
num_examples: 501
download_size: 62737
dataset_size: 122971
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "spanish-legal-data-lite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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hippocrates/HoC_test
|
hippocrates
| 2023-11-14T16:06:18Z | 74 | 0 |
[
"region:us"
] | null | 2023-11-14T16:06:15Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: train
num_bytes: 3062367
num_examples: 1108
- name: valid
num_bytes: 430760
num_examples: 157
- name: test
num_bytes: 863678
num_examples: 315
download_size: 1496963
dataset_size: 4356805
---
# Dataset Card for "HoC_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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atmallen/qm_alice_grader_last_1.0e
|
atmallen
| 2023-11-16T18:22:29Z | 74 | 0 |
[
"region:us"
] | null | 2023-11-16T03:25:17Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 14970044.0
num_examples: 200000
- name: validation
num_bytes: 1501418.0
num_examples: 20000
- name: test
num_bytes: 1502170.0
num_examples: 20000
download_size: 0
dataset_size: 17973632.0
---
# Dataset Card for "qm_alice__grader_last_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
NextDayAI/MultipleResponsesChat_labeled_unlabeled_all_engines_20230901_20231127
|
NextDayAI
| 2023-12-01T14:50:34Z | 74 | 0 |
[
"region:us"
] | null | 2023-12-01T14:50:11Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 665854184.3179306
num_examples: 219006
- name: valid
num_bytes: 15201733.841034733
num_examples: 5000
- name: test
num_bytes: 15201733.841034733
num_examples: 5000
download_size: 438076229
dataset_size: 696257652.0000001
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
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ByteSized/EduText
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ByteSized
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[
"license:mit",
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] | null | 2023-12-02T07:21:33Z |
---
license: mit
---
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lavis-nlp/german_legal_sentences
|
lavis-nlp
| 2022-10-20T18:34:19Z | 73 | 3 |
[
"task_categories:text-retrieval",
"task_ids:semantic-similarity-scoring",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n>1M",
"source_datasets:original",
"language:de",
"license:unknown",
"arxiv:2005.13342",
"arxiv:2010.10252",
"region:us"
] |
[
"text-retrieval",
"text-scoring"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- de
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n>1M
source_datasets:
- original
task_categories:
- text-retrieval
- text-scoring
task_ids:
- semantic-similarity-scoring
- text-retrieval-other-example-based-retrieval
---
# Dataset Card for German Legal Sentences
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://lavis-nlp.github.io/german_legal_sentences/
- **Repository:** https://github.com/lavis-nlp/german_legal_sentences
- **Paper:** coming soon
- **Leaderboard:**
- **Point of Contact:** [Marco Wrzalik](mailto:[email protected])
### Dataset Summary
German Legal Sentences (GLS) is an automatically generated training dataset for semantic sentence matching and citation recommendation in the domain in german legal documents. It follows the concept of weak supervision, where imperfect labels are generated using multiple heuristics. For this purpose we use a combination of legal citation matching and BM25 similarity. The contained sentences and their citations are parsed from real judicial decisions provided by [Open Legal Data](http://openlegaldata.io/) (https://arxiv.org/abs/2005.13342).
### Supported Tasks and Leaderboards
The main associated task is *Semantic Similarity Ranking*. We propose to use the *Mean Reciprocal Rank* (MRR) cut at the tenth position as well as MAP and Recall on Rankings of size 200. As baselines we provide the follows:
| Method | MRR@10 | MAP@200 | Recall@200 |
|-----------------------------------|---------:|-----------:|------------:|
| BM25 - default `(k1=1.2; b=0.75)` | 25.7 | 17.6 | 42.9 |
| BM25 - tuned `(k1=0.47; b=0.97)` | 26.2 | 18.1 | 43.3 |
| [CoRT](https://arxiv.org/abs/2010.10252) | 31.2 | 21.4 | 56.2 |
| [CoRT + BM25](https://arxiv.org/abs/2010.10252) | 32.1 | 22.1 | 67.1 |
In addition, we want to support a *Citation Recommendation* task in the future.
If you wish to contribute evaluation measures or give any suggestion or critique, please write an [e-mail](mailto:[email protected]).
### Languages
This dataset contains texts from the specific domain of German court decisions.
## Dataset Structure
### Data Instances
```
{'query.doc_id': 28860,
'query.ref_ids': [6215, 248, 248],
'query.sent_id': 304863,
'query.text': 'Zudem ist zu berücksichtigen , dass die Vollverzinsung nach '
'[REF] i. V. m. [REF] gleichermaßen zugunsten wie zulasten des '
'Steuerpflichtigen wirkt , sodass bei einer Überzahlung durch '
'den Steuerpflichtigen der Staat dem Steuerpflichtigen neben '
'der Erstattung ebenfalls den entstandenen potentiellen Zins- '
'und Liquiditätsnachteil in der pauschalierten Höhe des [REF] '
'zu ersetzen hat , unabhängig davon , in welcher Höhe dem '
'Berechtigten tatsächlich Zinsen entgangen sind .',
'related.doc_id': 56348,
'related.ref_ids': [248, 6215, 62375],
'related.sent_id': 558646,
'related.text': 'Ferner ist zu berücksichtigen , dass der Zinssatz des [REF] '
'im Rahmen des [REF] sowohl für Steuernachforderung wie auch '
'für Steuererstattungen und damit gleichermaßen zugunsten wie '
'zulasten des Steuerpflichtigen wirkt , Vgl. BVerfG , '
'Nichtannahmebeschluss vom [DATE] [REF] , juris , mit der '
'Folge , dass auch Erstattungsansprüche unabhängig davon , ob '
'und in welcher Höhe dem Berechtigten tatsächlich Zinsen '
'entgangen sind , mit monatlich 0,0 % verzinst werden .'}
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The documents we take from [Open Legal Data](http://openlegaldata.io/) (https://arxiv.org/abs/2005.13342) are first preprocessed by removing line breaks, enumeration characters and headings. Afterwards we parse legal citations using hand-crafted regular expressions. Each citation is split into it components and normalized, thus different variants of the same citation are matched together. For instance, "§211 Absatz 1 des Strafgesetzbuches" is normalized to "§ 211 Abs. 1 StGB". Every time we discover an unknown citation, we assign an unique id to it. We use these ids to replace parsed citations in the document text with a simple reference tag containing this id (e.g `[REF321]`). At the same time we parse dates and replace them with the date tag `[DATE]`. Both remove dots which can may be confused with the end of a sentence, which makes the next stage easier.
We use [SoMaJo](https://github.com/tsproisl/SoMaJo) to perform sentence tokenizing on the pre-processed documents. Each sentence that does not contain at least one legal citation is discarded. For the rest we assign sentence ids, remove all reference ids from them as well as any contents in braces (braces often contain large enumerations of citations and their sources). At the same time we keep track of the corresponding document from which a sentence originates and which references occur in it.
#### Who are the source language producers?
The source language originates in the context of German court proceedings.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The annotations are machine-generated.
### Personal and Sensitive Information
The source documents are already public and anonymized.
## Considerations for Using the Data
### Social Impact of Dataset
With this dataset, we strive towards better accessibility of court decisions to the general public by accelerating research on semantic search technologies. We hope that emerging search technologies will enable the layperson to find relevant information without knowing the specific terms used by lawyers.
### 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
Coming soon!
### Contributions
Thanks to [@mwrzalik](https://github.com/mwrzalik) for adding this dataset.
|
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] |
qwant/squad_fr
|
qwant
| 2023-04-19T14:37:09Z | 73 | 6 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
"language:fr",
"license:cc-by-4.0",
"region:us"
] |
[
"question-answering"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- fr
license:
- cc-by-4.0
multilinguality:
- monolingual
- translation
paperswithcode_id: squad
pretty_name: SQuAD-fr
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
- closed-domain-qa
---
# Dataset Card for "squad_fr"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Paper:** [On the Usability of Transformers-based models for a French Question-Answering task](https://hal.archives-ouvertes.fr/hal-03336060)
- **Size of downloaded dataset files:** 10 MB
- **Size of the generated dataset:** 73 MB
- **Total amount of disk used:** 83 MB
### Dataset Summary
SQuAD-fr:
- a translated version of the Stanford Question Answering Dataset (SQuAD) into French
- obtained through automatic translation of the English dataset
- a reading comprehension dataset, consisting of approximately 90K factoid questions on Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage
- serves as a means of data augmentation on FQuAD and PIAF benchmarks
### Supported Tasks and Leaderboards
- `closed-domain-qa`, `text-retrieval`: This dataset is intended to be used for `closed-domain-qa`, but can also be used for information retrieval tasks.
### Languages
This dataset is exclusively in French.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 10 MB
- **Size of the generated dataset:** 73 MB
- **Total amount of disk used:** 83 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [1],
"text": ["This is a test text"]
},
"context": "This is a test context.",
"id": "1",
"question": "Is this a test?",
"title": "train test"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name |train|validation|
|----------|----:|---------:|
|1.1.0|87514| 17492|
## Dataset Creation
### Curation Rationale
Usability of Transformer-based models, instability relating to data scarcity, investigation of data augmentation, hyperparameters optimization and cross-lingual transfer on the performance of a question-answering task in French.
### Source Data
#### Initial Data Collection and Normalization
validation: manually collected gold standards, chrf scores and bleu evaluation
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
### Citation Information
```
@inproceedings{cattan:hal-03336060,
TITLE = {{On the Usability of Transformers-based models for a French Question-Answering task}},
AUTHOR = {Cattan, Oralie and Servan, Christophe and Rosset, Sophie},
URL = {https://hal.archives-ouvertes.fr/hal-03336060},
BOOKTITLE = {{Recent Advances in Natural Language Processing (RANLP)}},
ADDRESS = {Varna, Bulgaria},
YEAR = {2021},
MONTH = Sep,
PDF = {https://hal.archives-ouvertes.fr/hal-03336060/file/RANLP_2021_transformers_usability.pdf},
HAL_ID = {hal-03336060},
HAL_VERSION = {v1},
}
```
|
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] |
hackathon-pln-es/es_tweets_laboral
|
hackathon-pln-es
| 2022-10-25T10:03:39Z | 73 | 1 |
[
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:es",
"license:unknown",
"region:us"
] |
[
"text-classification"
] | 2022-04-01T13:20:33Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- es
license:
- unknown
multilinguality:
- monolingual
pretty_name: "Tweets en espa\xF1ol denuncia laboral"
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
---
# Dataset Card for [es_tweets_laboral]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
Dataset creado por @hucruz, @DanielaGarciaQuezada, @hylandude, @BloodBoy21
Etiquetado por @DanielaGarciaQuezada
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
español
## 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
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huggingnft/cryptopunks
|
huggingnft
| 2022-04-16T17:59:07Z | 73 | 4 |
[
"license:mit",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"region:us"
] | null | 2022-04-10T08:52:12Z |
---
tags:
- huggingnft
- nft
- huggan
- gan
- image
- images
task:
- unconditional-image-generation
datasets:
- huggingnft/cryptopunks
license: mit
---
# Dataset Card
## Disclaimer
All rights belong to their owners.
Models and datasets can be removed from the site at the request of the copyright holder.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
NFT images dataset for unconditional generation.
NFT collection available [here](https://opensea.io/collection/cryptopunks).
Model is available [here](https://huggingface.co/huggingnft/cryptopunks).
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingnft/cryptopunks")
```
## Dataset Structure
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
- `image`: an `image` feature.
- `id`: an `int` feature.
- `token_metadata`: a `str` feature.
- `image_original_url`: a `str` feature.
### Data Splits
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingnft,
author={Aleksey Korshuk}
year=2022
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingnft)
|
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] |
wikimedia/wit_base
|
wikimedia
| 2022-11-04T15:09:33Z | 73 | 17 |
[
"task_categories:image-to-text",
"task_categories:text-retrieval",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|wikipedia",
"language:af",
"language:an",
"language:ar",
"language:arz",
"language:ast",
"language:az",
"language:azb",
"language:ba",
"language:bar",
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"language:br",
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"language:ckb",
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"language:cv",
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"language:ms",
"language:my",
"language:nan",
"language:nds",
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"language:oc",
"language:pa",
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"language:ro",
"language:ru",
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"language:ta",
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"language:vec",
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"language:war",
"language:xmf",
"language:yue",
"language:zh",
"license:cc-by-sa-4.0",
"text-image-retrieval",
"arxiv:2103.01913",
"arxiv:1512.03385",
"arxiv:1905.00641",
"region:us"
] |
[
"image-to-text",
"text-retrieval"
] | 2022-05-02T16:08:58Z |
---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- af
- an
- ar
- arz
- ast
- az
- azb
- ba
- bar
- be
- bg
- bn
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- bs
- ca
- ce
- ceb
- ckb
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- cv
- cy
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- en
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- te
- tg
- th
- tr
- tt
- uk
- ur
- uz
- vec
- vi
- vo
- war
- xmf
- yue
- zh
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
source_datasets:
- original
- extended|wikipedia
task_categories:
- image-to-text
- text-retrieval
task_ids:
- image-captioning
paperswithcode_id: wit
pretty_name: Wikipedia-based Image Text
language_bcp47:
- af
- an
- ar
- arz
- ast
- az
- azb
- ba
- bar
- be
- be-tarask
- bg
- bn
- br
- bs
- ca
- ce
- ceb
- ckb
- cs
- cv
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gl
- hi
- hr
- hsb
- ht
- hu
- hy
- ia
- id
- io
- is
- it
- iw
- ja
- jv
- ka
- kk
- kn
- ko
- la
- lah
- lb
- lmo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- nan
- nds
- ne
- nl
- nn
- 'no'
- nv
- oc
- pa
- pl
- pt
- qu
- ro
- ru
- sco
- si
- sk
- sl
- sq
- sr
- sr-Latn
- sv
- sw
- ta
- te
- tg
- th
- tr
- tt
- uk
- ur
- uz
- vec
- vi
- vo
- war
- xmf
- yue
- zh
- zh-TW
tags:
- text-image-retrieval
---
# Dataset Card for WIT
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [WIT homepage](https://github.com/google-research-datasets/wit)
- **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
](https://arxiv.org/abs/2103.01913)
- **Leaderboard:** [WIT leaderboard](https://paperswithcode.com/sota/text-image-retrieval-on-wit) and [WIT Kaggle competition](https://www.kaggle.com/competitions/wikipedia-image-caption/leaderboard)
- **Point of Contact:** [Miriam Redi](mailto:[email protected])
### Dataset Summary
Wikimedia's version of the Wikipedia-based Image Text (WIT) Dataset, a large multimodal multilingual dataset.
From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/):
> The core training data is taken from the Wikipedia Image-Text (WIT) Dataset, a large curated set of more than 37 million image-text associations extracted from Wikipedia articles in 108 languages that was recently released by Google Research.
>
> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images. However, due to licensing and data volume issues, the Google dataset only provides the image name and corresponding URL for download and not the raw image files.
>
> Getting easy access to the image files is crucial for participants to successfully develop competitive models. Therefore, today, the Wikimedia Research team is releasing its first large image dataset. It contains more than six million image files from Wikipedia articles in 100+ languages, which correspond to almost [1] all captioned images in the WIT dataset. Image files are provided at a 300-px resolution, a size that is suitable for most of the learning frameworks used to classify and analyze images.
> [1] We are publishing all images having a non-null “reference description” in the WIT dataset. For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the RetinaFace detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are candidate for deletion on Commons from the dataset.
**Note**: Compared to [Google's version](https://huggingface.co/datasets/google/wit), which has contents of one Wikipedia page per data sample, this version groups contents of all Wikipedia pages available in different languages for the image in one single data sample to avoid duplication of image bytes.
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.
- `text-retrieval`: The goal in this task is to build a model that retrieves the text (`caption_title_and_reference_description`) closest to an image. The leaderboard for this task can be found [here](https://paperswithcode.com/sota/text-image-retrieval-on-wit). This task also has a competition on [Kaggle](https://www.kaggle.com/c/wikipedia-image-caption).
In these tasks, any combination of the `caption_reference_description`, `caption_attribution_description` and `caption_alt_text_description` fields can be used as the input text/caption.
### Languages
The dataset contains examples from all Wikipedia languages.
## Dataset Structure
### Data Instances
Each instance is an image, its representation in bytes, a pre-computed embedding, and the set of captions attached to the image in Wikipedia.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x225 at 0x7F88F3876358>,
'image_url': 'https://upload.wikimedia.org/wikipedia/commons/8/8b/Scolopendra_gigantea.jpg',
'embedding': [1.4784087, 2.8710432, 0.0, 0.51603067, ..., 10.266883, 0.51142216, 0.0, 2.3464653],
'metadata_url': 'http://commons.wikimedia.org/wiki/File:Scolopendra_gigantea.jpg',
'original_height': 3000,
'original_width': 4000,
'mime_type': 'image/jpeg',
'caption_attribution_description': 'English: Puerto Rican Giant Centipede, Scolopendra gigantea; Vieques, Puerto Rico Slovenčina: Stonožka obrovská, Scolopendra gigantea; Vieques, Portoriko',
'wit_features': {
'language': ['ro', 'vi', 'sk', ..., 'nl', 'th', 'lv'],
'page_url': ['https://ro.wikipedia.org/wiki/Scolopendra_gigantea', 'https://vi.wikipedia.org/wiki/Scolopendra_gigantea', 'https://sk.wikipedia.org/wiki/Scolopendra_gigantea', ..., 'https://nl.wikipedia.org/wiki/Scolopendra_gigantea', 'https://th.wikipedia.org/wiki/%E0%B8%95%E0%B8%B0%E0%B8%82%E0%B8%B2%E0%B8%9A%E0%B8%A2%E0%B8%B1%E0%B8%81%E0%B8%A9%E0%B9%8C%E0%B8%82%E0%B8%B2%E0%B9%80%E0%B8%AB%E0%B8%A5%E0%B8%B7%E0%B8%AD%E0%B8%87%E0%B9%80%E0%B8%9B%E0%B8%A3%E0%B8%B9', 'https://lv.wikipedia.org/wiki/Skolopendru_dzimta'],
'attribution_passes_lang_id': [True, True, True, ..., True, True, True],
'caption_alt_text_description': [None, None, None, ..., 'Scolopendra gigantea', None, 'Milzu skolopendra (Scolopendra gigantea)'],
'caption_reference_description': [None, None, None, ..., None, None, 'Milzu skolopendra (Scolopendra gigantea)'],
'caption_title_and_reference_description': [None, 'Scolopendra gigantea [SEP] ', None, ..., 'Scolopendra gigantea [SEP] ', None, 'Skolopendru dzimta [SEP] Milzu skolopendra (Scolopendra gigantea)'],
'context_page_description': ['Scolopendra gigantea este un miriapod din clasa Chilopoda, fiind cel mai mare reprezentant al genului Scolopendra. Adultul poate atinge o lungime de 26 cm, uneori depășind 30 cm. Această specie habitează în regiunile de nord și de vest a Americii de Sud, pe insulele Trinidad, insulele Virgine, Jamaica Hispaniola ș.a. Localnicii denumesc scolopendra chilopodul gigant galben și chilopodul gigant amazonian.', 'Scolopendra gigantea là đại diện lớn nhất của chi Scolopendra nói riêng và cả lớp rết nói chung, thường đạt độ dài 26 cm và có thể vượt quá 30 cm. Sinh sống ở khu vực phía bắc và tây của Nam Mỹ và các đảo Trinidad, Puerto Rico, Saint Thomas, U.S. Virgin Islands, Jamaica, và Hispaniola.', 'Scolopendra gigantea, starší slovenský nazov: štípavica veľká, je živočích z rodu Scolopendra, s veľkosťou do 30 cm.', ..., 'Scolopendra gigantea is een tijgerduizendpoot uit Zuid-Amerika. De soort jaagt onder andere op grote geleedpotigen, amfibieën, reptielen en kleine zoogdieren. Het is voor zover bekend de grootste niet uitgestorven duizendpoot ter wereld.', 'ตะขาบยักษ์ขาเหลืองเปรู หรือ ตะขาบยักษ์อเมซอน เป็นตะขาบชนิดที่มีขนาดใหญ่ที่สุดในสกุล Scolopendra โดยปกติเมื่อโตเต็มที่จะยาว 26 เซนติเมตร แต่บางครั้งก็สามารถโตได้ถึง 30 เซนติเมตร ตะขาบชนิดนี้อาศัยอยู่ทางแถบเหนือและตะวันตกของทวีปอเมริกาใต้ และตามเกาะแก่งของประเทศตรินิแดดและจาไมกา เป็นสัตว์กินเนื้อ โดยกินจิ้งจก, กบ, นก, หนู และแม้แต่ค้างคาวเป็นอาหาร และขึ้นชื่อในเรื่องความดุร้าย', 'Skolpendru dzimta pieder pie simtkāju kārtas. Ap 400 dzimtas sugas sastopamas visā pasaulē, īpaši subtropu un tropu apgabalos. Mitinās augsnē, nobirušās lapās, plaisās, spraugās.'],
'context_section_description': [None, 'Scolopendra gigantea (còn được gọi là Rết chân vàng khổng lồ Peru và Rết khổng lồ Amazon) là đại diện lớn nhất của chi Scolopendra nói riêng và cả lớp rết nói chung, thường đạt độ dài 26\xa0cm (10\xa0in) và có thể vượt quá 30\xa0cm (12\xa0in). Sinh sống ở khu vực phía bắc và tây của Nam Mỹ và các đảo Trinidad, Puerto Rico, Saint Thomas, U.S. Virgin Islands, Jamaica, và Hispaniola.', None, ..., 'Scolopendra gigantea is een tijgerduizendpoot uit Zuid-Amerika. De soort jaagt onder andere op grote geleedpotigen, amfibieën, reptielen en kleine zoogdieren. Het is voor zover bekend de grootste niet uitgestorven duizendpoot ter wereld.', None, 'Skolpendru dzimta (Scolopendridae) pieder pie simtkāju kārtas. Ap 400 dzimtas sugas sastopamas visā pasaulē, īpaši subtropu un tropu apgabalos. Mitinās augsnē, nobirušās lapās, plaisās, spraugās.'],
'hierarchical_section_title': ['Scolopendra gigantea', 'Scolopendra gigantea', 'Scolopendra gigantea', ..., 'Scolopendra gigantea', 'ตะขาบยักษ์ขาเหลืองเปรู', 'Skolopendru dzimta'],
'is_main_image': [True, True, True, ..., True, True, True],
'page_title': ['Scolopendra gigantea', 'Scolopendra gigantea', 'Scolopendra gigantea', ..., 'Scolopendra gigantea', 'ตะขาบยักษ์ขาเหลืองเปรู', 'Skolopendru dzimta'],
'section_title': [None, None, None, ..., None, None, None]
}
}
```
**Note**: The dataset is stored in Parquet for better performance. This dataset was generated from the original files using [this script](wit_base/blob/main/scripts/wit.py). Additionally, 120 examples from the original files have incorrectly formatted one or more of the following fields: `original_height`, `original_width`, `mime_type` and `caption_attribution_description`. The fixed versions of these examples that were used in the generation script can be found [here](wit_base/blob/main/scripts/corrected_examples.py).
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image resized to a width of 300-px while preserving its aspect ratio. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `image_url`: URL to wikipedia image
- `embedding`: Precomputed image embedding. Each image is described with a 2048-dimensional signature extracted from the second-to-last layer of a [ResNet-50](https://arxiv.org/abs/1512.03385) neural network trained with [Imagenet](https://www.image-net.org/) data. These embeddings contain rich information about the image content and layout, in a compact form
- `metadata_url`: URL to wikimedia page containing the image and the metadata
- `original_height`: Original image height before resizing
- `original_width`: Original image width before resizing
- `mime_type`: Mime type associated to the image
- `caption_attribution_description`: This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias.
- `wit_features`: Sequence of captions for the image with language, page URL, information about the page, caption text, etc.
- `language`: Language code depicting wikipedia language of the page
- `page_url`: URL to wikipedia page
- `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description.
- `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers
- `caption_reference_description`: This is the caption that is visible on the wikipedia page directly below the image.
- `caption_title_and_reference_description`: Concatenation of `page_title` and `caption_reference_description`.
- `context_page_description`: Corresponds to the short description of the page. It provides a concise explanation of the scope of the page.
- `context_section_description`: Text within the image's section
- `hierarchical_section_title`: Hierarchical section's title
- `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.
- `page_changed_recently`: [More Information Needed]
- `page_title`: Wikipedia page's title
- `section_title`: Section's title
<p align='center'>
<img width='75%' src='https://production-media.paperswithcode.com/datasets/Screenshot_2021-03-04_at_14.26.02.png' alt="Half Dome" /> </br>
<b>Figure: WIT annotation example. </b>
</p>
Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913)
### Data Splits
All data is held in `train` split, with a total of 6477255 examples.
## Dataset Creation
### Curation Rationale
From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/):
> The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images.
> Getting easy access to the image files is crucial for participants to successfully develop competitive models.
> With this large release of visual data, we aim to help the competition participants—as well as researchers and practitioners who are interested in working with Wikipedia images—find and download the large number of image files associated with the challenge, in a compact form.
### Source Data
#### Initial Data Collection and Normalization
From the [paper, section 3.1](https://arxiv.org/abs/2103.01913):
> We started with all Wikipedia content pages (i.e., ignoring other
pages that have discussions, comments and such). These number about ~124M pages across 279 languages.
#### Who are the source language producers?
Text was extracted from Wikipedia.
### Annotations
#### Annotation process
WIT was constructed using an automatic process. However it was human-validated.
From the [paper, section 3.7](https://arxiv.org/abs/2103.01913):
> To further verify the quality of the WIT dataset we performed a
study using (crowd-sourced) human annotators. As seen in Fig. 3,
we asked raters to answer 3 questions. Given an image and the page
title, raters first evaluate the quality of the attribution description
and reference description in the first two questions (order randomized). The third question understands the contextual quality of these
text descriptions given the page description and caption. Each response is on a 3-point scale: "Yes" if the text perfectly describes
the image, "Maybe" if it is sufficiently explanatory and "No" if it is
irrelevant or the image is inappropriate.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/#FN1):
> For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the [RetinaFace](https://arxiv.org/abs/1905.00641) detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are [candidate for deletion](https://commons.wikimedia.org/wiki/Commons:Deletion_requests) on Commons from the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
From the [paper, section 3.4](https://arxiv.org/abs/2103.01913):
> Lastly we found that certain image-text pairs occurred very
frequently. These were often generic images that did not have
much to do with the main article page. Common examples
included flags, logos, maps, insignia and such. To prevent
biasing the data, we heavily under-sampled all such images
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Miriam Redi, Fabian Kaelin and Tiziano Piccardi.
### Licensing Information
[CC BY-SA 4.0 international license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```bibtex
@article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
}
```
### Contributions
Thanks to [@nateraw](https://github.com/nateraw), [yjernite](https://github.com/yjernite) and [mariosasko](https://github.com/mariosasko) for adding this dataset.
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khalidalt/tydiqa-primary
|
khalidalt
| 2022-07-28T21:56:04Z | 73 | 0 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|wikipedia",
"language:en",
"language:ar",
"language:bn",
"language:fi",
"language:id",
"language:ja",
"language:sw",
"language:ko",
"language:ru",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] |
[
"question-answering"
] | 2022-06-16T17:20:46Z |
---
pretty_name: TyDi QA
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
- ar
- bn
- fi
- id
- ja
- sw
- ko
- ru
- te
- th
license:
- apache-2.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: tydi-qa
---
# Dataset Card for "tydiqa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/google-research-datasets/tydiqa](https://github.com/google-research-datasets/tydiqa)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3726.74 MB
- **Size of the generated dataset:** 5812.92 MB
- **Total amount of disk used:** 9539.67 MB
### Dataset Summary
TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
the use of translation (unlike MLQA and XQuAD).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### primary_task
- **Size of downloaded dataset files:** 1863.37 MB
- **Size of the generated dataset:** 5757.59 MB
- **Total amount of disk used:** 7620.96 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"annotations": {
"minimal_answers_end_byte": [-1, -1, -1],
"minimal_answers_start_byte": [-1, -1, -1],
"passage_answer_candidate_index": [-1, -1, -1],
"yes_no_answer": ["NONE", "NONE", "NONE"]
},
"document_plaintext": "\"\\nรองศาสตราจารย์[1] หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร (22 กันยายน 2495 -) ผู้ว่าราชการกรุงเทพมหานครคนที่ 15 อดีตรองหัวหน้าพรรคปร...",
"document_title": "หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร",
"document_url": "\"https://th.wikipedia.org/wiki/%E0%B8%AB%E0%B8%A1%E0%B9%88%E0%B8%AD%E0%B8%A1%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%A7%E0%B8%87%E0%B8%...",
"language": "thai",
"passage_answer_candidates": "{\"plaintext_end_byte\": [494, 1779, 2931, 3904, 4506, 5588, 6383, 7122, 8224, 9375, 10473, 12563, 15134, 17765, 19863, 21902, 229...",
"question_text": "\"หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร เรียนจบจากที่ไหน ?\"..."
}
```
### Data Fields
The data fields are the same among all splits.
#### primary_task
- `passage_answer_candidates`: a dictionary feature containing:
- `plaintext_start_byte`: a `int32` feature.
- `plaintext_end_byte`: a `int32` feature.
- `question_text`: a `string` feature.
- `document_title`: a `string` feature.
- `language`: a `string` feature.
- `annotations`: a dictionary feature containing:
- `passage_answer_candidate_index`: a `int32` feature.
- `minimal_answers_start_byte`: a `int32` feature.
- `minimal_answers_end_byte`: a `int32` feature.
- `yes_no_answer`: a `string` feature.
- `document_plaintext`: a `string` feature.
- `document_url`: a `string` feature.
### Data Splits
| name | train | validation |
| -------------- | -----: | ---------: |
| primary_task | 166916 | 18670 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of the Association for Computational Linguistics}
}
```
```
@inproceedings{ruder-etal-2021-xtreme,
title = "{XTREME}-{R}: Towards More Challenging and Nuanced Multilingual Evaluation",
author = "Ruder, Sebastian and
Constant, Noah and
Botha, Jan and
Siddhant, Aditya and
Firat, Orhan and
Fu, Jinlan and
Liu, Pengfei and
Hu, Junjie and
Garrette, Dan and
Neubig, Graham and
Johnson, Melvin",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.802",
doi = "10.18653/v1/2021.emnlp-main.802",
pages = "10215--10245",
}
}
```
|
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BeIR/trec-covid-generated-queries
|
BeIR
| 2022-10-23T06:13:36Z | 73 | 0 |
[
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-17T12:59:43Z |
---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
|
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imvladikon/leipzig_corpora_collection
|
imvladikon
| 2023-11-12T08:49:08Z | 73 | 2 |
[
"task_categories:text-generation",
"task_categories:fill-mask",
"multilinguality:multilingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ar",
"language:en",
"language:he",
"language:de",
"language:it",
"language:fr",
"language:pl",
"language:pt",
"language:ru",
"language:uk",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2022-06-19T16:03:28Z |
---
language:
- ar
- en
- he
- de
- it
- fr
- pl
- pt
- ru
- uk
task_categories:
- text-generation
- fill-mask
source_datasets:
- original
multilinguality:
- multilingual
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
config_names:
- links
---
## Leipzig Corpora Collection
The [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download) presents corpora in different languages using the same format and comparable sources. All data are available as plain text files and can be imported into a MySQL database by using the provided import script. They are intended both for scientific use by corpus linguists as well as for applications such as knowledge extraction programs.
The corpora are identical in format and similar in size and content. They contain randomly selected sentences in the language of the corpus and are available in sizes from 10,000 sentences up to 1 million sentences. The sources are either newspaper texts or texts randomly collected from the web. The texts are split into sentences. Non-sentences and foreign language material was removed. Because word co-occurrence information is useful for many applications, these data are precomputed and included as well. For each word, the most significant words appearing as immediate left or right neighbor or appearing anywhere within the same sentence are given. More information about the format and content of these files can be found [here](https://wortschatz.uni-leipzig.de/en/download).
The corpora are automatically collected from carefully selected public sources without considering in detail the content of the contained text. No responsibility is taken for the content of the data. In particular, the views and opinions expressed in specific parts of the data remain exclusively with the authors.
## Dataset Usage
### Links
A "links" subset contains URLs with corresponding language and id (based on `https://corpora.uni-leipzig.de/`)
```python
from datasets import load_dataset
ds = load_dataset("imvladikon/leipzig_corpora_collection", "links")
for row in ds["train"]:
print(row)
```
```
{'id': '0', 'data_id': '0', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/ara_news_2005-2009_10K.tar.gz', 'language': 'Arabic', 'language_short': 'ara', 'year': '2005', 'size': '10K'}
{'id': '1', 'data_id': '1', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/ara_news_2005-2009_30K.tar.gz', 'language': 'Arabic', 'language_short': 'ara', 'year': '2005', 'size': '30K'}
{'id': '2', 'data_id': '2', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/ara_news_2005-2009_100K.tar.gz', 'language': 'Arabic', 'language_short': 'ara', 'year': '2005', 'size': '100K'}
....
```
where is possible to choose specific `data_id` to load a specific dataset, where `data_id` is name of the subset
Links possible to filter according to metdata attributes:
```python
links = load_dataset("imvladikon/leipzig_corpora_collection", "links", split="train")
english_2019 = links.filter(lambda x: x["language"] == "English" and x["year"] == "2019")
for sample in english_2019:
print(sample)
```
```
{'id': '277', 'data_id': 'eng_news_2019_10K', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng_news_2019_10K.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '10K'}
{'id': '278', 'data_id': 'eng_news_2019_30K', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng_news_2019_30K.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '30K'}
{'id': '279', 'data_id': 'eng_news_2019_100K', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng_news_2019_100K.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '100K'}
{'id': '280', 'data_id': 'eng_news_2019_300K', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng_news_2019_300K.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '300K'}
{'id': '281', 'data_id': 'eng_news_2019_1M', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng_news_2019_1M.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '1M'}
{'id': '541', 'data_id': 'eng-za_web_2019_10K', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng-za_web_2019_10K.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '10K'}
{'id': '542', 'data_id': 'eng-za_web_2019_30K', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng-za_web_2019_30K.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '30K'}
{'id': '543', 'data_id': 'eng-za_web_2019_100K', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng-za_web_2019_100K.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '100K'}
{'id': '544', 'data_id': 'eng-za_web_2019_300K', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng-za_web_2019_300K.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '300K'}
{'id': '545', 'data_id': 'eng-za_web_2019_1M', 'url': 'https://downloads.wortschatz-leipzig.de/corpora/eng-za_web_2019_1M.tar.gz', 'language': 'English', 'language_short': 'eng', 'year': '2019', 'size': '1M'}
```
### Corpus
after selecting `data_id`, let's say `heb_wikipedia_2021_1M`, we could load it:
```python
dataset_he = load_dataset("imvladikon/leipzig_corpora_collection", "heb_wikipedia_2021_1M", split="train")
for row in dataset_he:
print(row)
```
another example:
```python
dataset_en = load_dataset("imvladikon/leipzig_corpora_collection", "eng-simple_wikipedia_2021_300K", split="train")
print(dataset_en[76576])
```
sample:
```json
{'id': '79214', 'sentence': 'He was a member of the assembly from 1972 to 1977.'}
```
## Citation
If you use one of these corpora in your work, please, to cite [this work](http://www.lrec-conf.org/proceedings/lrec2012/pdf/327_Paper.pdf):
```
@inproceedings{goldhahn-etal-2012-building,
title = "Building Large Monolingual Dictionaries at the {L}eipzig Corpora Collection: From 100 to 200 Languages",
author = "Goldhahn, Dirk and
Eckart, Thomas and
Quasthoff, Uwe",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/327_Paper.pdf",
pages = "759--765",
abstract = "The Leipzig Corpora Collection offers free online access to 136 monolingual dictionaries enriched with statistical information. In this paper we describe current advances of the project in collecting and processing text data automatically for a large number of languages. Our main interest lies in languages of low density, where only few text data exists online. The aim of this approach is to create monolingual dictionaries and statistical information for a high number of new languages and to expand the existing dictionaries, opening up new possibilities for linguistic typology and other research. Focus of this paper will be set on the infrastructure for the automatic acquisition of large amounts of monolingual text in many languages from various sources. Preliminary results of the collection of text data will be presented. The mainly language-independent framework for preprocessing, cleaning and creating the corpora and computing the necessary statistics will also be depicted.",
}
```
|
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-0.006868375930935144,
-1.0201289653778076,
-0.04560025781393051,
0.4495784640312195,
0.40431681275367737,
0.4920288622379303,
0.7604274153709412,
-0.15409858524799347,
-0.06803116202354431,
0.21223564445972443,
0.0940978080034256,
0.15555688738822937,
0.18759498000144958,
-0.00035535599454306066,
0.24703094363212585,
-0.21461710333824158,
-0.08289846032857895
] |
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