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42
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| likes
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yashika0998/iot-23-preprocessed-allcolumns | yashika0998 | 2023-12-01T18:32:45Z | 0 | 0 | [
"task_categories:tabular-classification",
"task_categories:table-question-answering",
"language:en",
"code",
"region:us"
]
| [
"tabular-classification",
"table-question-answering"
]
| 2023-12-01T18:19:01Z | ---
dataset_info:
features:
- name: ts
dtype: float64
- name: uid
dtype: string
- name: id.orig_h
dtype: string
- name: id.orig_p
dtype: int64
- name: id.resp_h
dtype: string
- name: id.resp_p
dtype: int64
- name: proto
dtype: string
- name: service
dtype: string
- name: duration
dtype: float64
- name: orig_bytes
dtype: int64
- name: resp_bytes
dtype: int64
- name: conn_state
dtype: string
- name: local_orig
dtype: float64
- name: local_resp
dtype: float64
- name: missed_bytes
dtype: int64
- name: history
dtype: string
- name: orig_pkts
dtype: int64
- name: orig_ip_bytes
dtype: int64
- name: resp_pkts
dtype: int64
- name: resp_ip_bytes
dtype: int64
- name: label
dtype: string
splits:
- name: train
num_bytes: 1232978140
num_examples: 6046623
download_size: 274218995
dataset_size: 1232978140
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- tabular-classification
- table-question-answering
language:
- en
tags:
- code
---
# Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic
**Homepage:** [https://www.stratosphereips.org/datasets-iot23](https://www.stratosphereips.org/datasets-iot23)
This dataset contains a subset of the data from 20 captures of Malcious network traffic and 3 captures from live Benign Traffic on Internet of Things (IoT) devices. Created by Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga at the Avast AIC laboratory with the funding of Avast Software, this dataset is one of the best in the field for Intrusion Detection Systems (IDS) for IoT Devices [(Comparative Analysis of IoT Botnet Datasets)](https://doi.org/10.53070/bbd.1173687).
The selection of the subset was determined by [Aqeel Ahmed on Kaggle](https://www.kaggle.com/datasets/engraqeel/iot23preprocesseddata) and contains 6 million samples. The Kaggle upload, nor this one, have employed data balancing. The Kaggle card does not contain methodology to understand what criteria was used to select these samples. If you want ensure best practice, use this dataset to mock-up processing the data into a model before using the full dataset with data balancing. This will require processing the 8GB of conn.log.labelled files.
# Feature information:
All features originate from the [Zeek](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info) processing performed by the dataset creators. [See notes here for caviats for each column](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info).
<details>
<summary>Expand for feature names, descriptions, and datatypes</summary>
Name: ts
Desription: This is the time of the first packet.
Data Type: float64 - Timestamp
Name: uid
Description: A Zeek-defined unique identifier of the connection.
Data type: string
Name: id.orig_h
Description: The originator’s IP address.
Data type: string - for the form 255.255.255.255 for IPv4 or [aaaa:bbbb:cccc:dddd:eeee:ffff:1111:2222] for IPv6
Name: id.orig_p
Description: The originator’s port number.
Data type: int64 - uint64 in original
Name: id.resp_h
Description: The responder’s IP address.
Data type: string - for the form 255.255.255.255 for IPv4 or [aaaa:bbbb:cccc:dddd:eeee:ffff:1111:2222] for IPv6
Name: id.resp_p
Description: The responder’s port number.
Data type: int64 - uint64 in original
Name: proto
Description: The transport layer protocol of the connection.
Data type: string - enum(unknown_transport, tcp, udp, icmp). Only TCP and UDP in subset
Name: service
Description: An identification of an application protocol being sent over the connection.
Data type: optional string
Name: duration
Description: How long the connection lasted.
Data type: optional float64 - time interval
Name: orig_bytes
Description: The number of payload bytes the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_bytes
Description:The number of payload bytes the responder sent.
Data type: optional int64 - uint64 in original
Name: conn_state
Description: Value indicating connection state. (S0, S1, SF, REJ, S2, S3, RSTO, RSTR, RSTOS0, RSTRH, SH, SHR, OTH)
Data type: optional string
Name: local_orig
Description: If the connection is originated locally, this value will be T. If it was originated remotely it will be F.
Data type: optional float64 - bool in original but null for all columns
Name: local_resp
Description: If the connection is responded to locally, this value will be T. If it was responded to remotely it will be F.
Data type: optional float64 - bool in original but null for all columns
Name: missed_bytes
Description: Indicates the number of bytes missed in content gaps, which is representative of packet loss.
Data type: optional int64 - uint64 in original. default = 0
Name: history
Description: Records the state history of connections as a string of letters.
Data type: optional string
Name: orig_pkts
Description: Number of packets that the originator sent.
Data type: optional int64 - uint64 in original
Name: orig_ip_bytes
Description: Number of IP level bytes that the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_pkts
Description: Number of packets that the responder sent.
Data type: optional int64 - uint64 in original
Name: resp_ip_bytes
Description: Number of IP level bytes that the responder sent.
Data type: optional int64 - uint64 in original
Name: label
Description: Specifies if data point is benign or some form of malicious. See the dataset creators paper for descriptions of attack types
Data type: string - enum('PartOfAHorizontalPortScan', 'Okiru', 'DDoS', 'C&C-HeartBeat',
'Benign', 'C&C-Torii', 'C&C', 'C&C-FileDownload', 'Okiru-Attack',
'Attack', 'FileDownload', 'C&C-HeartBeat-FileDownload',
'C&C-Mirai')
NOTE: ts, uid, id.orig_h, id.resp_h SHOULD BE removed as they are dataset specific. Models should not be trained with specific timestamps or IP addresses (id.orig_h), as that can lead to over fitting to dataset specific times and addresses.
Further local_orig, local_resp SHOULD BE removed as they are null in all rows, so they are useless for training.
</details>
## Citation
If you are using this dataset for your research, please reference it as “Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4743746”
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Gunchar/hehehe | Gunchar | 2023-12-01T18:28:53Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T18:28:53Z | ---
license: mit
---
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|
zsy12345/telugu-asr | zsy12345 | 2023-12-01T19:10:04Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T18:32:26Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 38112606621.4
num_examples: 168076
download_size: 37317736585
dataset_size: 38112606621.4
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
BrookBvn/Ninja1 | BrookBvn | 2023-12-01T18:43:49Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T18:43:22Z | ---
license: openrail
---
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|
27aryan/amitabbachchan | 27aryan | 2023-12-01T18:59:31Z | 0 | 0 | [
"license:other",
"region:us"
]
| null | 2023-12-01T18:48:54Z | ---
license: other
license_name: other
license_link: LICENSE
---
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|
codys12/IPS0 | codys12 | 2023-12-01T21:10:51Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T19:15:53Z | ---
license: mit
---
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|
ctang/deontology_train_llama2_v2 | ctang | 2023-12-01T19:16:10Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T19:16:09Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15733419
num_examples: 16733
download_size: 1768896
dataset_size: 15733419
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
ctang/deontology_eval_llama2_v2 | ctang | 2023-12-01T19:29:34Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T19:29:33Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
splits:
- name: train
num_bytes: 913012
num_examples: 3426
download_size: 210840
dataset_size: 913012
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
JinglesDados/PretoJoia | JinglesDados | 2023-12-01T19:40:53Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T19:39:53Z | ---
license: openrail
---
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|
Gunchar/dataset-georgian | Gunchar | 2023-12-01T19:48:00Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T19:48:00Z | ---
license: mit
---
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|
judyhoffman/SkysScenes | judyhoffman | 2023-12-01T19:53:48Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T19:53:47Z | ---
license: mit
---
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|
judyhoffman/SkyScenes | judyhoffman | 2023-12-01T19:54:01Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T19:54:01Z | ---
license: mit
---
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|
hopee4/summer | hopee4 | 2023-12-01T20:13:08Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T20:12:48Z | ---
license: openrail
---
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|
Apinapi/Pamonha | Apinapi | 2023-12-01T20:19:03Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T20:18:44Z | ---
license: openrail
---
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|
mugeakbulut/turkish_Kadi_Sicilleri-ds-mini | mugeakbulut | 2023-12-02T09:01:25Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T20:24:51Z | ---
dataset_info:
features:
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splits:
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num_bytes: 1486810.797385621
num_examples: 413
- name: validation
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num_examples: 46
download_size: 810073
dataset_size: 1652412.0
configs:
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data_files:
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path: data/train-*
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path: data/validation-*
---
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|
array/socratis_image_text_emotion | array | 2023-12-01T20:47:36Z | 0 | 0 | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:conversational",
"license:mit",
"arxiv:2308.16741",
"region:us"
]
| [
"text-classification",
"image-classification",
"conversational"
]
| 2023-12-01T20:25:13Z | ---
license: mit
task_categories:
- text-classification
- image-classification
- conversational
---
# SOCRATIS: A benchmark of diverse open-ended emotional reactions to image-caption pairs.
### ICCV WECIA Workshop 2023 (oral)
[Project Page](https://kdeng55.github.io/socratis-website/), [Paper](https://arxiv.org/abs/2308.16741)
We release a benchmark which contains 18K diverse emotions and reasons for feeling them on 2K image-caption pairs.
Our current preliminary findings have shown that Humans prefer human-written emotional reactions over machine-generated by more than two times.
We also find that current metrics fail to correlate with human preference, leaving significant room for research!
We release the data publicly.
`test.json` contains the testing data in the following format:
```
{
unique_id: [[image_path, caption, emotions, explanations, anonymized_demographics], ...]
}
```
The `unique_id` is a unique id for a image-caption pair. Each `unique_id` key has a list of entries from diverse workers.
Each entry consists of the emotions and the explanations for feeling that emotion. Demographics may be missing for many annotations since they were optional and some workers opted to not disclose it. All data is anonymized.
The image files are at: https://drive.google.com/file/d/1J8SiUEfKqc5rfxE1nwZUrG1Hcz7Djc3G/view?usp=sharing. | [
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|
KayoSilva88777/Allan | KayoSilva88777 | 2023-12-01T20:30:58Z | 0 | 0 | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2023-12-01T20:30:02Z | ---
license: bigscience-openrail-m
---
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|
okyleng/kyle | okyleng | 2023-12-01T20:49:28Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T20:47:37Z | ---
license: openrail
---
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|
Doutran/datasetcd1mylla | Doutran | 2023-12-01T22:02:08Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T20:53:02Z | ---
license: openrail
---
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|
sdadasfgdfgfdg/Gumball | sdadasfgdfgfdg | 2023-12-01T21:04:31Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T21:03:52Z | ---
license: openrail
---
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|
JeremiahZ/humaneval_x_with_stats | JeremiahZ | 2023-12-01T21:18:27Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T21:18:24Z | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: declaration
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: example_test
dtype: string
- name: llvm_ir
dtype: string
- name: wat
dtype: string
- name: number_of_lines
dtype: int64
- name: number_of_chars
dtype: int64
- name: wat_number_of_chars
dtype: int64
- name: wat_number_of_lines
dtype: int64
splits:
- name: train
num_bytes: 4393622
num_examples: 161
download_size: 1046507
dataset_size: 4393622
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "humaneval_x_with_stats"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
JeremiahZ/mbxp_with_stats | JeremiahZ | 2023-12-01T21:19:28Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T21:19:26Z | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: prompt
dtype: string
- name: description
dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
- name: canonical_solution
dtype: string
- name: llvm_ir
dtype: string
- name: wat
dtype: string
- name: number_of_lines
dtype: int64
- name: number_of_chars
dtype: int64
- name: wat_number_of_chars
dtype: int64
- name: wat_number_of_lines
dtype: int64
splits:
- name: test
num_bytes: 13572947
num_examples: 773
download_size: 2868972
dataset_size: 13572947
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "mbxp_with_stats"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
c4ba/gabrielvieira | c4ba | 2023-12-01T23:32:01Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T21:41:54Z | ---
license: openrail
---
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|
Doutran/setmyllacia1.zip | Doutran | 2023-12-01T22:00:24Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T22:00:24Z | ---
license: openrail
---
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|
Doutran/myllasetcdone | Doutran | 2023-12-01T22:05:42Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T22:04:40Z | ---
license: openrail
---
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|
ThiagoKRHS/ricardojuarez | ThiagoKRHS | 2023-12-01T22:24:07Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T22:23:31Z | ---
license: openrail
---
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|
Capsekai/Holiday-Niji | Capsekai | 2023-12-01T22:35:13Z | 0 | 0 | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-12-01T22:33:33Z | ---
license: creativeml-openrail-m
---
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|
phyloforfun/HLT_Kew_WCVP_SLTPvA_v1-0_tiny__T20-OCR-C25-L25-E50-R10 | phyloforfun | 2023-12-01T22:56:17Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T22:56:16Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 104894
num_examples: 100
download_size: 23586
dataset_size: 104894
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
phyloforfun/HLT_Kew_WCVP_SLTPvA_v1-0_small__T20-OCR-C25-L25-E50-R10 | phyloforfun | 2023-12-01T22:56:45Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T22:56:41Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1045913
num_examples: 1000
download_size: 150662
dataset_size: 1045913
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
phyloforfun/HLT_Kew_WCVP_SLTPvA_v1-0_large__T20-OCR-C25-L25-E50-R10 | phyloforfun | 2023-12-01T22:58:44Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T22:58:35Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 104633782
num_examples: 100000
download_size: 14989427
dataset_size: 104633782
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
AdapterOcean/physics_dataset_standardized_std | AdapterOcean | 2023-12-03T16:20:15Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T23:05:09Z | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 50580506
num_examples: 40000
download_size: 22905844
dataset_size: 50580506
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Parikshith/grow-1-monolingual-ha-en-comet | Parikshith | 2023-12-02T00:20:04Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T23:25:26Z | ---
dataset_info:
features:
- name: src
dtype: string
- name: mt
dtype: string
- name: score
dtype: float64
splits:
- name: small
num_bytes: 24923873
num_examples: 100000
download_size: 16390887
dataset_size: 24923873
configs:
- config_name: default
data_files:
- split: small
path: data/small-*
---
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|
phyloforfun/HLT_Kew_WCVP_SLTPvA_v1-0_full__T20-OCR-C25-L25-E50-R10 | phyloforfun | 2023-12-01T23:29:50Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T23:28:01Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1488879842
num_examples: 1422868
download_size: 189039917
dataset_size: 1488879842
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Danieldlima21/youtopia | Danieldlima21 | 2023-12-01T23:50:17Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T23:47:47Z | ---
license: openrail
---
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|
aatherton2024/inuitparrrallel | aatherton2024 | 2023-12-02T00:32:28Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T00:04:49Z | ---
configs:
- config_name: default
data_files:
- split: en
path: data/en-*
- split: fr
path: data/fr-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: en
num_bytes: 439558
num_examples: 10192
- name: fr
num_bytes: 734362
num_examples: 10192
download_size: 490449
dataset_size: 1173920
---
# Dataset Card for "inuitparrrallel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Raspberry-ai/az_cos_mango_zara_dataset_v1 | Raspberry-ai | 2023-12-02T00:35:03Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T00:32:28Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
- name: brand_name
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 985313903.55
num_examples: 8790
download_size: 3350926096
dataset_size: 985313903.55
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "az_cos_mango_zara_dataset_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
KAUE2006/BussundaShrek | KAUE2006 | 2023-12-02T00:39:00Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T00:38:19Z | ---
license: openrail
---
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|
KayoSilva88777/CarlosFernandes | KayoSilva88777 | 2023-12-02T01:23:41Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T01:14:29Z | ---
license: apache-2.0
---
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|
Umbrellos/gobbleygordmsm | Umbrellos | 2023-12-02T02:37:02Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T01:19:17Z | ---
license: openrail
---
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|
judy93536/news_api_all_agree_5k | judy93536 | 2023-12-02T01:26:38Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T01:26:33Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 743397
num_examples: 4031
- name: test
num_bytes: 182240
num_examples: 1006
download_size: 516419
dataset_size: 925637
---
# Dataset Card for "news_api_all_agree_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
KayoSilva88777/CarlosF | KayoSilva88777 | 2023-12-02T01:28:34Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T01:27:56Z | ---
license: apache-2.0
---
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|
Parikshith/grow-1-monolingual-ha-en-comet-wmt21 | Parikshith | 2023-12-02T02:19:58Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T02:07:29Z | ---
dataset_info:
features:
- name: src
dtype: string
- name: mt
dtype: string
- name: score
dtype: float64
splits:
- name: small
num_bytes: 24923873
num_examples: 100000
download_size: 16395230
dataset_size: 24923873
configs:
- config_name: default
data_files:
- split: small
path: data/small-*
---
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|
RenatoBC/rodrigosilva | RenatoBC | 2023-12-02T02:11:58Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T02:11:13Z | ---
license: openrail
---
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|
Carlosgg14/GON | Carlosgg14 | 2023-12-02T03:16:45Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T03:14:38Z | ---
license: openrail
---
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|
R99SMG/lists | R99SMG | 2023-12-02T03:25:18Z | 0 | 0 | [
"license:other",
"region:us"
]
| null | 2023-12-02T03:24:41Z | ---
license: other
license_name: nlp-list-news
license_link: LICENSE
---
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|
Deojoandco/capstone_fromgpt_without_gold_v13_all | Deojoandco | 2023-12-02T06:44:38Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T04:04:57Z | ---
dataset_info:
features:
- name: dialog_id
dtype: int64
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: gold_tags
dtype: string
- name: gpt_success
dtype: bool
- name: gpt_response
dtype: string
- name: gold_tags_tokens_count
dtype: int64
- name: GPT_TAGS_FOUND
dtype: bool
- name: gpt_output_tags
dtype: string
- name: gpt_output_tag_tokens_count
dtype: int64
- name: GPT_MI_FOUND
dtype: bool
- name: gpt_tags_token_count
dtype: int64
- name: gpt_tags
dtype: string
- name: tag_token_count_match
dtype: bool
- name: precision
dtype: float64
- name: recall
dtype: float64
- name: f1
dtype: float64
- name: accuracy
dtype: float64
splits:
- name: test
num_bytes: 13736
num_examples: 12
- name: train
num_bytes: 126588
num_examples: 76
- name: validation
num_bytes: 23470
num_examples: 12
download_size: 88672
dataset_size: 163794
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "capstone_fromgpt_without_gold_v13_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Anoop03031988/news_summary | Anoop03031988 | 2023-12-02T04:30:06Z | 0 | 0 | [
"language:en",
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T04:26:11Z | ---
license: apache-2.0
language:
- en
pretty_name: news_summarizer
--- | [
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|
DJR1987/andybogard | DJR1987 | 2023-12-02T05:28:49Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T05:04:33Z | ---
license: openrail
---
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|
adamjweintraut/bart-finetuned-eli5_lfqa_top_rouge_eval_2023-12-02 | adamjweintraut | 2023-12-02T05:07:35Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T05:07:33Z | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: q_id
dtype: string
- name: question
dtype: string
- name: predicted
sequence: string
- name: label
sequence: string
- name: rougeL_P
dtype: float64
- name: rougeL_R
dtype: float64
- name: rougeL_F
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 7392399
num_examples: 2500
download_size: 4419764
dataset_size: 7392399
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
robinsyihab/QQPR-triplets-ID | robinsyihab | 2023-12-02T06:10:33Z | 0 | 0 | [
"task_categories:text-classification",
"task_categories:question-answering",
"size_categories:10M<n<100M",
"language:id",
"region:us"
]
| [
"text-classification",
"question-answering"
]
| 2023-12-02T06:02:07Z | ---
task_categories:
- text-classification
- question-answering
language:
- id
size_categories:
- 10M<n<100M
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|
kavyamanohar/Malayalam-word-freq | kavyamanohar | 2023-12-02T08:34:20Z | 0 | 0 | [
"license:cc-by-4.0",
"region:us"
]
| null | 2023-12-02T06:39:28Z | ---
license: cc-by-4.0
---
## Word Frequency Profile of Malayalam
The repo contains Malayalam words and their frequencies as obtained from AI4Bharat [Indic NLP corpus](https://github.com/AI4Bharat/indicnlp_corpus).
There is an associated python script to plot the word frequnecy profile.
| [
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|
Danieldlima21/raluka | Danieldlima21 | 2023-12-02T07:17:23Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-02T07:16:11Z | ---
license: openrail
---
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|
JiHunWang/cartoonizer-dataset | JiHunWang | 2023-12-02T07:25:57Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T07:25:54Z | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: cartoonized_image
dtype: image
splits:
- name: train
num_bytes: 32429437.0
num_examples: 50
download_size: 32431565
dataset_size: 32429437.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
automated-research-group/llama2_7b_chat-boolq-results_jacksee | automated-research-group | 2023-12-02T08:01:47Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T08:01:45Z | ---
dataset_info:
config_name: '{''do_sample''=False, ''beams''=1}'
features:
- name: id
dtype: string
- name: prediction
dtype: string
- name: bool_accuracy
dtype: bool
splits:
- name: train
num_bytes: 503592
num_examples: 3270
download_size: 265378
dataset_size: 503592
configs:
- config_name: '{''do_sample''=False, ''beams''=1}'
data_files:
- split: train
path: '{''do_sample''=False, ''beams''=1}/train-*'
---
# Dataset Card for "llama2_7b_chat-boolq-results_jacksee"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
quocanh34/youtube_dataset_test_final | quocanh34 | 2023-12-02T08:55:08Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T08:33:14Z | ---
dataset_info:
features:
- name: audio
dtype: 'null'
- name: transcription
dtype: 'null'
- name: w2v2_transcription
dtype: 'null'
- name: WER
dtype: 'null'
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 1165
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
AdityaNG/BengaluruEmbeddings | AdityaNG | 2023-12-03T13:16:24Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-02T08:51:00Z | ---
license: mit
---
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|
alpayariyak/tot-synthia | alpayariyak | 2023-12-02T09:26:20Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T09:26:06Z | ---
dataset_info:
features:
- name: system
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: without_RAG
num_bytes: 62222837
num_examples: 15154
- name: with_RAG
num_bytes: 65164762
num_examples: 15631
download_size: 33575363
dataset_size: 127387599
configs:
- config_name: default
data_files:
- split: without_RAG
path: data/without_RAG-*
- split: with_RAG
path: data/with_RAG-*
---
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|
taku-taku/lora | taku-taku | 2023-12-02T10:05:50Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T09:47:52Z | ---
license: apache-2.0
---
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|
Enterprisium/fuego-20231202-105909-72867e | Enterprisium | 2023-12-02T14:26:29Z | 0 | 0 | [
"fuego",
"region:us"
]
| null | 2023-12-02T09:59:10Z | ---
tags:
- fuego
fuego:
id: 20231202-105909-72867e
status: done
script: run_glue.py
requirements_file: requirements.txt
space_id: Enterprisium/fuego-20231202-105909-72867e
space_hardware: cpu-basic
github_repo_id: huggingface/transformers
github_repo_branch: main
github_repo_sha: 2c658b5a4282f2e824b4e23dc3bcda7ef27d5827
---
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|
0wand/arcania | 0wand | 2023-12-02T10:27:30Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-02T10:27:30Z | ---
license: mit
---
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|
ahishamm/isic_binary_augmented_enhanced | ahishamm | 2023-12-02T10:33:07Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T10:32:56Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 97594170.25
num_examples: 17250
- name: test
num_bytes: 45157103.104
num_examples: 7786
download_size: 152316448
dataset_size: 142751273.354
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
smcproject/ml-phonetic-lexicon | smcproject | 2023-12-02T16:06:40Z | 0 | 0 | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:ml",
"license:cc-by-4.0",
"region:us"
]
| [
"text2text-generation"
]
| 2023-12-02T10:36:41Z | ---
license: cc-by-4.0
task_categories:
- text2text-generation
language:
- ml
pretty_name: Malayalam Grapheme Phoneme Dataset
configs:
- config_name: commonword
data_files: commonword.tsv
sep: "\t"
- config_name: english_loan
data_files: english_loan.tsv
sep: "\t"
- config_name: noun
data_files: noun.tsv
sep: "\t"
- config_name: personname
data_files: personname.tsv
sep: "\t"
- config_name: placename
data_files: placename.tsv
sep: "\t"
- config_name: pronoun
data_files: pronoun.tsv
sep: "\t"
- config_name: propernoun
data_files: propernoun.tsv
sep: "\t"
- config_name: sanskrit_loan
data_files: sanskrit_loan.tsv
sep: "\t"
- config_name: verb
data_files: verb.tsv
sep: "\t"
size_categories:
- 100K<n<1M
---
## Malayalam Phonetic Lexicon
This dataset contains words in Malayalam script and their pronunciation in International Phonetic Alphabet (IPA)
The words in the lexicon are sourced from
1) The most frequest 100 thousand words from [Indic NLP](https://github.com/AI4Bharat/indicnlp_corpus) corpus
2) Curated collection of word categories from [Mlmorph](https://pypi.org/project/mlmorph/) project
This pronunciations are created using [Mlphon](https://pypi.org/project/mlphon/) python Library.
### Applications
- Ready to use pronunciation lexicons for ASR and TTS
- To train datadriven grapheme to phoneme models
If you are using this dataset for any research, cite
```@ARTICLE{kavya2022mlphon,
author={Manohar, Kavya and Jayan, A. R. and Rajan, Rajeev},
journal={IEEE Access},
title={Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers},
year={2022},
volume={10},
number={},
pages={97555-97575},
doi={10.1109/ACCESS.2022.3204403}}
``` | [
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|
lylcst/ultrafeedback_fined | lylcst | 2023-12-02T11:21:28Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-02T10:39:41Z | ---
license: mit
---
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|
anhlbt/tts_dataset | anhlbt | 2023-12-02T13:39:56Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T10:55:09Z | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: w2v2_transcription
dtype: string
- name: WER
dtype: int64
splits:
- name: train
num_bytes: 18131497.022292994
num_examples: 135
download_size: 18027741
dataset_size: 18131497.022292994
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
utsabbarmanju/jeebonananda_das_bangla_poems | utsabbarmanju | 2023-12-02T15:52:33Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T11:42:07Z | ---
license: apache-2.0
---
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|
p7u/new-gpt | p7u | 2023-12-02T11:49:31Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-02T11:49:31Z | ---
license: mit
---
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|
hajar817/CV_10_0_fa_pseudo_labelled_whisper_medium_fa | hajar817 | 2023-12-02T15:53:45Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:02:11Z | ---
dataset_info:
config_name: fa
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 691558828.976
num_examples: 24672
- name: validation
num_bytes: 388165287.79
num_examples: 9937
- name: test
num_bytes: 424025317.777
num_examples: 9937
download_size: 1244651173
dataset_size: 1503749434.543
configs:
- config_name: fa
data_files:
- split: train
path: fa/train-*
- split: validation
path: fa/validation-*
- split: test
path: fa/test-*
---
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|
open-llm-leaderboard/details_Azure99__blossom-v3_1-mistral-7b | open-llm-leaderboard | 2023-12-04T10:15:34Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:03:34Z | ---
pretty_name: Evaluation run of Azure99/blossom-v3_1-mistral-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Azure99/blossom-v3_1-mistral-7b](https://huggingface.co/Azure99/blossom-v3_1-mistral-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 6 run(s). Each run can be\
\ found as a specific 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_Azure99__blossom-v3_1-mistral-7b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-04T10:15:27.415703](https://huggingface.co/datasets/open-llm-leaderboard/details_Azure99__blossom-v3_1-mistral-7b/blob/main/results_2023-12-04T10-15-27.415703.json)(note\
\ that their might be results for other tasks in 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\": 0.46929492039423804,\n\
\ \"acc_stderr\": 0.013746490739560038\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.46929492039423804,\n \"acc_stderr\": 0.013746490739560038\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Azure99/blossom-v3_1-mistral-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T12_03_31.211153
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-03-31.211153.parquet'
- split: 2023_12_02T12_59_48.772371
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-59-48.772371.parquet'
- split: 2023_12_02T22_05_15.192157
path:
- '**/details_harness|gsm8k|5_2023-12-02T22-05-15.192157.parquet'
- split: 2023_12_03T15_22_48.296021
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-22-48.296021.parquet'
- split: 2023_12_03T16_24_04.733758
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-24-04.733758.parquet'
- split: 2023_12_04T10_15_27.415703
path:
- '**/details_harness|gsm8k|5_2023-12-04T10-15-27.415703.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T10-15-27.415703.parquet'
- config_name: results
data_files:
- split: 2023_12_02T12_03_31.211153
path:
- results_2023-12-02T12-03-31.211153.parquet
- split: 2023_12_02T12_59_48.772371
path:
- results_2023-12-02T12-59-48.772371.parquet
- split: 2023_12_02T22_05_15.192157
path:
- results_2023-12-02T22-05-15.192157.parquet
- split: 2023_12_03T15_22_48.296021
path:
- results_2023-12-03T15-22-48.296021.parquet
- split: 2023_12_03T16_24_04.733758
path:
- results_2023-12-03T16-24-04.733758.parquet
- split: 2023_12_04T10_15_27.415703
path:
- results_2023-12-04T10-15-27.415703.parquet
- split: latest
path:
- results_2023-12-04T10-15-27.415703.parquet
---
# Dataset Card for Evaluation run of Azure99/blossom-v3_1-mistral-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Azure99/blossom-v3_1-mistral-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Azure99/blossom-v3_1-mistral-7b](https://huggingface.co/Azure99/blossom-v3_1-mistral-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 6 run(s). Each run can be found as a specific 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_Azure99__blossom-v3_1-mistral-7b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T10:15:27.415703](https://huggingface.co/datasets/open-llm-leaderboard/details_Azure99__blossom-v3_1-mistral-7b/blob/main/results_2023-12-04T10-15-27.415703.json)(note that their might be results for other tasks in 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": 0.46929492039423804,
"acc_stderr": 0.013746490739560038
},
"harness|gsm8k|5": {
"acc": 0.46929492039423804,
"acc_stderr": 0.013746490739560038
}
}
```
### 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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|
Chewan/ch-ag-bbc | Chewan | 2023-12-02T12:05:54Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T12:05:54Z | ---
license: apache-2.0
---
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|
AIFEG/BenchGPT | AIFEG | 2023-12-04T07:42:01Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T12:15:36Z | ---
license: apache-2.0
---
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|
casey-martin/MedInstruct | casey-martin | 2023-12-02T12:32:38Z | 0 | 0 | [
"task_categories:text-generation",
"language:en",
"medical",
"arxiv:2302.13971",
"arxiv:2307.09288",
"arxiv:2212.10560",
"region:us"
]
| [
"text-generation"
]
| 2023-12-02T12:21:30Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/MedInstruct-52k.json
- split: test
path: data/MedInstruct-test.jsonl
task_categories:
- text-generation
language:
- en
tags:
- medical
---
# MedInstruct
<hr>
This is the repo for *MedInstruct*, which is a dataset of synthetically generated medical instructions. The repo contains:
- The 52K medical instruction-response dataset [*MedInstruct-52k*](https://github.com/XZhang97666/AlpaCare/blob/master/data/MedInstruct-52k.json) used for fine-tuning *AlpaCare*, and corresponding [clinican-crafted seed task](https://github.com/XZhang97666/AlpaCare/blob/master/data/med_seed.json) to generate instruction.
- A 217 clinical craft free-form instruction evaluation test set,[*MedInstruct-test*](https://github.com/XZhang97666/AlpaCare/blob/master/data/MedInstruct-test.jsonl).
- The code for:
1. [medical task generation](https://github.com/XZhang97666/AlpaCare/tree/master/test_generation);
2. [fine-tuning LLaMA series models](https://github.com/XZhang97666/AlpaCare/tree/master/training);
3. [instrcution-tuned model response generation](https://github.com/XZhang97666/AlpaCare/tree/master/test_generation);
4. [response evaluation via LLMs](https://github.com/XZhang97666/AlpaCare/tree/master/evaluation).
## Overview
*AlpaCare* models contain 4 models (7B/13B - LLaMA[1]/LLaMA-2[2]) tuned on a 52k medical instruction-following dataset *MedInstruct-52k*, following Alpaca[3] and Self-Instruct[4]. You can find our model weights at:
| Version | Link |
| --- | --- |
| *AlpaCare* -LLaMA_7B |[https://huggingface.co/xz97/AlpaCare-llama1-7b](https://huggingface.co/xz97/AlpaCare-llama1-7b)|
| *AlpaCare* -LLaMA2_7B |[https://huggingface.co/xz97/AlpaCare-llama2-7b](https://huggingface.co/xz97/AlpaCare-llama2-7b)|
| *AlpaCare* -LLaMA_13B |[https://huggingface.co/xz97/AlpaCare-llama-13b](https://huggingface.co/xz97/AlpaCare-llama-13b)|
| *AlpaCare* -LLaMA2_13B |[https://huggingface.co/xz97/AlpaCare-llama2-13b](https://huggingface.co/xz97/AlpaCare-llama2-13b)|
[1]: LLaMA: Open and Efficient Foundation Language Models. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample. https://arxiv.org/abs/2302.13971v1
[2] Llama 2: Open foundation and fine-tuned chat models. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. https://arxiv.org/abs/2307.09288
[3]: Stanford Alpaca: An Instruction-following LLaMA Model.Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto. https://crfm.stanford.edu/2023/03/13/alpaca.html
[4]: Self-Instruct: Aligning Language Model with Self Generated Instructions. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi. https://arxiv.org/abs/2212.10560
## Data Release
[*MedInstruct*](https://huggingface.co/datasets/xz97/MedInstruct) contains:
- MedInstruct datasets:
1. *MedInstruct-52K*: 52 medical instruction-following data we used for fine-tuning *AlpaCare* models
2. *MedInstruct-test*: 217 clinican craft free-form instruction evulation tasks with reference responses generated by `gpt-4`, `gpt-tubro-3.5`, `text-davinci-003` and `claude-2`.
All files is a list of dictionaries in JSON/JSONL format, each dictionary contains the following fields:
- `instruction`: `str`, the medical task the model should perform. Each of instrcutions in *MedInstruct-52K* and *MedInstruct-test* is unique. The instrcutions in *MedInstruct-52K* are generated by OpenAI `gpt-4`, while *MedInstruct-test* are clinian-craft.
- `input`: `str`, optional medical context or example for the task to further explain the instruction and increase the task diversity.
- `output`: `str`, the answer to the instruction as generated by `text-davinci-003`.
- `difficulity`: `str`, medical knolwedge difficulty level scale from 1-5 evulated by clinicans following the [scoring system](plots/scoresystem.pdf). This only appear in *MedInstruct-test*.
- Instructions:
1. Clinican-crafted instruction seed set, containing 167 instances.
2. All medical instructions generated by OpenAI `gpt-4` with/without filtering.
</details>
## Data Generation Process
We following the data generation pipeline from [self-instruct](https://github.com/yizhongw/self-instruct) and [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), and modified several points:
*Instructions*:
- Beginning with a [medical task seed set](https://github.com/XZhang97666/AlpaCare/blob/master/data/med_seed.json) that contains 167 clinical-craft clinical tasks.
- Utilizing the GPT-4 engine for instruction data generation.
- Crafting a [prompt](https://github.com/XZhang97666/AlpaCare/blob/master/prompt/instruction_prompt.txt) to serve as the medical instruction generation guidelines for GPT-4.
- To reduce costs in task generation and introduce greater randomness, 12 instructions are generated at once. This is followed by the selection of 3 random seed instructions from the seed set.
*Responses*:
- The GPT-3.5-turbo engine is used to synthesize responses, processing each instruction individually.
Here's an illustration of an instruction-response pair:
```
{
instruction: "Rewrite this instruction on how to use an inhaler to be more patient-friendly and less technical."
input: "Exhale deeply, engage mouthpiece of inhaler, depress canister while simultaneously inhaling long and deep, hold the breath for ten seconds, then gently exhale"
output: "Here's a more patient-friendly version of the instruction on how to use an inhaler:
1. Take a deep breath and completely breathe out.
2. Put the mouthpiece of the inhaler into your mouth and seal your lips around it properly.
3. Press down the canister while breathing in slowly and deeply at the same time.
4. Hold your breath for ten seconds. 5. Finally, breathe out gently."
}
``` | [
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|
open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca | open-llm-leaderboard | 2023-12-02T12:35:45Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:35:32Z | ---
pretty_name: Evaluation run of ericzzz/falcon-rw-1b-instruct-openorca
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ericzzz/falcon-rw-1b-instruct-openorca](https://huggingface.co/ericzzz/falcon-rw-1b-instruct-openorca)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T12:35:28.593271](https://huggingface.co/datasets/open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca/blob/main/results_2023-12-02T12-35-28.593271.json)(note\
\ that their might be results for other tasks in 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\": 0.03411675511751327,\n\
\ \"acc_stderr\": 0.005000212600773262\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.03411675511751327,\n \"acc_stderr\": 0.005000212600773262\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ericzzz/falcon-rw-1b-instruct-openorca
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T12_35_28.593271
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-35-28.593271.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-35-28.593271.parquet'
- config_name: results
data_files:
- split: 2023_12_02T12_35_28.593271
path:
- results_2023-12-02T12-35-28.593271.parquet
- split: latest
path:
- results_2023-12-02T12-35-28.593271.parquet
---
# Dataset Card for Evaluation run of ericzzz/falcon-rw-1b-instruct-openorca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ericzzz/falcon-rw-1b-instruct-openorca
- **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 [ericzzz/falcon-rw-1b-instruct-openorca](https://huggingface.co/ericzzz/falcon-rw-1b-instruct-openorca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T12:35:28.593271](https://huggingface.co/datasets/open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca/blob/main/results_2023-12-02T12-35-28.593271.json)(note that their might be results for other tasks in 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": 0.03411675511751327,
"acc_stderr": 0.005000212600773262
},
"harness|gsm8k|5": {
"acc": 0.03411675511751327,
"acc_stderr": 0.005000212600773262
}
}
```
### 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_llm-jp__llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0 | open-llm-leaderboard | 2023-12-02T12:37:18Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:37:13Z | ---
pretty_name: Evaluation run of llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_llm-jp__llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T12:37:10.361445](https://huggingface.co/datasets/open-llm-leaderboard/details_llm-jp__llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0/blob/main/results_2023-12-02T12-37-10.361445.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T12_37_10.361445
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-37-10.361445.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-37-10.361445.parquet'
- config_name: results
data_files:
- split: 2023_12_02T12_37_10.361445
path:
- results_2023-12-02T12-37-10.361445.parquet
- split: latest
path:
- results_2023-12-02T12-37-10.361445.parquet
---
# Dataset Card for Evaluation run of llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_llm-jp__llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T12:37:10.361445](https://huggingface.co/datasets/open-llm-leaderboard/details_llm-jp__llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0/blob/main/results_2023-12-02T12-37-10.361445.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
HamdanXI/paradetox-refined-dataset | HamdanXI | 2023-12-02T12:46:08Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:46:07Z | ---
dataset_info:
features:
- name: en_toxic_comment
dtype: string
- name: en_neutral_comment
dtype: string
- name: edit_ops
sequence:
sequence: string
- name: masked_comment
dtype: string
splits:
- name: train
num_bytes: 5592956
num_examples: 19744
download_size: 2314734
dataset_size: 5592956
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
open-llm-leaderboard/details_GeneZC__MiniChat-1.5-3B | open-llm-leaderboard | 2023-12-02T12:48:25Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:48:21Z | ---
pretty_name: Evaluation run of GeneZC/MiniChat-1.5-3B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [GeneZC/MiniChat-1.5-3B](https://huggingface.co/GeneZC/MiniChat-1.5-3B) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_GeneZC__MiniChat-1.5-3B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T12:48:18.451769](https://huggingface.co/datasets/open-llm-leaderboard/details_GeneZC__MiniChat-1.5-3B/blob/main/results_2023-12-02T12-48-18.451769.json)(note\
\ that their might be results for other tasks in 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\": 0.24184988627748294,\n\
\ \"acc_stderr\": 0.011794861371318703\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.24184988627748294,\n \"acc_stderr\": 0.011794861371318703\n\
\ }\n}\n```"
repo_url: https://huggingface.co/GeneZC/MiniChat-1.5-3B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T12_48_18.451769
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-48-18.451769.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-48-18.451769.parquet'
- config_name: results
data_files:
- split: 2023_12_02T12_48_18.451769
path:
- results_2023-12-02T12-48-18.451769.parquet
- split: latest
path:
- results_2023-12-02T12-48-18.451769.parquet
---
# Dataset Card for Evaluation run of GeneZC/MiniChat-1.5-3B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/GeneZC/MiniChat-1.5-3B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [GeneZC/MiniChat-1.5-3B](https://huggingface.co/GeneZC/MiniChat-1.5-3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_GeneZC__MiniChat-1.5-3B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T12:48:18.451769](https://huggingface.co/datasets/open-llm-leaderboard/details_GeneZC__MiniChat-1.5-3B/blob/main/results_2023-12-02T12-48-18.451769.json)(note that their might be results for other tasks in 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": 0.24184988627748294,
"acc_stderr": 0.011794861371318703
},
"harness|gsm8k|5": {
"acc": 0.24184988627748294,
"acc_stderr": 0.011794861371318703
}
}
```
### 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?
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### 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
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### Citation Information
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### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_amazon__MistralLite | open-llm-leaderboard | 2023-12-02T12:51:30Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:51:26Z | ---
pretty_name: Evaluation run of amazon/MistralLite
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [amazon/MistralLite](https://huggingface.co/amazon/MistralLite) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_amazon__MistralLite\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T12:51:23.360186](https://huggingface.co/datasets/open-llm-leaderboard/details_amazon__MistralLite/blob/main/results_2023-12-02T12-51-23.360186.json)(note\
\ that their might be results for other tasks in 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\": 0.01061410159211524,\n\
\ \"acc_stderr\": 0.002822713322387704\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.01061410159211524,\n \"acc_stderr\": 0.002822713322387704\n\
\ }\n}\n```"
repo_url: https://huggingface.co/amazon/MistralLite
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T12_51_23.360186
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-51-23.360186.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-51-23.360186.parquet'
- config_name: results
data_files:
- split: 2023_12_02T12_51_23.360186
path:
- results_2023-12-02T12-51-23.360186.parquet
- split: latest
path:
- results_2023-12-02T12-51-23.360186.parquet
---
# Dataset Card for Evaluation run of amazon/MistralLite
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/amazon/MistralLite
- **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 [amazon/MistralLite](https://huggingface.co/amazon/MistralLite) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_amazon__MistralLite",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T12:51:23.360186](https://huggingface.co/datasets/open-llm-leaderboard/details_amazon__MistralLite/blob/main/results_2023-12-02T12-51-23.360186.json)(note that their might be results for other tasks in 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": 0.01061410159211524,
"acc_stderr": 0.002822713322387704
},
"harness|gsm8k|5": {
"acc": 0.01061410159211524,
"acc_stderr": 0.002822713322387704
}
}
```
### 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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|
EduAlmeida/brendataset | EduAlmeida | 2023-12-02T12:58:22Z | 0 | 0 | [
"license:openrail++",
"region:us"
]
| null | 2023-12-02T12:55:51Z | ---
license: openrail++
---
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|
open-llm-leaderboard/details_01-ai__Yi-6B-200K | open-llm-leaderboard | 2023-12-02T12:58:21Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:56:53Z | ---
pretty_name: Evaluation run of 01-ai/Yi-6B-200K
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [01-ai/Yi-6B-200K](https://huggingface.co/01-ai/Yi-6B-200K) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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_01-ai__Yi-6B-200K\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T12:58:15.129366](https://huggingface.co/datasets/open-llm-leaderboard/details_01-ai__Yi-6B-200K/blob/main/results_2023-12-02T12-58-15.129366.json)(note\
\ that their might be results for other tasks in 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\": 0.3032600454890068,\n\
\ \"acc_stderr\": 0.0126615026634187\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.3032600454890068,\n \"acc_stderr\": 0.0126615026634187\n\
\ }\n}\n```"
repo_url: https://huggingface.co/01-ai/Yi-6B-200K
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T12_56_50.280611
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-56-50.280611.parquet'
- split: 2023_12_02T12_57_04.291453
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-57-04.291453.parquet'
- split: 2023_12_02T12_57_29.899454
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-57-29.899454.parquet'
- split: 2023_12_02T12_58_15.129366
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-58-15.129366.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-58-15.129366.parquet'
- config_name: results
data_files:
- split: 2023_12_02T12_56_50.280611
path:
- results_2023-12-02T12-56-50.280611.parquet
- split: 2023_12_02T12_57_04.291453
path:
- results_2023-12-02T12-57-04.291453.parquet
- split: 2023_12_02T12_57_29.899454
path:
- results_2023-12-02T12-57-29.899454.parquet
- split: 2023_12_02T12_58_15.129366
path:
- results_2023-12-02T12-58-15.129366.parquet
- split: latest
path:
- results_2023-12-02T12-58-15.129366.parquet
---
# Dataset Card for Evaluation run of 01-ai/Yi-6B-200K
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/01-ai/Yi-6B-200K
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [01-ai/Yi-6B-200K](https://huggingface.co/01-ai/Yi-6B-200K) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_01-ai__Yi-6B-200K",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T12:58:15.129366](https://huggingface.co/datasets/open-llm-leaderboard/details_01-ai__Yi-6B-200K/blob/main/results_2023-12-02T12-58-15.129366.json)(note that their might be results for other tasks in 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": 0.3032600454890068,
"acc_stderr": 0.0126615026634187
},
"harness|gsm8k|5": {
"acc": 0.3032600454890068,
"acc_stderr": 0.0126615026634187
}
}
```
### 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_Azure99__blossom-v3-mistral-7b | open-llm-leaderboard | 2023-12-02T12:57:21Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T12:57:16Z | ---
pretty_name: Evaluation run of Azure99/blossom-v3-mistral-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Azure99/blossom-v3-mistral-7b](https://huggingface.co/Azure99/blossom-v3-mistral-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Azure99__blossom-v3-mistral-7b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T12:57:13.954407](https://huggingface.co/datasets/open-llm-leaderboard/details_Azure99__blossom-v3-mistral-7b/blob/main/results_2023-12-02T12-57-13.954407.json)(note\
\ that their might be results for other tasks in 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\": 0.4670204700530705,\n\
\ \"acc_stderr\": 0.013742492794163416\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.4670204700530705,\n \"acc_stderr\": 0.013742492794163416\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Azure99/blossom-v3-mistral-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T12_57_13.954407
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-57-13.954407.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-57-13.954407.parquet'
- config_name: results
data_files:
- split: 2023_12_02T12_57_13.954407
path:
- results_2023-12-02T12-57-13.954407.parquet
- split: latest
path:
- results_2023-12-02T12-57-13.954407.parquet
---
# Dataset Card for Evaluation run of Azure99/blossom-v3-mistral-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Azure99/blossom-v3-mistral-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Azure99/blossom-v3-mistral-7b](https://huggingface.co/Azure99/blossom-v3-mistral-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Azure99__blossom-v3-mistral-7b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T12:57:13.954407](https://huggingface.co/datasets/open-llm-leaderboard/details_Azure99__blossom-v3-mistral-7b/blob/main/results_2023-12-02T12-57-13.954407.json)(note that their might be results for other tasks in 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": 0.4670204700530705,
"acc_stderr": 0.013742492794163416
},
"harness|gsm8k|5": {
"acc": 0.4670204700530705,
"acc_stderr": 0.013742492794163416
}
}
```
### 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_llm-jp__llm-jp-13b-instruct-full-jaster-v1.0 | open-llm-leaderboard | 2023-12-02T13:00:09Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:00:04Z | ---
pretty_name: Evaluation run of llm-jp/llm-jp-13b-instruct-full-jaster-v1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [llm-jp/llm-jp-13b-instruct-full-jaster-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-v1.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_llm-jp__llm-jp-13b-instruct-full-jaster-v1.0\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:00:01.308695](https://huggingface.co/datasets/open-llm-leaderboard/details_llm-jp__llm-jp-13b-instruct-full-jaster-v1.0/blob/main/results_2023-12-02T13-00-01.308695.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-v1.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_00_01.308695
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-00-01.308695.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-00-01.308695.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_00_01.308695
path:
- results_2023-12-02T13-00-01.308695.parquet
- split: latest
path:
- results_2023-12-02T13-00-01.308695.parquet
---
# Dataset Card for Evaluation run of llm-jp/llm-jp-13b-instruct-full-jaster-v1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-v1.0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [llm-jp/llm-jp-13b-instruct-full-jaster-v1.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-jaster-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_llm-jp__llm-jp-13b-instruct-full-jaster-v1.0",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:00:01.308695](https://huggingface.co/datasets/open-llm-leaderboard/details_llm-jp__llm-jp-13b-instruct-full-jaster-v1.0/blob/main/results_2023-12-02T13-00-01.308695.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_Weyaxi__OpenOrca-Zephyr-7B | open-llm-leaderboard | 2023-12-02T13:01:47Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:01:43Z | ---
pretty_name: Evaluation run of Weyaxi/OpenOrca-Zephyr-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/OpenOrca-Zephyr-7B](https://huggingface.co/Weyaxi/OpenOrca-Zephyr-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__OpenOrca-Zephyr-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:01:40.868867](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__OpenOrca-Zephyr-7B/blob/main/results_2023-12-02T13-01-40.868867.json)(note\
\ that their might be results for other tasks in 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\": 0.4624715693707354,\n\
\ \"acc_stderr\": 0.013733636059107756\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.4624715693707354,\n \"acc_stderr\": 0.013733636059107756\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/OpenOrca-Zephyr-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_01_40.868867
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-01-40.868867.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-01-40.868867.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_01_40.868867
path:
- results_2023-12-02T13-01-40.868867.parquet
- split: latest
path:
- results_2023-12-02T13-01-40.868867.parquet
---
# Dataset Card for Evaluation run of Weyaxi/OpenOrca-Zephyr-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/OpenOrca-Zephyr-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 [Weyaxi/OpenOrca-Zephyr-7B](https://huggingface.co/Weyaxi/OpenOrca-Zephyr-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__OpenOrca-Zephyr-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:01:40.868867](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__OpenOrca-Zephyr-7B/blob/main/results_2023-12-02T13-01-40.868867.json)(note that their might be results for other tasks in 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": 0.4624715693707354,
"acc_stderr": 0.013733636059107756
},
"harness|gsm8k|5": {
"acc": 0.4624715693707354,
"acc_stderr": 0.013733636059107756
}
}
```
### 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_Weyaxi__test-help-steer-filtered-orig | open-llm-leaderboard | 2023-12-02T13:28:33Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:28:26Z | ---
pretty_name: Evaluation run of Weyaxi/test-help-steer-filtered-orig
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/test-help-steer-filtered-orig](https://huggingface.co/Weyaxi/test-help-steer-filtered-orig)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__test-help-steer-filtered-orig\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:28:23.925402](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__test-help-steer-filtered-orig/blob/main/results_2023-12-02T13-28-23.925402.json)(note\
\ that their might be results for other tasks in 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\": 0.09628506444275967,\n\
\ \"acc_stderr\": 0.008125264128215877\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.09628506444275967,\n \"acc_stderr\": 0.008125264128215877\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/test-help-steer-filtered-orig
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_28_23.925402
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-28-23.925402.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-28-23.925402.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_28_23.925402
path:
- results_2023-12-02T13-28-23.925402.parquet
- split: latest
path:
- results_2023-12-02T13-28-23.925402.parquet
---
# Dataset Card for Evaluation run of Weyaxi/test-help-steer-filtered-orig
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/test-help-steer-filtered-orig
- **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 [Weyaxi/test-help-steer-filtered-orig](https://huggingface.co/Weyaxi/test-help-steer-filtered-orig) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__test-help-steer-filtered-orig",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:28:23.925402](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__test-help-steer-filtered-orig/blob/main/results_2023-12-02T13-28-23.925402.json)(note that their might be results for other tasks in 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": 0.09628506444275967,
"acc_stderr": 0.008125264128215877
},
"harness|gsm8k|5": {
"acc": 0.09628506444275967,
"acc_stderr": 0.008125264128215877
}
}
```
### 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_Weyaxi__neural-chat-7b-v3-1-Nebula-v2-7B | open-llm-leaderboard | 2023-12-02T13:35:50Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:35:43Z | ---
pretty_name: Evaluation run of Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B](https://huggingface.co/Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__neural-chat-7b-v3-1-Nebula-v2-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:35:39.622054](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__neural-chat-7b-v3-1-Nebula-v2-7B/blob/main/results_2023-12-02T13-35-39.622054.json)(note\
\ that their might be results for other tasks in 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\": 0.04624715693707354,\n\
\ \"acc_stderr\": 0.005784991662691895\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.04624715693707354,\n \"acc_stderr\": 0.005784991662691895\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_35_39.622054
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-35-39.622054.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-35-39.622054.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_35_39.622054
path:
- results_2023-12-02T13-35-39.622054.parquet
- split: latest
path:
- results_2023-12-02T13-35-39.622054.parquet
---
# Dataset Card for Evaluation run of Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B
- **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 [Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B](https://huggingface.co/Weyaxi/neural-chat-7b-v3-1-Nebula-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__neural-chat-7b-v3-1-Nebula-v2-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:35:39.622054](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__neural-chat-7b-v3-1-Nebula-v2-7B/blob/main/results_2023-12-02T13-35-39.622054.json)(note that their might be results for other tasks in 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": 0.04624715693707354,
"acc_stderr": 0.005784991662691895
},
"harness|gsm8k|5": {
"acc": 0.04624715693707354,
"acc_stderr": 0.005784991662691895
}
}
```
### 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_Weyaxi__zephyr-beta-Nebula-v2-7B | open-llm-leaderboard | 2023-12-02T13:42:30Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:42:23Z | ---
pretty_name: Evaluation run of Weyaxi/zephyr-beta-Nebula-v2-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/zephyr-beta-Nebula-v2-7B](https://huggingface.co/Weyaxi/zephyr-beta-Nebula-v2-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__zephyr-beta-Nebula-v2-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:42:20.652326](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__zephyr-beta-Nebula-v2-7B/blob/main/results_2023-12-02T13-42-20.652326.json)(note\
\ that their might be results for other tasks in 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\": 0.17513267626990145,\n\
\ \"acc_stderr\": 0.010469307043157914\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.17513267626990145,\n \"acc_stderr\": 0.010469307043157914\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/zephyr-beta-Nebula-v2-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_42_20.652326
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-42-20.652326.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-42-20.652326.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_42_20.652326
path:
- results_2023-12-02T13-42-20.652326.parquet
- split: latest
path:
- results_2023-12-02T13-42-20.652326.parquet
---
# Dataset Card for Evaluation run of Weyaxi/zephyr-beta-Nebula-v2-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/zephyr-beta-Nebula-v2-7B
- **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 [Weyaxi/zephyr-beta-Nebula-v2-7B](https://huggingface.co/Weyaxi/zephyr-beta-Nebula-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__zephyr-beta-Nebula-v2-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:42:20.652326](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__zephyr-beta-Nebula-v2-7B/blob/main/results_2023-12-02T13-42-20.652326.json)(note that their might be results for other tasks in 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": 0.17513267626990145,
"acc_stderr": 0.010469307043157914
},
"harness|gsm8k|5": {
"acc": 0.17513267626990145,
"acc_stderr": 0.010469307043157914
}
}
```
### 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_yec019__fbopt-350m-8bit | open-llm-leaderboard | 2023-12-02T13:46:22Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:46:14Z | ---
pretty_name: Evaluation run of yec019/fbopt-350m-8bit
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yec019/fbopt-350m-8bit](https://huggingface.co/yec019/fbopt-350m-8bit) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_yec019__fbopt-350m-8bit\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:46:11.364082](https://huggingface.co/datasets/open-llm-leaderboard/details_yec019__fbopt-350m-8bit/blob/main/results_2023-12-02T13-46-11.364082.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.01288855193328279,\n\
\ \"acc_stderr\": 0.0031069012664996336\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.01288855193328279,\n \"acc_stderr\": 0.0031069012664996336\n\
\ }\n}\n```"
repo_url: https://huggingface.co/yec019/fbopt-350m-8bit
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_46_11.364082
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-46-11.364082.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-46-11.364082.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_46_11.364082
path:
- results_2023-12-02T13-46-11.364082.parquet
- split: latest
path:
- results_2023-12-02T13-46-11.364082.parquet
---
# Dataset Card for Evaluation run of yec019/fbopt-350m-8bit
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yec019/fbopt-350m-8bit
- **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 [yec019/fbopt-350m-8bit](https://huggingface.co/yec019/fbopt-350m-8bit) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_yec019__fbopt-350m-8bit",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:46:11.364082](https://huggingface.co/datasets/open-llm-leaderboard/details_yec019__fbopt-350m-8bit/blob/main/results_2023-12-02T13-46-11.364082.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.01288855193328279,
"acc_stderr": 0.0031069012664996336
},
"harness|gsm8k|5": {
"acc": 0.01288855193328279,
"acc_stderr": 0.0031069012664996336
}
}
```
### 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_TinyLlama__TinyLlama-1.1B-Chat-v0.6 | open-llm-leaderboard | 2023-12-02T13:49:59Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:49:54Z | ---
pretty_name: Evaluation run of TinyLlama/TinyLlama-1.1B-Chat-v0.6
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TinyLlama__TinyLlama-1.1B-Chat-v0.6\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:49:51.667624](https://huggingface.co/datasets/open-llm-leaderboard/details_TinyLlama__TinyLlama-1.1B-Chat-v0.6/blob/main/results_2023-12-02T13-49-51.667624.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.02122820318423048,\n\
\ \"acc_stderr\": 0.003970449129848635\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.02122820318423048,\n \"acc_stderr\": 0.003970449129848635\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_49_51.667624
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-49-51.667624.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-49-51.667624.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_49_51.667624
path:
- results_2023-12-02T13-49-51.667624.parquet
- split: latest
path:
- results_2023-12-02T13-49-51.667624.parquet
---
# Dataset Card for Evaluation run of TinyLlama/TinyLlama-1.1B-Chat-v0.6
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6
- **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 [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TinyLlama__TinyLlama-1.1B-Chat-v0.6",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:49:51.667624](https://huggingface.co/datasets/open-llm-leaderboard/details_TinyLlama__TinyLlama-1.1B-Chat-v0.6/blob/main/results_2023-12-02T13-49-51.667624.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.02122820318423048,
"acc_stderr": 0.003970449129848635
},
"harness|gsm8k|5": {
"acc": 0.02122820318423048,
"acc_stderr": 0.003970449129848635
}
}
```
### 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_TinyLlama__TinyLlama-1.1B-intermediate-step-955k-token-2T | open-llm-leaderboard | 2023-12-02T13:50:04Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:49:59Z | ---
pretty_name: Evaluation run of TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TinyLlama__TinyLlama-1.1B-intermediate-step-955k-token-2T\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:49:56.962139](https://huggingface.co/datasets/open-llm-leaderboard/details_TinyLlama__TinyLlama-1.1B-intermediate-step-955k-token-2T/blob/main/results_2023-12-02T13-49-56.962139.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.013646702047005308,\n\
\ \"acc_stderr\": 0.0031957470754808434\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.013646702047005308,\n \"acc_stderr\": 0.0031957470754808434\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_49_56.962139
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-49-56.962139.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-49-56.962139.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_49_56.962139
path:
- results_2023-12-02T13-49-56.962139.parquet
- split: latest
path:
- results_2023-12-02T13-49-56.962139.parquet
---
# Dataset Card for Evaluation run of TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
- **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 [TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TinyLlama__TinyLlama-1.1B-intermediate-step-955k-token-2T",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:49:56.962139](https://huggingface.co/datasets/open-llm-leaderboard/details_TinyLlama__TinyLlama-1.1B-intermediate-step-955k-token-2T/blob/main/results_2023-12-02T13-49-56.962139.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.013646702047005308,
"acc_stderr": 0.0031957470754808434
},
"harness|gsm8k|5": {
"acc": 0.013646702047005308,
"acc_stderr": 0.0031957470754808434
}
}
```
### 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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|
giorgoskyriacou2009tv/Itv-1 | giorgoskyriacou2009tv | 2023-12-02T13:51:21Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-02T13:51:20Z | ---
license: apache-2.0
---
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|
open-llm-leaderboard/details_Korabbit__Llama-2-7b-chat-hf-afr-200step-merged | open-llm-leaderboard | 2023-12-02T13:52:39Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:52:30Z | ---
pretty_name: Evaluation run of Korabbit/Llama-2-7b-chat-hf-afr-200step-merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Korabbit/Llama-2-7b-chat-hf-afr-200step-merged](https://huggingface.co/Korabbit/Llama-2-7b-chat-hf-afr-200step-merged)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Korabbit__Llama-2-7b-chat-hf-afr-200step-merged\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:52:27.757521](https://huggingface.co/datasets/open-llm-leaderboard/details_Korabbit__Llama-2-7b-chat-hf-afr-200step-merged/blob/main/results_2023-12-02T13-52-27.757521.json)(note\
\ that their might be results for other tasks in 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\": 0.18953752843062927,\n\
\ \"acc_stderr\": 0.010795837931896377\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.18953752843062927,\n \"acc_stderr\": 0.010795837931896377\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Korabbit/Llama-2-7b-chat-hf-afr-200step-merged
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_52_27.757521
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-52-27.757521.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-52-27.757521.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_52_27.757521
path:
- results_2023-12-02T13-52-27.757521.parquet
- split: latest
path:
- results_2023-12-02T13-52-27.757521.parquet
---
# Dataset Card for Evaluation run of Korabbit/Llama-2-7b-chat-hf-afr-200step-merged
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Korabbit/Llama-2-7b-chat-hf-afr-200step-merged
- **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 [Korabbit/Llama-2-7b-chat-hf-afr-200step-merged](https://huggingface.co/Korabbit/Llama-2-7b-chat-hf-afr-200step-merged) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Korabbit__Llama-2-7b-chat-hf-afr-200step-merged",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:52:27.757521](https://huggingface.co/datasets/open-llm-leaderboard/details_Korabbit__Llama-2-7b-chat-hf-afr-200step-merged/blob/main/results_2023-12-02T13-52-27.757521.json)(note that their might be results for other tasks in 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": 0.18953752843062927,
"acc_stderr": 0.010795837931896377
},
"harness|gsm8k|5": {
"acc": 0.18953752843062927,
"acc_stderr": 0.010795837931896377
}
}
```
### 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_Weyaxi__HelpSteer-filtered-7B | open-llm-leaderboard | 2023-12-02T13:56:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:56:12Z | ---
pretty_name: Evaluation run of Weyaxi/HelpSteer-filtered-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/HelpSteer-filtered-7B](https://huggingface.co/Weyaxi/HelpSteer-filtered-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:56:09.449355](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B/blob/main/results_2023-12-02T13-56-09.449355.json)(note\
\ that their might be results for other tasks in 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\": 0.33434420015163,\n\
\ \"acc_stderr\": 0.012994634003332771\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.33434420015163,\n \"acc_stderr\": 0.012994634003332771\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/HelpSteer-filtered-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_56_09.449355
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-56-09.449355.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-56-09.449355.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_56_09.449355
path:
- results_2023-12-02T13-56-09.449355.parquet
- split: latest
path:
- results_2023-12-02T13-56-09.449355.parquet
---
# Dataset Card for Evaluation run of Weyaxi/HelpSteer-filtered-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/HelpSteer-filtered-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 [Weyaxi/HelpSteer-filtered-7B](https://huggingface.co/Weyaxi/HelpSteer-filtered-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:56:09.449355](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B/blob/main/results_2023-12-02T13-56-09.449355.json)(note that their might be results for other tasks in 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": 0.33434420015163,
"acc_stderr": 0.012994634003332771
},
"harness|gsm8k|5": {
"acc": 0.33434420015163,
"acc_stderr": 0.012994634003332771
}
}
```
### 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_Kiddyz__testllm-c2 | open-llm-leaderboard | 2023-12-02T13:57:18Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:57:14Z | ---
pretty_name: Evaluation run of Kiddyz/testllm-c2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Kiddyz/testllm-c2](https://huggingface.co/Kiddyz/testllm-c2) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Kiddyz__testllm-c2\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:57:11.515401](https://huggingface.co/datasets/open-llm-leaderboard/details_Kiddyz__testllm-c2/blob/main/results_2023-12-02T13-57-11.515401.json)(note\
\ that their might be results for other tasks in 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\": 0.47384382107657314,\n\
\ \"acc_stderr\": 0.013753627037255047\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.47384382107657314,\n \"acc_stderr\": 0.013753627037255047\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Kiddyz/testllm-c2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_57_11.515401
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-57-11.515401.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-57-11.515401.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_57_11.515401
path:
- results_2023-12-02T13-57-11.515401.parquet
- split: latest
path:
- results_2023-12-02T13-57-11.515401.parquet
---
# Dataset Card for Evaluation run of Kiddyz/testllm-c2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Kiddyz/testllm-c2
- **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 [Kiddyz/testllm-c2](https://huggingface.co/Kiddyz/testllm-c2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Kiddyz__testllm-c2",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:57:11.515401](https://huggingface.co/datasets/open-llm-leaderboard/details_Kiddyz__testllm-c2/blob/main/results_2023-12-02T13-57-11.515401.json)(note that their might be results for other tasks in 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": 0.47384382107657314,
"acc_stderr": 0.013753627037255047
},
"harness|gsm8k|5": {
"acc": 0.47384382107657314,
"acc_stderr": 0.013753627037255047
}
}
```
### 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_Weyaxi__SynthIA-v1.3-Nebula-v2-7B | open-llm-leaderboard | 2023-12-02T13:57:39Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:57:34Z | ---
pretty_name: Evaluation run of Weyaxi/SynthIA-v1.3-Nebula-v2-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/SynthIA-v1.3-Nebula-v2-7B](https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:57:31.778075](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B/blob/main/results_2023-12-02T13-57-31.778075.json)(note\
\ that their might be results for other tasks in 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\": 0.2486732373009856,\n\
\ \"acc_stderr\": 0.01190614722287996\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.2486732373009856,\n \"acc_stderr\": 0.01190614722287996\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_57_31.778075
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-57-31.778075.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-57-31.778075.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_57_31.778075
path:
- results_2023-12-02T13-57-31.778075.parquet
- split: latest
path:
- results_2023-12-02T13-57-31.778075.parquet
---
# Dataset Card for Evaluation run of Weyaxi/SynthIA-v1.3-Nebula-v2-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B
- **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 [Weyaxi/SynthIA-v1.3-Nebula-v2-7B](https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:57:31.778075](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B/blob/main/results_2023-12-02T13-57-31.778075.json)(note that their might be results for other tasks in 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": 0.2486732373009856,
"acc_stderr": 0.01190614722287996
},
"harness|gsm8k|5": {
"acc": 0.2486732373009856,
"acc_stderr": 0.01190614722287996
}
}
```
### 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_PulsarAI__Nebula-v2-7B | open-llm-leaderboard | 2023-12-02T13:58:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T13:58:11Z | ---
pretty_name: Evaluation run of PulsarAI/Nebula-v2-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PulsarAI/Nebula-v2-7B](https://huggingface.co/PulsarAI/Nebula-v2-7B) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:58:09.073163](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B/blob/main/results_2023-12-02T13-58-09.073163.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.3169067475360121,\n\
\ \"acc_stderr\": 0.012815868296721373\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.3169067475360121,\n \"acc_stderr\": 0.012815868296721373\n\
\ }\n}\n```"
repo_url: https://huggingface.co/PulsarAI/Nebula-v2-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_58_09.073163
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-58-09.073163.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-58-09.073163.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_58_09.073163
path:
- results_2023-12-02T13-58-09.073163.parquet
- split: latest
path:
- results_2023-12-02T13-58-09.073163.parquet
---
# Dataset Card for Evaluation run of PulsarAI/Nebula-v2-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PulsarAI/Nebula-v2-7B
- **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 [PulsarAI/Nebula-v2-7B](https://huggingface.co/PulsarAI/Nebula-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:58:09.073163](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B/blob/main/results_2023-12-02T13-58-09.073163.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.3169067475360121,
"acc_stderr": 0.012815868296721373
},
"harness|gsm8k|5": {
"acc": 0.3169067475360121,
"acc_stderr": 0.012815868296721373
}
}
```
### 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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|
magixxixx/shopee-product-reviews-on-computer-category | magixxixx | 2023-12-02T14:14:58Z | 0 | 0 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:tl",
"language:en",
"shopee",
"reviews",
"region:us"
]
| [
"text-classification"
]
| 2023-12-02T14:01:02Z | ---
task_categories:
- text-classification
language:
- tl
- en
tags:
- shopee
- reviews
pretty_name: Shopee reviews on computer category, 20k positives and 20k negatives
size_categories:
- 10K<n<100K
--- | [
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|
open-llm-leaderboard/details_BEE-spoke-data__NanoLlama-GQA-L10-A32_KV8-v13-KI | open-llm-leaderboard | 2023-12-02T14:06:08Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:06:03Z | ---
pretty_name: Evaluation run of BEE-spoke-data/NanoLlama-GQA-L10-A32_KV8-v13-KI
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BEE-spoke-data/NanoLlama-GQA-L10-A32_KV8-v13-KI](https://huggingface.co/BEE-spoke-data/NanoLlama-GQA-L10-A32_KV8-v13-KI)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BEE-spoke-data__NanoLlama-GQA-L10-A32_KV8-v13-KI\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:06:00.673559](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__NanoLlama-GQA-L10-A32_KV8-v13-KI/blob/main/results_2023-12-02T14-06-00.673559.json)(note\
\ that their might be results for other tasks in 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\": 0.009097801364670205,\n\
\ \"acc_stderr\": 0.002615326510775673\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.009097801364670205,\n \"acc_stderr\": 0.002615326510775673\n\
\ }\n}\n```"
repo_url: https://huggingface.co/BEE-spoke-data/NanoLlama-GQA-L10-A32_KV8-v13-KI
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T14_06_00.673559
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-06-00.673559.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-06-00.673559.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_06_00.673559
path:
- results_2023-12-02T14-06-00.673559.parquet
- split: latest
path:
- results_2023-12-02T14-06-00.673559.parquet
---
# Dataset Card for Evaluation run of BEE-spoke-data/NanoLlama-GQA-L10-A32_KV8-v13-KI
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/BEE-spoke-data/NanoLlama-GQA-L10-A32_KV8-v13-KI
- **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 [BEE-spoke-data/NanoLlama-GQA-L10-A32_KV8-v13-KI](https://huggingface.co/BEE-spoke-data/NanoLlama-GQA-L10-A32_KV8-v13-KI) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_BEE-spoke-data__NanoLlama-GQA-L10-A32_KV8-v13-KI",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:06:00.673559](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__NanoLlama-GQA-L10-A32_KV8-v13-KI/blob/main/results_2023-12-02T14-06-00.673559.json)(note that their might be results for other tasks in 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": 0.009097801364670205,
"acc_stderr": 0.002615326510775673
},
"harness|gsm8k|5": {
"acc": 0.009097801364670205,
"acc_stderr": 0.002615326510775673
}
}
```
### 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_NurtureAI__Starling-LM-11B-alpha-v1 | open-llm-leaderboard | 2023-12-02T14:12:24Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:12:19Z | ---
pretty_name: Evaluation run of NurtureAI/Starling-LM-11B-alpha-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NurtureAI/Starling-LM-11B-alpha-v1](https://huggingface.co/NurtureAI/Starling-LM-11B-alpha-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NurtureAI__Starling-LM-11B-alpha-v1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:12:16.708140](https://huggingface.co/datasets/open-llm-leaderboard/details_NurtureAI__Starling-LM-11B-alpha-v1/blob/main/results_2023-12-02T14-12-16.708140.json)(note\
\ that their might be results for other tasks in 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\": 0.5094768764215315,\n\
\ \"acc_stderr\": 0.01377001065116882\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.5094768764215315,\n \"acc_stderr\": 0.01377001065116882\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NurtureAI/Starling-LM-11B-alpha-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T14_12_16.708140
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-12-16.708140.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-12-16.708140.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_12_16.708140
path:
- results_2023-12-02T14-12-16.708140.parquet
- split: latest
path:
- results_2023-12-02T14-12-16.708140.parquet
---
# Dataset Card for Evaluation run of NurtureAI/Starling-LM-11B-alpha-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NurtureAI/Starling-LM-11B-alpha-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 [NurtureAI/Starling-LM-11B-alpha-v1](https://huggingface.co/NurtureAI/Starling-LM-11B-alpha-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_NurtureAI__Starling-LM-11B-alpha-v1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:12:16.708140](https://huggingface.co/datasets/open-llm-leaderboard/details_NurtureAI__Starling-LM-11B-alpha-v1/blob/main/results_2023-12-02T14-12-16.708140.json)(note that their might be results for other tasks in 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": 0.5094768764215315,
"acc_stderr": 0.01377001065116882
},
"harness|gsm8k|5": {
"acc": 0.5094768764215315,
"acc_stderr": 0.01377001065116882
}
}
```
### 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_feidfoe__Metamath-reproduce-7b | open-llm-leaderboard | 2023-12-02T14:22:31Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:22:26Z | ---
pretty_name: Evaluation run of feidfoe/Metamath-reproduce-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [feidfoe/Metamath-reproduce-7b](https://huggingface.co/feidfoe/Metamath-reproduce-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_feidfoe__Metamath-reproduce-7b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:22:23.494556](https://huggingface.co/datasets/open-llm-leaderboard/details_feidfoe__Metamath-reproduce-7b/blob/main/results_2023-12-02T14-22-23.494556.json)(note\
\ that their might be results for other tasks in 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\": 0.5815011372251706,\n\
\ \"acc_stderr\": 0.013588287284030881\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5815011372251706,\n \"acc_stderr\": 0.013588287284030881\n\
\ }\n}\n```"
repo_url: https://huggingface.co/feidfoe/Metamath-reproduce-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T14_22_23.494556
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-22-23.494556.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-22-23.494556.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_22_23.494556
path:
- results_2023-12-02T14-22-23.494556.parquet
- split: latest
path:
- results_2023-12-02T14-22-23.494556.parquet
---
# Dataset Card for Evaluation run of feidfoe/Metamath-reproduce-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/feidfoe/Metamath-reproduce-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 [feidfoe/Metamath-reproduce-7b](https://huggingface.co/feidfoe/Metamath-reproduce-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_feidfoe__Metamath-reproduce-7b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:22:23.494556](https://huggingface.co/datasets/open-llm-leaderboard/details_feidfoe__Metamath-reproduce-7b/blob/main/results_2023-12-02T14-22-23.494556.json)(note that their might be results for other tasks in 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": 0.5815011372251706,
"acc_stderr": 0.013588287284030881
},
"harness|gsm8k|5": {
"acc": 0.5815011372251706,
"acc_stderr": 0.013588287284030881
}
}
```
### 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_Weyaxi__Dolphin-Nebula-7B | open-llm-leaderboard | 2023-12-02T14:25:46Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:25:41Z | ---
pretty_name: Evaluation run of Weyaxi/Dolphin-Nebula-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/Dolphin-Nebula-7B](https://huggingface.co/Weyaxi/Dolphin-Nebula-7B) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__Dolphin-Nebula-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:25:38.586013](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Dolphin-Nebula-7B/blob/main/results_2023-12-02T14-25-38.586013.json)(note\
\ that their might be results for other tasks in 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\": 0.3305534495830174,\n\
\ \"acc_stderr\": 0.012957496367085026\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.3305534495830174,\n \"acc_stderr\": 0.012957496367085026\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/Dolphin-Nebula-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T14_25_38.586013
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-25-38.586013.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-25-38.586013.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_25_38.586013
path:
- results_2023-12-02T14-25-38.586013.parquet
- split: latest
path:
- results_2023-12-02T14-25-38.586013.parquet
---
# Dataset Card for Evaluation run of Weyaxi/Dolphin-Nebula-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/Dolphin-Nebula-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 [Weyaxi/Dolphin-Nebula-7B](https://huggingface.co/Weyaxi/Dolphin-Nebula-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__Dolphin-Nebula-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:25:38.586013](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Dolphin-Nebula-7B/blob/main/results_2023-12-02T14-25-38.586013.json)(note that their might be results for other tasks in 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": 0.3305534495830174,
"acc_stderr": 0.012957496367085026
},
"harness|gsm8k|5": {
"acc": 0.3305534495830174,
"acc_stderr": 0.012957496367085026
}
}
```
### 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_CausalLM__14B | open-llm-leaderboard | 2023-12-02T14:28:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:28:11Z | ---
pretty_name: Evaluation run of CausalLM/14B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CausalLM/14B](https://huggingface.co/CausalLM/14B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CausalLM__14B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:28:09.246570](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__14B/blob/main/results_2023-12-02T14-28-09.246570.json)(note\
\ that their might be results for other tasks in 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\": 0.5860500379075056,\n\
\ \"acc_stderr\": 0.013566991960151781\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5860500379075056,\n \"acc_stderr\": 0.013566991960151781\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CausalLM/14B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T14_28_09.246570
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-28-09.246570.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-28-09.246570.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_28_09.246570
path:
- results_2023-12-02T14-28-09.246570.parquet
- split: latest
path:
- results_2023-12-02T14-28-09.246570.parquet
---
# Dataset Card for Evaluation run of CausalLM/14B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CausalLM/14B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [CausalLM/14B](https://huggingface.co/CausalLM/14B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CausalLM__14B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:28:09.246570](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__14B/blob/main/results_2023-12-02T14-28-09.246570.json)(note that their might be results for other tasks in 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": 0.5860500379075056,
"acc_stderr": 0.013566991960151781
},
"harness|gsm8k|5": {
"acc": 0.5860500379075056,
"acc_stderr": 0.013566991960151781
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_vihangd__dopeyplats-1.1b-2T-v1 | open-llm-leaderboard | 2023-12-02T14:28:44Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-02T14:28:39Z | ---
pretty_name: Evaluation run of vihangd/dopeyplats-1.1b-2T-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [vihangd/dopeyplats-1.1b-2T-v1](https://huggingface.co/vihangd/dopeyplats-1.1b-2T-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vihangd__dopeyplats-1.1b-2T-v1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:28:36.585578](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__dopeyplats-1.1b-2T-v1/blob/main/results_2023-12-02T14-28-36.585578.json)(note\
\ that their might be results for other tasks in 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\": 0.016679302501895376,\n\
\ \"acc_stderr\": 0.0035275958887224295\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.016679302501895376,\n \"acc_stderr\": 0.0035275958887224295\n\
\ }\n}\n```"
repo_url: https://huggingface.co/vihangd/dopeyplats-1.1b-2T-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T14_28_36.585578
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-28-36.585578.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-28-36.585578.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_28_36.585578
path:
- results_2023-12-02T14-28-36.585578.parquet
- split: latest
path:
- results_2023-12-02T14-28-36.585578.parquet
---
# Dataset Card for Evaluation run of vihangd/dopeyplats-1.1b-2T-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vihangd/dopeyplats-1.1b-2T-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 [vihangd/dopeyplats-1.1b-2T-v1](https://huggingface.co/vihangd/dopeyplats-1.1b-2T-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_vihangd__dopeyplats-1.1b-2T-v1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:28:36.585578](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__dopeyplats-1.1b-2T-v1/blob/main/results_2023-12-02T14-28-36.585578.json)(note that their might be results for other tasks in 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": 0.016679302501895376,
"acc_stderr": 0.0035275958887224295
},
"harness|gsm8k|5": {
"acc": 0.016679302501895376,
"acc_stderr": 0.0035275958887224295
}
}
```
### 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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|
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