id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
AdapterOcean/python3-standardized_cluster_23 | 2023-10-23T02:39:12.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:39:11 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 68385464
num_examples: 6344
download_size: 15705863
dataset_size: 68385464
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "python3-standardized_cluster_23"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 582 | [
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] |
AdapterOcean/data-standardized_cluster_5_std | 2023-10-23T02:39:23.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:39:19 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 19250752
num_examples: 17694
download_size: 8207788
dataset_size: 19250752
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_5_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 669 | [
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] |
AdapterOcean/python3-standardized_cluster_24_std | 2023-10-23T02:39:56.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:39:55 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 8311186
num_examples: 5062
download_size: 1502193
dataset_size: 8311186
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "python3-standardized_cluster_24_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 670 | [
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] |
AdapterOcean/python3-standardized_cluster_24 | 2023-10-23T02:39:59.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:39:58 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 28865437
num_examples: 2531
download_size: 5589715
dataset_size: 28865437
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "python3-standardized_cluster_24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 581 | [
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AdapterOcean/data-standardized_cluster_6_std | 2023-10-23T02:40:24.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:40:22 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 10822742
num_examples: 10908
download_size: 4495600
dataset_size: 10822742
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_6_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 669 | [
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AdapterOcean/data-standardized_cluster_7_std | 2023-10-23T02:41:22.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:41:20 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2745371
num_examples: 2426
download_size: 1134621
dataset_size: 2745371
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_7_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 666 | [
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AdapterOcean/data-standardized_cluster_8_std | 2023-10-23T02:42:19.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:42:17 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9094499
num_examples: 8844
download_size: 3833740
dataset_size: 9094499
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_8_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 666 | [
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] |
AdapterOcean/data-standardized_cluster_9_std | 2023-10-23T02:43:19.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:43:17 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 10069274
num_examples: 7628
download_size: 4280007
dataset_size: 10069274
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_9_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 668 | [
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] |
AdapterOcean/data-standardized_cluster_10_std | 2023-10-23T02:44:20.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:44:17 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 18457392
num_examples: 16730
download_size: 7954385
dataset_size: 18457392
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_10_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 670 | [
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AdapterOcean/data-standardized_cluster_11_std | 2023-10-23T02:45:21.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:45:19 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9622067
num_examples: 7804
download_size: 4176276
dataset_size: 9622067
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_11_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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AdapterOcean/data-standardized_cluster_12_std | 2023-10-23T02:46:19.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:46:16 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 6875828
num_examples: 6416
download_size: 2875603
dataset_size: 6875828
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_12_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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AdapterOcean/data-standardized_cluster_13_std | 2023-10-23T02:47:18.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:47:16 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9016228
num_examples: 8678
download_size: 3788318
dataset_size: 9016228
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_13_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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AdapterOcean/data-standardized_cluster_14_std | 2023-10-23T02:48:17.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:48:15 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 11228260
num_examples: 10578
download_size: 4685303
dataset_size: 11228260
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_14_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 670 | [
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AdapterOcean/data-standardized_cluster_15_std | 2023-10-23T02:49:14.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:49:12 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 6861311
num_examples: 6246
download_size: 2962223
dataset_size: 6861311
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_15_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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] |
AdapterOcean/data-standardized_cluster_16_std | 2023-10-23T02:50:13.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:50:09 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9175738
num_examples: 8716
download_size: 3946637
dataset_size: 9175738
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_16_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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] |
AdapterOcean/data-standardized_cluster_17_std | 2023-10-23T02:51:11.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:51:09 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 4535547
num_examples: 3716
download_size: 1978928
dataset_size: 4535547
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_17_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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AdapterOcean/data-standardized_cluster_18_std | 2023-10-23T02:52:11.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:52:09 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9051592
num_examples: 8532
download_size: 3910771
dataset_size: 9051592
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_18_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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AdapterOcean/data-standardized_cluster_19_std | 2023-10-23T02:53:09.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:53:07 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9165871
num_examples: 8554
download_size: 4002670
dataset_size: 9165871
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_19_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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AdapterOcean/data-standardized_cluster_20_std | 2023-10-23T02:54:11.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:54:07 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 19282668
num_examples: 16764
download_size: 8304570
dataset_size: 19282668
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_20_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 670 | [
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AdapterOcean/data-standardized_cluster_21_std | 2023-10-23T02:55:12.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:55:10 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9681643
num_examples: 9462
download_size: 4191325
dataset_size: 9681643
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_21_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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] |
AdapterOcean/data-standardized_cluster_22_std | 2023-10-23T02:56:13.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:56:10 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 27474399
num_examples: 25474
download_size: 11969072
dataset_size: 27474399
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_22_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 671 | [
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] |
AdapterOcean/data-standardized_cluster_23_std | 2023-10-23T02:57:15.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:57:13 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 7951640
num_examples: 6750
download_size: 3438336
dataset_size: 7951640
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_23_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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] |
AdapterOcean/data-standardized_cluster_24_std | 2023-10-23T02:58:14.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T02:58:11 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 18943226
num_examples: 16868
download_size: 8113695
dataset_size: 18943226
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_24_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 670 | [
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] |
AILab-CVC/obelics_seed2_tokens | 2023-10-25T02:10:47.000Z | [
"license:cc-by-4.0",
"region:us"
] | AILab-CVC | null | null | 1 | 0 | 2023-10-23T03:08:19 | ---
license: cc-by-4.0
---
Part of the OBELISC data set, including 32 Million samples, please refer to `dataset.py` to use this data | 134 | [
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siafuller/music_test | 2023-10-23T04:02:06.000Z | [
"region:us"
] | siafuller | null | null | 0 | 0 | 2023-10-23T04:01:36 | Entry not found | 15 | [
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chain87333/wrecking | 2023-10-23T04:21:33.000Z | [
"region:us"
] | chain87333 | null | null | 0 | 0 | 2023-10-23T04:21:16 | Entry not found | 15 | [
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avame2424/new_fall | 2023-10-23T04:24:25.000Z | [
"region:us"
] | avame2424 | null | null | 0 | 0 | 2023-10-23T04:23:57 | Entry not found | 15 | [
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nicklashansen/tdmpc2 | 2023-10-26T01:04:59.000Z | [
"license:mit",
"arxiv:2310.16828",
"region:us"
] | nicklashansen | null | null | 2 | 0 | 2023-10-23T04:26:38 | ---
license: mit
---
# Dataset Card for TD-MPC2
Official dataset release for the paper
[Scalable, Robust World Models for Continuous Control](https://nicklashansen.github.io/td-mpc2) by
[Nicklas Hansen](https://nicklashansen.github.io), [Hao Su](https://cseweb.ucsd.edu/~haosu)\*, [Xiaolong Wang](https://xiaolonw.github.io)\* (UC San Diego)
**Quick links:** [[Website]](https://nicklashansen.github.io/td-mpc2) [[Paper]](https://arxiv.org/abs/2310.16828) [[Models]](https://huggingface.co/nicklashansen/tdmpc2)
## Dataset Details
We open-source all data corresponding to the 80-task and 30-task datasets used in our multi-task experiments. The two datasets contain 545M and 345M transitions, respectively. The data is obtained from the replay buffers of 240 single-task TD-MPC2 agents, and thus contain a wide variety of behaviors ranging from random to expert policies. This section aims to provide further details about the released datasets.
### Dataset Description
- **Curated by:** [Nicklas Hansen](https://nicklashansen.github.io) (UC San Diego)
- **License:** MIT
### Dataset Sources
- **Repository:** [https://github.com/nicklashansen/tdmpc2](https://github.com/nicklashansen/tdmpc2)
- **Paper:** [https://arxiv.org/abs/2310.16828](https://arxiv.org/abs/2310.16828)
### Source Data
Our data is collected by 240 single-task TD-MPC2 agents trained on 104 continuous control tasks from DMControl, Meta-World, Maniskill2, and MyoSuite.
| Dataset | Embodiments | Max obs | Max action | Episodes | Transitions | Size |
|---------|:-----------:|:-------:|:----------:|:--------:|:-----------:|:----:|
| MT80 | 12 | 39 | 6 | 2.69M | 545M | 34 GB |
| MT30 | 11 | 24 | 6 | 690k | 345M | 20 GB |
See the [official website](https://nicklashansen.github.io/td-mpc2/dataset) for visualization of TD-MPC2 agents performing all of 80 tasks from the dataset.
## Citation
If you find our work useful, please consider citing the paper as follows:
**BibTeX:**
```
@misc{hansen2023tdmpc2,
title={TD-MPC2: Scalable, Robust World Models for Continuous Control},
author={Nicklas Hansen and Hao Su and Xiaolong Wang},
year={2023},
eprint={2310.16828},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
## Contact
Correspondence to: [Nicklas Hansen](https://nicklashansen.github.io) | 2,323 | [
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brian43454/law | 2023-10-23T04:33:28.000Z | [
"region:us"
] | brian43454 | null | null | 0 | 0 | 2023-10-23T04:32:56 | Entry not found | 15 | [
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andew7887863/hdza | 2023-10-23T04:39:34.000Z | [
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-q_k_v_o_proj | 2023-10-23T04:40:19.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-23T04:40:12 | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-q_k_v_o_proj\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T04:40:08.123829](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-q_k_v_o_proj/blob/main/results_2023-10-23T04-40-08.123829.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.35979446308724833,\n\
\ \"em_stderr\": 0.0049150348561349125,\n \"f1\": 0.39900062919463114,\n\
\ \"f1_stderr\": 0.004825664226319409,\n \"acc\": 0.46081554692422627,\n\
\ \"acc_stderr\": 0.01059065324860096\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.35979446308724833,\n \"em_stderr\": 0.0049150348561349125,\n\
\ \"f1\": 0.39900062919463114,\n \"f1_stderr\": 0.004825664226319409\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14025777103866566,\n \
\ \"acc_stderr\": 0.009565108281428673\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7813733228097869,\n \"acc_stderr\": 0.011616198215773246\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T04_40_08.123829
path:
- '**/details_harness|drop|3_2023-10-23T04-40-08.123829.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T04-40-08.123829.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T04_40_08.123829
path:
- '**/details_harness|gsm8k|5_2023-10-23T04-40-08.123829.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T04-40-08.123829.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T04_40_08.123829
path:
- '**/details_harness|winogrande|5_2023-10-23T04-40-08.123829.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T04-40-08.123829.parquet'
- config_name: results
data_files:
- split: 2023_10_23T04_40_08.123829
path:
- results_2023-10-23T04-40-08.123829.parquet
- split: latest
path:
- results_2023-10-23T04-40-08.123829.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-q_k_v_o_proj",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T04:40:08.123829](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-q_k_v_o_proj/blob/main/results_2023-10-23T04-40-08.123829.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.35979446308724833,
"em_stderr": 0.0049150348561349125,
"f1": 0.39900062919463114,
"f1_stderr": 0.004825664226319409,
"acc": 0.46081554692422627,
"acc_stderr": 0.01059065324860096
},
"harness|drop|3": {
"em": 0.35979446308724833,
"em_stderr": 0.0049150348561349125,
"f1": 0.39900062919463114,
"f1_stderr": 0.004825664226319409
},
"harness|gsm8k|5": {
"acc": 0.14025777103866566,
"acc_stderr": 0.009565108281428673
},
"harness|winogrande|5": {
"acc": 0.7813733228097869,
"acc_stderr": 0.011616198215773246
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,483 | [
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Eididkd/Documents.zip | 2023-10-23T05:08:06.000Z | [
"region:us"
] | Eididkd | null | null | 0 | 0 | 2023-10-23T04:43:04 | Entry not found | 15 | [
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] |
arminmrm93/wiki_with_embedding1 | 2023-10-23T04:51:54.000Z | [
"region:us"
] | arminmrm93 | null | null | 0 | 0 | 2023-10-23T04:51:22 | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 834134749
num_examples: 64586
download_size: 597776213
dataset_size: 834134749
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wiki_with_embedding1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 594 | [
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w | 2023-10-23T05:19:39.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-23T05:19:33 | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE1_17w](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T05:19:29.097153](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w/blob/main/results_2023-10-23T05-19-29.097153.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.36398909395973156,\n\
\ \"em_stderr\": 0.00492738070289941,\n \"f1\": 0.4025178271812084,\n\
\ \"f1_stderr\": 0.004834808125344133,\n \"acc\": 0.45268383345111485,\n\
\ \"acc_stderr\": 0.010561270649266652\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.36398909395973156,\n \"em_stderr\": 0.00492738070289941,\n\
\ \"f1\": 0.4025178271812084,\n \"f1_stderr\": 0.004834808125344133\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1326762699014405,\n \
\ \"acc_stderr\": 0.009343929131442216\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7726913970007893,\n \"acc_stderr\": 0.011778612167091087\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T05_19_29.097153
path:
- '**/details_harness|drop|3_2023-10-23T05-19-29.097153.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T05-19-29.097153.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T05_19_29.097153
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-19-29.097153.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-19-29.097153.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T05_19_29.097153
path:
- '**/details_harness|winogrande|5_2023-10-23T05-19-29.097153.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T05-19-29.097153.parquet'
- config_name: results
data_files:
- split: 2023_10_23T05_19_29.097153
path:
- results_2023-10-23T05-19-29.097153.parquet
- split: latest
path:
- results_2023-10-23T05-19-29.097153.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE1_17w](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:19:29.097153](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w/blob/main/results_2023-10-23T05-19-29.097153.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.36398909395973156,
"em_stderr": 0.00492738070289941,
"f1": 0.4025178271812084,
"f1_stderr": 0.004834808125344133,
"acc": 0.45268383345111485,
"acc_stderr": 0.010561270649266652
},
"harness|drop|3": {
"em": 0.36398909395973156,
"em_stderr": 0.00492738070289941,
"f1": 0.4025178271812084,
"f1_stderr": 0.004834808125344133
},
"harness|gsm8k|5": {
"acc": 0.1326762699014405,
"acc_stderr": 0.009343929131442216
},
"harness|winogrande|5": {
"acc": 0.7726913970007893,
"acc_stderr": 0.011778612167091087
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,315 | [
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj | 2023-10-23T05:27:24.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-23T05:27:17 | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T05:27:14.109658](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj/blob/main/results_2023-10-23T05-27-14.109658.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3719588926174497,\n\
\ \"em_stderr\": 0.004949726013193945,\n \"f1\": 0.4084679110738261,\n\
\ \"f1_stderr\": 0.004843145937750956,\n \"acc\": 0.4480415851620389,\n\
\ \"acc_stderr\": 0.010535274120903989\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3719588926174497,\n \"em_stderr\": 0.004949726013193945,\n\
\ \"f1\": 0.4084679110738261,\n \"f1_stderr\": 0.004843145937750956\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1281273692191054,\n \
\ \"acc_stderr\": 0.009206398549980031\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827948\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T05_27_14.109658
path:
- '**/details_harness|drop|3_2023-10-23T05-27-14.109658.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T05-27-14.109658.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T05_27_14.109658
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-27-14.109658.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-27-14.109658.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T05_27_14.109658
path:
- '**/details_harness|winogrande|5_2023-10-23T05-27-14.109658.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T05-27-14.109658.parquet'
- config_name: results
data_files:
- split: 2023_10_23T05_27_14.109658
path:
- results_2023-10-23T05-27-14.109658.parquet
- split: latest
path:
- results_2023-10-23T05-27-14.109658.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:27:14.109658](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-q_k_v_o_proj/blob/main/results_2023-10-23T05-27-14.109658.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.3719588926174497,
"em_stderr": 0.004949726013193945,
"f1": 0.4084679110738261,
"f1_stderr": 0.004843145937750956,
"acc": 0.4480415851620389,
"acc_stderr": 0.010535274120903989
},
"harness|drop|3": {
"em": 0.3719588926174497,
"em_stderr": 0.004949726013193945,
"f1": 0.4084679110738261,
"f1_stderr": 0.004843145937750956
},
"harness|gsm8k|5": {
"acc": 0.1281273692191054,
"acc_stderr": 0.009206398549980031
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827948
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,457 | [
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w | 2023-10-23T05:30:44.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-23T05:30:37 | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE2_3w](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T05:30:33.523762](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w/blob/main/results_2023-10-23T05-30-33.523762.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.19316275167785235,\n\
\ \"em_stderr\": 0.004042912227684817,\n \"f1\": 0.2405106963087243,\n\
\ \"f1_stderr\": 0.004012764038516629,\n \"acc\": 0.44387175953656505,\n\
\ \"acc_stderr\": 0.010404181547690496\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.19316275167785235,\n \"em_stderr\": 0.004042912227684817,\n\
\ \"f1\": 0.2405106963087243,\n \"f1_stderr\": 0.004012764038516629\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1197877179681577,\n \
\ \"acc_stderr\": 0.008944213403553045\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827948\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T05_30_33.523762
path:
- '**/details_harness|drop|3_2023-10-23T05-30-33.523762.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T05-30-33.523762.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T05_30_33.523762
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-30-33.523762.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-30-33.523762.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T05_30_33.523762
path:
- '**/details_harness|winogrande|5_2023-10-23T05-30-33.523762.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T05-30-33.523762.parquet'
- config_name: results
data_files:
- split: 2023_10_23T05_30_33.523762
path:
- results_2023-10-23T05-30-33.523762.parquet
- split: latest
path:
- results_2023-10-23T05-30-33.523762.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE2_3w](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:30:33.523762](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w/blob/main/results_2023-10-23T05-30-33.523762.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.19316275167785235,
"em_stderr": 0.004042912227684817,
"f1": 0.2405106963087243,
"f1_stderr": 0.004012764038516629,
"acc": 0.44387175953656505,
"acc_stderr": 0.010404181547690496
},
"harness|drop|3": {
"em": 0.19316275167785235,
"em_stderr": 0.004042912227684817,
"f1": 0.2405106963087243,
"f1_stderr": 0.004012764038516629
},
"harness|gsm8k|5": {
"acc": 0.1197877179681577,
"acc_stderr": 0.008944213403553045
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827948
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,307 | [
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj | 2023-10-23T05:42:03.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-23T05:41:56 | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T05:41:52.177937](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj/blob/main/results_2023-10-23T05-41-52.177937.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.08137583892617449,\n\
\ \"em_stderr\": 0.0027999889835206245,\n \"f1\": 0.13315016778523447,\n\
\ \"f1_stderr\": 0.0029419319985989354,\n \"acc\": 0.4454347335673805,\n\
\ \"acc_stderr\": 0.010395126943573653\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08137583892617449,\n \"em_stderr\": 0.0027999889835206245,\n\
\ \"f1\": 0.13315016778523447,\n \"f1_stderr\": 0.0029419319985989354\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12054586808188021,\n \
\ \"acc_stderr\": 0.008968608285309067\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838238\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T05_41_52.177937
path:
- '**/details_harness|drop|3_2023-10-23T05-41-52.177937.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T05-41-52.177937.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T05_41_52.177937
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-41-52.177937.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-41-52.177937.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T05_41_52.177937
path:
- '**/details_harness|winogrande|5_2023-10-23T05-41-52.177937.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T05-41-52.177937.parquet'
- config_name: results
data_files:
- split: 2023_10_23T05_41_52.177937
path:
- results_2023-10-23T05-41-52.177937.parquet
- split: latest
path:
- results_2023-10-23T05-41-52.177937.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:41:52.177937](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w-gate_up_down_proj/blob/main/results_2023-10-23T05-41-52.177937.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.08137583892617449,
"em_stderr": 0.0027999889835206245,
"f1": 0.13315016778523447,
"f1_stderr": 0.0029419319985989354,
"acc": 0.4454347335673805,
"acc_stderr": 0.010395126943573653
},
"harness|drop|3": {
"em": 0.08137583892617449,
"em_stderr": 0.0027999889835206245,
"f1": 0.13315016778523447,
"f1_stderr": 0.0029419319985989354
},
"harness|gsm8k|5": {
"acc": 0.12054586808188021,
"acc_stderr": 0.008968608285309067
},
"harness|winogrande|5": {
"acc": 0.7703235990528808,
"acc_stderr": 0.011821645601838238
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,535 | [
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] |
mingfengxue/OccuQuest | 2023-10-23T06:17:07.000Z | [
"region:us"
] | mingfengxue | null | null | 0 | 0 | 2023-10-23T05:56:00 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: estate
path: data/estate-*
- split: quora
path: data/quora-*
dataset_info:
features:
- name: category
dtype: string
- name: occupation
dtype: string
- name: topic
dtype: string
- name: messages
list:
- name: role
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 330314955
num_examples: 114090
- name: validation
num_bytes: 7314741
num_examples: 2500
- name: test
num_bytes: 718046
num_examples: 250
- name: estate
num_bytes: 703613
num_examples: 250
- name: quora
num_bytes: 45540
num_examples: 250
download_size: 139074820
dataset_size: 339096895
---
# Dataset Card for "OccuQuest"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,044 | [
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bodyrockweightlossprogram/bodyrockweightlossprogram | 2023-10-23T06:06:38.000Z | [
"region:us"
] | bodyrockweightlossprogram | null | null | 0 | 0 | 2023-10-23T06:01:52 | Are you on a quest for effective weight loss? Look no further than the **BodyRock Weight Loss Program**. This comprehensive article will provide insights into the program, its workings, benefits, and more.
[Click Here To Get It From Official Website](https://snoppymart.com/get-bodyrock-weight-loss-program)
What is the BodyRock Weight Loss Program?
-----------------------------------------
The BodyRock Weight Loss Program is a holistic approach to shedding unwanted pounds and improving your overall fitness. It combines structured workout plans with nutrition guidance and a supportive community to help you achieve your weight loss goals.
[.png)](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7jsS74t__JEGqdZnwC7XrRBc7-de0S526lEFpxhOw1Sap1uEUSOq9rAP9mL97KQlTg6XnR_6gBDz8FEzQF5CPL2vaQBNLVemF4ArL-dnqNHiHPvQedO48HGgN0RSKtfQjQlgrVoC0cp8Klc1-rjCu9sN-WX5lg1Flv8fHW3dZfuKxTYXclKC2awrYXnM/s1671/Screenshot%20(1435).png)
How Does the Program Work?
--------------------------
The program works by offering a combination of high-intensity interval training (HIIT) workouts and a carefully curated meal plan. These workouts are designed to boost your metabolism, burn calories, and build lean muscle, while the meal plan focuses on balanced and nutritious eating.
[.png)](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmRXGW0GCZp-tNiDOEJ46KVF-f-S8H6zlTJsMn7UInzCHTwf48S4QfwpsMmi18JjaVCI940qvn7AxYHcbtAEY8p80Kx3hNkyh01LGUxAQxYT-jBbJ0s7XlnCe2674gJaQuBUPQy6OZzSpTHirQiPLU3AWZXKlpp5M8FV4mGxeIUuo3xs-bFG3nkJWQMYw/s1681/Screenshot%20(1436).png)
Benefits of BodyRock Weight Loss Program
----------------------------------------
* Rapid fat loss
* Increased energy levels
* Enhanced endurance and strength
* Improved overall fitness
* Personalized approach to weight loss
Success Stories
---------------
Many individuals have achieved remarkable results through the BodyRock Weight Loss Program. Their inspiring stories attest to the program's effectiveness and its ability to transform lives.
[.png)](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhVRNGSktDlCwaIRZz_OSJJB5dN_O5iM4WW4ml7uyCaVfHkLeqEwUE3kZiQ9zd0Kl-Mj-FzyZtmlp13muvuYX_YdyIaQm5su9tdWUjsAgrnsSlxbqaZpCZ1byo5wNH3pumkLvyzPbjuUzE4RAgiCyBP7us2pZJJFXHXnhlIMC_3EBrDQ_nuDwYRE0aQmZw/s1751/Screenshot%20(1438).png)
⏩ Tips for Success
To succeed with the program, it's important to stay consistent, stay hydrated, and maintain a positive mindset. A support system can make a significant difference in your journey to a healthier you.
⏩ Customization Options
The program recognizes that one size does not fit all, and offers customization options to cater to individual needs and goals. This personalization ensures that you get the best results.
⏩ Workout Plans
BodyRock provides a wide variety of workout plans that can be tailored to your fitness level and preferences. From beginner to advanced, there's something for everyone.
⏩ Nutrition and Meal Planning
Eating right is a critical component of weight loss, and BodyRock's meal plans are designed to make this process easier. You'll have access to nutritious recipes and guidance on portion control.
⏩ Tracking Progress
Tracking your progress is essential for staying motivated. The program offers tools to monitor your weight loss journey, helping you celebrate your successes.
⏩ Community Support
The BodyRock community is a supportive and encouraging group of like-minded individuals who can offer advice, motivation, and inspiration throughout your journey.
⏩ Expert Guidance
The program is led by experienced trainers and nutritionists who provide expert guidance and answer questions, ensuring you have all the support you need.
[.png)](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgEIQ3QhxhvOsN0CT0YUTHZcZBolCATbt3TYOvmKrTbkpzSlkmrJHTFqWVKsHA5MowHK3g7HrBaqLWFSnCWgsduDFWm8B1Vj6TIdtG0vCk0QF2NzYqZlEgVm3J45XENzIiuWaq038CJjVntx6SGr6PFoDV0Iob76oTrghFYnkFxdPgLXpUatSMd-E8biq4/s1683/Screenshot%20(1437).pnghttps://snoppymart.com/get-bodyrock-weight-loss-program)
Common Questions
----------------
1. Is the program suitable for beginners?
2. How quickly can I expect to see results?
3. Can I do the workouts at home?
4. Is the meal plan customizable?
5. What kind of equipment do I need for the workouts?
Conclusion
----------
The BodyRock Weight Loss Program is a comprehensive solution for those looking to shed pounds and boost their overall fitness. With personalized plans, expert guidance, and a supportive community, it's a proven way to achieve your weight loss goals.
[Click Here To Get It From Official Website](https://snoppymart.com/get-bodyrock-weight-loss-program)
FAQs
----
Is the BodyRock program suitable for people of all fitness levels?
➣ Yes, the program offers workout plans for beginners to advanced levels.
How quickly can I expect to see results?
➣ Results may vary, but many participants notice positive changes within a few weeks.
Can I do the workouts at home?
➣ Absolutely, most of the workouts can be done in the comfort of your home.
Is the meal plan customizable?
➣ Yes, you can customize your meal plan based on your dietary preferences and goals.
In conclusion, the BodyRock Weight Loss Program offers a holistic approach to achieving your weight loss goals. With personalized workout and meal plans, expert guidance, and a supportive community, it's a proven path to a healthier and fitter you. [Start your journey today](https://snoppymart.com/get-bodyrock-weight-loss-program)! | 6,698 | [
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj | 2023-10-23T06:02:33.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-23T06:02:26 | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T06:02:22.448569](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj/blob/main/results_2023-10-23T06-02-22.448569.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.10098573825503356,\n\
\ \"em_stderr\": 0.0030856947694457384,\n \"f1\": 0.15267407718120746,\n\
\ \"f1_stderr\": 0.0031959753495490175,\n \"acc\": 0.4472367612449459,\n\
\ \"acc_stderr\": 0.010567855433819127\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.10098573825503356,\n \"em_stderr\": 0.0030856947694457384,\n\
\ \"f1\": 0.15267407718120746,\n \"f1_stderr\": 0.0031959753495490175\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1288855193328279,\n \
\ \"acc_stderr\": 0.009229580761400269\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237985\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T06_02_22.448569
path:
- '**/details_harness|drop|3_2023-10-23T06-02-22.448569.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T06-02-22.448569.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T06_02_22.448569
path:
- '**/details_harness|gsm8k|5_2023-10-23T06-02-22.448569.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T06-02-22.448569.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T06_02_22.448569
path:
- '**/details_harness|winogrande|5_2023-10-23T06-02-22.448569.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T06-02-22.448569.parquet'
- config_name: results
data_files:
- split: 2023_10_23T06_02_22.448569
path:
- results_2023-10-23T06-02-22.448569.parquet
- split: latest
path:
- results_2023-10-23T06-02-22.448569.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T06:02:22.448569](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-gate_up_down_proj/blob/main/results_2023-10-23T06-02-22.448569.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.10098573825503356,
"em_stderr": 0.0030856947694457384,
"f1": 0.15267407718120746,
"f1_stderr": 0.0031959753495490175,
"acc": 0.4472367612449459,
"acc_stderr": 0.010567855433819127
},
"harness|drop|3": {
"em": 0.10098573825503356,
"em_stderr": 0.0030856947694457384,
"f1": 0.15267407718120746,
"f1_stderr": 0.0031959753495490175
},
"harness|gsm8k|5": {
"acc": 0.1288855193328279,
"acc_stderr": 0.009229580761400269
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237985
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,545 | [
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aai520-group6/squad_v2 | 2023-10-25T11:53:11.000Z | [
"region:us"
] | aai520-group6 | null | null | 0 | 0 | 2023-10-23T06:05:06 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 116732025
num_examples: 130319
- name: validation
num_bytes: 11661091
num_examples: 11873
download_size: 0
dataset_size: 128393116
---
# Dataset Card for "squad_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 773 | [
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fittar/visually_grounded_embeddings | 2023-10-23T14:50:10.000Z | [
"language:en",
"license:mit",
"arxiv:2206.08823",
"region:us"
] | fittar | null | null | 0 | 0 | 2023-10-23T07:40:00 | ---
license: mit
language:
- en
---
# Visually Grounded embeddings for Fast-text and GloVe
This repository contains multiple visually grounded word embedding models.
All of these embeddings have been effectively infused with visual information from images.<br>
They have been proven to show stronger correlations (compared to textual embeddings)
to human judgments on various word similarities and relatedness benchmarks.
# Usage
All of the models are encoded in [gensim](https://pypi.org/project/gensim/) format.
Loading the model:
```python
import gensim
model_g = gensim.models.KeyedVectors.load_word2vec_format('path_to_embeddings' , binary=True)
#retrieve the most similar words
print(model_g.most_similar('together',topn=10))
[('togther', 0.6425853967666626), ('togehter', 0.6374243497848511), ('togeather', 0.6196791529655457),
('togather', 0.5998020172119141), ('togheter', 0.5819681882858276),('toghether', 0.5738174319267273),
('2gether', 0.5187329053878784), ('togethor', 0.501663088798523), ('gether', 0.49128714203834534),
('toegther', 0.48457157611846924)]
print(model_g.most_similar('sad',topn=10))
[('saddening', 0.6763913631439209), ('depressing', 0.6676110029220581), ('saddened', 0.6352651715278625),
('sorrowful', 0.6336953043937683), ('heartbreaking', 0.6180269122123718), ('heartbroken', 0.6099187135696411),
('tragic', 0.6039361953735352), ('pathetic', 0.5848405361175537), ('Sad', 0.5826965570449829),
('mournful', 0.5742306709289551)]
#find the outlier word
print(model_g.doesnt_match(['fire', 'water', 'land', 'sea', 'air', 'car']))
car
```
where 'path_to_embeddings' is the path to the embeddings you intend to use.
# Which embeddings to use
Under the **Files and Versions** tab, you can see the list of 4 available embeddings.
The following embedding files are from the paper [Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training](https://aclanthology.org/2021.conll-1.12/):
- v_glove_1024d_1.0
- v_fasttext_1024d_1.0
The following embedding files are from the paper [Language with Vision: a Study on Grounded Word and Sentence Embeddings](https://arxiv.org/pdf/2206.08823.pdf):
- v_glove_1024d_2.0
- v_glove_300_d_2.0
All of them come with 1024-dimensional word vectors except v_glove_300_d_2.0 which contains 300-dimensional word vectors.
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] |
pizzagatakasugi/dataset1000 | 2023-10-23T07:49:15.000Z | [
"region:us"
] | pizzagatakasugi | null | null | 0 | 0 | 2023-10-23T07:49:05 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: precedence_name
dtype: string
- name: follower_name
dtype: string
- name: sfen
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 64924488
num_examples: 51961
- name: validation
num_bytes: 7105288
num_examples: 5716
- name: test
num_bytes: 71402
num_examples: 58
download_size: 30878718
dataset_size: 72101178
---
# Dataset Card for "dataset1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 681 | [
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bodyrockweightloss/bodyrockweightloss | 2023-10-23T07:52:37.000Z | [
"region:us"
] | bodyrockweightloss | null | null | 0 | 0 | 2023-10-23T07:51:50 | **[BodyRock Weight Loss Program](https://snoppymart.com/get-bodyrock-weight-loss-program)** Health Club is a Fitness and Wellness outlet that offers a wide range of services to help you reach your fitness goals. It provides a variety of classes such as yoga, Pilates, Zumba, and more. It also offers personal training, nutrition counseling, and massage therapy. The club has a fully equipped gym with the latest equipment and a wide range of free weights. The club also has a sauna, steam room, and a swimming pool. Body Rock Health Club is dedicated to helping you achieve your fitness goals and live a healthier lifestyle.
[.png)](https://snoppymart.com/get-bodyrock-weight-loss-program)
In a world saturated with weight loss programs promising quick fixes and instant results, it can be challenging to navigate through the noise and find a program that not only helps you shed pounds but also promotes a sustainable and healthy lifestyle. One such program that has gained attention in recent years is the Bodyrock Weight Loss Program. In this article, we'll take a closer look at what Bodyrock is all about, and why it may be the right choice for those seeking a long-term solution to their weight loss journey.
### **[Official website Here >> BodyRock Weight Loss Program <<](https://snoppymart.com/get-bodyrock-weight-loss-program)**
### [BodyRock](https://www.facebook.com/people/BodyRock-Weight-Loss-Program/61552741376544/): A Fitness Pioneer
BodyRock is no stranger to the fitness world. Established as a fitness company in 2008, BodyRock has been at the forefront of creating effective and engaging workout programs. With a strong online presence and a legion of dedicated followers, the company is known for its unique approach to fitness.
### The Burn & Boost Bootcamp Concept
The Burn & Boost Bootcamp is a comprehensive weight loss program designed to help participants shed unwanted pounds, boost their fitness levels, and improve overall health. It is a carefully curated plan that combines cutting-edge workout routines, nutritional guidance, and a supportive community to create a holistic approach to weight loss.
### Key Features of the Burn & Boost Bootcamp
**Diverse Workouts:** One of the program’s most exciting aspects is its workout variety. The Burn & Boost Bootcamp offers a range of exercises, from high-intensity interval training (HIIT) to strength training, ensuring that participants never get bored and consistently challenge their bodies.
**Online Accessibility:** **[BodyRock](https://snoppymart.com/get-bodyrock-weight-loss-program)** recognizes the convenience of home workouts, and the Burn & Boost Bootcamp can be accessed from the comfort of your own space. This flexibility makes it an ideal choice for those with busy schedules.
**Nutritional Guidance:** Effective weight loss involves more than just exercise. The program includes nutritional guidance to help participants make healthier food choices, understand portion control, and maintain a balanced diet.
**Supportive Community:** The **[BodyRock](https://snoppymart.com/get-bodyrock-weight-loss-program)** community is a pivotal element of the program. Participants can interact with like-minded individuals, share their experiences, and receive encouragement and support throughout their weight loss journey.
**Results-Driven:** The Burn & Boost Bootcamp is designed to deliver results. Participants often report improved fitness levels, fat loss, muscle gain, and an overall sense of well-being.
**[Official website Here >> BodyRock Weight Loss Program <<](https://snoppymart.com/get-bodyrock-weight-loss-program)**
### Benefits of the Burn & Boost Bootcamp
**Sustainable Weight Loss:** The program focuses on long-term success, encouraging participants to adopt sustainable fitness and dietary habits.
**Convenience:** With no need for a gym membership, participants can work out at their own pace and on their own schedule.
**Motivation and Accountability:** The supportive community and regularly updated workout routines keep participants motivated and accountable.
**Health Improvement:** Beyond weight loss, the program can lead to improved cardiovascular health, increased energy levels, and reduced stress.
**Accessible to All:** The Burn & Boost Bootcamp is designed for individuals of all fitness levels, from beginners to experienced athletes.
[.png)](https://snoppymart.com/get-bodyrock-weight-loss-program)
### The [Bodyrock](https://snoppymart.com/get-bodyrock-weight-loss-program) Approach
The **[Bodyrock Weight Loss Program](https://snoppymart.com/get-bodyrock-weight-loss-program)** is not just about losing weight; it's about transforming your entire lifestyle. The program emphasizes the importance of a balanced approach, combining exercise, nutrition, and mindset to achieve holistic health and fitness.
**Exercise:** The cornerstone of **[Bodyrock](https://snoppymart.com/get-bodyrock-weight-loss-program)** is its workout regimen. These workouts, which can be accessed online, vary in intensity and duration, making them suitable for people of all fitness levels. Whether you're a beginner or an experienced athlete, Bodyrock has a wide range of workout routines that can be tailored to your needs. The workouts often blend elements of high-intensity interval training (HIIT) and strength training to maximize calorie burn and muscle development.
**Nutrition:** Alongside exercise, the **[Bodyrock program](https://snoppymart.com/get-bodyrock-weight-loss-program)** places a strong emphasis on a balanced diet. Their website offers a variety of meal plans, recipes, and nutritional guidance that help participants make healthier food choices. The program promotes a sustainable and sensible approach to eating, avoiding fad diets and instead focusing on real, whole foods.
**Mindset:** Weight loss is not just about physical transformation; it's about mental well-being as well. Bodyrock understands this and offers resources for developing a positive mindset. Their content includes motivational articles, meditation sessions, and advice on setting realistic goals. This holistic approach sets **[Bodyrock](https://snoppymart.com/get-bodyrock-weight-loss-program)** apart from many other weight loss programs.
### Sustainability
The **[Bodyrock Weight Loss Program](https://snoppymart.com/get-bodyrock-weight-loss-program)**'s focus on sustainability is one of its defining features. Unlike crash diets or excessive exercise programs, Bodyrock aims to help participants create lasting changes in their lives. This includes adopting healthier habits that will remain with them long after they've reached their weight loss goals.
### Realistic Expectations
**[BodyRock](https://snoppymart.com/get-bodyrock-weight-loss-program)** is transparent about what participants can expect. The program doesn't promise overnight results or drastic transformations within a few weeks. Instead, it encourages participants to set achievable goals and work steadily towards them. This approach is a refreshing departure from many programs that make unrealistic claims.
**[Official website Here >> BodyRock Weight Loss Program <<](https://snoppymart.com/get-bodyrock-weight-loss-program)**
### Is [BodyRock](https://snoppymart.com/get-bodyrock-weight-loss-program) Right for You?
While the **[BodyRock Weight Loss Program](https://snoppymart.com/get-bodyrock-weight-loss-program)** offers numerous benefits and has garnered a devoted following, it may not be the perfect fit for everyone. Before embarking on this program, it's essential to consider the following factors:
**Time Commitment:** The workouts can be intense, and you'll need to set aside time in your schedule to complete them.
**Budget:** Some participants may find the associated costs of workout equipment or premium access to the program's resources a bit high.
**Self-Motivation:** Like any fitness program, your success ultimately depends on your dedication and self-motivation.
[](https://snoppymart.com/get-bodyrock-weight-loss-program)
### Conclusion
The **[BodyRock Weight Loss Program](https://snoppymart.com/get-bodyrock-weight-loss-program)** is a comprehensive and holistic approach to weight loss and overall health. By combining exercise, nutrition, and mindset, it addresses the critical elements needed for sustainable change. The program's community support and emphasis on realistic expectations make it an appealing choice for those who are committed to transforming their lives in a healthy and balanced way. Before starting any weight loss program, it's advisable to consult with a healthcare professional to ensure it's a safe and suitable choice for your individual needs and circumstances. | 9,558 | [
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BangumiBase/kimitobokunosaigonosenjouaruiwasekaigahajimaruseisen | 2023-10-23T09:27:08.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-23T08:26:25 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kimi To Boku No Saigo No Senjou Arui Wa Sekai Ga Hajimaru Seisen
This is the image base of bangumi Kimi to Boku no Saigo no Senjou Arui wa Sekai ga Hajimaru Seisen, we detected 20 characters, 1108 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 203 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 11 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 14 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 36 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 72 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 11 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 14 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 97 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 229 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 46 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 14 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 25 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 17 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 99 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 29 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 13 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 8 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 66 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 7 | [Download](18/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 97 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| 7,589 | [
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datajuicer/redpajama-arxiv-refined-by-data-juicer | 2023-10-23T08:37:41.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:35:29 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 1M<n<10M
---
# RedPajama -- ArXiv (refined by Data-Juicer)
A refined version of ArXiv dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-arxiv-refine-result.jsonl) (About 85GB).
## Dataset Information
- Number of samples: 1,655,259 (Keep ~95.99% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-arxivrecipes-arxiv'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.516 # 3sigma
max_ratio: 0.915 # 3sigma
- average_line_length_filter: # for code
max_len: 682 # 3sigma
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.00076 # 3sigma
#- language_id_score_filter: # remove language filter
- maximum_line_length_filter: # for code
max_len: 4000
- perplexity_filter:
lang: en
max_ppl: 8000
- special_characters_filter:
max_ratio: 0.6
- text_length_filter:
max_len: 350000
- words_num_filter:
lang: en
tokenization: true
min_num: 20
max_num: 100000
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.574 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,343 | [
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datajuicer/alpaca-cot-zh-refined-by-data-juicer | 2023-10-23T09:24:30.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"license:apache-2.0",
"data-juicer",
"Fine-tuning",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:35:58 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
tags:
- data-juicer
- Fine-tuning
size_categories:
- 1M<n<10M
---
# Alpaca-CoT -- ZH (refined by Data-Juicer)
A refined Chinese version of Alpaca-CoT dataset by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to fine-tune a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/SFT/alpaca-cot-zh-refine_result.jsonl) (About 18.7GB).
## Dataset Information
- Number of samples: 9,873,214 (Keep ~46.58% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-alpaca-cot-zh'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- document_deduplicator:
lowercase: true
ignore_non_character: true
- alphanumeric_filter:
tokenization: false
min_ratio: 0.10
- character_repetition_filter:
rep_len: 10
max_ratio: 0.6
- flagged_words_filter:
lang: zh
tokenization: true
use_words_aug: true
max_ratio: 0.017
- text_length_filter:
min_len: 10
- document_simhash_deduplicator:
tokenization: character
window_size: 4
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 10
hamming_distance: 8
``` | 1,718 | [
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datajuicer/redpajama-cc-2023-06-refined-by-data-juicer | 2023-10-23T08:41:32.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:40:01 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10M<n<100M
---
# RedPajama -- CommonCrawl-2023-06 (refined by Data-Juicer)
A refined version of CommonCrawl-2023-06 dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-cc-refine-results/redpajama-cc-2023-06-refine-result.jsonl) (About 310GB).
## Dataset Information
- Number of samples: 50,643,699 (Keep ~45.46% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-cc-2013-06'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.7508 # 3sigma
max_ratio: 0.8591 # 3sigma -- 1036821
- average_line_length_filter: # for code
max_len: 1500 # < 3sigma -- 395868
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3 # > 3sigma -- 195026
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0015 # 3sigma -- 287896
- language_id_score_filter: # remove language filter
min_score: 0.793 # 3sigma -- 2173246
- maximum_line_length_filter: # for code
max_len: 5000 # < 3sigma -- 797111
- perplexity_filter:
lang: en
max_ppl: 5000 # 3sigma -- 942162
- special_characters_filter:
min_ratio: 0.15 # > 3sigma
max_ratio: 0.35 # > 3sigma -- 1155090
- text_length_filter:
max_len: 58187 # 3sigma -- 1165902
- words_num_filter:
lang: en
tokenization: true
min_num: 20
max_num: 11529 # 3sigma -- 1185363
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.2962 # 3sigma -- 2407282
``` | 2,660 | [
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datajuicer/redpajama-cc-2022-05-refined-by-data-juicer | 2023-10-23T08:44:21.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:42:57 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10M<n<100M
---
# RedPajama -- CommonCrawl-2022-05 (refined by Data-Juicer)
A refined version of CommonCrawl-2022-05 dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-cc-refine-results/redpajama-cc-2022-05-refine-result.jsonl) (About 265GB).
## Dataset Information
- Number of samples: 42,648,496 (Keep ~45.34% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-cc-2022-05'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.7514 # 3sigma
max_ratio: 0.8577 # 3sigmai -- 888003
- average_line_length_filter: # for code
max_len: 1500 # < 3sigma -- 447069
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3 # > 3sigma -- 145890 samples
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0012 # 3sigma -- 319395
- language_id_score_filter: # remove language filter
min_score: 0.791 # 3sigma -- 1823528
- maximum_line_length_filter: # for code
max_len: 5000 # < 3sigma -- 791612
- perplexity_filter:
lang: en
max_ppl: 5000 # < 3sigma -- 654459
- special_characters_filter:
min_ratio: 0.15 # > 3sigma
max_ratio: 0.35 # > 3sigma
- text_length_filter:
max_len: 59265 # 3sigma -- 1046590
- words_num_filter:
lang: en
tokenization: true
min_num: 20 # > 3sigma
max_num: 11860 # 3sigma -- 1036780
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.3117 # 3sigma -- 2089703
``` | 2,671 | [
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] |
datajuicer/redpajama-stack-code-refined-by-data-juicer | 2023-10-23T08:47:08.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:45:44 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10M<n<100M
---
# RedPajama & TheStack -- Github Code (refined by Data-Juicer)
A refined version of Github Code dataset in RedPajama & TheStack by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-stack-code-refine-result.jsonl) (About 232GB).
## Dataset Information
- Number of samples: 49,279,344 (Keep ~52.09% from the original dataset)
## Refining Recipe
### RedPajama code refinement
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-code-rp'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- clean_copyright_mapper:
- alphanumeric_filter:
tokenization: False
min_ratio: 0.4
max_ratio: 0.8
- alphanumeric_filter:
tokenization: True
min_ratio: 1.5
max_ratio: 3
- average_line_length_filter:
min_len: 15
max_len: 100
- character_repetition_filter:
rep_len: 10
min_ratio: 0.05
max_ratio: 0.3
- maximum_line_length_filter:
min_len: 50
max_len: 500
- text_length_filter:
min_len: 300
- words_num_filter:
lang: en
tokenization: False
min_num: 30
max_num: 5000
- word_repetition_filter:
lang: en
tokenization: False
rep_len: 10
max_ratio: 0.1
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
```
### TheStack code refinement (only max_stars_count >= 20)
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-the-stack'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
text_key: 'content'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- clean_copyright_mapper:
- alphanumeric_filter: # 18766
tokenization: false
min_ratio: 0.2 # < 3sigma (0.3791)
max_ratio: 0.9163 # 3sigma
- alphanumeric_filter: # 146432
tokenization: true
min_ratio: 0.546 # 3sigma
max_ratio: 3.65 # 3sigma
- average_line_length_filter: # for code
min_len: 10 # > 3sigma (0) -- 48790
max_len: 150 # < 3sigma (15603) -- 233275
- character_repetition_filter:
max_ratio: 0.36 # 3sigma -- 346875
- maximum_line_length_filter: # for code
max_len: 1000 # remove 256670 samples
- text_length_filter:
max_len: 96714 # 3sigma -- 190006
- words_num_filter:
min_num: 20 # remove 1504958 samples
max_num: 6640 # 3sigma -- remove 179847 samples
- word_repetition_filter:
rep_len: 10
max_ratio: 0.357 # 3sigma -- 598462
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
```
### Merge and Remove Duplicate Samples
```yaml
project_name: 'Data-Juicer-recipes-code'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl' # path to your dataset result file
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 4,480 | [
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datajuicer/the-pile-europarl-refined-by-data-juicer | 2023-10-23T09:21:31.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:47:08 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10K<n<100K
---
# The Pile -- EuroParl (refined by Data-Juicer)
A refined version of EuroParl dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-europarl-refine-result.jsonl) (About 2.2GB).
## Dataset Information
- Number of samples: 61,601 (Keep ~88.23% from the original dataset)
## Refining Recipe
```yaml
# global parameters
# global parameters
project_name: 'Data-Juicer-recipes-EuroParl'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.75 # <3sigma (0.779)
max_ratio: 0.90 # >3sigma(0.878)
- average_line_length_filter: # for code
max_len: 588 # 3sigma
- character_repetition_filter:
rep_len: 10
max_ratio: 0.16 # >3sigma (0.114)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0007 # 3sigma
- language_id_score_filter:
min_score: 0.7
- maximum_line_length_filter: # for code
max_len: 4000 # >3sigma (3104)
- perplexity_filter:
lang: en
max_ppl: 7596 #(3sigma)
- special_characters_filter:
max_ratio: 0.3 # > 3sigma (0.243)
- text_length_filter:
max_len: 2e5
- words_num_filter:
tokenization: true
min_num: 20
max_num: 1e5 # 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.2 # > 3sigma (0.185)
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,440 | [
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datajuicer/redpajama-pile-stackexchange-refined-by-data-juicer | 2023-10-23T08:50:23.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:48:37 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10M<n<100M
---
# RedPajama & The Pile -- StackExchange (refined by Data-Juicer)
A refined version of StackExchange dataset in RedPajama & The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original merged dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-pile-stackexchange-refine-result.jsonl) (About 71GB).
## Dataset Information
- Number of samples: 26,309,203 (Keep ~57.89% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-stack-exchange'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.35 # <3sigma
max_ratio: 0.943 # 3sigma
- average_line_length_filter: # for code
min_len: 20 # >3sigma
max_len: 400 # >3sigma
- character_repetition_filter:
rep_len: 10
max_ratio: 0.4 # >3sigma (0.12)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.01 # >3sigma
- language_id_score_filter: # remove language filter
min_score: 0.1 # <3sigma
- maximum_line_length_filter: # for code
min_len: 80
- perplexity_filter:
lang: en
max_ppl: 10000 # >3sigma
- special_characters_filter:
min_ratio: 0.232 # 3sigma
max_ratio: 0.7 # >3sigma
- text_length_filter:
min_len: 200
- words_num_filter:
lang: en
tokenization: true
min_num: 100
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.8 # >3sigma
- document_simhash_deduplicator: #26309203 left
tokenization: space
window_size: 3
lowercase: true
ignore_pattern: '\n\n'
num_blocks: 9
hamming_distance: 7
``` | 2,518 | [
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datajuicer/redpajama-wiki-refined-by-data-juicer | 2023-10-23T08:52:08.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:50:55 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10M<n<100M
---
# RedPajama -- Wikipedia (refined by Data-Juicer)
A refined version of Wikipedia dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-wiki-refine-result.jsonl) (About 68GB).
## Dataset Information
- Number of samples: 26,990,659 (Keep ~90.47% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-wiki'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.6 # <3sigma (0.735)
max_ratio: 0.884 # 3sigma
- average_line_length_filter: # for code
max_len: 192 # 3sigma
- character_repetition_filter:
rep_len: 10
max_ratio: 0.4 # >3sigma (0.197)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0019 # 3sigma
- language_id_score_filter:
min_score: 0.689 # 3sigma
- maximum_line_length_filter: # for code
max_len: 1630 # 3sigma tbd
- perplexity_filter:
lang: en
max_ppl: 6887 # 3sigma
- special_characters_filter:
max_ratio: 0.5 # >3sigma (0.34)
- text_length_filter:
max_len: 18221 # 3sigma
- words_num_filter:
lang: en
tokenization: true
min_num: 20
max_num: 6086 # 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.3 # 3sigma (0.194)
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,442 | [
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datajuicer/redpajama-cc-2021-04-refined-by-data-juicer | 2023-10-23T08:53:56.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:52:52 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10M<n<100M
---
# RedPajama -- CommonCrawl-2021-04 (refined by Data-Juicer)
A refined version of CommonCrawl-2021-04 dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-cc-refine-results/redpajama-cc-2021-04-refine-result.jsonl) (About 284GB).
## Dataset Information
- Number of samples: 44,724,752 (Keep ~45.23% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-cc-2021-04'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.7494 # 3sigma
max_ratio: 0.8595 # 3sigma -- 1001790
- average_line_length_filter: # for code
max_len: 1500 # < 3sigma (2817) -- 541131
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3 # > 3sigma (0.1463) -- 159152
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0019 # 3sigma -- 184714
- language_id_score_filter: # remove language filter
min_score: 0.786 # 3sigma -- 1995115
- maximum_line_length_filter: # for code
max_len: 5000 # < 3sigma -- 1076085
- perplexity_filter:
lang: en
max_ppl: 5000 # < 3sigma -- 906649
- special_characters_filter:
min_ratio: 0.15 # > 3sigma
max_ratio: 0.35 # > 3sigma -- 1046590
- text_length_filter:
max_len: 61592 # 3sigma -- 1114727
- words_num_filter:
lang: en
tokenization: true
min_num: 20 # > 3sigma
max_num: 12241 # 3sigma -- 1120334
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.3105 # 3sigma -- 2234933
``` | 2,690 | [
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datajuicer/redpajama-cc-2020-05-refined-by-data-juicer | 2023-10-23T08:55:35.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:54:30 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10M<n<100M
---
# RedPajama -- CommonCrawl-2020-05 (refined by Data-Juicer)
A refined version of CommonCrawl-2020-05 dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-cc-refine-results/redpajama-cc-2020-05-refine-result.jsonl) (About 297GB).
## Dataset Information
- Number of samples: 42,612,596 (Keep ~46.90% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-cc-2020-05'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.7469 # 3sigma
max_ratio: 0.8609 # 3sigma
- average_line_length_filter: # for code
max_len: 1500 # < 3sigma -- 332621
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3 # > 3sigma -- 170501
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.002 # 3sigma -- 167167
- language_id_score_filter: # remove language filter
min_score: 0.774 # 3sigma -- 1943513
- maximum_line_length_filter: # for code
max_len: 5000 # < 3sigma -- 845490
- perplexity_filter:
lang: en
max_ppl: 5000 # < 3sigma -- 909218
- special_characters_filter:
min_ratio: 0.15 # > 3sigma
max_ratio: 0.35 # > 3sigma -- 1134347
- text_length_filter:
max_len: 68161 # 3sigma -- 1145902
- words_num_filter:
lang: en
tokenization: true
min_num: 20 # remove 7913 samples
max_num: 13644 # 3sigma -- 1148810
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.328 # 3sigma -- 2125070
``` | 2,672 | [
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datajuicer/redpajama-cc-2019-30-refined-by-data-juicer | 2023-10-23T08:57:10.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:56:10 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 10M<n<100M
---
# RedPajama -- CommonCrawl-2019-30 (refined by Data-Juicer)
A refined version of CommonCrawl-2019-30 dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-cc-refine-results/redpajama-cc-2019-30-refine-result.jsonl) (About 240GB).
## Dataset Information
- Number of samples: 36,557,283 (Keep ~45.08% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-cc-2019-30'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter: # 770218
tokenization: false
min_ratio: 0.7489 # 3sigma
max_ratio: 0.8585 # 3sigma
- average_line_length_filter: # for code
max_len: 1500 # < 3sigma (2689) -- 177520
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3 # > 3sigma (0.1491) -- 151703
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0025 # 3sigma -- 101540
- language_id_score_filter: # remove language filter
min_score: 0.788 # 3sigma -- 1622574
- maximum_line_length_filter: # for code
max_len: 5000 # < 3sigma (8775) -- 485806
- perplexity_filter:
lang: en
max_ppl: 5000 # < 3sigma (6723) -- 676914
- special_characters_filter:
min_ratio: 0.15 # > 3sigma (0.104)
max_ratio: 0.35 # > 3sigma (0.322) -- 859797
- text_length_filter:
max_len: 65589 # 3sigma -- 975142
- words_num_filter:
lang: en
tokenization: true
min_num: 20 # > 3sigma -- 196
max_num: 13030 # 3sigma -- 989078
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.279 # 3sigma -- 1716308
``` | 2,722 | [
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datajuicer/alpaca-cot-en-refined-by-data-juicer | 2023-10-23T09:23:29.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"data-juicer",
"fine-tuning",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:56:37 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- fine-tuning
size_categories:
- 10M<n<100M
---
# Alpaca-CoT -- EN (refined by Data-Juicer)
A refined English version of Alpaca-CoT dataset by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to fine-tune a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/SFT/alpaca-cot-en-refine_result.jsonl) (About 226GB).
## Dataset Information
- Number of samples: 72,855,345 (Keep ~54.48% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-alpaca-cot-en'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- document_deduplicator:
lowercase: true
ignore_non_character: true
- alphanumeric_filter:
tokenization: false
min_ratio: 0.1
- character_repetition_filter:
rep_len: 10
max_ratio: 0.6
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.017
- maximum_line_length_filter:
min_len: 20
- text_length_filter:
min_len: 30
- document_simhash_deduplicator:
tokenization: space
window_size: 3
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 9
hamming_distance: 7
``` | 1,740 | [
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datajuicer/redpajama-book-refined-by-data-juicer | 2023-10-23T08:59:08.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:57:47 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 100K<n<1M
---
# RedPajama -- Book (refined by Data-Juicer)
A refined version of Book dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-book-refine-result.jsonl) (About 91GB).
## Dataset Information
- Number of samples: 195,983 (Keep ~95.51% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-book'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.55 # <3sigma (0.697)
max_ratio: 0.854 # 3sigma
- average_line_length_filter: # for code
max_len: 500 # >3sigma (364)
- character_repetition_filter:
rep_len: 10
max_ratio: 0.2 # >3sigma (0.12)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.00047 # 3sigma
- language_id_score_filter: # remove language filter
min_score: 0.2
- maximum_line_length_filter: # for code
max_len: 13381 # 3sigma
- perplexity_filter:
lang: en
max_ppl: 6000 # <3sigma (16516)
- special_characters_filter:
max_ratio: 0.5 # >3sigma (0.32)
- words_num_filter:
lang: en
tokenization: true
min_num: 1000
max_num: 539754 # 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.194 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,400 | [
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datajuicer/redpajama-c4-refined-by-data-juicer | 2023-10-23T09:00:40.000Z | [
"task_categories:text-generation",
"size_categories:100M<n<1B",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T08:59:29 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 100M<n<1B
---
# RedPajama -- C4 (refined by Data-Juicer)
A refined version of C4 dataset in RedPajama by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/redpajama-c4-refine-result.jsonl) (About 832GB).
## Dataset Information
- Number of samples: 344,491,171 (Keep ~94.42% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-c4'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl' # path to your dataset result file
np: 50 # number of subprocess to process your dataset
open_tracer: True
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.65 # <3sigma (0.740)
max_ratio: 0.9 # >3sigma (0.867)
- average_line_length_filter: # for code
max_len: 3000 # >3sigma (1277)
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3 # >3sigma (0.168)
- language_id_score_filter:
min_score: 0.6
- maximum_line_length_filter: # for code
max_len: 4000 # >3sigma (2017)
- perplexity_filter:
lang: en
max_ppl: 6000 #(>3sigma 4543)
- special_characters_filter:
max_ratio: 0.4 # > 3sigma (0.303)
- words_num_filter:
tokenization: true
min_num: 20
max_num: 10000
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.231 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,293 | [
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kunalkhan370/my-civitai-models | 2023-11-01T19:05:30.000Z | [
"region:us"
] | kunalkhan370 | null | null | 0 | 0 | 2023-10-23T09:02:05 | Entry not found | 15 | [
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datajuicer/the-pile-hackernews-refined-by-data-juicer | 2023-10-23T09:11:09.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T09:02:21 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 100K<n<1M
---
# The Pile -- HackerNews (refined by Data-Juicer)
A refined version of HackerNews dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-hackernews-refine-result.jsonl) (About 1.8G).
## Dataset Information
- Number of samples: 371,331 (Keep ~99.55% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-HackerNews'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 48 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
#- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.2 #<3sigma
- average_line_length_filter:
min_len: 15 # >3sigma
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3 # >3sigma
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.05 # >3sigma
- language_id_score_filter:
min_score: 0.2 # <3sigma
- maximum_line_length_filter:
min_len: 20 # >3sigma
- perplexity_filter:
lang: en
max_ppl: 10000 # >3sigma
- special_characters_filter:
max_ratio: 0.7 # >3sigma
- text_length_filter:
min_len: 100 # > 3sigma
- words_num_filter:
lang: en
tokenization: true
min_num: 30 # > 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.8 # > 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,332 | [
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xinyu1205/recognize-anything-plus-model-tag-descriptions | 2023-10-23T12:12:12.000Z | [
"task_categories:zero-shot-classification",
"language:en",
"license:apache-2.0",
"image recognition",
"region:us"
] | xinyu1205 | null | null | 1 | 0 | 2023-10-23T09:05:21 | ---
license: apache-2.0
language:
- en
tags:
- image recognition
task_categories:
- zero-shot-classification
---
# Recognize Anything Plus Tag Descriptions Card
## Dataset details
**Dataset type:**
These tag descriptions files come from the RAM++ by calling GPT api.
**Dataset date:**
Recognize Anything Plus Tag Descriptions was collected in October 2023
**Paper or resources for more information:**
https://github.com/xinyu1205/recognize-anything
**Where to send questions or comments about the model:**
https://github.com/xinyu1205/recognize-anything/issues
## Intended use
**Primary intended uses:**
The primary use of Recognize Anything Plus Model is research on fundamental image recognition models.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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open-llm-leaderboard/details_golaxy__gogpt2-13b | 2023-10-23T09:09:11.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-23T09:09:03 | ---
pretty_name: Evaluation run of golaxy/gogpt2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [golaxy/gogpt2-13b](https://huggingface.co/golaxy/gogpt2-13b) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_golaxy__gogpt2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T09:08:59.644904](https://huggingface.co/datasets/open-llm-leaderboard/details_golaxy__gogpt2-13b/blob/main/results_2023-10-23T09-08-59.644904.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.25041946308724833,\n\
\ \"em_stderr\": 0.004436932707454965,\n \"f1\": 0.319959102348994,\n\
\ \"f1_stderr\": 0.004400567822301105,\n \"acc\": 0.3496193990687978,\n\
\ \"acc_stderr\": 0.00851200635523702\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.25041946308724833,\n \"em_stderr\": 0.004436932707454965,\n\
\ \"f1\": 0.319959102348994,\n \"f1_stderr\": 0.004400567822301105\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02047005307050796,\n \
\ \"acc_stderr\": 0.003900413385915719\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6787687450670876,\n \"acc_stderr\": 0.013123599324558321\n\
\ }\n}\n```"
repo_url: https://huggingface.co/golaxy/gogpt2-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T09_08_59.644904
path:
- '**/details_harness|drop|3_2023-10-23T09-08-59.644904.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T09-08-59.644904.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T09_08_59.644904
path:
- '**/details_harness|gsm8k|5_2023-10-23T09-08-59.644904.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T09-08-59.644904.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T09_08_59.644904
path:
- '**/details_harness|winogrande|5_2023-10-23T09-08-59.644904.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T09-08-59.644904.parquet'
- config_name: results
data_files:
- split: 2023_10_23T09_08_59.644904
path:
- results_2023-10-23T09-08-59.644904.parquet
- split: latest
path:
- results_2023-10-23T09-08-59.644904.parquet
---
# Dataset Card for Evaluation run of golaxy/gogpt2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/golaxy/gogpt2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [golaxy/gogpt2-13b](https://huggingface.co/golaxy/gogpt2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_golaxy__gogpt2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T09:08:59.644904](https://huggingface.co/datasets/open-llm-leaderboard/details_golaxy__gogpt2-13b/blob/main/results_2023-10-23T09-08-59.644904.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.25041946308724833,
"em_stderr": 0.004436932707454965,
"f1": 0.319959102348994,
"f1_stderr": 0.004400567822301105,
"acc": 0.3496193990687978,
"acc_stderr": 0.00851200635523702
},
"harness|drop|3": {
"em": 0.25041946308724833,
"em_stderr": 0.004436932707454965,
"f1": 0.319959102348994,
"f1_stderr": 0.004400567822301105
},
"harness|gsm8k|5": {
"acc": 0.02047005307050796,
"acc_stderr": 0.003900413385915719
},
"harness|winogrande|5": {
"acc": 0.6787687450670876,
"acc_stderr": 0.013123599324558321
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,097 | [
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datajuicer/the-pile-freelaw-refined-by-data-juicer | 2023-10-23T09:12:32.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T09:10:03 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 1M<n<10M
---
# The Pile -- FreeLaw (refined by Data-Juicer)
A refined version of FreeLaw dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-freelaw-refine-result.jsonl) (About 45GB).
## Dataset Information
- Number of samples: 2,942,612 (Keep ~82.61% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-freelaw'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.3 # <3sigma (0.436)
- average_line_length_filter: # for code
max_len: 697 # 3sigma TBD
- character_repetition_filter:
rep_len: 10
max_ratio: 0.4 # >3sigma (0.350)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0053 # 3sigma
- language_id_score_filter:
min_score: 0.5 # < 3sigma (0.583)
- maximum_line_length_filter: # for code
max_len: 4229 # 3sigma
- perplexity_filter:
lang: en
max_ppl: 5322 # 3sigma
- special_characters_filter:
max_ratio: 0.7 # > 3sigma (0.626)
- stopwords_filter: # not use
lang: en
tokenization: true
min_ratio: 0.1 # > 3sigma (0.07)
- text_length_filter:
max_len: 84026 # 3sigma
- words_num_filter:
lang: en
tokenization: true
min_num: 100
max_num: 15208 # 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.155 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,522 | [
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datajuicer/the-pile-uspto-refined-by-data-juicer | 2023-10-23T09:18:18.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T09:15:24 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 1M<n<10M
---
# The Pile -- USPTO (refined by Data-Juicer)
A refined version of USPTO dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-uspto-refine-result.jsonl) (About 18G).
## Dataset Information
- Number of samples: 4,516,283 (Keep ~46.77% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-uspto'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl' # path to your dataset result file
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.7 # <3sigma (0.758)
- average_line_length_filter: # for code
max_len: 2000 # >3sigma (1307)
- character_repetition_filter:
rep_len: 10
max_ratio: 0.2 # >3sigma (0.189)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0016 # 3sigma
- language_id_score_filter:
min_score: 0.6
- maximum_line_length_filter: # for code
max_len: 3061 # 3sigma
- perplexity_filter:
lang: en
max_ppl: 4000 # 3sigma
- special_characters_filter:
max_ratio: 0.3 # > 3sigma (0.274)
- text_length_filter:
max_len: 21556 # 3sigma
- words_num_filter:
lang: en
tokenization: true
min_num: 100
max_num: 6000 # 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.169 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,421 | [
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datajuicer/the-pile-pubmed-central-refined-by-data-juicer | 2023-10-23T09:30:08.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T09:26:13 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 1M<n<10M
---
# The Pile -- PubMed Central (refined by Data-Juicer)
A refined version of PubMed Central dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-pubmed-central-refine-result.jsonl) (About 83G).
## Dataset Information
- Number of samples: 2,694,860 (Keep ~86.96% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-pubmed-central'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter: # 89217
tokenization: false
min_ratio: 0.2787 # 3sigma
- average_line_length_filter: # for code
max_len: 1200 # < 3sigma (1478) -- 7410
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3741 # 3sigma -- 65849
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.00195 # 3sigma -- 8305
- language_id_score_filter: # remove language filter
min_score: 0.5 # 272359
- maximum_line_length_filter: # for code
max_len: 7328 # remove 23808 samples
- perplexity_filter:
lang: en
max_ppl: 8000 # remove 173883 samples
- special_characters_filter:
max_ratio: 0.842 # remove 87661 samples
- text_length_filter:
max_len: 136028 # 3sigma -- 15118
- words_num_filter:
lang: en
tokenization: true
min_num: 20 # remove 176537 samples
max_num: 23305 # remove 15016 samples
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.5981 # 3sigma -- 93843
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,577 | [
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qwertysingh/data3 | 2023-10-23T09:32:06.000Z | [
"region:us"
] | qwertysingh | null | null | 0 | 0 | 2023-10-23T09:30:40 | ---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
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{}
---
# Dataset Card for Dataset Name
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[More Information Needed] | 4,563 | [
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Wauplin/test_empty_data_card | 2023-10-23T09:31:20.000Z | [
"prodigy",
"region:us"
] | Wauplin | null | null | 0 | 0 | 2023-10-23T09:31:18 | ---
tags:
- prodigy
---
# Dataset Card for Dataset Name
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[More Information Needed] | 4,154 | [
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datajuicer/the-pile-pubmed-abstracts-refined-by-data-juicer | 2023-10-23T09:34:32.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T09:31:40 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 100K<n<1M
---
# The Pile -- PubMed Abstracts (refined by Data-Juicer)
A refined version of PubMed Abstracts dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-pubmed-abstract-refine-result.jsonl) (About 24G).
## Dataset Information
- Number of samples: 371,331 (Keep ~99.55% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-pubmed-abstract'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter: # 4068
tokenization: false
min_ratio: 0.7 # < 3sigma (0.773)
max_ratio: 0.881 # 3sigma
- average_line_length_filter: # for code
max_len: 2100 # > 3sigma (1471) -- 7410
- character_repetition_filter:
rep_len: 10
max_ratio: 0.2 # > 3sigma (0.1458) -- 6060
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.00232 # 3sigma
- language_id_score_filter: # remove language filter
min_score: 0.5
- maximum_line_length_filter: # for code
max_len: 4000 # remove 8202 samples
- perplexity_filter:
lang: en
max_ppl: 4000 # remove 10284 samples
- special_characters_filter:
max_ratio: 0.38 # remove 5532 samples
- text_length_filter:
max_len: 4000 # > 3sigma -- 10873
- words_num_filter:
lang: en
tokenization: true
min_num: 20 # remove 10790 samples
max_num: 700 # remove 22709 samples
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.0887 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 3 # small window size for short texts
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 10
hamming_distance: 8 # larger hamming distance threshold for short texts
``` | 2,685 | [
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damian0815/perlin-1k | 2023-10-23T09:52:31.000Z | [
"size_categories:n<1K",
"license:mit",
"region:us"
] | damian0815 | null | null | 0 | 0 | 2023-10-23T09:37:12 | ---
license: mit
size_categories:
- n<1K
---
1000 images at 768x768 of 3 octaves of Perlin noise, at various brightness and contrast levels. eg:
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Ahmed167/floor_plans_cleaned | 2023-10-23T09:39:10.000Z | [
"region:us"
] | Ahmed167 | null | null | 0 | 0 | 2023-10-23T09:37:46 | ---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
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[More Information Needed] | 4,563 | [
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hapboy/test1 | 2023-10-23T09:38:17.000Z | [
"region:us"
] | hapboy | null | null | 0 | 0 | 2023-10-23T09:38:17 | Entry not found | 15 | [
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datajuicer/the-pile-nih-refined-by-data-juicer | 2023-10-23T09:46:07.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"data-juicer",
"pretraining",
"region:us"
] | datajuicer | null | null | 0 | 0 | 2023-10-23T09:38:24 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 100K<n<1M
---
# The Pile -- NIHExPorter (refined by Data-Juicer)
A refined version of NIHExPorter dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-hin-refine-result.jsonl) (About 2.0G).
## Dataset Information
- Number of samples: 858,492 (Keep ~91.36% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-Hin'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.75 # <3sigma (0.800)
max_ratio: 0.866
- average_line_length_filter:
max_len: 10000 # >3sigma (5425)
- character_repetition_filter:
rep_len: 10
max_ratio: 0.2 # >3sigma (0.127)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0003 # 3sigma
- language_id_score_filter:
min_score: 0.7
- perplexity_filter:
lang: en
max_ppl: 1669 #(3sigma)
- special_characters_filter:
max_ratio: 0.3 # > 3sigma (0.218)
- words_num_filter:
tokenization: true
min_num: 20
max_num: 2000
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.104 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` | 2,252 | [
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grsilva/rebel_portuguese | 2023-10-23T18:11:26.000Z | [
"language:pt",
"license:mit",
"region:us"
] | grsilva | null | null | 0 | 0 | 2023-10-23T09:40:44 | ---
license: mit
language:
- pt
pretty_name: rebel_pt
---
This is a dataset that was created to re-train [REBEL](https://github.com/Babelscape/rebel) to work better for the Portuguese language.
This dataset was generated using [CROCODILE](https://github.com/Babelscape/crocodile), which was adapted to use a Portuguese specific model (pt_core_news_sm) instead of their default multi-language model (xx_ent_wiki_sm).
The dataset comes with a train, test, dev and train_dev splits. The train_dev split accounts for 80% of the dataset with the remaining 20% being the training data. The train and dev split was generated from the 80% train_dev data which was further split into an 80/20.
The split for the dataset ends up being:
* Train_dev -> 80% of the data
* Test -> 20% of the data
* Train -> 64% of the data
* Dev -> 16% of the data | 839 | [
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] |
Arsture/ideal-girlfriend | 2023-10-23T10:30:21.000Z | [
"region:us"
] | Arsture | null | null | 0 | 0 | 2023-10-23T09:47:47 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 36234
num_examples: 88
download_size: 10043
dataset_size: 36234
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ideal-girlfriend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 436 | [
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joseph34961971/ntuadl2023hw1 | 2023-10-23T10:20:01.000Z | [
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qgyd2021/visuelle2 | 2023-10-23T10:15:16.000Z | [
"size_categories:100M<n<1B",
"arxiv:2204.06972",
"region:us"
] | qgyd2021 | null | null | 0 | 0 | 2023-10-23T10:14:09 | ---
size_categories:
- 100M<n<1B
---
Visuelle 2.0
https://arxiv.org/abs/2204.06972 | 83 | [
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] |
theblackcat102/llava-pretrain | 2023-10-23T10:57:17.000Z | [
"region:us"
] | theblackcat102 | null | null | 0 | 0 | 2023-10-23T10:14:11 | ---
dataset_info:
features:
- name: image
dtype: image
- name: conversations
dtype: string
splits:
- name: train
num_bytes: 32172470547.488
num_examples: 558128
download_size: 27759109881
dataset_size: 32172470547.488
---
# Dataset Card for "llava-pretrain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 420 | [
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phanvancongthanh/pubchem_enamine_dedup | 2023-10-24T08:56:19.000Z | [
"region:us"
] | phanvancongthanh | null | null | 0 | 0 | 2023-10-23T10:17:17 | ---
dataset_info:
features:
- name: standardized_smiles
dtype: string
splits:
- name: train
num_bytes: 47338232
num_examples: 906545
download_size: 24899243
dataset_size: 47338232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "pubchem_enamine_dedup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 469 | [
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] |
Arsture/ideal-girlfriend2 | 2023-10-23T10:31:29.000Z | [
"region:us"
] | Arsture | null | null | 0 | 0 | 2023-10-23T10:31:26 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 36234
num_examples: 88
download_size: 10043
dataset_size: 36234
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ideal-girlfriend2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 437 | [
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Falah/celebrity_art_prompts | 2023-10-23T11:03:56.000Z | [
"region:us"
] | Falah | null | null | 0 | 0 | 2023-10-23T10:48:19 | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 163783
num_examples: 1000
download_size: 32791
dataset_size: 163783
---
# Dataset Card for "celebrity_art_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 362 | [
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koaning/repo_id | 2023-10-23T10:50:36.000Z | [
"region:us"
] | koaning | null | null | 0 | 0 | 2023-10-23T10:50:36 | Entry not found | 15 | [
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fun1021183/cvt2_GS3_0 | 2023-10-23T11:16:57.000Z | [
"region:us"
] | fun1021183 | null | null | 0 | 0 | 2023-10-23T11:13:07 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 1832280306.75
num_examples: 13258
- name: test
num_bytes: 640923801.75
num_examples: 4722
download_size: 2373732866
dataset_size: 2473204108.5
---
# Dataset Card for "cvt2_GS3_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 469 | [
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getphytagelabsmycosoothe/PhytAgeLabsMycoSoothe | 2023-10-23T11:21:36.000Z | [
"region:us"
] | getphytagelabsmycosoothe | null | null | 0 | 0 | 2023-10-23T11:17:49 | What sets PhytAge Labs MycoSoothe apart is its proactive approach—it doesn't just eliminate existing fungus; it strives to prevent its unwelcome return. This means enduring relief and newfound confidence in your daily life. Keep in mind that individual experiences may vary, but with MycoSoothe, you're taking a positive step towards healthier, fungus-free skin, hair, and nails.
[EXCLUSIVE PROMO OFFER: Click Here to Buy MycoSoothe Advance Nail Health Support Formula at the Best Price Available Online](https://snoppymart.com/us-mycosoothe)
MycoSoothe Reviews
------------------
Are you plagued by relentless fungal infections that simply refuse to go away? Well, fret no more, because MycoSoothe is here to offer you the ultimate solution. MycoSoothe stands as a revolutionary oral supplement designed specifically to combat fungi with its groundbreaking ingredients.
Nails, often overlooked, actually play a pivotal role in our daily lives. From intricate tasks like food preparation to simple actions like gripping objects, healthy nails are essential. When fungal growth disrupts the harmony of our nails, it not only affects their appearance but also poses potential health risks.
Nail-related issues are far more common than one might imagine, with thousands of individuals grappling with nail health concerns. In the quest for solutions, many have come across a plethora of products in the market, each claiming to address these issues uniquely.
Among these contenders, [MycoSoothe](https://snoppymart.com/mycosoothe/) emerges as a trailblazing nail health support product, offering a holistic approach to combat skin and nail fungus while nurturing long-term nail health.
Nail health goes beyond mere aesthetics; our nails are steadfast companions in our daily endeavors. Unhealthy nails can lead to significant inconveniences. Consider how frequently we use our hands for various activities, from opening doors to eating – all of which could potentially lead to health problems if our nails are not in optimal condition.
The global prevalence of nail health issues highlights the significance of this often overlooked aspect of well-being. The realm of supplements is filled with promises of quick fixes for nail health problems. Sadly, most of these products fall short of their claims, leaving consumers disillusioned and disheartened.
Now, bid farewell to those days of ineffective treatments and welcome a revolutionary approach to fungal defense with MycoSoothe. Our potent formula is meticulously crafted to strike at the very core of fungal issues, delivering results that are truly unparalleled. MycoSoothe empowers you to regain control over your health and finally wave goodbye to those persistent fungal problems.
PhytAge Labs MycoSoothe is an oral supplement armed with cutting-edge ingredients to combat fungus effectively. It serves as a beacon of hope, offering a way to address these concerns more efficiently. So, if you're weary of the endless struggle against fungal infections, it's high time to consider MycoSoothe as your trusted ally in the quest for better health and nail rejuvenation.
[.png)](https://snoppymart.com/us-mycosoothe)
What is [MycoSoothe](https://snoppymart.com/mycosoothe/)?
---------------------------------------------------------
Mycosoothe is a potent natural remedy designed to effectively combat fungal infections from within. This safe and efficient supplement is formulated with a unique blend of ingredients that work in tandem to target the root cause of fungal infections, providing long-lasting relief and preventing future outbreaks.
One of the key components of Mycosoothe is tea tree oil, renowned for its robust antifungal properties. Throughout history, tea tree oil has been used to treat various skin issues, including fungal infections. By incorporating this natural wonder, Mycosoothe offers a tried-and-true solution, instilling confidence in its effectiveness.
However, Mycosoothe doesn't stop at tea tree oil; it also harnesses the power of other potent ingredients such as grapefruit seed extract, garlic extract, and oregano extract. These carefully selected substances are chosen for their antifungal, antibacterial, and immune-boosting qualities, providing your body with the tools it needs to combat fungal infections effectively.
Unlike topical treatments that offer only temporary relief, Mycosoothe works internally to address the underlying harmful cause of fungal infections. It not only eliminates existing ailments but also plays a preventive role by bolstering your immune system and restoring your body's natural balance, reducing the likelihood of future infections.
[.png)](https://snoppymart.com/us-mycosoothe)
PhytAge Labs, the manufacturer of Mycosoothe, assures the safety of this medication and emphasizes that it contains the appropriate therapeutic dosages. All ingredients are sourced from reliable, natural sources, and this prescription-free antifungal remedy is readily available online, providing a convenient and accessible solution for those in need.
[BEST DEAL OFFER! Buy MycoSoothe Toenail Supplement Directly from the Manufacturer at the Lowest Price!](https://snoppymart.com/us-mycosoothe)
How Does MycoSoothe Work?
-------------------------
MycoSoothe is a revolutionary solution designed to combat chronic fungal attacks on your nails, scalp, or skin. This exceptional formula not only prioritizes your safety but also harnesses the power of nature to revitalize and enhance the strength and beauty of your nails. By incorporating MycoSoothe into your daily routine, you can bid farewell to issues like discoloration and persistent fungal attacks, experiencing a remarkable transformation in your nail health. Curious about how it works? Here’s how the magic behind this extraordinary formulation works:
* **Revitalization from Within:** MycoSoothe addresses the root causes of brittle and yellow nails, often linked to nutrient deficiencies. By providing a generous dose of essential vitamins and minerals with each intake, this supplement is meticulously designed to improve the texture, tone, strength, and overall appearance of your nails. Just two pills a day act as your armor against common nail issues, making them a robust and healthy part of your body.
* **Defense Against the Unwanted:** MycoSoothe is well blended with the best of the best antioxidants and anti-inflammatory compounds, precisely composed to target the core of fungal cell walls. It acts as a natural fortress against these harmful invaders, inhibiting their replication and preventing infections and swelling. This defense mechanism safeguards your nails with style and confidence.
* **Energizing Your Blood:** MycoSoothe's unique blend of vitamins and minerals promotes the production of robust red blood cells, optimizing blood flow beneath your nails. This enhanced circulation ensures your nails receive the nutrients and oxygen they need, resulting in healthier, more vibrant nail color and reduced risk of discoloration.
* **Transformation with Protein Power:** Brittle nails become a thing of the past with MycoSoothe. It fuels nail strength to boost and strengthen the keratin production, making your nails resilient and stunning. Additionally, this supplement contributes to elevated collagen levels, thanks to the inclusion of vitamin C. Collagen is a vital protein that enhances the structure and integrity of your nail tissues, further improving their shape and appearance.
[MycoSoothe](https://snoppymart.com/mycosoothe/) Ingredients
------------------------------------------------------------
MycoSoothe contains a powerful combination of ingredients, including a dynamic trio of active components: ascorbic acid (vitamin C), vitamin E, and Selenium. However, these are just the beginning. Each serving of MycoSoothe includes a proprietary blend with over 15 natural nutrients, creating a symphony of health benefits that can elevate your nail, hair, and skin care routine to new heights. Let's delve into the key ingredients that make MycoSoothe a passport to a healthier, more vibrant you:
* **Vitamin C (Ascorbic Acid)**
Vitamin C, also known as ascorbic acid, is a cornerstone ingredient in MycoSoothe, renowned for its exceptional benefits in nail, hair, and skin care. Its primary strength lies in its capacity to supercharge collagen levels, the fundamental protein that maintains the integrity and strength of your nails. Particularly important for toenails, where cell regeneration is rapid, vitamin C plays a pivotal role in ensuring nail health. When combined with MycoSoothe's array of nutrients, it forms a robust defense against nail plate deterioration and the development of unsightly white spots.
* **Vitamin E**
Vitamin E is another essential component of MycoSoothe, offering a wide range of benefits. This nutrient strengthens, hydrates, and rejuvenates damaged toenails, thanks to its rich alpha-tocopherol content. Alpha-tocopherol acts as a protective shield against oxidative damage to skin and nail cells. Furthermore, its moisturizing properties contribute to maintaining lustrous and well-hydrated skin and cuticles. Vitamin E plays a crucial role in nurturing nail health and preserving its natural beauty.
* **Selenium (20 mcg)**
MycoSoothe includes 20 mcg of Selenium, a pivotal mineral in the formula. Selenium takes on the role of an antioxidant, shielding rapidly growing nail cells from the potentially harmful impact of free radicals. It serves as a steadfast guardian, defending these delicate cells against oxidative harm and relentless fungal attacks. Beyond nail health, according to the official MycoSoothe website, it is suggested that Selenium may also enhance heart health by promoting healthy blood flow throughout the body, including your precious nails. In essence, it acts as a stalwart soldier in the battle against fungal nail invasions.
* **Turmeric**
Turmeric, a potent anti-inflammatory powerhouse, is a key component of MycoSoothe. It promotes healthy blood flow, providing protection against fungal intruders that could compromise the well-being of your nails.
* **Soursop**
Rich in antioxidants, soursop contributes to the elimination of free radicals and toxins from your system. Beyond nail health, it offers support for heart health and optimal gut function.
* **Green Tea**
Green tea is a source of bioactive nutrients that energize nail cells, strengthen and secure the immune system to act as a vigilant guardian against chronic fungal infections.
* **Raspberry**
This red gem is known for its defensive properties against various ailments, enhancing cell growth and division, and preventing nail discoloration.
* **Essiac Tea**
Rich in antioxidants and metabolism-boosting nutrients, Essiac tea takes on resilient toenail fungus, fortifying keratin and collagen formation to banish brittleness.
* **Burdock Leaf**
Renowned for its support of the urinary system, burdock leaf also contributes to expanding immunity and supporting blood purification, thereby bolstering liver and kidney functions.
* **Mushroom Complex (Maitake, Shiitake, and Reishi)**
Ancient mushrooms such as Maitake, Shiitake, and Reishi fortify the immune system, combat fungal infections, enhance sleep quality, and revitalize the body's metabolic processes.
* **Pomegranate**
Pomegranate, bursting with essential vitamins and minerals, enhances blood flow, minimizes inflammations, and effectively combats harmful nail fungus.
* **Olive Leaf**
Rich in antioxidants and anti-fungal components, olive leaf rejuvenates scalp, skin, and nail health, working harmoniously with other MycoSoothe nutrients to hydrate and strengthen your nails.
* **Panax Asian Ginseng**
Panax Asian Ginseng, a nutrient enriched with antioxidant, anti-inflammatory, and anti-fungal properties, energizes your body, supports cognitive functions, and contributes to overall well-being.
[MycoSoothe](https://snoppymart.com/mycosoothe/) For Sale: Pricing and Where to Buy?
------------------------------------------------------------------------------------
You can conveniently purchabuy se MycoSoothe from the official website, www.trymycosoothe.com. It's important to emphasize that the official website is the sole reliable source for authentic MycoSoothe supplements. To ensure the legitimacy of the product, it is strongly recommended to make your purchases exclusively through the official website.
Claims of MycoSoothe being available from local merchants, vendors, or franchisees should be disregarded, as they lack the necessary authorization. Bottles purchased from other sources may potentially be counterfeit. Here is the pricing information available on the official website:
* One bottle: Priced at $69.95, this package provides a one-month supply of MycoSoothe. It includes standard shipping fees, multiple payment methods, and is backed by a 90-day money-back guarantee.
* Two bottles: You can purchase two bottles for a total of $119.90, which equates to $59.95 per bottle. This package offers a two-month supply, with no shipping fees, multiple payment methods, 2 free e-books, and a 90-day money-back guarantee.
* Four bottles: The four-bottle package is available for $199.80, making each bottle $49.95. It provides a four-month supply, free shipping within the US, 2 free e-books, and a 90-day money-back guarantee.
Not Happy with MycoSoothe Results? - 90-Days Money Back Guarantee?
------------------------------------------------------------------
MycoSoothe takes customer satisfaction seriously and offers a 90-day return policy. If you are not satisfied with your purchase, you can request a refund within 90 days from the date of purchase. The refund process is straightforward and does not require you to provide explanations or reasons.
To initiate a refund, simply contact MycoSoothe's customer care team through the provided email or phone number, which are prominently displayed on the official website and supplement bottle. Once they receive your refund request, they will promptly review your order details and other relevant information to expedite the refund process.
Conclusion - MycoSoothe Reviews
-------------------------------
If you're in search of an effective and natural solution to combat fungal infections, MycoSoothe supplement is definitely worth considering. Its potent blend of natural ingredients is designed to work from the inside out, addressing fungal infections at their core and preventing their recurrence. With consistent use, you can finally bid farewell to those persistent fungal issues and enjoy the benefits of healthy, fungus-free skin.
Don't let fungal infections continue to impact your life and well-being. Empower yourself to take control and fight back with MycoSoothe, the groundbreaking oral supplement that delivers tangible results. Say goodbye to discomfort, irritation, and frustration, and say hello to healthier, happier skin.
[.png)](https://snoppymart.com/us-mycosoothe)
With MycoSoothe, your fungal infections won't stand a chance against the power of its revolutionary ingredients. It's time to seize control and reclaim your skin's health and vitality. Try MycoSoothe today and experience the transformation for yourself – because you deserve to live without the burden of fungal infections.
[Click Here to Buy MycoSoothe For Toenail Fungus From the Official Website Now at Discounted Price!](https://snoppymart.com/us-mycosoothe) | 16,375 | [
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Azure99/blossom-orca-v1 | 2023-10-23T11:38:50.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"language:en",
"license:apache-2.0",
"region:us"
] | Azure99 | null | null | 0 | 0 | 2023-10-23T11:26:57 | ---
license: apache-2.0
task_categories:
- text-generation
- text2text-generation
language:
- zh
- en
size_categories:
- 100K<n<1M
---
# BLOSSOM ORCA V1
### 介绍
Blossom Orca V1是一个基于OpenOrca衍生而来的中英双语指令数据集，适用于指令微调。
本数据集从OpenOrca中抽取了系统提示和指令，首先将其翻译为中文并校验翻译结果，再使用指令调用gpt-3.5-turbo-0613模型生成响应，并过滤掉包含自我认知以及拒绝回答的响应，以便后续对齐。此外，为了确保响应风格的一致性以及中英数据配比，本数据集还对未翻译的原始指令也进行了相同的调用，最终得到了1:1的中英双语指令数据。
相比直接对原始OpenOrca进行翻译的中文数据集，Blossom Orca的一致性及质量更高。
本次发布了全量数据的30%，包含中英双语各100K，共计200K记录。
### 语言
以中文和英文为主。
### 数据集结构
数据集包含两个文件：blossom-orca-v1-chinese-100k.json和blossom-orca-v1-english-100k.json，分别对应中文和英文的数据。
每条数据代表一个完整的对话，包含id和conversations两个字段。
- id：字符串，代表原始OpenOrca的指令id。
- conversations：对象数组，每个对象包含role、content两个字段，role的取值为system、user或assistant，分别代表系统提示、用户输入和助手输出，content则为对应的内容。
### 数据集限制
本数据集的所有响应均由gpt-3.5-turbo-0613生成，并未经过严格的数据校验，可能包含不准确甚至严重错误的回答。此外，由于过滤了拒答响应，仅使用本数据集训练的模型，可能不会拒绝非法的请求。 | 881 | [
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TrainingDataPro/knee-x-rays | 2023-10-23T11:56:17.000Z | [
"task_categories:image-classification",
"task_categories:image-to-image",
"language:en",
"license:cc-by-nc-nd-4.0",
"medical",
"code",
"region:us"
] | TrainingDataPro | null | null | 1 | 0 | 2023-10-23T11:52:46 | ---
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- image-to-image
language:
- en
tags:
- medical
- code
---
# Knee X-rays
The dataset consists of a collection of knee X-ray images in **.jpg and .dcm** formats. The images are organized into folders based on different medical conditions. Each folder contains images depicting specific knee problems.
### Types of diseases and conditions in the dataset:
*Arthritis, Fracture, Stage 1 of Osteoarthritis, Stage 2 of Osteoarthritis, Stage 3 of Osteoarthritis and Stage 4 of Osteoarthritis*
The dataset is a valuable resource for medical research and the field of **musculoskeletal disorders**. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for **automated detection, diagnosis, and classification** of these conditions.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=knee-x-rays) to discuss your requirements, learn about the price and buy the dataset.
# Content
### The folder "files" includes 6 folders:
- corresponding to name of the disease/condition and including x-rays of people with this disease/condition (**arthritis, fracture, stage 1 of OA, stage 2 of OA, stage 3 of OA and stage 4 of OA**)
- including x-rays in 2 different formats: **.jpg and .dcm**.
### File with the extension .csv includes the following information for each media file:
- **dcm**: link to access the .dcm file,
- **jpg**: link to access the .jpg file,
- **type**: name of the disease or condition on the x-ray
# Medical data might be collected in accordance with your requirements.
## **[TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=knee-x-rays)** provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/trainingdata-pro** | 2,319 | [
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explosion/ner-fashion-brands | 2023-10-23T12:04:31.000Z | [
"prodigy",
"region:us"
] | explosion | null | null | 0 | 0 | 2023-10-23T12:01:36 | ---
tags:
- prodigy
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: section
dtype: string
- name: _input_hash
dtype: int64
- name: _task_hash
dtype: int64
- name: tokens
list:
- name: end
dtype: int64
- name: id
dtype: int64
- name: start
dtype: int64
- name: text
dtype: string
- name: spans
list:
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dtype: int64
- name: input_hash
dtype: int64
- name: label
dtype: string
- name: source
dtype: string
- name: start
dtype: int64
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dtype: string
- name: token_end
dtype: int64
- name: token_start
dtype: int64
- name: _session_id
dtype: 'null'
- name: _view_id
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2222165
num_examples: 1235
- name: eval
num_bytes: 898819
num_examples: 500
download_size: 839865
dataset_size: 3120984
---
# Ner Fashion Brands
This dataset originally appear as part of
[this tutorial](https://github.com/explosion/projects/tree/v3/tutorials/ner_fashion_brands). The goal
of the dataset is to detect fashion brands in Reddit Comments.
For more details, be sure to read [this blogpost](https://explosion.ai/blog/sense2vec-reloaded#annotation). | 1,492 | [
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] |
Qdrant/arxiv-titles-instructorxl-embeddings | 2023-10-23T13:41:40.000Z | [
"task_categories:sentence-similarity",
"task_categories:feature-extraction",
"size_categories:1M<n<10M",
"language:en",
"region:us"
] | Qdrant | null | null | 0 | 0 | 2023-10-23T12:04:14 | ---
language:
- en
pretty_name: InstructorXL embeddings of the Arxiv.org titles
task_categories:
- sentence-similarity
- feature-extraction
size_categories:
- 1M<n<10M
---
# arxiv-titles-instructorxl-embeddings
This dataset contains 768-dimensional embeddings generated from the [arxiv](https://arxiv.org/)
paper titles using [InstructorXL](https://huggingface.co/hkunlp/instructor-xl) model. Each
vector has an abstract used to create it, along with the DOI (Digital Object Identifier). The
dataset was created using precomputed embeddings exposed by the [Alexandria Index](https://alex.macrocosm.so/download).
## Generation process
The embeddings have been generated using the following instruction:
```text
Represent the Research Paper title for retrieval; Input:
```
The following code snippet shows how to generate embeddings using the InstructorXL model:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-xl')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Research Paper title for retrieval; Input:"
embeddings = model.encode([[instruction, sentence]])
``` | 1,178 | [
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] |
explosion/ner-drugs | 2023-10-23T12:22:59.000Z | [
"prodigy",
"region:us"
] | explosion | null | null | 0 | 0 | 2023-10-23T12:10:43 | ---
tags:
- prodigy
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: section
dtype: string
- name: _input_hash
dtype: int64
- name: _task_hash
dtype: int64
- name: tokens
list:
- name: end
dtype: int64
- name: id
dtype: int64
- name: start
dtype: int64
- name: text
dtype: string
- name: answer
dtype: string
- name: spans
list:
- name: end
dtype: int64
- name: label
dtype: string
- name: start
dtype: int64
- name: token_end
dtype: int64
- name: token_start
dtype: int64
splits:
- name: train
num_bytes: 2486903
num_examples: 1477
- name: eval
num_bytes: 849130
num_examples: 500
download_size: 0
dataset_size: 3336033
---
# Dataset Card for `ner-drugs`
This dataset was originally part of [this tutorial](https://github.com/explosion/projects/tree/v3/tutorials/ner_drugs). The goal of the
dataset is to find references to drugs in Reddit discussions. | 1,181 | [
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mycosoothereviewshere/mycosoothereviewshere | 2023-10-23T12:20:59.000Z | [
"region:us"
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] |
haseong8012/child-100K | 2023-10-23T16:26:49.000Z | [
"region:us"
] | haseong8012 | null | null | 0 | 0 | 2023-10-23T13:55:13 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: audio
sequence: float32
splits:
- name: train
num_bytes: 19504038988
num_examples: 100000
download_size: 17224747032
dataset_size: 19504038988
---
# Dataset Card for "child-100K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 490 | [
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AdapterOcean/datasci-standardized_unified | 2023-10-23T13:57:20.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T13:57:17 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 4474152
num_examples: 1982
download_size: 2284059
dataset_size: 4474152
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "datasci-standardized_unified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 499 | [
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] |
AdapterOcean/datasci-standardized_embedded | 2023-10-23T13:57:33.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T13:57:30 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 12600352
num_examples: 1982
download_size: 6271462
dataset_size: 12600352
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "datasci-standardized_embedded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 544 | [
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] |
AdapterOcean/datasci-standardized_cluster_0_std | 2023-10-23T13:57:51.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T13:57:48 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2931034
num_examples: 2588
download_size: 1480621
dataset_size: 2931034
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "datasci-standardized_cluster_0_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 669 | [
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] |
AdapterOcean/datasci-standardized_cluster_1_std | 2023-10-23T13:58:22.000Z | [
"region:us"
] | AdapterOcean | null | null | 0 | 0 | 2023-10-23T13:58:20 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1707624
num_examples: 1376
download_size: 840372
dataset_size: 1707624
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "datasci-standardized_cluster_1_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 668 | [
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mariakmurphy55/testingdatasetcards | 2023-10-23T14:23:13.000Z | [
"size_categories:n<1K",
"language:en",
"license:cc0-1.0",
"region:us"
] | mariakmurphy55 | null | null | 0 | 0 | 2023-10-23T14:07:20 | ---
license: cc0-1.0
language:
- en
pretty_name: linregdata
size_categories:
- n<1K
---
# Dataset Card for Testingdatasetcards
Very Simple Multiple Linear Regression Dataset
## Dataset Details
### Dataset Description
<!-- This is a very simple multiple linear regression dataset for beginners.
This dataset has only three columns and twenty rows.
There are only two independent variables and one dependent variable. The independent variables are 'age' and 'experience'.
The dependent variable is 'income'. -->
- **Curated by:** HUSSAIN NASIR KHAN (Kaggle)
- **Shared by [optional]:** Maria Murphy
- **Language(s) (NLP):** English
- **License:** CC0: Public Domain
## Uses
Intended for practice with linear regression.
## Dataset Structure
Contains three columns (age, experience, income) and twenty observations. | 826 | [
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Liokay/b | 2023-10-23T14:10:53.000Z | [
"region:us"
] | Liokay | null | null | 0 | 0 | 2023-10-23T14:10:53 | Entry not found | 15 | [
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] |
vwxyzjn/test-dataset-bug | 2023-10-23T14:12:27.000Z | [
"region:us"
] | vwxyzjn | null | null | 0 | 0 | 2023-10-23T14:10:53 | ---
dataset_info:
features:
- name: data
sequence: int64
splits:
- name: train
num_bytes: 40
num_examples: 2
- name: remove_CritiqueRequest_10_18_2023_1697667530
num_bytes: 40
num_examples: 2
download_size: 2130
dataset_size: 80
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: remove_CritiqueRequest_10_18_2023_1697667530
path: data/remove_CritiqueRequest_10_18_2023_1697667530-*
---
# Dataset Card for "test-dataset-bug"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 641 | [
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] |
vwxyzjn/test-dataset-bug2 | 2023-10-23T14:11:19.000Z | [
"region:us"
] | vwxyzjn | null | null | 0 | 0 | 2023-10-23T14:11:18 | ---
dataset_info:
features:
- name: data
sequence: int64
splits:
- name: remove_CritiqueRequest_10_18_2023_1697667530
num_bytes: 40
num_examples: 2
download_size: 1065
dataset_size: 40
configs:
- config_name: default
data_files:
- split: remove_CritiqueRequest_10_18_2023_1697667530
path: data/remove_CritiqueRequest_10_18_2023_1697667530-*
---
# Dataset Card for "test-dataset-bug2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 548 | [
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vwxyzjn/test-dataset-bug3 | 2023-10-23T14:13:10.000Z | [
"region:us"
] | vwxyzjn | null | null | 0 | 0 | 2023-10-23T14:13:08 | ---
dataset_info:
features:
- name: data
sequence: int64
splits:
- name: train
num_bytes: 40
num_examples: 2
- name: remove_CritiqueRequest_10_18_2023_1697667530
num_bytes: 40
num_examples: 2
download_size: 2130
dataset_size: 80
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: remove_CritiqueRequest_10_18_2023_1697667530
path: data/remove_CritiqueRequest_10_18_2023_1697667530-*
---
# Dataset Card for "test-dataset-bug3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 642 | [
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-0.0012712478637695312,
-0.0296173095703125,
-0.0309906005859375,
0.056243896484375,
-0.0219268798828125,
-0.1060791015625,
0.006687164306640625,
0.03363037109375,
0.01061248779296875,
0.01050567626953125,
0.044158935546875,
-0.0195465087890625,
0.03326416015625,
0.004650115966796875,
0.03076171875,
-0.0262451171875,
-0.03936767578125,
-0.0134735107421875,
-0.005725860595703125,
-0.005401611328125,
-0.0408935546875
]
] |
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