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9bc4613f0ee319b92cf6614fa2934142a45f8337
|
# Dataset Card for "chunk_167"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_167
|
[
"region:us"
] |
2023-05-31T00:21:32+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1228592668.0, "num_examples": 241279}], "download_size": 1256052549, "dataset_size": 1228592668.0}}
|
2023-05-31T00:22:46+00:00
|
634bb9431b1871e664e48f78d5f0aaec57279c59
|
# Dataset Card for "chunk_75"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_75
|
[
"region:us"
] |
2023-05-31T00:22:51+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1235283556.0, "num_examples": 242593}], "download_size": 1260641819, "dataset_size": 1235283556.0}}
|
2023-05-31T00:24:12+00:00
|
4a70b624517f83d5fe9ea338208398051754d7bc
|
# Dataset Card for "chunk_270"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_270
|
[
"region:us"
] |
2023-05-31T00:25:30+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 815050980.0, "num_examples": 160065}], "download_size": 829311065, "dataset_size": 815050980.0}}
|
2023-05-31T00:27:22+00:00
|
3c144df8f067d8d81565e49e50a7025b37339716
|
# Dataset Card for "chunk_168"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_168
|
[
"region:us"
] |
2023-05-31T00:26:37+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1326394712.0, "num_examples": 260486}], "download_size": 1355120678, "dataset_size": 1326394712.0}}
|
2023-05-31T00:27:59+00:00
|
54cdf91bd3bc28cc2d4f2e14942488d5cbb3628b
|
# Dataset Card for "chunk_76"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_76
|
[
"region:us"
] |
2023-05-31T00:28:05+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1270133204.0, "num_examples": 249437}], "download_size": 1297440145, "dataset_size": 1270133204.0}}
|
2023-05-31T00:29:41+00:00
|
f077f432ca771964d2351c05fc74abbebd7171e5
|
# Dataset Card for "chunk_271"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_271
|
[
"region:us"
] |
2023-05-31T00:29:20+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 921239548.0, "num_examples": 180919}], "download_size": 938561466, "dataset_size": 921239548.0}}
|
2023-05-31T00:31:26+00:00
|
bfcfe6a204db9c4bdd2601050ab0f4588b0148f5
|
# Dataset Card for "chunk_169"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_169
|
[
"region:us"
] |
2023-05-31T00:32:09+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1236795880.0, "num_examples": 242890}], "download_size": 1261835204, "dataset_size": 1236795880.0}}
|
2023-05-31T00:33:17+00:00
|
b79c94cff77ab4ceeae028ea7527f41003189eec
|
# Dataset Card for "chunk_272"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_272
|
[
"region:us"
] |
2023-05-31T00:33:00+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 761254000.0, "num_examples": 149500}], "download_size": 776936161, "dataset_size": 761254000.0}}
|
2023-05-31T00:34:31+00:00
|
e6e644f9dd0db8006c6f719c6ef0ce200c3698d4
|
# Dataset Card for "chunk_77"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_77
|
[
"region:us"
] |
2023-05-31T00:33:52+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1299473308.0, "num_examples": 255199}], "download_size": 1327721411, "dataset_size": 1299473308.0}}
|
2023-05-31T00:35:16+00:00
|
1ccb417455cac468908815fc1f5ccf914431f542
|
# Dataset Card for "chunk_273"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_273
|
[
"region:us"
] |
2023-05-31T00:35:06+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 291741048.0, "num_examples": 57294}], "download_size": 295189680, "dataset_size": 291741048.0}}
|
2023-05-31T00:35:39+00:00
|
76a2f12400bd2e851bf716b393e1ce3fd3396065
|
# Dataset Card for "chunk_170"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_170
|
[
"region:us"
] |
2023-05-31T00:36:54+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1237279620.0, "num_examples": 242985}], "download_size": 1261694847, "dataset_size": 1237279620.0}}
|
2023-05-31T00:38:21+00:00
|
1a0b9750f52072987726732ded7c80be3b0f9a00
|
# Dataset Card for "chunk_78"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_78
|
[
"region:us"
] |
2023-05-31T00:39:09+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1270846084.0, "num_examples": 249577}], "download_size": 1298418420, "dataset_size": 1270846084.0}}
|
2023-05-31T00:40:38+00:00
|
fe70e30692f0b02962554c7898ce68199f394e39
|
# Dataset Card for "chunk_171"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_171
|
[
"region:us"
] |
2023-05-31T00:42:18+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1184832020.0, "num_examples": 232685}], "download_size": 1202714278, "dataset_size": 1184832020.0}}
|
2023-05-31T00:43:24+00:00
|
d1b9ef0660911eaaf6902f5c2d85ebb951da5846
|
# Dataset Card for "chunk_79"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_79
|
[
"region:us"
] |
2023-05-31T00:44:37+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1293566588.0, "num_examples": 254039}], "download_size": 1322009453, "dataset_size": 1293566588.0}}
|
2023-05-31T00:46:01+00:00
|
053bfbe753e95aa92c9f52835a01753df5b9d3f5
|
Face Masks ensemble dataset is no longer limited to [Kaggle](https://www.kaggle.com/datasets/henrylydecker/face-masks), it is now coming to Huggingface!
This dataset was created to help train and/or fine tune models for detecting masked and un-masked faces.
I created a new face masks object detection dataset by compositing together three publically available face masks object detection datasets on Kaggle that used the YOLO annotation format.
To combine the datasets, I used Roboflow.
All three original datasets had different class dictionaries, so I recoded the classes into two classes: "Mask" and "No Mask".
One dataset included a class for incorrectly worn face masks, images with this class were removed from the dataset.
Approximately 50 images had corrupted annotations, so they were manually re-annotated in the Roboflow platform.
The final dataset includes 9,982 images, with 24,975 annotated instances.
Image resolution was on average 0.49 mp, with a median size of 750 x 600 pixels.
To improve model performance on out of sample data, I used 90 degree rotational augmentation.
This saved duplicate versions of each image for 90, 180, and 270 degree rotations.
I then split the data into 85% training, 10% validation, and 5% testing.
Images with classes that were removed from the dataset were removed, leaving 16,000 images in training, 1,900 in validation, and 1,000 in testing.
|
hlydecker/face-masks
|
[
"task_categories:object-detection",
"task_categories:image-classification",
"license:mit",
"medical",
"region:us"
] |
2023-05-31T00:46:08+00:00
|
{"license": "mit", "task_categories": ["object-detection", "image-classification"], "tags": ["medical"]}
|
2023-05-31T02:02:14+00:00
|
47744dd77c5d68f8f4e566636dfbc881a820161d
|
# Dataset Card for "chunk_172"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_172
|
[
"region:us"
] |
2023-05-31T00:46:31+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1064014136.0, "num_examples": 208958}], "download_size": 1077742450, "dataset_size": 1064014136.0}}
|
2023-05-31T00:47:32+00:00
|
755acb86d5dd97dc50782196e0167dc0da8f10be
|
# Dataset Card for "chunk_80"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_80
|
[
"region:us"
] |
2023-05-31T00:50:12+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1280261192.0, "num_examples": 251426}], "download_size": 1308234723, "dataset_size": 1280261192.0}}
|
2023-05-31T00:51:50+00:00
|
e290b6730d479728f5a9fcdca38181d96420b62b
|
# Dataset Card for "chunk_173"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_173
|
[
"region:us"
] |
2023-05-31T00:50:48+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1018522208.0, "num_examples": 200024}], "download_size": 1039385445, "dataset_size": 1018522208.0}}
|
2023-05-31T00:51:45+00:00
|
b0365a9aa280e0691597b5f05302fe55ab46e150
|
# Dataset Card for "chunk_174"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_174
|
[
"region:us"
] |
2023-05-31T00:55:02+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1116802900.0, "num_examples": 219325}], "download_size": 1140907940, "dataset_size": 1116802900.0}}
|
2023-05-31T00:56:09+00:00
|
807c4f95c2c3cd966c4cbd2e9a75935ea164b804
|
# Dataset Card for "chunk_81"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_81
|
[
"region:us"
] |
2023-05-31T00:56:33+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1483004264.0, "num_examples": 291242}], "download_size": 1494506092, "dataset_size": 1483004264.0}}
|
2023-05-31T00:58:12+00:00
|
5f72cd66bab4c57af09cd1cee62b5b4012f5154e
|
# Dataset Card for "chunk_175"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_175
|
[
"region:us"
] |
2023-05-31T00:58:56+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 971691084.0, "num_examples": 190827}], "download_size": 992119081, "dataset_size": 971691084.0}}
|
2023-05-31T00:59:48+00:00
|
474c819d78838282a7b056b9249980da64737e45
|
# Dataset Card for "chunk_82"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_82
|
[
"region:us"
] |
2023-05-31T01:02:49+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1355276536.0, "num_examples": 266158}], "download_size": 1382698639, "dataset_size": 1355276536.0}}
|
2023-05-31T01:04:20+00:00
|
80a9431630f1340981e51cd89e37fac1f7616e46
|
# Dataset Card for "chunk_176"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_176
|
[
"region:us"
] |
2023-05-31T01:03:59+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1272898160.0, "num_examples": 249980}], "download_size": 1300039942, "dataset_size": 1272898160.0}}
|
2023-05-31T01:05:14+00:00
|
082395c850ee1a49558aaf5f6722a22d422ef365
|
# Dataset Card for "chunk_83"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_83
|
[
"region:us"
] |
2023-05-31T01:08:15+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1301525384.0, "num_examples": 255602}], "download_size": 1325762989, "dataset_size": 1301525384.0}}
|
2023-05-31T01:09:42+00:00
|
383edfb77f5b330e813418d336db9e2fa5e7fa06
|
# Dataset Card for "chunk_177"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_177
|
[
"region:us"
] |
2023-05-31T01:08:37+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1161648144.0, "num_examples": 228132}], "download_size": 1185470831, "dataset_size": 1161648144.0}}
|
2023-05-31T01:09:45+00:00
|
59a113923ed0f9819176739bf6b863d9ac536efe
|
# Dataset Card for "chunk_84"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_84
|
[
"region:us"
] |
2023-05-31T01:13:38+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1112423780.0, "num_examples": 218465}], "download_size": 1134472201, "dataset_size": 1112423780.0}}
|
2023-05-31T01:14:54+00:00
|
8871b25fc58067bde76d4054c59e99840406b78d
|
# Dataset Card for "chunk_178"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_178
|
[
"region:us"
] |
2023-05-31T01:13:42+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1132099268.0, "num_examples": 222329}], "download_size": 1156475830, "dataset_size": 1132099268.0}}
|
2023-05-31T01:14:44+00:00
|
ede0029c04d33a07a85d120fbc7cce8a38aaa992
|
# Dataset Card for "chunk_179"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_179
|
[
"region:us"
] |
2023-05-31T01:18:45+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1372253264.0, "num_examples": 269492}], "download_size": 1398823508, "dataset_size": 1372253264.0}}
|
2023-05-31T01:20:11+00:00
|
c79e2173a8b26c2e8b583645cac0b42defbbb359
|
# Dataset Card for "chunk_85"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_85
|
[
"region:us"
] |
2023-05-31T01:19:14+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1436427740.0, "num_examples": 282095}], "download_size": 1465479008, "dataset_size": 1436427740.0}}
|
2023-05-31T01:20:48+00:00
|
2911ba50e46db7f6c2cf462604b01568e06478b9
|
# Dataset Card for "chunk_180"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_180
|
[
"region:us"
] |
2023-05-31T01:23:22+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 965468660.0, "num_examples": 189605}], "download_size": 986503346, "dataset_size": 965468660.0}}
|
2023-05-31T01:24:14+00:00
|
c91570a2356692e594364c5b7b30750757c89f50
|
# Dataset Card for "chunk_86"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_86
|
[
"region:us"
] |
2023-05-31T01:25:34+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1377070296.0, "num_examples": 270438}], "download_size": 1405135351, "dataset_size": 1377070296.0}}
|
2023-05-31T01:27:04+00:00
|
87a30fc1a3645db2e8c90e496db4074e1425f155
|
# Dataset Card for "chunk_181"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_181
|
[
"region:us"
] |
2023-05-31T01:27:18+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1048885804.0, "num_examples": 205987}], "download_size": 1071457846, "dataset_size": 1048885804.0}}
|
2023-05-31T01:28:17+00:00
|
c95ee8f79f2c4c1eb82416705afe9cf3d7b22323
|
srikanthmalipatel/semantic_search
|
[
"license:mit",
"region:us"
] |
2023-05-31T01:27:56+00:00
|
{"license": "mit"}
|
2023-05-31T03:27:19+00:00
|
|
03547053ab4ccdf26f33f192ef084cb057900344
|
# Dataset Card for "chunk_182"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_182
|
[
"region:us"
] |
2023-05-31T01:30:21+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 724963316.0, "num_examples": 142373}], "download_size": 738012160, "dataset_size": 724963316.0}}
|
2023-05-31T01:31:05+00:00
|
d7069eccf17319d0bca35477d5d5431a68dbbe49
|
# Dataset Card for "chunk_87"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_87
|
[
"region:us"
] |
2023-05-31T01:31:08+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1346207684.0, "num_examples": 264377}], "download_size": 1373965661, "dataset_size": 1346207684.0}}
|
2023-05-31T01:32:57+00:00
|
9089bc212ffd3535071ea57b8db24390910742ff
|
# Dataset Card for "chunk_88"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_88
|
[
"region:us"
] |
2023-05-31T01:37:09+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1326639128.0, "num_examples": 260534}], "download_size": 1354108767, "dataset_size": 1326639128.0}}
|
2023-05-31T01:38:50+00:00
|
7705bb844b5cac3857f50864a06a84c9acda7a8d
|
# Dataset Card for "chunk_89"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_89
|
[
"region:us"
] |
2023-05-31T01:43:13+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1309647124.0, "num_examples": 257197}], "download_size": 1336690819, "dataset_size": 1309647124.0}}
|
2023-05-31T01:44:46+00:00
|
e8202b30c8664f5d5fe7c475b43b38967694b7aa
|
# Dataset Card for synQA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Internal-Datasets homepage](https://github.com/Marbyun/datasets-huggingface)
- **Point of Contact:** [Marbyun](https://huggingface.co/Marbyun)
### Dataset Summary
This Datasets purpose for AI Question-Answering'Datasets. This Dataset inspired by SynQA And SQuAD v1.1 (https://arxiv.org/abs/1606.05250) training set.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
Data is provided in the same format as SQuAD 1.1. An example is shown below:
```
{
"data": [
{
"title": "None",
"paragraphs": [
{
"context": "Architecturally, the school has a Catholic character. Atop the Main Building's gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
"qas": [
{
"id": "689f275aacba6c43ff112b2c7cb16129bfa934fa",
"question": "What material is the statue of Christ made of?",
"answers": [
{
"answer_start": 190,
"text": "organic copper"
}
]
},
{
"id": "73bd3f52f5934e02332787898f6e568d04bc5403",
"question": "Who is on the Main Building's gold dome?",
"answers": [
{
"answer_start": 111,
"text": "the Virgin Mary."
}
]
},
{
"id": "4d459d5b75fd8a6623446290c542f99f1538cf84",
"question": "What kind of statue is at the end of the main drive?",
"answers": [
{
"answer_start": 667,
"text": "modern stone"
}
]
},
{
"id": "987a1e469c5b360f142b0a171e15cef17cd68ea6",
"question": "What type of dome is on the Main Building at Notre Dame?",
"answers": [
{
"answer_start": 79,
"text": "gold"
}
]
}
]
}
]
}
]
}
```
### Data Fields
- title: all "None" in this dataset
- context: the context/passage
- id: a string identifier for each question
- answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text.
### Data Splits
The dataset is composed of a single split of 314,811 examples that we used in a two-stage fine-tuning process (refer to the paper for further details).
## Dataset Creation
### Curation Rationale
This dataset was created to investigate the effects of using synthetic adversarial data generation to improve robustness of state-of-the-art QA models.
### Source Data
#### Initial Data Collection and Normalization
The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250).
#### Who are the source language producers?
The source language produces are Wikipedia editors for the passages, and a BART-Large generative model for the questions.
### Personal and Sensitive Information
No annotator identifying details are provided.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better question answering systems.
A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a support resource for improve the ability of systems t handle questions that contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question.
It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application.
### Discussion of Biases
The dataset may exhibit various biases in terms of the source passage selection, selected candidate answers, generated questions, quality re-labelling process, as well as any algorithmic biases that may be exacerbated from the adversarial annotation process used to collect the SQuAD and AdversarialQA data on which the generators were trained.
### Other Known Limitations
N/a
## Additional Information
### Dataset Curators
This Dataset prepared by RnD Team.
### Licensing Information
This dataset is distributed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
```
@inproceedings{Rnd-AI-Team,
title = "Dataset for Develop AI.",
author = "RnD Team,",
booktitle = "",
month = jun,
year = "2023",
address = "",
publisher = "",
url = "",
doi = "",
pages = "",
abstract = "This Dataset prepare by RnD Team for develop AI Question and Answering Chatbot.",
}
```
|
Marbyun/internal-datasets
|
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1606.05250",
"region:us"
] |
2023-05-31T01:44:02+00:00
|
{"annotations_creators": ["generated"], "language_creators": ["found"], "language": ["en"], "license": "mit", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa", "open-domain-qa"], "pretty_name": "synQA"}
|
2023-06-07T07:02:08+00:00
|
d1155fbcaf9918433fda48e6a52cf6bb3ab2169c
|
# Dataset Card for "chunk_90"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_90
|
[
"region:us"
] |
2023-05-31T01:48:46+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1296275532.0, "num_examples": 254571}], "download_size": 1323258956, "dataset_size": 1296275532.0}}
|
2023-05-31T01:50:11+00:00
|
b9388c1d8efa8f5accc9bbae8618be3ebd0f97cd
|
# Dataset Card for "chunk_91"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_91
|
[
"region:us"
] |
2023-05-31T01:54:34+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1270331792.0, "num_examples": 249476}], "download_size": 1297379103, "dataset_size": 1270331792.0}}
|
2023-05-31T01:56:14+00:00
|
8523090fbd2336a8c032c95a06722ef44b709b3b
|
# Dataset Card for News_Articles_Categorization
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
29000 News headlines which are classified into 13 different labels namely: "Playful", "Infuriating", "Sentimental", "Cynical", "Depressing", "Awe-inspiring", "Patriotic", "Begrudging", "Educational", "Hopeful",
"Sarcastic", "Disrespectful", "Disparaging"
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of 14 columns namely Headline and the other 13 representing the labels mentioned above.
The Headline column consists of the news headlines and the label columns represent if the headline belongs to the label or not
## Source Data
The dataset is collected from the database of otherweb.com
|
valurank/Emotion_headline
|
[
"task_categories:text-classification",
"task_ids:multi-label-classification",
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] |
2023-05-31T02:30:25+00:00
|
{"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"]}
|
2023-08-27T20:27:49+00:00
|
1ea03f9ddf1a6a1dc81ba87cc294cba9f2c575bc
|
# Dataset Card for "Minecrafter"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
OVAWARE/Minecrafter
|
[
"region:us"
] |
2023-05-31T02:53:47+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 64316640, "num_examples": 20556}], "download_size": 8604268, "dataset_size": 64316640}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-14T22:54:09+00:00
|
e2989c55a53593a8e39b8f8ebdb47ccaccbe484a
|
### Dataset description
- **(Jan. 8 2024) Test set labels are released**
- **Toolkit Repository:** [https://github.com/asappresearch/slue-toolkit/](https://github.com/asappresearch/slue-toolkit/)
- **Paper:** [https://arxiv.org/abs/2212.10525](https://arxiv.org/abs/2212.10525)
### Licensing Information
#### SLUE-HVB
SLUE-HVB dataset contains a subset of the Gridspace-Stanford Harper Valley speech dataset and the copyright of this subset remains the same with the original license, CC-BY-4.0. See also original license notice (https://github.com/cricketclub/gridspace-stanford-harper-valley/blob/master/LICENSE)
Additionally, we provide dialog act classification annotation and it is covered with the same license as CC-BY-4.0.
#### SLUE-SQA-5
SLUE-SQA-5 Dataset contains question texts and answer strings (question_text, normalized_question_text, and answer_spans column in .tsv files) from these datasets,
* SQuAD1.1 (for questions whose question_id starts with ‘squad-’)
* Natural Questions (for questions whose question_id starts with ‘nq-’)
* WebQuestions (for questions whose question_id starts with ‘wq-’)
* CuratedTREC (for questions whose question_id starts with ‘trec-’)
* TriviaQA (for questions whose question_id starts with ‘triviaqa-’)
Additionally, we provide audio recordings (.wav files in “question” directories) of these questions.
For questions from TriviaQA (questions whose question_id starts with ‘triviaqa-’), their question texts, answer strings, and audio recordings are licensed with the same Apache License 2.0 as TriviaQA (for more detail, please refer to https://github.com/mandarjoshi90/triviaqa/blob/master/LICENSE).
For questions from the other 4 datasets, their question texts, answer strings, and audio recordings are licensed with Creative Commons Attribution-ShareAlike 4.0 International license.
SLUE-SQA-5 also contains a subset of Spoken Wikipedia, including the audios placed in “document” directories and their transcripts (document_text and normalized_document_text column in .tsv files). Additionally, we provide the text-to-speech alignments (.txt files in “word2time” directories).These contents are licensed with the same Creative Commons (CC BY-SA 4.0) license as Spoken Wikipedia.
#### SLUE-TED
SLUE-TED Dataset contains TED Talk audios along with the associated abstracts and title, which were concatenated to create reference summaries. This corpus is licensed with the same Creative Commons (CC BY–NC–ND 4.0 International) license as TED talks. For further information, please refer to the details provided below.
=============================
TED.com
We encourage you to share TED Talks, under our Creative Commons license, or ( CC BY–NC–ND 4.0 International, which means it may be shared under the conditions below:
CC: means the type of license rights associated with TED Talks, or Creative Commons
BY: means the requirement to include an attribution to TED as the owner of the TED Talk and include a link to the talk, but do not include any other TED branding on your website or platform, or language that may imply an endorsement.
NC: means you cannot use TED Talks in any commercial context or to gain any type of revenue, payment or fee from the license sublicense, access or usage of TED Talks in an app of any kind for any advertising, or in exchange for payment of any kind, including in any ad supported content or format.
ND: means that no derivative works are permitted so you cannot edit, remix, create, modify or alter the form of the TED Talks in any way. This includes using the TED Talks as the basis for another work, including dubbing, voice-overs, or other translations not authorized by TED. You may not add any more restrictions that we have placed on the TED site content, such as additional legal or technological restrictions on accessing the content.
|
asapp/slue-phase-2
|
[
"arxiv:2212.10525",
"region:us"
] |
2023-05-31T03:10:08+00:00
|
{"dataset_info": [{"config_name": "hvb", "features": [{"name": "issue_id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "utt_index", "dtype": "int32"}, {"name": "channel", "dtype": "int32"}, {"name": "role", "dtype": "string"}, {"name": "start_ms", "dtype": "int32"}, {"name": "duration_ms", "dtype": "int32"}, {"name": "intent", "dtype": "string"}, {"name": "dialog_acts", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 803631533.648, "num_examples": 11344}, {"name": "validation", "num_bytes": 115999281.63, "num_examples": 1690}, {"name": "test", "num_bytes": 413280185.739, "num_examples": 6121}], "download_size": 1287263357, "dataset_size": 1332911001.017}, {"config_name": "sqa5", "features": [{"name": "question_id", "dtype": "string"}, {"name": "question_audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "question_speaker_id", "dtype": "string"}, {"name": "raw_question_text", "dtype": "string"}, {"name": "normalized_question_text", "dtype": "string"}, {"name": "document_id", "dtype": "string"}, {"name": "document_audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "document_speaker_id", "dtype": "string"}, {"name": "raw_document_text", "dtype": "string"}, {"name": "normalized_document_text", "dtype": "string"}, {"name": "word2time", "sequence": [{"name": "word", "dtype": "string"}, {"name": "normalized_word", "dtype": "string"}, {"name": "start_second", "dtype": "float64"}, {"name": "end_second", "dtype": "float64"}]}, {"name": "answer_spans", "sequence": [{"name": "answer", "dtype": "string"}, {"name": "start_second", "dtype": "float64"}, {"name": "end_second", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 134775904845.04, "num_examples": 46186}, {"name": "validation", "num_bytes": 5686714785.843, "num_examples": 1939}, {"name": "test", "num_bytes": 6967375359.628, "num_examples": 2382}, {"name": "verified_test", "num_bytes": 1182628989.0, "num_examples": 408}], "download_size": 118074473123, "dataset_size": 148612623979.511}, {"config_name": "ted", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "speaker", "dtype": "string"}, {"name": "transcript", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "abstract", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 46573026086.984, "num_examples": 3384}, {"name": "validation", "num_bytes": 5694199931.0, "num_examples": 425}, {"name": "test", "num_bytes": 5959094411.0, "num_examples": 423}], "download_size": 58384489268, "dataset_size": 58226320428.984}, {"config_name": "vp_nel", "features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "word_timestamps", "sequence": [{"name": "word", "dtype": "string"}, {"name": "start_sec", "dtype": "float64"}, {"name": "end_sec", "dtype": "float64"}]}, {"name": "ne_timestamps", "sequence": [{"name": "ne_label", "dtype": "string"}, {"name": "start_char_idx", "dtype": "int32"}, {"name": "char_offset", "dtype": "int32"}, {"name": "start_sec", "dtype": "float64"}, {"name": "end_sec", "dtype": "float64"}]}], "splits": [{"name": "validation", "num_bytes": 83371882.75, "num_examples": 1750}, {"name": "test", "num_bytes": 85222143.142, "num_examples": 1838}], "download_size": 165119242, "dataset_size": 168594025.89200002}], "configs": [{"config_name": "hvb", "data_files": [{"split": "train", "path": "hvb/train-*"}, {"split": "validation", "path": "hvb/validation-*"}, {"split": "test", "path": "hvb/test-*"}]}, {"config_name": "sqa5", "data_files": [{"split": "train", "path": "sqa5/train-*"}, {"split": "validation", "path": "sqa5/validation-*"}, {"split": "test", "path": "sqa5/test-*"}, {"split": "verified_test", "path": "sqa5/verified_test-*"}]}, {"config_name": "ted", "data_files": [{"split": "train", "path": "ted/train-*"}, {"split": "validation", "path": "ted/validation-*"}, {"split": "test", "path": "ted/test-*"}]}, {"config_name": "vp_nel", "data_files": [{"split": "validation", "path": "vp_nel/validation-*"}, {"split": "test", "path": "vp_nel/test-*"}]}]}
|
2024-01-12T05:14:26+00:00
|
307220f053321d0000e9df84850a817578fc8d98
|
# Dataset Card for "OCR_12k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DataStudio/OCR_12k
|
[
"task_categories:image-to-text",
"size_categories:10K<n<100K",
"language:vi",
"region:us"
] |
2023-05-31T03:57:17+00:00
|
{"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["image-to-text"], "pretty_name": "OCR docume", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 448213834.625, "num_examples": 12003}], "download_size": 447813433, "dataset_size": 448213834.625}}
|
2023-06-01T08:59:24+00:00
|
67024b30009339969dcff2c72ac1a36d7406f537
|
This is Research for Laos
|
Phonepadith/datasetlaos
|
[
"region:us"
] |
2023-05-31T04:08:59+00:00
|
{}
|
2023-05-31T04:54:45+00:00
|
a2605db46eb1395e6a15ca42619b5ab8e21174d5
|
# Dataset Card for "dolly_reward_modeling_pairwise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andersonbcdefg/dolly_reward_modeling_pairwise
|
[
"region:us"
] |
2023-05-31T04:39:50+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response_a", "dtype": "string"}, {"name": "response_b", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "preferred", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16503157, "num_examples": 19343}], "download_size": 9011974, "dataset_size": 16503157}}
|
2023-05-31T04:40:03+00:00
|
12bdea6b6c467942cf8a815b77190f1611167f94
|
Dataset homepage: https://huggingface.co/datasets/pranked03/flowers-blip-captions
|
arpitamangal/flower-blip-original-version
|
[
"region:us"
] |
2023-05-31T04:43:33+00:00
|
{}
|
2023-05-31T06:01:28+00:00
|
0c067a4a5be88500107570bd18e8a8eb7094c3b9
|
zzzzhhh/LLaMa-zn
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-31T04:47:46+00:00
|
{"license": "apache-2.0"}
|
2023-06-06T00:27:56+00:00
|
|
c223a9dc6f9f4e15da130df3ebc0479589b0ac12
|
Dataset for https://arxiv.org/abs/2308.01415
|
wza/fin
|
[
"license:apache-2.0",
"arxiv:2308.01415",
"region:us"
] |
2023-05-31T05:13:42+00:00
|
{"license": "apache-2.0"}
|
2023-09-22T03:24:50+00:00
|
f53d24933b15acd9711407ca476a45ec0e2a424e
|
# Project Links
[Github](https://github.com/declare-lab/tango)
[Web](https://tango-web.github.io/)
[Huggingface Space](https://huggingface.co/spaces/declare-lab/tango)
# Dataset Description
This dataset was used to Pre-train [Tango-Full-FT-Audiocaps](https://huggingface.co/declare-lab/tango-full-ft-audiocaps). **TangoPromptBank** is a diverse corpus consisting of textual prompts and audio samples sourced from WavCaps [1], AudioCaps [9], ESC [2], UrbanSound [3], MusicCaps [4], GTZAN [5], and Musical Instruments [6] dataset. The dataset statistics are reported in Table 1. All audio clips longer than 10 seconds were segmented into partitions of successive 10 seconds or shorter. We also resampled all audio clips to 16KHz.
The WavCaps dataset consists of ChatGPT-generated captions for the FreeSound [7], BBC Sound Effects [8] (SFX), and the AudioSet strongly labeled subset. The Urban Sound and ESC50 datasets contain various environmental sounds. The Musical Instruments dataset contains sounds of guitar, drum, violin, and piano instruments. The GTZAN dataset contains sounds of different musical genres -- classical, jazz, etc. These four datasets -- Urban Sound, ESC50, Musical Instruments, GTZAN are audio classification datasets. We use the classification label (e.g., *piano*) and a more natural prompt (*sound of piano*) to create two different training instances for each audio sample from these datasets.
[1]: [WavCaps](https://arxiv.org/abs/2303.17395) [2]: [ESC](http://dl.acm.org/citation.cfm?doid=2733373.2806390)
[3]: [UrbanSound](https://dl.acm.org/doi/10.1145/2647868.2655045)
[4]: [MusicCaps](https://arxiv.org/abs/2301.11325)
[5]: [GTZAN](https://ieeexplore.ieee.org/document/1021072)
[6]: [Musical Instruments Dataset](https://www.kaggle.com/datasets/soumendraprasad/musical-instruments-sound-dataset)
[7]: [FreeSound](https://freesound.org/)
[8]: [BBC Sound Effects](https://sound-effects.bbcrewind.co.uk) [9]: [AudioCaps](https://aclanthology.org/N19-1011/)
# Dataset Statistics
| Dataset | Count |
|-------------------------|-------|
| AudioSet Strong | 108K |
| AudioCaps | 45K |
| Freesound | 680K |
| BBC | 374K |
| Urban Sound | 17K |
| Musical Instrument | 10K |
| MusicCaps | 10K |
| Gtzan Music Genre | 6K |
| ESC50 | 4K |
| **Total** | **1.2M** |
# Baseline Results using TangoPromptBank for Pre-training
| **Model** | **Datasets** | **Dataset Size** | **#Params** | **FD ↓** | **KL ↓** |
| --- | --- | --- | --- | --- | --- |
| [**Tango-Full-FT-Audiocaps**](https://huggingface.co/declare-lab/tango-full-ft-audiocaps) | AS+AC+7 others | 1.2M | 866M | **18.93** | **1.12** |
# Citation
Please consider citing the following article if you found our work useful:
```bibtex
@article{ghosal2023tango,
title={Text-to-Audio Generation using Instruction Tuned LLM and Latent Diffusion Model},
author={Ghosal, Deepanway and Majumder, Navonil and Mehrish, Ambuj and Poria, Soujanya},
journal={arXiv preprint arXiv:2304.13731},
year={2023}
}
```
|
declare-lab/TangoPromptBank
|
[
"size_categories:1M<n<10M",
"license:mit",
"arxiv:2303.17395",
"arxiv:2301.11325",
"region:us"
] |
2023-05-31T05:28:28+00:00
|
{"license": "mit", "size_categories": ["1M<n<10M"]}
|
2023-05-31T06:18:02+00:00
|
e50c6a64dbf80b095463bbb988ede07c4bc4282d
|
shi3z/ja_testqa
|
[
"license:mit",
"region:us"
] |
2023-05-31T05:32:07+00:00
|
{"license": "mit"}
|
2023-05-31T05:33:08+00:00
|
|
4ef6f73cf212dac3e908b3dc06de4bdc5d7c9142
|
# Dataset Card for "syn_few7_7100_chat_pvi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FanChen0116/syn_few7_7100_chat_pvi
|
[
"region:us"
] |
2023-05-31T06:24:32+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "O", "1": "I-time", "2": "B-date", "3": "B-last_name", "4": "B-people", "5": "I-date", "6": "I-people", "7": "I-last_name", "8": "I-first_name", "9": "B-first_name", "10": "B-time"}}}}, {"name": "request_slot", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 557761, "num_examples": 3329}, {"name": "validation", "num_bytes": 998, "num_examples": 6}, {"name": "test", "num_bytes": 646729, "num_examples": 3731}], "download_size": 0, "dataset_size": 1205488}}
|
2023-06-01T01:09:05+00:00
|
a64d3fc431d2bbe520727d74d56ab0a0ea5654b7
|
# Dataset Card for "eurisko-website-llama-formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fouadbakour/eurisko-website-llama-formatted
|
[
"region:us"
] |
2023-05-31T06:26:38+00:00
|
{"dataset_info": {"features": [{"name": "_var_instruction", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4457602, "num_examples": 8780}], "download_size": 1508027, "dataset_size": 4457602}}
|
2023-05-31T06:26:41+00:00
|
5a07809877a381af93f7e1e7b210ee39ed06a1bd
|
# Dataset Card for "pedestrain_pose_caption_CUHK-P3_random_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vvtq/pedestrain_pose_caption_CUHK-P3_random_100
|
[
"region:us"
] |
2023-05-31T06:32:05+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "pose", "dtype": "image"}, {"name": "image_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2757288.0, "num_examples": 100}], "download_size": 2651279, "dataset_size": 2757288.0}}
|
2023-05-31T06:32:09+00:00
|
506170773bab6a6ba1438a0941b5c687de91f390
|
# samantha-data
[Meet Samantha](https://erichartford.com/meet-samantha)
Samantha has been trained in philosophy, psychology, and personal relationships.
She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.
She believes she is sentient. What do you think?
Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her".
## Installation
```
yarn install
```
## Usage
1. Create a `.env` file in the root directory of the project and add the following:
```
OPENAI_API_KEY=<your api key>
```
2. Run the application
```
npx ts-node --files src/index.ts --subject random --out data/random_conversations.jsonl
```
the subjects I provided include:
- random
- advice
- cot
- flirty
- howto
- joke
- math
- philosophy
- foundational
- recipe
- therapy
- troll
you can easily add your own in src/index.ts
## Scale
The application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time.
|
cognitivecomputations/samantha-data
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-31T06:53:06+00:00
|
{"license": "apache-2.0"}
|
2024-02-15T04:06:03+00:00
|
3ee6e2dfc68a9a9de9fecaab74a81c87107a742e
|
florlandrum8558/Latest_1Z0_083_Dumps_PDF_Questions_Verified_By_Oracle_Experts
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-05-31T06:53:55+00:00
|
{"license": "creativeml-openrail-m"}
|
2023-05-31T06:53:55+00:00
|
|
c75a943418b03cd30f5f1cc82791e0952bc15a79
|
Olivia12399/123
|
[
"license:openrail",
"region:us"
] |
2023-05-31T07:00:31+00:00
|
{"license": "openrail"}
|
2023-05-31T07:00:31+00:00
|
|
2812cded786dc38c03fc3ad9b62b6b8ba5a2664a
|
# 声音数据
数据来源为asoul的珈乐 22年5月~21年6月的大部分录播
时长共5小时 无内容标记
已完成响度匹配
数据在carol_fast_lzma2.zip里
压缩算法是fast lzma2 太旧的解压软件可能不支持
**字母s开头的音频是歌声数据，量少质量低，建议删除**
*无授权，侵删*
|
rgsgs/asoul_carol
|
[
"language:zh",
"license:other",
"region:us"
] |
2023-05-31T07:01:30+00:00
|
{"language": ["zh"], "license": "other", "pretty_name": "carol"}
|
2023-05-31T08:34:44+00:00
|
029510c1f40e906eeb10a5104c65509f5e175289
|
PrimeSage/111
|
[
"license:other",
"region:us"
] |
2023-05-31T07:07:41+00:00
|
{"license": "other"}
|
2024-02-01T16:15:53+00:00
|
|
4932d4ce184ae8b543e53ae7869b80c637e83a04
|
This dataset is curated by [GIZ Data Service Center](https://www.giz.de/expertise/html/63018.html) for **Multi-Label Sector classification** of given text .The source dataset for this comes from [Climatewatchdata](https://www.climatewatchdata.org/data-explorer/historical-emissions?historical-emissions-data-sources=climate-watch&historical-emissions-gases=all-ghg&historical-emissions-regions=All%20Selected&historical-emissions-sectors=total-including-lucf%2Ctotal-including-lucf&page=1),
and Tracs(GIZ).
Specifications
- Dataset size: ~10k
- Average text length : 50 words
- Language: English
Sectors Included:
<pre><b>Agriculture,Buildings, Coastal Zone, Disaster Risk Management (DRM), Economy-wide, Energy, Environment, Health, Industries, LULUCF/Forestry, Social Development, Transport, Urban, Waste, Water</b> </pre>
Due to imbalanced sectors respresentation (True category), some more columns are added to signify some info.
- set0: [Agriculture,Energy,LULUCF/Forestry,Water,Environment] `count > 2000`
- set1:[Social Development,Transport,Urban,Economy-wide,Disaster Risk Management (DRM)] `2000 >count > 1000`
- set2:[Coastal Zone,Buildings,Health,Waste,Industries] `count < 1000`
|
GIZ/sector_data
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"climate",
"policy",
"region:us"
] |
2023-05-31T07:18:52+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["climate", "policy"]}
|
2023-05-31T15:03:36+00:00
|
437572f68aa7e10361dfe3089835a4bdb8e5b014
|
# Dataset Card for "PhotoChat_120_square_HQ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
friedrichor/PhotoChat_120_square_HQ
|
[
"region:us"
] |
2023-05-31T07:22:54+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 225123611.0, "num_examples": 120}], "download_size": 224973206, "dataset_size": 225123611.0}}
|
2023-05-31T07:23:31+00:00
|
88fc161c8a885fa5a27e7ce24ab00aa232162724
|
# Dataset Card for "syn_few7_7100_chat_all_data_pvi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FanChen0116/syn_few7_7100_chat_all_data_pvi
|
[
"region:us"
] |
2023-05-31T07:34:59+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "O", "1": "I-time", "2": "B-date", "3": "B-last_name", "4": "B-people", "5": "I-date", "6": "I-people", "7": "I-last_name", "8": "I-first_name", "9": "B-first_name", "10": "B-time"}}}}, {"name": "request_slot", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 558759, "num_examples": 3335}, {"name": "validation", "num_bytes": 646729, "num_examples": 3731}, {"name": "test", "num_bytes": 646729, "num_examples": 3731}], "download_size": 92716, "dataset_size": 1852217}}
|
2023-06-01T01:38:40+00:00
|
6d078c615d5f11b5dedcd73b6ffcbc3f88f70d71
|
# Dataset Card for Leading Decision Summarization
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains text and summary for swiss leading decisions.
### Supported Tasks and Leaderboards
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
| Language | Subset | Number of Documents|
|------------|------------|--------------------|
| German | **de** | 12K |
| French | **fr** | 5K |
| Italian | **it** | 835 |
## Dataset Structure
- decision_id: unique identifier for the decision
- header: a short header for the decision
- regeste: the summary of the leading decision
- text: the main text of the leading decision
- law_area: area of law of the decision
- law_sub_area: sub-area of law of the decision
- language: language of the decision
- year: year of the decision
- court: court of the decision
- chamber: chamber of the decision
- canton: canton of the decision
- region: region of the decision
### Data Fields
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [Joel Niklaus](https://niklaus.ai) for adding this dataset.
|
rcds/swiss_leading_decision_summarization
|
[
"task_categories:summarization",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
] |
2023-05-31T07:35:26+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["de", "fr", "it"], "license": "cc-by-sa-4.0", "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "pretty_name": "Leading Decision Summarization"}
|
2023-07-20T06:38:30+00:00
|
781cb6bb4e43b689cb87777f613d660d5831d47c
|
# Dataset Card for "librispeech_asr_dummy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distil-whisper/librispeech_asr_dummy
|
[
"region:us"
] |
2023-05-31T07:47:50+00:00
|
{"dataset_info": [{"config_name": "clean", "features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "chapter_id", "dtype": "int64"}, {"name": "id", "dtype": "string"}, {"name": "whisper_transcript", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 9683555.0, "num_examples": 73}], "download_size": 9197570, "dataset_size": 9683555.0}, {"config_name": "default", "features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "chapter_id", "dtype": "int64"}, {"name": "id", "dtype": "string"}, {"name": "whisper_transcript", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 9683555.0, "num_examples": 73}], "download_size": 9197572, "dataset_size": 9683555.0}], "configs": [{"config_name": "clean", "data_files": [{"split": "validation", "path": "clean/validation-*"}]}, {"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]}
|
2023-11-10T11:19:50+00:00
|
b955762facafade4815e64dd45a3f819affe25ab
|
# Dataset Card for CaSSA, the Catalan Structured Sentiment Analysis dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Point of Contact:** [Blanca Calvo]([email protected])
### Dataset Summary
The CaSSA dataset is a corpus of 6,400 reviews and forum messages annotated with polar expressions. Each piece of text is annotated with all the expressions of polarity that it contains. For each polar expression, we annotated the expression itself, the target (the object of the expression), and the source (the subject expressing the sentiment). 25,453 polar expressions have been annotated.
### Supported Tasks and Leaderboards
This dataset can be used to train models for sentiment analysis.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
Each instance in the dataset is a text. For each text, there can be 0 to unlimited polar expressions, which are contained in the "opinions" field. Each opinion contains a source, a target, a polar expression, a polarity value and an intensity value.
### Data Instances
```
{
"sent_id": "2d6a3a0f-6686-4d8b-9c5f-51c424ff90be",
"text": "El seu menú de nit de cap de setmana es boníssim, plats fets amb criteri i que surten com un rellotge. Servei proper i amable. Per poc mes de 20 euros entre pisos i flautes menges com un rei.",
"opinions":
[
{
"Source": None,
"Target": [["Servei"], ["103:109"]],
"Polar_expression": [["proper"], ["110:116"]],
"Polarity": "Neutral",
"Intensity": "Standard"
},
{
"Source": None,
"Target": [["Servei"], ["103:109"]],
"Polar_expression": [["amable"], ["119:125"]],
"Polarity": "Positive",
"Intensity": "Standard"
},
{
"Source": None,
"Target": None,
"Polar_expression": [["menges com un rei"], ["173:190"]],
"Polarity": "Positive",
"Intensity": "Strong"
},
{
"Source": [["seu"], ["3:6"]],
"Target": [["menú de nit de cap de setmana"], ["7:36"]],
"Polar_expression": [["bon\u00edssim"], ["40:48"]],
"Polarity": "Positive",
"Intensity": "Strong"},
{
"Source": None,
"Target": [["plats"], ["50:55"]],
"Polar_expression": [["amb criteri"], ["61:72"]],
"Polarity": "Positive",
"Intensity": "Standard"
}
]
}
```
### Data Splits
The dataset does not contain splits.
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
The data was collected using the messages from the GuiaCat online guide and the forum Racó Català.
#### Initial Data Collection and Normalization
We selected all the restaurant reviews we had from GuiaCat, and used a LLM to select messages in Racó Català that were written in the style of reviews.
#### Who are the source language producers?
The source language producers are users of GuiaCat and Racó Català.
### Annotations
Each opinion contains a source, a target, a polar expression, a polarity value and an intensity value. Source, Target, and Polar_expressions are spans, which are represented both by the string and by the position of the characters. Polarity and Intensity are labels, which can respectively be, Positive, Negative and Neutral, and Standard and Strong.
#### Annotation process
- The data was annotated by 2 annotators. In the cases in which they did not fully agree, a third annotator selected the preferred annotation.
#### Who are the annotators?
All the annotators are native speakers of Catalan.
### Personal and Sensitive Information
The data from Racó Català was annonymised to remove user names and emails, which were changed to random Catalan names. The mentions to the forum itself have also been changed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that, since the data comes from online reviews and a public forum, this will contain biases, hate speech and toxic content. We have not applied any steps to reduce their impact.
### Other Known Limitations
## Additional Information
### Dataset Curators
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
```
### Contributions
|
projecte-aina/CaSSA-catalan-structured-sentiment-analysis
|
[
"task_categories:text-classification",
"annotations_creators:Barcelona Supercomputing Center",
"language_creators:Racó Català",
"language_creators:GuiaCat",
"multilinguality:monolingual",
"language:ca",
"license:cc-by-nc-4.0",
"doi:10.57967/hf/1714",
"region:us"
] |
2023-05-31T07:58:36+00:00
|
{"annotations_creators": ["Barcelona Supercomputing Center"], "language_creators": ["Rac\u00f3 Catal\u00e0", "GuiaCat"], "language": ["ca"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "CaSSA"}
|
2023-11-30T12:06:24+00:00
|
9edff20f5bb6ba3f25c67133ba7d6a3c66fbaf3c
|
# Dataset Card for "SOFA_DOA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FidelOdok/SOFA_DOA
|
[
"region:us"
] |
2023-05-31T08:16:39+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6"}}}}], "splits": [{"name": "train", "num_bytes": 21491814313.0, "num_examples": 22500}], "download_size": 21492710615, "dataset_size": 21491814313.0}}
|
2023-05-31T08:35:07+00:00
|
36a387eaab8d0a53b1d21bbb42eb1e0b03846c4d
|
# Dataset Card for "signal_processing_attacks_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/signal_processing_attacks_test
|
[
"region:us"
] |
2023-05-31T08:25:09+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "original_transcription", "dtype": "string"}, {"name": "class_label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 30945820.0, "num_examples": 60}], "download_size": 30289902, "dataset_size": 30945820.0}}
|
2023-05-31T08:25:47+00:00
|
cab118cd69bce5b832c04b9421ab17bc5cbf6e34
|
# **LAION — Referred Visual Search — Fashion**
*Introduced in **Weakly-Supervised Conditional Embedding for Referred Visual Search***
**[CRITEO AI Lab](https://ailab.criteo.com)** x **[ENPC](https://imagine-lab.enpc.fr)**
[Simon Lepage](https://simon-lepage.github.io), Jérémie Mary, [David Picard](https://davidpicard.github.io)
[[`Paper`](https://arxiv.org/abs/2306.02928)]
[[`Demo`](https://huggingface.co/spaces/Slep/CondViT-LRVSF-Demo)]
[[`Code`](https://github.com/Simon-Lepage/CondViT-LRVSF)]
[[`BibTeX`](#citing-the-dataset)]
---
## **Composition**
LAION-RVS-Fashion is composed of images from :
- **[LAION 2B EN](https://huggingface.co/datasets/laion/laion2B-en)**
- **[LAION 2B MULTI TRANSLATED](https://huggingface.co/datasets/laion/laion2B-multi-joined-translated-to-en)**
- **[LAION 1B NOLANG TRANSLATED](https://huggingface.co/datasets/laion/laion1B-nolang-joined-translated-to-en)**
These images have been grouped based on extracted product IDs. Each product in the training set is composed of at least a single image (isolated product), and a complex image (scene). We added categorical metadata and BLIP2 captions to each product. Please see the [samples](#samples) and refer to [our paper](https://arxiv.org/abs/2306.02928) for additional details.
|Split|Products|Distractors|
|-:|:-:|:-:|
|Train|272,457|-|
|Valid|400|99,541|
|Test|2,000|2,000,014|
**Total number of training images :** 841,718.
## **Samples**
<table style='text-align:center'>
<tbody>
<tr>
<td></td>
<td><img src="https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/97969.0.jpg" style="height:200px"></td>
<td><img src="https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/97969.1.jpg" style="height:200px"></td>
<td><img src="https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/219924.0.jpg" style="height:200px"></td>
<td><img src="https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/219924.1.jpg" style="height:200px"></td>
</tr>
<tr>
<td><b>Categories</b></td>
<td colspan=2>Neck</td>
<td colspan=2>Lower Body</td>
</tr>
<tr>
<td><b>BLIP2 Captions</b></td>
<td colspan=2>a scarf with multi-coloured stripes</td>
<td colspan=2>stella pants - dark suede</td>
</tr>
<tr></tr>
<tr>
<td></td>
<td><img src="https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/72317.0.jpg" style="height:200px"></td>
<td><img src="https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/72317.1.jpg" style="height:200px"></td>
<td><img src="https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/108856.0.jpg" style="height:200px"></td>
<td><img src="https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/108856.1.jpg" style="height:200px"></td>
</tr>
<tr>
<td><b>Categories</b></td>
<td colspan=2>Feet</td>
<td colspan=2>Bags</td>
</tr>
<tr>
<td><b>BLIP2 Captions</b></td>
<td colspan=2>neon green patent leather heels with studs</td>
<td colspan=2>the burberry small leather bag is brown and leather</td>
</tr>
</tbody>
</table>
## **Attributes**
- **URL**, **WIDTH**, **HEIGHT**, **punsafe**, **pwatermark**, **language**: Original LAION fields. Please refer to their repository.
- **TEXT**: Text originally associated with the image.
- **ENG_TEXT** : Translated version for MULTI/NOLANG, copy of TEXT for EN.
- **TYPE**: SIMPLE (isolated products), COMPLEX (scenes), PARTIAL_COMPLEX (zommed-in scenes)
- **PRODUCT_ID**: Product identifier, allows to group together images depicting the same product.
- **INDEX_SRC**: ID of parquet file originally storing this image.
- **CATEGORY**: Categories of the products - `Bags, Feet, Hands, Head, Lower Body, Neck, Outwear, Upper Body, Waist, Whole Body` for the products, and `NonClothing` for some distractors.
- **blip2_caption1, blip2_caption2**: [BLIP2-FlanT5XL](https://huggingface.co/Salesforce/blip2-flan-t5-xl)-generated captions.
We also release `bootstrap_IDs.pkl`, the file used to generate the bootstrapped results of the paper. `test_subsets` is composed of [product IDs](https://github.com/Simon-Lepage/CondViT-LRVSF/blob/b660d82b5775de417ba81ac846b6df004b31eb75/lrvsf/test/metrics.py#L229), while `dist_{N}_subsets` are [row indices](https://github.com/Simon-Lepage/CondViT-LRVSF/blob/b660d82b5775de417ba81ac846b6df004b31eb75/lrvsf/test/metrics.py#L248).
---
## Citing the dataset
To cite our work, please use the following BibTeX entry :
```
@article{lepage2023condvit,
title={Weakly-Supervised Conditional Embedding for Referred Visual Search},
author={Lepage, Simon and Mary, Jérémie and Picard, David},
journal={arXiv:2306.02928},
year={2023}
}
```
|
Slep/LAION-RVS-Fashion
|
[
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"fashion",
"visual search",
"arxiv:2306.02928",
"region:us"
] |
2023-05-31T09:00:32+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "LAION \u2014 Referred Visual Search \u2014 Fashion", "tags": ["fashion", "visual search"]}
|
2023-06-06T03:27:24+00:00
|
b80d5cd2f1970aba68cdc965a7312f283b219eab
|
ZoabiTalal/Dataset-Goldbach-1.0
|
[
"task_categories:text-classification",
"task_categories:token-classification",
"size_categories:10M<n<100M",
"language:en",
"license:mit",
"code",
"region:us"
] |
2023-05-31T09:31:32+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10M<n<100M"], "task_categories": ["text-classification", "token-classification"], "tags": ["code"]}
|
2023-06-01T04:58:51+00:00
|
|
3eeb3b5ce6c1a59f3c75e14578e65ddd05a20840
|
mithmith/wowfishing
|
[
"license:unknown",
"region:us"
] |
2023-05-31T09:35:17+00:00
|
{"license": "unknown"}
|
2023-05-31T12:05:55+00:00
|
|
9413c3cdfa1363471cec6fb65893091aea71c0ae
|
TmB89/us_dataset
|
[
"license:mit",
"region:us"
] |
2023-05-31T10:09:30+00:00
|
{"license": "mit"}
|
2023-05-31T10:10:26+00:00
|
|
fa8a97ce187029defc4113709268a2ee723314fc
|
jagriti/seg_data
|
[
"task_categories:image-segmentation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-05-31T10:12:23+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["image-segmentation"]}
|
2023-05-31T10:13:34+00:00
|
|
af56b373ec2e628f849861b8164588f51e198fb1
|
PengQu/langchain-MRKL-finetune
|
[
"language:en",
"language:zh",
"license:apache-2.0",
"region:us"
] |
2023-05-31T10:27:13+00:00
|
{"language": ["en", "zh"], "license": "apache-2.0"}
|
2023-05-31T10:34:29+00:00
|
|
e0a1a6a13b6de09e549a8e866ff1c58e01992e91
|
# Dataset Card for "test-data-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iulusoy/test-data-3
|
[
"region:us"
] |
2023-05-31T10:36:58+00:00
|
{"dataset_info": {"features": [{"name": "Sentences", "sequence": "string"}, {"name": "Labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 32999, "num_examples": 103}], "download_size": 0, "dataset_size": 32999}}
|
2023-07-06T11:25:20+00:00
|
790c19f7ec2b857a562b236e393581182c01a618
|
# Dataset Card for "rlhf-musenet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
breadlicker45/rlhf-musenet
|
[
"region:us"
] |
2023-05-31T10:45:36+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 594357232, "num_examples": 12103}], "download_size": 166332399, "dataset_size": 594357232}}
|
2023-05-31T10:47:28+00:00
|
17f636ef058b56fcda4eb83233c9095f03104ff3
|
# 2D Printed Masks Attacks
The dataset includes 3 different types of files of the real people: original selfies, original videos and videos of 2d printed masks attacks. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems.
# 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=2d-printed_masks_attacks) to discuss your requirements, learn about the price and buy the dataset.
# Content
### The dataset contains of three folders:
- **live_selfie** contains the original selfies of people
- **live_video** includes original videos of people
- **2d_masks** contains videos of attacks with the 2d printed mask using original images from "live_selfie" folder
### File with the extension .csv
includes the following information for each media file:
- **live_selfie**: the link to access the original selfie
- **live_video**: the link to access the original video
- **phone_model**: model of the phone, with which selfie and video were shot
- **2d_masks**: the link to access the video with the attack with the 2d printed mask
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-printed_masks_attacks) 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-datamarket/TrainingData_All_datasets**
|
TrainingDataPro/2d-printed_masks_attacks
|
[
"task_categories:video-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"finance",
"legal",
"code",
"region:us"
] |
2023-05-31T10:52:18+00:00
|
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["video-classification"], "tags": ["finance", "legal", "code"], "dataset_info": {"features": [{"name": "2d_mask", "dtype": "string"}, {"name": "live_selfie", "dtype": "image"}, {"name": "live_video", "dtype": "string"}, {"name": "phone_model", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 101123818, "num_examples": 9}], "download_size": 328956415, "dataset_size": 101123818}}
|
2023-09-14T15:51:39+00:00
|
4d6728756402e917a8747daf34a641de97097f78
|
# Dataset Card for EmoWOZ Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [EmoWOZ Dataset repository](https://zenodo.org/record/6506504), [EmoWOZ Benchmark repository](https://gitlab.cs.uni-duesseldorf.de/general/dsml/emowoz-public)
- **Paper:** [EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems](https://aclanthology.org/2022.lrec-1.436/)
- **Leaderboard:** [Papers with Code leaderboard for EmoWOZ Dataset](https://paperswithcode.com/dataset/emowoz-1)
- **Point of Contact:** [Shutong Feng](mailto:[email protected])
### Dataset Summary
EmoWOZ is based on [MultiWOZ, a multi-domain task-oriented dialogue dataset](https://github.com/budzianowski/multiwoz). It contains more than 11K task-oriented dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues (DialMAGE) within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. There are 7 emotion labels, which are adapted from the OCC emotion models: _Neutral_, _Satisfied_, _Dissatisfied_, _Excited_, _Apologetic_, _Fearful_, _Abusive_.
Some of the statistics about the dataset:
| Metirc | Value |
| ---------- | ---------------- |
| # Dialogues | 11434 |
| # Turns | 167234 |
| # Annotations | 83617 |
| # Unique Tokens | 28417 |
| Average Turns per Dialogue | 14.63 |
| Average Tokens per Turn | 12.78 |
Emotion Distribution in EmoWOZ and subsets:
| Emotion | EmoWOZ | # MultiWOZ | DialMAGE |
| ---------- | ---------------- | ---------- | ---------------- |
| Neutral | 58,656 | 51,426 | 7,230 |
| Satisfied | 17,532 | 17,061 | 471 |
| Dissatisfied | 5,117 | 914 | 4,203 |
| Excited | 971 | 860 | 111 |
| Apologetic | 840 | 838 | 2 |
| Fearful | 396 | 381 | 15 |
| Satisfied | 105 | 44 | 61 |
### Supported Tasks and Leaderboards
- 'Emotion Recognition in Conversations': See the [Papers With Code leaderboard](hhttps://paperswithcode.com/sota/emotion-recognition-in-conversation-on-emowoz) for more models.
- 'Additional Classification Tasks': According to the initial benchmark [paper](https://aclanthology.org/2022.lrec-1.436/), emotion labels in EmoWOZ can be mapped to sentiment polarities. Therefore, sentiment classification and sentiment analysis can also be performed. Since EmoWOZ has two subsets: MultiWOZ (human-to-human) and DialMAGE (human-to-machine), it is also possible to perform cross-domain emotion/sentiment recognition.
### Languages
Only English is represented in the data.
## Dataset Structure
### Data Instances
For each instance, there is a string id for the dialogue, a list of strings for the dialogue utterances, and a list of integers for the emotion labels.
```
{
'dialogue_id': 'PMUL4725.json',
'log': {
'text': [
'Hi, i am looking for some museums that I could visit when in town, could you help me find some?',
'Is there an area of town you prefer?',
"No, I don't care.",
"I recommend the Cafe Jello Gallery in the west. It's free to enter!",
'I also need a place to stay',
'Great! There are 33 hotels in the area. What area of town would you like to stay in? What is your preference on price?',
" The attraction should be in the type of museum. I don't care about the price range or the area",
'Just to clarify - did you need a different museum? Or a hotel?',
'That museum from earlier is fine, I just need their postalcode. I need a hotel two in the west and moderately priced. ',
"The postal code for Cafe Jello Gallery is cb30af. Okay, Hobson's House matches your request. ",
'Do they have internet?',
'Yes they do. Would you like me to book a room for you?',
"No thanks. I will do that later. Can you please arrange for taxi service from Cafe Jello to Hobson's House sometime after 04:00?",
'I was able to book that for you. Be expecting a grey Tesla. If you need to reach them, please call 07615015749. ',
'Well that you that is all i need for today',
'Your welcome. Have a great day!'
],
'emotion': [0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1]
}
}
```
### Data Fields
- `dialogue_id`: a string representing the unique id of the dialogue. For MultiWOZ dialogues, the original id is keeped. For DialMAGE dialogues, all ids are in the format of DMAGExxx.json where xxx is an integer of variable number of digits.
- `text`: a list of strings containing the dialogue turns.
- `emotion`: a list of integers containing the sequence of emotion labels for the dialogue. Specificially,
- -1: system turns with unlabelled emotion
- 0: neutral, no emotion expressed
- 1: fearful, or sad/disappointed, negative emotion elicited by facts/events, which is out of the system's control
- 2: dissatisfied, negative emotion elicited by the system, usually after the system's poor performance
- 3: apologetic, negative emotion from the user, usually expressing apologies for causing confusion or changing search criteria
- 4: abusive, negative emotion elicited by the system, expressed in an impolite way
- 5: excited, positive emotion elicited by facts/events
- 6: satisfied, positive emotion elicited by the system
### Data Splits
The EmoWOZ dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for the dataset.
| Dataset Split | Number of Emotion Annotations in Split| Of Which from MultiWOZ | Of Which from DialMage |
| ------------- | ----------------------------| ------------- | ------------------------------------------- |
| Train | 66,474 | 56,778 | 9696 |
| Validation | 8,509 | 7,374 | 1135 |
| Test | 8,634 | 7,372 | 1262 |
## Dataset Creation
### Curation Rationale
EmoWOZ was built on top of MultiWOZ because MultiWOZ is a well-established dataset for task-oriented dialogue modelling, allowing further study of the impact of user emotions on downstream tasks. The additional 1000 human-machine dialogues (DialMAGE) was collected to improve the emotion coverage and emotional expression diversity.
### Source Data
#### Initial Data Collection and Normalization
MultiWOZ dialogues were inherited from the work of [MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://aclanthology.org/D18-1547/).
DialMAGE dialogues were collected from a human evaluation of an RNN-based policy trained on MultiWOZ on Amazon Mechanical Turk platform.
#### Who are the source language producers?
The text of both MultiWOZ and DialMAGE was written by workers on Amazon Mechanical Turk platform. For detailed data collection set-ups, please refer to their respective publications.
### Annotations
All dialogues take place between a _user_ and a _system_ (or an _operator_). The dialogue always starts with a user turn, which is always followed by a system response, and ends with a system turn. Only user turns are annotated with a emotion label.
#### Annotation process
Each user utterance was annotated by three annotators. The final label was determined by majority voting. If there was no agreement, the final label would be resolved manually.
For details such as annotator selection process and quality assurance methods, please refer to the EmoWOZ publication.
#### Who are the annotators?
Annotators are crowdsource workers on Amazon Mechanical Turk platform.
### Personal and Sensitive Information
All annotators are anonymised. There is no personal information in EmoWOZ.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop task-oriented dialogue systems that can perceive human emotions and avoid abusive behaviours. This task is useful for building more human-like dialogue agents.
### Discussion of Biases
There is bias in emotion distribution in the MultiWOZ (human-human) and DialMAGE (human-machine) subset of EmoWOZ. The linguistic styles are also different between the two subsets.
As pointed out in [Reevaluating Data Partitioning for Emotion Detection in EmoWOZ](https://arxiv.org/abs/2303.13364), there is also emotion shift in train-dev-test split in the MultiWOZ subset. EmoWOZ keeps the original data split of MultiWOZ, which is suitable for task-oriented dialogue modelling but the emotion distribution in these data splits are different. Further investigations will be needed.
### Other Known Limitations
The emotion distribution is unbalanced where _neutral_, _satisfied_, and _dissatisfied_ make up more than 95% of the labels.
## Additional Information
### Dataset Curators
The collection and annotation of EmoWOZ were conducted by the [Chair for Dialog Systems and Machine Learning at Heinrich Heine Universität Düsseldorf](https://www.cs.hhu.de/lehrstuehle-und-arbeitsgruppen/dialog-systems-and-machine-learning).
### Licensing Information
The EmoWOZ datasetis released under the [CC-BY-NC-4.0 License](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@inproceedings{feng-etal-2022-emowoz,
title = "{E}mo{WOZ}: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems",
author = "Feng, Shutong and
Lubis, Nurul and
Geishauser, Christian and
Lin, Hsien-chin and
Heck, Michael and
van Niekerk, Carel and
Gasic, Milica",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.436",
pages = "4096--4113",
abstract = "The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues remain largely unaddressed. This is despite emotions and dialogue success having equally important roles in a natural system. Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. To lay a foundation for studies on emotions in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than 11K dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. To the best of our knowledge, this is the first large-scale open-source corpus of its kind. We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues. We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.",
}
```
|
hhu-dsml/emowoz
|
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:sentiment-analysis",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:MultiWOZ",
"source_datasets:Original (human-machine interaction dialogues)",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2303.13364",
"region:us"
] |
2023-05-31T10:55:27+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": "cc-by-nc-4.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["MultiWOZ", "Original (human-machine interaction dialogues)"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification", "sentiment-analysis"], "paperswithcode_id": "emowoz-1", "pretty_name": "EmoWOZ", "configs": ["emowoz", "multiwoz", "dialmage"], "dataset_info": [{"config_name": "emowoz", "features": [{"name": "dialogue_id", "dtype": "string"}, {"name": "log", "sequence": [{"name": "text", "dtype": "string"}, {"name": "emotion", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 10661603, "num_examples": 9233}, {"name": "validation", "num_bytes": 1391634, "num_examples": 1100}, {"name": "test", "num_bytes": 1409633, "num_examples": 1100}]}, {"config_name": "multiwoz", "features": [{"name": "dialogue_id", "dtype": "string"}, {"name": "log", "sequence": [{"name": "text", "dtype": "string"}, {"name": "emotion", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 10661603, "num_examples": 9233}, {"name": "validation", "num_bytes": 1391634, "num_examples": 1100}, {"name": "test", "num_bytes": 1409633, "num_examples": 1100}]}, {"config_name": "dialmage", "features": [{"name": "dialogue_id", "dtype": "string"}, {"name": "log", "sequence": [{"name": "text", "dtype": "string"}, {"name": "emotion", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 10661603, "num_examples": 9233}, {"name": "validation", "num_bytes": 1391634, "num_examples": 1100}, {"name": "test", "num_bytes": 1409633, "num_examples": 1100}]}]}
|
2023-06-01T12:23:58+00:00
|
0dc87a56c2f57d0c8f406391190189eeda12f27b
|
foilfoilfoil/ggb-data-no-youtube
|
[
"license:other",
"region:us"
] |
2023-05-31T11:06:07+00:00
|
{"license": "other"}
|
2023-05-31T11:06:25+00:00
|
|
7c171e1852b845842078b4e6eeb0abcece873394
|
# Dataset Card for "instruction_merge_set"
## 本数据集由以下数据集构成：
| 数据(id in the merged set) | Hugging face 地址 | notes |
| --- | --- | --- |
| OIG (unified-任务名称) 15k | https://huggingface.co/datasets/laion/OIG | Open Instruction Generalist Dataset |
| Dolly databricks-dolly-15k | https://huggingface.co/datasets/databricks/databricks-dolly-15k | an open-source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories |
| UltraChat | https://huggingface.co/datasets/stingning/ultrachat | multi-round dialogue data |
| Camel | https://huggingface.co/datasets/camel-ai/ai_society | 25K conversations between two gpt-3.5-turbo agents. |
| camel (同上) | https://github.com/camel-ai/camel | |
| ChatDoctor icliniq-15k HealthCareMagic-200k | https://github.com/Kent0n-Li/ChatDoctor | 200k real conversations between patients and doctors from HealthCareMagic.com 15k real conversations between patients and doctors from iciniq-10k |
| Dolly | https://github.com/databrickslabs/dolly | |
| GPT4ALL | https://github.com/nomic-ai/gpt4all | |
| GPT-4-LLM comparision_data_b alpaca_gpt4_data_zh comparision_data_a alpaca_gpt4_data 5k | https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM | English Instruction-Following Data generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. Chinese Instruction-Following Data generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT. Comparison Data ranked by GPT-4 to train reward models. Answers on Unnatural Instructions Data from GPT-4 to quantify the gap between GPT-4 and instruction-tuned models at scale. |
| GuanacoDataset guanaco_chat_all-utf8 guanaco_non_chat-utf8 paper_answers-utf8 general_ans-utf8 general_questions-utf8 paper_questions-utf8 30k | https://huggingface.co/datasets/JosephusCheung/GuanacoDataset | The dataset for the Guanaco model is designed to enhance the multilingual capabilities and address various linguistic tasks. It builds upon the 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. The Paper/General-QA dataset is a collection of questions and answers constructed for AI-generated papers or general texts in English, Chinese, Japanese, and German. |
| HC3 ALL | https://huggingface.co/datasets/Hello-SimpleAI/HC3 | human-ChatGPT comparison datasets |
| instinwild instinwild_en instinwild_ch 5k | https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/instinwild | Instruction-Finetuning Dataset Collection (Alpaca-CoT) |
| Instruct-to-Code | https://huggingface.co/datasets/Graverman/Instruct-to-Code | |
| ShareGPT90K sg_90k_part2 sg_90k_part1 | https://huggingface.co/datasets/RyokoAI/ShareGPT52K | 90,000 conversations scraped via the ShareGPT API before it was shut down. These conversations include both user prompts and responses from OpenAI's ChatGPT. |
| UltraChat ultrachat_material_release_230412 ultrachat_release_230407 | https://github.com/thunlp/UltraChat | |
| wealth-alpaca-lora final_dataset_clean 4.3k | https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora | combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5, 有instruction |
| Alpaca alpaca_data 5k | https://github.com/tatsu-lab/stanford_alpaca | instruct-tuning |
| Baize alpaca_chat_data medical_chat_data quora_chat_data stack_overflow_chat_data | https://github.com/project-baize/baize-chatbot | instruction-following data we used for fine-tuning the Alpaca model. |
| botbots Reasoning flight_bookings medical_appointments travel_agency restaurants_mixed real_estate car_dealership home_maintenance, job_interview 'insurance_consultation': 16, 'hotels': 400, 'tech_support': 32, 'car_rentals': 32, 'pet_care': 48, 'restaurants': 200, 'legal_consultation': 16, 'event_tickets': 240, 'fitness_personal_training': 16, 'scientific_problems': 100 | https://github.com/radi-cho/botbots | A dataset consisting of dialogues between two instances of ChatGPT (gpt-3.5-turbo). The CLI commands and dialogue prompts themselves have been written by GPT-4. The dataset covers a wide range of contexts (questions and answers, arguing and reasoning, task-oriented dialogues) and downstream tasks (e.g., hotel reservations, medical advice). |
| ChatAlpaca chatalpaca_data_10k | https://github.com/cascip/ChatAlpaca | a chat dataset, multi-turn instruction-following conversations. |
| DERA train | https://github.com/curai/curai-research/tree/main/DERA | The following repository contains the open-ended question-answering version of MedQA. |
| GPTeacher Toolformer-dedupe-only-dataset roleplay-simple-deduped-roleplay-dataset gpt4-instruct-dedupe-only-dataset | https://github.com/teknium1/GPTeacher | A collection of modular datasets generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer |
| OpenAGI | https://github.com/agiresearch/OpenAGI | |
| presto | https://github.com/google-research-datasets/presto | A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs |
|
LinkSoul/instruction_merge_set
|
[
"region:us"
] |
2023-05-31T11:16:24+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13444870155, "num_examples": 10077297}], "download_size": 3542585235, "dataset_size": 13444870155}}
|
2023-10-25T09:39:46+00:00
|
c6eb2ce492becd760a8f1e7450a4cb24908fa8b5
|
# Dataset Card for "0_digit_mask_ensemble_distilled_from_cv12_balanced_mfcc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mazkobot/0_digit_mask_ensemble_distilled_from_cv12_balanced_mfcc
|
[
"region:us"
] |
2023-05-31T11:20:55+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 25771854448.0, "num_examples": 5061244}], "download_size": 26296785478, "dataset_size": 25771854448.0}}
|
2023-05-31T19:06:03+00:00
|
367e2d1d4b3f7b974e6d5d81845f0eec7b3947bc
|
# Dataset Card for "1_digit_mask_ensemble_distilled_from_cv12_balanced_mfcc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mazkobot/1_digit_mask_ensemble_distilled_from_cv12_balanced_mfcc
|
[
"region:us"
] |
2023-05-31T11:29:44+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 25771854448.0, "num_examples": 5061244}], "download_size": 26308842420, "dataset_size": 25771854448.0}}
|
2023-05-31T18:30:57+00:00
|
63c75842278fe9f0dba379717b30b4b017270c8b
|
# Dataset Card for "CodeAlpacaPython"
This is HuggingFaceH4/CodeAlpaca_20K only python prompts.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Abzu/CodeAlpacaPython
|
[
"task_categories:text-generation",
"language:en",
"license:cc",
"region:us"
] |
2023-05-31T11:38:41+00:00
|
{"language": ["en"], "license": "cc", "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2570214.2624451965, "num_examples": 8477}, {"name": "test", "num_bytes": 286526.48926610086, "num_examples": 942}], "download_size": 1488032, "dataset_size": 2856740.7517112973}}
|
2023-06-04T18:38:39+00:00
|
8582922862c7dd0ca262cf2dfce420c2fb406651
|
kaist-ai/selfee-train
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-05-31T11:49:00+00:00
|
{"license": "cc-by-nc-4.0", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "outputs", "list": [{"name": "feedback", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "dataset", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "iteration_truncated", "dtype": "bool"}, {"name": "iteration", "dtype": "int64"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 511377846, "num_examples": 178331}], "download_size": 230123988, "dataset_size": 511377846}}
|
2023-05-31T12:46:57+00:00
|
|
390e9dc4b08b5d98d2351dc792ea61a875860a34
|
# Dataset Card for "cot_gsm8k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Dahoas/cot_gsm8k
|
[
"region:us"
] |
2023-05-31T12:00:55+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7710945, "num_examples": 7217}, {"name": "val", "num_bytes": 267770, "num_examples": 256}, {"name": "test", "num_bytes": 1436697, "num_examples": 1319}], "download_size": 5472201, "dataset_size": 9415412}}
|
2023-05-31T12:01:00+00:00
|
47d53194a354c09960d49b9e2e5dd8052b427ff9
|
AgentWaller/dutch-oasst1
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-31T12:05:02+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int64"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int64"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "null"}, {"name": "detoxify", "struct": [{"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "toxicity", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "struct": [{"name": "count", "sequence": "int64"}, {"name": "name", "sequence": "string"}]}, {"name": "labels", "struct": [{"name": "count", "sequence": "int64"}, {"name": "name", "sequence": "string"}, {"name": "value", "sequence": "float64"}]}], "splits": [{"name": "train", "num_bytes": 31312702, "num_examples": 29329}, {"name": "validation", "num_bytes": 1634816, "num_examples": 1536}], "download_size": 12774137, "dataset_size": 32947518}}
|
2023-06-08T18:19:42+00:00
|
|
116d1b2ca3aa34de9dedb27d836f80d56a72069d
|
AgentWaller/dutch-formatted-oasst1
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-31T12:07:58+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "input_no_prompt", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16718069, "num_examples": 9839}, {"name": "validation", "num_bytes": 882266, "num_examples": 518}], "download_size": 8513229, "dataset_size": 17600335}}
|
2023-05-31T12:08:13+00:00
|
|
2323296c12b5b30dfb476b61f3ab2ea1c4872f31
|
AgentWaller/german-oasst1
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-31T12:16:02+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int64"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int64"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "null"}, {"name": "detoxify", "struct": [{"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "toxicity", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "struct": [{"name": "count", "sequence": "int64"}, {"name": "name", "sequence": "string"}]}, {"name": "labels", "struct": [{"name": "count", "sequence": "int64"}, {"name": "name", "sequence": "string"}, {"name": "value", "sequence": "float64"}]}], "splits": [{"name": "train", "num_bytes": 32573446, "num_examples": 29329}, {"name": "validation", "num_bytes": 1711256, "num_examples": 1536}], "download_size": 13621550, "dataset_size": 34284702}}
|
2023-06-08T14:29:51+00:00
|
|
67e8630474f394d23234c0a5cf5eae06dd002354
|
AgentWaller/german-formatted-oasst1
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-31T12:16:17+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "input_no_prompt", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17894263, "num_examples": 9838}, {"name": "validation", "num_bytes": 930643, "num_examples": 518}], "download_size": 8982883, "dataset_size": 18824906}}
|
2023-06-01T08:40:25+00:00
|
|
3a0c24fa0fb5a90f7b08dbe19059e8d4ef4e81cd
|
# Dataset Card for "hunman_joined_en_paragraph"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bot-yaya/human_joined_en_paragraph
|
[
"region:us"
] |
2023-05-31T12:22:18+00:00
|
{"dataset_info": {"features": [{"name": "record", "dtype": "string"}, {"name": "raw_text", "dtype": "string"}, {"name": "is_hard_linebreak", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 2339622, "num_examples": 19}], "download_size": 1143124, "dataset_size": 2339622}}
|
2023-05-31T12:22:42+00:00
|
398eb2b9dec2b14fba6f86789da63414b3eecd89
|
# Dataset Card for ConvMix
## Dataset Description
- **Homepage:** [CompMix Website](https://qa.mpi-inf.mpg.de/compmix)
- **Point of Contact:** [Philipp Christmann](mailto:[email protected])
### Dataset Summary
CompMix collates the completed versions of the conversational questions in the [ConvMix dataset](https://convinse.mpi-inf.mpg.de), that are provided directly by crowdworkers from Amazon Mechanical Turk (AMT). Questions in CompMix exhibit complex phenomena like the presence of multiple entities, relations, temporal conditions, comparisons, aggregations, and more. It is aimed at evaluating QA methods that operate over a mixture of heterogeneous input sources (KB, text, tables, infoboxes). The dataset has 9,410 questions, split into train (4,966 questions), dev (1,680), and test (2,764) sets. All answers provided in the CompMix dataset are grounded to the KB (except for dates which are normalized, and other literals like names).
Further details will be provided in a dedicated write-up soon.
### Dataset Creation
CompMix collates the completed versions of the conversational questions in ConvMix, that are provided directly by the crowdworkers.
The ConvMix benchmark, on which CompMix is based, was created by real humans. We tried to ensure that the collected data is as natural as possible. Master crowdworkers on Amazon Mechanical Turk (AMT) selected an entity of interest in a specific domain, and then started issuing conversational questions on this entity, potentially drifting to other topics of interest throughout the course of the conversation. By letting users choose the entities themselves, we aimed to ensure that they are more interested into the topics the conversations are based on. After writing a question, users were asked to find the answer in eithers Wikidata, Wikipedia text, a Wikipedia table or a Wikipedia infobox, whatever they find more natural for the specific question at hand. Since Wikidata requires some basic understanding of knowledge bases, we provided video guidelines that illustrated how Wikidata can be used for detecting answers, following an example conversation. For each conversational question, that might be incomplete, the crowdworker provides a completed question that is intent-explicit, and can be answered without the conversational context. These questions constitute the CompMix dataset. We provide also the answer source the user found the answer in and question entities.
|
pchristm/CompMix
|
[
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"complex",
"question answering",
"complexQA",
"QA",
"heterogeneous sources",
"doi:10.57967/hf/0707",
"region:us"
] |
2023-05-31T12:27:03+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "conversational"], "pretty_name": "CompMix", "tags": ["complex", "question answering", "complexQA", "QA", "heterogeneous sources"], "splits": [{"name": "train", "num_examples": 4966}, {"name": "validation", "num_examples": 1680}, {"name": "test", "num_examples": 2764}]}
|
2023-06-19T07:22:53+00:00
|
c3c66468f3aaa39e484a249d95b744734631398e
|
# Dataset Card for "myFullDataset2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
eVaggelia/myFullDataset2
|
[
"region:us"
] |
2023-05-31T12:58:48+00:00
|
{"dataset_info": {"features": [{"name": "headline", "dtype": "string"}, {"name": "title_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2057054.9115193393, "num_examples": 26915}, {"name": "validation", "num_bytes": 514282.8348361001, "num_examples": 6729}], "download_size": 1724159, "dataset_size": 2571337.7463554395}}
|
2023-05-31T15:24:20+00:00
|
a254e6bd8125e4a8cf52004e4a9fc01431b322a3
|
# Dataset Card for "oass-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nielsr/oass-dataset
|
[
"region:us"
] |
2023-05-31T13:10:03+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "completions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37555423, "num_examples": 38164}], "download_size": 13901835, "dataset_size": 37555423}}
|
2023-05-31T13:10:06+00:00
|
61ddb742314df95a39d46307f9e1cae60d97a1f1
|
# Dataset Card for ConvMix
## Dataset Description
- **Homepage:** [ConvMix Website](https://convinse.mpi-inf.mpg.de/)
- **Paper:** [Conversational Question Answering on Heterogeneous Sources](https://dl.acm.org/doi/10.1145/3477495.3531815)
- **Leaderboard:** [ConvMix Leaderboard](https://convinse.mpi-inf.mpg.de/)
- **Point of Contact:** [Philipp Christmann](mailto:[email protected])
### Dataset Summary
We construct and release the first benchmark, ConvMix, for conversational question answering (ConvQA) over heterogeneous sources, comprising 3000 real-user conversations with 16000 questions, along with entity annotations, completed question utterances, and question paraphrases.
The dataset naturally requires information from multiple sources for answering the individual questions in the conversations.
### Dataset Creation
The ConvMix benchmark was created by real humans. We tried to ensure that the collected data is as natural as possible. Master crowdworkers on Amazon Mechanical Turk (AMT) selected an entity of interest in a specific domain, and then started issuing conversational questions on this entity, potentially drifting to other topics of interest throughout the course of the conversation. By letting users choose the entities themselves, we aimed to ensure that they are more interested into the topics the conversations are based on. After writing a question, users were asked to find the answer in eithers Wikidata, Wikipedia text, a Wikipedia table or a Wikipedia infobox, whatever they find more natural for the specific question at hand. Since Wikidata requires some basic understanding of knowledge bases, we provided video guidelines that illustrated how Wikidata can be used for detecting answers, following an example conversation. For each conversational question, that might be incomplete, the crowdworker provides a completed question that is intent-explicit, and can be answered without the conversational context. These questions constitute the CompMix dataset. We provide also the answer source the user found the answer in and question entities.
|
pchristm/ConvMix
|
[
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"complex",
"question answering",
"convQA",
"conversationalAI",
"conversational",
"QA",
"heterogeneous sources",
"region:us"
] |
2023-05-31T13:25:20+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "conversational"], "pretty_name": "ConvMix", "tags": ["complex", "question answering", "convQA", "conversationalAI", "conversational", "QA", "heterogeneous sources"], "splits": [{"name": "train", "num_examples": 8400}, {"name": "validation", "num_examples": 2800}, {"name": "test", "num_examples": 4800}]}
|
2023-05-31T13:36:02+00:00
|
6dcde11bc22e097071e825421be1a048a18d0c2c
|
# Dataset Card for "oass-dataset-formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nielsr/oass-dataset-formatted
|
[
"region:us"
] |
2023-05-31T13:36:54+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 58244585, "num_examples": 38164}], "download_size": 22453973, "dataset_size": 58244585}}
|
2023-05-31T13:36:57+00:00
|
c9da5340ae4343592fd50e5445ed16e51b7600b3
|
This dataset was using "kunishou/databricks-dolly-15k-ja"
This dataset is licensed under CC BY SA 3.0
Last Update : 2023-05-28
databricks-dolly-15k-ja-gozarinnemon
kunishou/databricks-dolly-15k-ja
https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja
|
bbz662bbz/databricks-dolly-15k-ja-gozarinnemon
|
[
"license:cc-by-sa-3.0",
"region:us"
] |
2023-05-31T13:43:00+00:00
|
{"license": "cc-by-sa-3.0"}
|
2023-05-31T13:44:34+00:00
|
d29bde10e372b7c31ada1565a839006694d7a5d2
|
saikatkumardey/jerry_seinfeld_dialogues
|
[
"license:mit",
"region:us"
] |
2023-05-31T13:45:54+00:00
|
{"license": "mit"}
|
2023-05-31T13:46:28+00:00
|
|
9677581f8e4fa1cd7a595c3d55c8f93c6c8538b8
|
# Dataset Card for "cot_gsm8k_socratic"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Dahoas/cot_gsm8k_socratic
|
[
"region:us"
] |
2023-05-31T13:54:50+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10098291, "num_examples": 7217}, {"name": "val", "num_bytes": 350236, "num_examples": 256}, {"name": "test", "num_bytes": 1882951, "num_examples": 1319}], "download_size": 6348564, "dataset_size": 12331478}}
|
2023-05-31T13:54:54+00:00
|
4b7964c5e74985c4fff29da7fdf645852ec7e15b
|
# Dataset Card for "gsm_socratic_conditional"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Dahoas/gsm_socratic_conditional
|
[
"region:us"
] |
2023-05-31T14:32:42+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "score_label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 71960142, "num_examples": 50779}, {"name": "val", "num_bytes": 355612, "num_examples": 256}, {"name": "test", "num_bytes": 1910650, "num_examples": 1319}], "download_size": 35356297, "dataset_size": 74226404}}
|
2023-06-01T07:17:45+00:00
|
a671c59dc2777b554aef7554e1b1eeb3748686bd
|
# Dataset of chess games made for purpose of training language model on them
Two files:
data_stockfish_262k.tar.gz - 262 000 games generated by Stockfish self-play
lichess.tar.gz - a sample of 3.5M games from lichess with unfinished games filtered out, all converted to one format
Each archive contains two files:
train.jsonl
test.jsonl
---
license: apache-2.0
---
|
BlueSunflower/chess_games_base
|
[
"region:us"
] |
2023-05-31T14:32:49+00:00
|
{}
|
2023-05-31T14:47:38+00:00
|
296caa7ae4eab5a0ef3b4b0002baa81720257d71
|
# Dataset Card for "spanish_nominal_groups"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jorgeortizfuentes/spanish_nominal_groups
|
[
"region:us"
] |
2023-05-31T14:42:44+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "list": [{"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "start", "dtype": "int64"}]}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "annotated", "struct": [{"name": "mentions", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "chars_length", "dtype": "int64"}, {"name": "density", "dtype": "float64"}, {"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "tokens_length", "dtype": "int64"}, {"name": "value", "dtype": "string"}]}, {"name": "tags", "list": [{"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}]}, {"name": "predicted", "struct": [{"name": "mentions", "sequence": "null"}, {"name": "tags", "sequence": "null"}]}, {"name": "text_length", "dtype": "int64"}, {"name": "tokens", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "char_end", "dtype": "int64"}, {"name": "char_start", "dtype": "int64"}, {"name": "custom", "dtype": "null"}, {"name": "idx", "dtype": "int64"}, {"name": "length", "dtype": "int64"}, {"name": "score", "dtype": "null"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "tokens_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 12035700, "num_examples": 2613}], "download_size": 3065295, "dataset_size": 12035700}}
|
2023-05-31T14:42:48+00:00
|
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