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6c1a1284eb3557055d7c57b91cd7e68e3252b32c |
## Dataset Description
- **Homepage:** [Human Action Recognition (HAR) Dataset](https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset)
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
## Dataset Summary
A dataset from [kaggle](https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset). origin: https://dphi.tech/challenges/data-sprint-76-human-activity-recognition/233/data
### Introduction
- The dataset features 15 different classes of Human Activities.
- The dataset contains about 12k+ labelled images including the validation images.
- Each image has only one human activity category and are saved in separate folders of the labelled classes
### PROBLEM STATEMENT
- Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios.
- Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities.
- Your Task is to build an Image Classification Model using CNN that classifies to which class of activity a human is performing.
### About Files
- Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’ which contain the images of the respective human activities.
- Test - contains 5400 images of Human Activities. For these images you are required to make predictions as the respective class names -'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’.
- Testing_set.csv - this is the order of the predictions for each image that is to be submitted on the platform. Make sure the predictions you download are with their image’s filename in the same order as given in this file.
- sample_submission: This is a csv file that contains the sample submission for the data sprint.
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label. All `test` data is labeled 0.
### Class Label Mappings:
```
{
'calling': 0,
'clapping': 1,
'cycling': 2,
'dancing': 3,
'drinking': 4,
'eating': 5,
'fighting': 6,
'hugging': 7,
'laughing': 8,
'listening_to_music': 9,
'running': 10,
'sitting': 11,
'sleeping': 12,
'texting': 13,
'using_laptop': 14
}
```
### Data Splits
| | train | test |
|---------------|--------|-----:|
| # of examples | 12600 | 5400 |
### Data Size
- download: 311.96 MiB
- generated: 312.59 MiB
- total: 624.55 MiB
```pycon
>>> from datasets import load_dataset
>>> ds = load_dataset("Bingsu/Human_Action_Recognition")
>>> ds
DatasetDict({
test: Dataset({
features: ['image', 'labels'],
num_rows: 5400
})
train: Dataset({
features: ['image', 'labels'],
num_rows: 12600
})
})
>>> ds["train"].features
{'image': Image(decode=True, id=None),
'labels': ClassLabel(num_classes=15, names=['calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop'], id=None)}
>>> ds["train"][0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=240x160>,
'labels': 11}
``` | Bingsu/Human_Action_Recognition | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:odbl",
"region:us"
] | 2022-06-09T01:00:52+00:00 | {"language": ["en"], "license": ["odbl"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["image-classification"], "pretty_name": "Human Action Recognition"} | 2022-07-05T01:48:56+00:00 |
e86a47f2f12179b719e73ae853eb4547c40c5cf1 | AkaGuda/fate | [
"license:other",
"region:us"
] | 2022-06-09T05:51:23+00:00 | {"license": "other"} | 2022-06-09T05:51:23+00:00 |
|
f46f986fff162cdbfe9f35874a08d9cec2446b6e | # AutoTrain Dataset for project: quality-customer-reviews
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project quality-customer-reviews.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": " Love this truck, I think it is light years better than the competition. I have driven or owned all [...]",
"target": 1
},
{
"text": " I purchased this to haul our 4 horse trailer since the standard iterations of the domestic vehicles[...]",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=5, names=['good', 'great', 'ok', 'poor', 'terrible'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 9166 |
| valid | 2295 |
| florentgbelidji/autotrain-data-quality-customer-reviews | [
"task_categories:text-classification",
"language:en",
"region:us"
] | 2022-06-09T08:35:36+00:00 | {"language": ["en"], "task_categories": ["text-classification"]} | 2022-10-25T09:29:24+00:00 |
f666ec81588b1b9df9f93bcbc0ee19a5ca264ad9 |
# Dataset Card for Quick, Draw!
## 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:** [Quick, Draw! homepage](https://quickdraw.withgoogle.com/data)
- **Repository:** [Quick, Draw! repository](https://github.com/googlecreativelab/quickdraw-dataset)
- **Paper:** [A Neural Representation of Sketch Drawings](https://arxiv.org/abs/1704.03477v4)
- **Leaderboard:** [Quick, Draw! Doodle Recognition Challenge](https://www.kaggle.com/competitions/quickdraw-doodle-recognition/leaderboard)
- **Point of Contact:** [Quick, Draw! support](mailto:[email protected])
### Dataset Summary
The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given sketch into one of 345 classes.
The (closed) leaderboard for this task is available [here](https://www.kaggle.com/competitions/quickdraw-doodle-recognition/leaderboard).
### Languages
English.
## Dataset Structure
### Data Instances
#### `raw`
A data point comprises a drawing and its metadata.
```
{
'key_id': '5475678961008640',
'word': 0,
'recognized': True,
'timestamp': datetime.datetime(2017, 3, 28, 13, 28, 0, 851730),
'countrycode': 'MY',
'drawing': {
'x': [[379.0, 380.0, 381.0, 381.0, 381.0, 381.0, 382.0], [362.0, 368.0, 375.0, 380.0, 388.0, 393.0, 399.0, 404.0, 409.0, 410.0, 410.0, 405.0, 397.0, 392.0, 384.0, 377.0, 370.0, 363.0, 356.0, 348.0, 342.0, 336.0, 333.0], ..., [477.0, 473.0, 471.0, 469.0, 468.0, 466.0, 464.0, 462.0, 461.0, 469.0, 475.0, 483.0, 491.0, 499.0, 510.0, 521.0, 531.0, 540.0, 548.0, 558.0, 566.0, 576.0, 583.0, 590.0, 595.0, 598.0, 597.0, 596.0, 594.0, 592.0, 590.0, 589.0, 588.0, 586.0]],
'y': [[1.0, 7.0, 15.0, 21.0, 27.0, 32.0, 32.0], [17.0, 17.0, 17.0, 17.0, 16.0, 16.0, 16.0, 16.0, 18.0, 23.0, 29.0, 32.0, 32.0, 32.0, 29.0, 27.0, 25.0, 23.0, 21.0, 19.0, 17.0, 16.0, 14.0], ..., [151.0, 146.0, 139.0, 131.0, 125.0, 119.0, 113.0, 107.0, 102.0, 99.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 100.0, 102.0, 104.0, 105.0, 110.0, 115.0, 121.0, 126.0, 131.0, 137.0, 142.0, 148.0, 150.0]],
't': [[0, 84, 100, 116, 132, 148, 260], [573, 636, 652, 660, 676, 684, 701, 724, 796, 838, 860, 956, 973, 979, 989, 995, 1005, 1012, 1020, 1028, 1036, 1053, 1118], ..., [8349, 8446, 8468, 8484, 8500, 8516, 8541, 8557, 8573, 8685, 8693, 8702, 8710, 8718, 8724, 8732, 8741, 8748, 8757, 8764, 8773, 8780, 8788, 8797, 8804, 8965, 8996, 9029, 9045, 9061, 9076, 9092, 9109, 9167]]
}
}
```
#### `preprocessed_simplified_drawings`
The simplified version of the dataset generated from the `raw` data with the simplified vectors, removed timing information, and the data positioned and scaled into a 256x256 region.
The simplification process was:
1.Align the drawing to the top-left corner, to have minimum values of 0.
2.Uniformly scale the drawing, to have a maximum value of 255.
3.Resample all strokes with a 1 pixel spacing.
4.Simplify all strokes using the [Ramer-Douglas-Peucker algorithm](https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm) with an epsilon value of 2.0.
```
{
'key_id': '5475678961008640',
'word': 0,
'recognized': True,
'timestamp': datetime.datetime(2017, 3, 28, 15, 28),
'countrycode': 'MY',
'drawing': {
'x': [[31, 32], [27, 37, 38, 35, 21], [25, 28, 38, 39], [33, 34, 32], [5, 188, 254, 251, 241, 185, 45, 9, 0], [35, 35, 43, 125, 126], [35, 76, 80, 77], [53, 50, 54, 80, 78]],
'y': [[0, 7], [4, 4, 6, 7, 3], [5, 10, 10, 7], [4, 33, 44], [50, 50, 54, 83, 86, 90, 86, 77, 52], [85, 91, 92, 96, 90], [35, 37, 41, 47], [34, 23, 22, 23, 34]]
}
}
```
#### `preprocessed_bitmaps` (default configuration)
This configuration contains the 28x28 grayscale bitmap images that were generated from the simplified data, but are aligned to the center of the drawing's bounding box rather than the top-left corner. The code that was used for generation is available [here](https://github.com/googlecreativelab/quickdraw-dataset/issues/19#issuecomment-402247262).
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x10B5B102828>,
'label': 0
}
```
#### `sketch_rnn` and `sketch_rnn_full`
The `sketch_rnn_full` configuration stores the data in the format suitable for inputs into a recurrent neural network and was used for for training the [Sketch-RNN](https://arxiv.org/abs/1704.03477) model. Unlike `sketch_rnn` where the samples have been randomly selected from each category, the `sketch_rnn_full` configuration contains the full data for each category.
```
{
'word': 0,
'drawing': [[132, 0, 0], [23, 4, 0], [61, 1, 0], [76, 0, 0], [22, -4, 0], [152, 0, 0], [50, -5, 0], [36, -10, 0], [8, 26, 0], [0, 69, 0], [-2, 11, 0], [-8, 10, 0], [-56, 24, 0], [-23, 14, 0], [-99, 40, 0], [-45, 6, 0], [-21, 6, 0], [-170, 2, 0], [-81, 0, 0], [-29, -9, 0], [-94, -19, 0], [-48, -24, 0], [-6, -16, 0], [2, -36, 0], [7, -29, 0], [23, -45, 0], [13, -6, 0], [41, -8, 0], [42, -2, 1], [392, 38, 0], [2, 19, 0], [11, 33, 0], [13, 0, 0], [24, -9, 0], [26, -27, 0], [0, -14, 0], [-8, -10, 0], [-18, -5, 0], [-14, 1, 0], [-23, 4, 0], [-21, 12, 1], [-152, 18, 0], [10, 46, 0], [26, 6, 0], [38, 0, 0], [31, -2, 0], [7, -2, 0], [4, -6, 0], [-10, -21, 0], [-2, -33, 0], [-6, -11, 0], [-46, 1, 0], [-39, 18, 0], [-19, 4, 1], [-122, 0, 0], [-2, 38, 0], [4, 16, 0], [6, 4, 0], [78, 0, 0], [4, -8, 0], [-8, -36, 0], [0, -22, 0], [-6, -2, 0], [-32, 14, 0], [-58, 13, 1], [-96, -12, 0], [-10, 27, 0], [2, 32, 0], [102, 0, 0], [1, -7, 0], [-27, -17, 0], [-4, -6, 0], [-1, -34, 0], [-64, 8, 1], [129, -138, 0], [-108, 0, 0], [-8, 12, 0], [-1, 15, 0], [12, 15, 0], [20, 5, 0], [61, -3, 0], [24, 6, 0], [19, 0, 0], [5, -4, 0], [2, 14, 1]]
}
```
### Data Fields
#### `raw`
- `key_id`: A unique identifier across all drawings.
- `word`: Category the player was prompted to draw.
- `recognized`: Whether the word was recognized by the game.
- `timestamp`: When the drawing was created.
- `countrycode`: A two letter country code ([ISO 3166-1 alpha-2](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2)) of where the player was located.
- `drawing`: A dictionary where `x` and `y` are the pixel coordinates, and `t` is the time in milliseconds since the first point. `x` and `y` are real-valued while `t` is an integer. `x`, `y` and `t` match in lenght and are represented as lists of lists where each sublist corresponds to a single stroke. The raw drawings can have vastly different bounding boxes and number of points due to the different devices used for display and input.
#### `preprocessed_simplified_drawings`
- `key_id`: A unique identifier across all drawings.
- `word`: Category the player was prompted to draw.
- `recognized`: Whether the word was recognized by the game.
- `timestamp`: When the drawing was created.
- `countrycode`: A two letter country code ([ISO 3166-1 alpha-2](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2)) of where the player was located.
- `drawing`: A simplified drawing represented as a dictionary where `x` and `y` are the pixel coordinates. The simplification processed is described in the `Data Instances` section.
#### `preprocessed_bitmaps` (default configuration)
- `image`: A `PIL.Image.Image` object containing the 28x28 grayscale bitmap. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `label`: Category the player was prompted to draw.
<details>
<summary>
Click here to see the full class labels mapping:
</summary>
|id|class|
|---|---|
|0|aircraft carrier|
|1|airplane|
|2|alarm clock|
|3|ambulance|
|4|angel|
|5|animal migration|
|6|ant|
|7|anvil|
|8|apple|
|9|arm|
|10|asparagus|
|11|axe|
|12|backpack|
|13|banana|
|14|bandage|
|15|barn|
|16|baseball bat|
|17|baseball|
|18|basket|
|19|basketball|
|20|bat|
|21|bathtub|
|22|beach|
|23|bear|
|24|beard|
|25|bed|
|26|bee|
|27|belt|
|28|bench|
|29|bicycle|
|30|binoculars|
|31|bird|
|32|birthday cake|
|33|blackberry|
|34|blueberry|
|35|book|
|36|boomerang|
|37|bottlecap|
|38|bowtie|
|39|bracelet|
|40|brain|
|41|bread|
|42|bridge|
|43|broccoli|
|44|broom|
|45|bucket|
|46|bulldozer|
|47|bus|
|48|bush|
|49|butterfly|
|50|cactus|
|51|cake|
|52|calculator|
|53|calendar|
|54|camel|
|55|camera|
|56|camouflage|
|57|campfire|
|58|candle|
|59|cannon|
|60|canoe|
|61|car|
|62|carrot|
|63|castle|
|64|cat|
|65|ceiling fan|
|66|cell phone|
|67|cello|
|68|chair|
|69|chandelier|
|70|church|
|71|circle|
|72|clarinet|
|73|clock|
|74|cloud|
|75|coffee cup|
|76|compass|
|77|computer|
|78|cookie|
|79|cooler|
|80|couch|
|81|cow|
|82|crab|
|83|crayon|
|84|crocodile|
|85|crown|
|86|cruise ship|
|87|cup|
|88|diamond|
|89|dishwasher|
|90|diving board|
|91|dog|
|92|dolphin|
|93|donut|
|94|door|
|95|dragon|
|96|dresser|
|97|drill|
|98|drums|
|99|duck|
|100|dumbbell|
|101|ear|
|102|elbow|
|103|elephant|
|104|envelope|
|105|eraser|
|106|eye|
|107|eyeglasses|
|108|face|
|109|fan|
|110|feather|
|111|fence|
|112|finger|
|113|fire hydrant|
|114|fireplace|
|115|firetruck|
|116|fish|
|117|flamingo|
|118|flashlight|
|119|flip flops|
|120|floor lamp|
|121|flower|
|122|flying saucer|
|123|foot|
|124|fork|
|125|frog|
|126|frying pan|
|127|garden hose|
|128|garden|
|129|giraffe|
|130|goatee|
|131|golf club|
|132|grapes|
|133|grass|
|134|guitar|
|135|hamburger|
|136|hammer|
|137|hand|
|138|harp|
|139|hat|
|140|headphones|
|141|hedgehog|
|142|helicopter|
|143|helmet|
|144|hexagon|
|145|hockey puck|
|146|hockey stick|
|147|horse|
|148|hospital|
|149|hot air balloon|
|150|hot dog|
|151|hot tub|
|152|hourglass|
|153|house plant|
|154|house|
|155|hurricane|
|156|ice cream|
|157|jacket|
|158|jail|
|159|kangaroo|
|160|key|
|161|keyboard|
|162|knee|
|163|knife|
|164|ladder|
|165|lantern|
|166|laptop|
|167|leaf|
|168|leg|
|169|light bulb|
|170|lighter|
|171|lighthouse|
|172|lightning|
|173|line|
|174|lion|
|175|lipstick|
|176|lobster|
|177|lollipop|
|178|mailbox|
|179|map|
|180|marker|
|181|matches|
|182|megaphone|
|183|mermaid|
|184|microphone|
|185|microwave|
|186|monkey|
|187|moon|
|188|mosquito|
|189|motorbike|
|190|mountain|
|191|mouse|
|192|moustache|
|193|mouth|
|194|mug|
|195|mushroom|
|196|nail|
|197|necklace|
|198|nose|
|199|ocean|
|200|octagon|
|201|octopus|
|202|onion|
|203|oven|
|204|owl|
|205|paint can|
|206|paintbrush|
|207|palm tree|
|208|panda|
|209|pants|
|210|paper clip|
|211|parachute|
|212|parrot|
|213|passport|
|214|peanut|
|215|pear|
|216|peas|
|217|pencil|
|218|penguin|
|219|piano|
|220|pickup truck|
|221|picture frame|
|222|pig|
|223|pillow|
|224|pineapple|
|225|pizza|
|226|pliers|
|227|police car|
|228|pond|
|229|pool|
|230|popsicle|
|231|postcard|
|232|potato|
|233|power outlet|
|234|purse|
|235|rabbit|
|236|raccoon|
|237|radio|
|238|rain|
|239|rainbow|
|240|rake|
|241|remote control|
|242|rhinoceros|
|243|rifle|
|244|river|
|245|roller coaster|
|246|rollerskates|
|247|sailboat|
|248|sandwich|
|249|saw|
|250|saxophone|
|251|school bus|
|252|scissors|
|253|scorpion|
|254|screwdriver|
|255|sea turtle|
|256|see saw|
|257|shark|
|258|sheep|
|259|shoe|
|260|shorts|
|261|shovel|
|262|sink|
|263|skateboard|
|264|skull|
|265|skyscraper|
|266|sleeping bag|
|267|smiley face|
|268|snail|
|269|snake|
|270|snorkel|
|271|snowflake|
|272|snowman|
|273|soccer ball|
|274|sock|
|275|speedboat|
|276|spider|
|277|spoon|
|278|spreadsheet|
|279|square|
|280|squiggle|
|281|squirrel|
|282|stairs|
|283|star|
|284|steak|
|285|stereo|
|286|stethoscope|
|287|stitches|
|288|stop sign|
|289|stove|
|290|strawberry|
|291|streetlight|
|292|string bean|
|293|submarine|
|294|suitcase|
|295|sun|
|296|swan|
|297|sweater|
|298|swing set|
|299|sword|
|300|syringe|
|301|t-shirt|
|302|table|
|303|teapot|
|304|teddy-bear|
|305|telephone|
|306|television|
|307|tennis racquet|
|308|tent|
|309|The Eiffel Tower|
|310|The Great Wall of China|
|311|The Mona Lisa|
|312|tiger|
|313|toaster|
|314|toe|
|315|toilet|
|316|tooth|
|317|toothbrush|
|318|toothpaste|
|319|tornado|
|320|tractor|
|321|traffic light|
|322|train|
|323|tree|
|324|triangle|
|325|trombone|
|326|truck|
|327|trumpet|
|328|umbrella|
|329|underwear|
|330|van|
|331|vase|
|332|violin|
|333|washing machine|
|334|watermelon|
|335|waterslide|
|336|whale|
|337|wheel|
|338|windmill|
|339|wine bottle|
|340|wine glass|
|341|wristwatch|
|342|yoga|
|343|zebra|
|344|zigzag|
</details>
#### `sketch_rnn` and `sketch_rnn_full`
- `word`: Category the player was prompted to draw.
- `drawing`: An array of strokes. Strokes are represented as 3-tuples consisting of x-offset, y-offset, and a binary variable which is 1 if the pen is lifted between this position and the next, and 0 otherwise.
<details>
<summary>
Click here to see the code for visualizing drawings in Jupyter Notebook or Google Colab:
</summary>
```python
import numpy as np
import svgwrite # pip install svgwrite
from IPython.display import SVG, display
def draw_strokes(drawing, factor=0.045):
"""Displays vector drawing as SVG.
Args:
drawing: a list of strokes represented as 3-tuples
factor: scaling factor. The smaller the scaling factor, the bigger the SVG picture and vice versa.
"""
def get_bounds(data, factor):
"""Return bounds of data."""
min_x = 0
max_x = 0
min_y = 0
max_y = 0
abs_x = 0
abs_y = 0
for i in range(len(data)):
x = float(data[i, 0]) / factor
y = float(data[i, 1]) / factor
abs_x += x
abs_y += y
min_x = min(min_x, abs_x)
min_y = min(min_y, abs_y)
max_x = max(max_x, abs_x)
max_y = max(max_y, abs_y)
return (min_x, max_x, min_y, max_y)
data = np.array(drawing)
min_x, max_x, min_y, max_y = get_bounds(data, factor)
dims = (50 + max_x - min_x, 50 + max_y - min_y)
dwg = svgwrite.Drawing(size=dims)
dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))
lift_pen = 1
abs_x = 25 - min_x
abs_y = 25 - min_y
p = "M%s,%s " % (abs_x, abs_y)
command = "m"
for i in range(len(data)):
if (lift_pen == 1):
command = "m"
elif (command != "l"):
command = "l"
else:
command = ""
x = float(data[i,0])/factor
y = float(data[i,1])/factor
lift_pen = data[i, 2]
p += command+str(x)+","+str(y)+" "
the_color = "black"
stroke_width = 1
dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill("none"))
display(SVG(dwg.tostring()))
```
</details>
> **Note**: Sketch-RNN takes for input strokes represented as 5-tuples with drawings padded to a common maximum length and prefixed by the special start token `[0, 0, 1, 0, 0]`. The 5-tuple representation consists of x-offset, y-offset, and p_1, p_2, p_3, a binary one-hot vector of 3 possible pen states: pen down, pen up, end of sketch. More precisely, the first two elements are the offset distance in the x and y directions of the pen from the previous point. The last 3 elements represents a binary one-hot vector of 3 possible states. The first pen state, p1, indicates that the pen is currently touching the paper, and that a line will be drawn connecting the next point with the current point. The second pen state, p2, indicates that the pen will be lifted from the paper after the current point, and that no line will be drawn next. The final pen state, p3, indicates that the drawing has ended, and subsequent points, including the current point, will not be rendered.
><details>
> <summary>
> Click here to see the code for converting drawings to Sketch-RNN input format:
> </summary>
>
> ```python
> def to_sketch_rnn_format(drawing, max_len):
> """Converts a drawing to Sketch-RNN input format.
>
> Args:
> drawing: a list of strokes represented as 3-tuples
> max_len: maximum common length of all drawings
>
> Returns:
> NumPy array
> """
> drawing = np.array(drawing)
> result = np.zeros((max_len, 5), dtype=float)
> l = len(drawing)
> assert l <= max_len
> result[0:l, 0:2] = drawing[:, 0:2]
> result[0:l, 3] = drawing[:, 2]
> result[0:l, 2] = 1 - result[0:l, 3]
> result[l:, 4] = 1
> # Prepend special start token
> result = np.vstack([[0, 0, 1, 0, 0], result])
> return result
> ```
>
></details>
### Data Splits
In the configurations `raw`, `preprocessed_simplified_drawings` and `preprocessed_bitamps` (default configuration), all the data is contained in the training set, which has 50426266 examples.
`sketch_rnn` and `sketch_rnn_full` have the data split into training, validation and test split. In the `sketch_rnn` configuration, 75K samples (70K Training, 2.5K Validation, 2.5K Test) have been randomly selected from each category. Therefore, the training set contains 24150000 examples, the validation set 862500 examples and the test set 862500 examples. The `sketch_rnn_full` configuration has the full (training) data for each category, which leads to the training set having 43988874 examples, the validation set 862500 and the test set 862500 examples.
## Dataset Creation
### Curation Rationale
From the GitHub repository:
> The Quick Draw Dataset is a collection of 50 million drawings across [345 categories](categories.txt), contributed by players of the game [Quick, Draw!](https://quickdraw.withgoogle.com). The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located. You can browse the recognized drawings on [quickdraw.withgoogle.com/data](https://quickdraw.withgoogle.com/data).
>
> We're sharing them here for developers, researchers, and artists to explore, study, and learn from
### Source Data
#### Initial Data Collection and Normalization
This dataset contains vector drawings obtained from [Quick, Draw!](https://quickdraw.withgoogle.com/), an online game where the players are asked to draw objects belonging to a particular object class in less than 20 seconds.
#### Who are the source language producers?
The participants in the [Quick, Draw!](https://quickdraw.withgoogle.com/) game.
### Annotations
#### Annotation process
The annotations are machine-generated and match the category the player was prompted to draw.
#### Who are the annotators?
The annotations are machine-generated.
### Personal and Sensitive Information
Some sketches are known to be problematic (see https://github.com/googlecreativelab/quickdraw-dataset/issues/74 and https://github.com/googlecreativelab/quickdraw-dataset/issues/18).
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
## Additional Information
### Dataset Curators
Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim and Nick Fox-Gieg.
### Licensing Information
The data is made available by Google, Inc. under the [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license.
### Citation Information
```bibtex
@article{DBLP:journals/corr/HaE17,
author = {David Ha and
Douglas Eck},
title = {A Neural Representation of Sketch Drawings},
journal = {CoRR},
volume = {abs/1704.03477},
year = {2017},
url = {http://arxiv.org/abs/1704.03477},
archivePrefix = {arXiv},
eprint = {1704.03477},
timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/HaE17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | quickdraw | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1704.03477",
"region:us"
] | 2022-06-09T08:56:43+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "quick-draw-dataset", "pretty_name": "Quick, Draw!", "dataset_info": [{"config_name": "raw", "features": [{"name": "key_id", "dtype": "string"}, {"name": "word", "dtype": {"class_label": {"names": {"0": "aircraft carrier", "1": "airplane", "2": "alarm clock", "3": "ambulance", "4": "angel", "5": "animal migration", "6": "ant", "7": "anvil", "8": "apple", "9": "arm", "10": "asparagus", "11": "axe", "12": "backpack", "13": "banana", "14": "bandage", "15": "barn", "16": "baseball bat", "17": "baseball", "18": "basket", "19": "basketball", "20": "bat", "21": "bathtub", "22": "beach", "23": "bear", "24": "beard", "25": "bed", "26": "bee", "27": "belt", "28": "bench", "29": "bicycle", "30": "binoculars", "31": "bird", "32": "birthday cake", "33": "blackberry", "34": "blueberry", "35": "book", "36": "boomerang", "37": "bottlecap", "38": "bowtie", "39": "bracelet", "40": "brain", "41": "bread", "42": "bridge", "43": "broccoli", "44": "broom", "45": "bucket", "46": "bulldozer", "47": "bus", "48": "bush", "49": "butterfly", "50": "cactus", "51": "cake", "52": "calculator", "53": "calendar", "54": "camel", "55": "camera", "56": "camouflage", "57": "campfire", "58": "candle", "59": "cannon", "60": "canoe", "61": "car", "62": "carrot", "63": "castle", "64": "cat", "65": "ceiling fan", "66": "cell phone", "67": "cello", "68": "chair", "69": "chandelier", "70": "church", "71": "circle", "72": "clarinet", "73": "clock", "74": "cloud", "75": "coffee cup", "76": "compass", "77": "computer", "78": "cookie", "79": "cooler", "80": "couch", "81": "cow", "82": "crab", "83": "crayon", "84": "crocodile", "85": "crown", "86": "cruise ship", "87": "cup", "88": "diamond", "89": "dishwasher", "90": "diving board", "91": "dog", "92": "dolphin", "93": "donut", "94": "door", "95": "dragon", "96": "dresser", "97": "drill", "98": "drums", "99": "duck", "100": "dumbbell", "101": "ear", "102": "elbow", "103": "elephant", "104": "envelope", "105": "eraser", "106": "eye", "107": "eyeglasses", "108": "face", "109": "fan", "110": "feather", "111": "fence", "112": "finger", "113": "fire hydrant", "114": "fireplace", "115": "firetruck", "116": "fish", "117": "flamingo", "118": "flashlight", "119": "flip flops", "120": "floor lamp", "121": "flower", "122": "flying saucer", "123": "foot", "124": "fork", "125": "frog", "126": "frying pan", "127": "garden hose", "128": "garden", "129": "giraffe", "130": "goatee", "131": "golf club", "132": "grapes", "133": "grass", "134": "guitar", "135": "hamburger", "136": "hammer", "137": "hand", "138": "harp", "139": "hat", "140": "headphones", "141": "hedgehog", "142": "helicopter", "143": "helmet", "144": "hexagon", "145": "hockey puck", "146": "hockey stick", "147": "horse", "148": "hospital", "149": "hot air balloon", "150": "hot dog", "151": "hot tub", "152": "hourglass", "153": "house plant", "154": "house", "155": "hurricane", "156": "ice cream", "157": "jacket", "158": "jail", "159": "kangaroo", "160": "key", "161": "keyboard", "162": "knee", "163": "knife", "164": "ladder", "165": "lantern", "166": "laptop", "167": "leaf", "168": "leg", "169": "light bulb", "170": "lighter", "171": "lighthouse", "172": "lightning", "173": "line", "174": "lion", "175": "lipstick", "176": "lobster", "177": "lollipop", "178": "mailbox", "179": "map", "180": "marker", "181": "matches", "182": "megaphone", "183": "mermaid", "184": "microphone", "185": "microwave", "186": "monkey", "187": "moon", "188": "mosquito", "189": "motorbike", "190": "mountain", "191": "mouse", "192": "moustache", "193": "mouth", "194": "mug", "195": "mushroom", "196": "nail", "197": "necklace", "198": "nose", "199": "ocean", "200": "octagon", "201": "octopus", "202": "onion", "203": "oven", "204": "owl", "205": "paint can", "206": "paintbrush", "207": "palm tree", "208": "panda", "209": "pants", "210": "paper clip", "211": "parachute", "212": "parrot", "213": "passport", "214": "peanut", "215": "pear", "216": "peas", "217": "pencil", "218": "penguin", "219": "piano", "220": "pickup truck", "221": "picture frame", "222": "pig", "223": "pillow", "224": "pineapple", "225": "pizza", "226": "pliers", "227": "police car", "228": "pond", "229": "pool", "230": "popsicle", "231": "postcard", "232": "potato", "233": "power outlet", "234": "purse", "235": "rabbit", "236": "raccoon", "237": "radio", "238": "rain", "239": "rainbow", "240": "rake", "241": "remote control", "242": "rhinoceros", "243": "rifle", "244": "river", "245": "roller coaster", "246": "rollerskates", "247": "sailboat", "248": "sandwich", "249": "saw", "250": "saxophone", "251": "school bus", "252": "scissors", "253": "scorpion", "254": "screwdriver", "255": "sea turtle", "256": "see saw", "257": "shark", "258": "sheep", "259": "shoe", "260": "shorts", "261": "shovel", "262": "sink", "263": "skateboard", "264": "skull", "265": "skyscraper", "266": "sleeping bag", "267": "smiley face", "268": "snail", "269": "snake", "270": "snorkel", "271": "snowflake", "272": "snowman", "273": "soccer ball", "274": "sock", "275": "speedboat", "276": "spider", "277": "spoon", "278": "spreadsheet", "279": "square", "280": "squiggle", "281": "squirrel", "282": "stairs", "283": "star", "284": "steak", "285": "stereo", "286": "stethoscope", "287": "stitches", "288": "stop sign", "289": "stove", "290": "strawberry", "291": "streetlight", "292": "string bean", "293": "submarine", "294": "suitcase", "295": "sun", "296": "swan", "297": "sweater", "298": "swing set", "299": "sword", "300": "syringe", "301": "t-shirt", "302": "table", "303": "teapot", "304": "teddy-bear", "305": "telephone", "306": "television", "307": "tennis racquet", "308": "tent", "309": "The Eiffel Tower", "310": "The Great Wall of China", "311": "The Mona Lisa", "312": "tiger", "313": "toaster", "314": "toe", "315": "toilet", "316": "tooth", "317": "toothbrush", "318": "toothpaste", "319": "tornado", "320": "tractor", "321": "traffic light", "322": "train", "323": "tree", "324": "triangle", "325": "trombone", "326": "truck", "327": "trumpet", "328": "umbrella", "329": "underwear", "330": "van", "331": "vase", "332": "violin", "333": "washing machine", "334": "watermelon", "335": "waterslide", "336": "whale", "337": "wheel", "338": "windmill", "339": "wine bottle", "340": "wine glass", "341": "wristwatch", "342": "yoga", "343": "zebra", "344": "zigzag"}}}}, {"name": "recognized", "dtype": "bool"}, {"name": "timestamp", "dtype": "timestamp[us, tz=UTC]"}, {"name": "countrycode", "dtype": "string"}, {"name": "drawing", "sequence": [{"name": "x", "sequence": "float32"}, {"name": "y", "sequence": "float32"}, {"name": "t", "sequence": "int32"}]}], "splits": [{"name": "train", "num_bytes": 134763164880, "num_examples": 50426266}], "download_size": 194810597157, "dataset_size": 134763164880}, {"config_name": "preprocessed_simplified_drawings", "features": [{"name": "key_id", "dtype": "string"}, {"name": "word", "dtype": {"class_label": {"names": {"0": "aircraft carrier", "1": "airplane", "2": "alarm clock", "3": "ambulance", "4": "angel", "5": "animal migration", "6": "ant", "7": "anvil", "8": "apple", "9": "arm", "10": "asparagus", "11": "axe", "12": "backpack", "13": "banana", "14": "bandage", "15": "barn", "16": "baseball bat", "17": "baseball", "18": "basket", "19": "basketball", "20": "bat", "21": "bathtub", "22": "beach", "23": "bear", "24": "beard", "25": "bed", "26": "bee", "27": "belt", "28": "bench", "29": "bicycle", "30": "binoculars", "31": "bird", "32": "birthday cake", "33": "blackberry", "34": "blueberry", "35": "book", "36": "boomerang", "37": "bottlecap", "38": "bowtie", "39": "bracelet", "40": "brain", "41": "bread", "42": "bridge", "43": "broccoli", "44": "broom", "45": "bucket", "46": "bulldozer", "47": "bus", "48": "bush", "49": "butterfly", "50": "cactus", "51": "cake", "52": "calculator", "53": "calendar", "54": "camel", "55": "camera", "56": "camouflage", "57": "campfire", "58": "candle", "59": "cannon", "60": "canoe", "61": "car", "62": "carrot", "63": "castle", "64": "cat", "65": "ceiling fan", "66": "cell phone", "67": "cello", "68": "chair", "69": "chandelier", "70": "church", "71": "circle", "72": "clarinet", "73": "clock", "74": "cloud", "75": "coffee cup", "76": "compass", "77": "computer", "78": "cookie", "79": "cooler", "80": "couch", "81": "cow", "82": "crab", "83": "crayon", "84": "crocodile", "85": "crown", "86": "cruise ship", "87": "cup", "88": "diamond", "89": "dishwasher", "90": "diving board", "91": "dog", "92": "dolphin", "93": "donut", "94": "door", "95": "dragon", "96": "dresser", "97": "drill", "98": "drums", "99": "duck", "100": "dumbbell", "101": "ear", "102": "elbow", "103": "elephant", "104": "envelope", "105": "eraser", "106": "eye", "107": "eyeglasses", "108": "face", "109": "fan", "110": "feather", "111": "fence", "112": "finger", "113": "fire hydrant", "114": "fireplace", "115": "firetruck", "116": "fish", "117": "flamingo", "118": "flashlight", "119": "flip flops", "120": "floor lamp", "121": "flower", "122": "flying saucer", "123": "foot", "124": "fork", "125": "frog", "126": "frying pan", "127": "garden hose", "128": "garden", "129": "giraffe", "130": "goatee", "131": "golf club", "132": "grapes", "133": "grass", "134": "guitar", "135": "hamburger", "136": "hammer", "137": "hand", "138": "harp", "139": "hat", "140": "headphones", "141": "hedgehog", "142": "helicopter", "143": "helmet", "144": "hexagon", "145": "hockey puck", "146": "hockey stick", "147": "horse", "148": "hospital", "149": "hot air balloon", "150": "hot dog", "151": "hot tub", "152": "hourglass", "153": "house plant", "154": "house", "155": "hurricane", "156": "ice cream", "157": "jacket", "158": "jail", "159": "kangaroo", "160": "key", "161": "keyboard", "162": "knee", "163": "knife", "164": "ladder", "165": "lantern", "166": "laptop", "167": "leaf", "168": "leg", "169": "light bulb", "170": "lighter", "171": "lighthouse", "172": "lightning", "173": "line", "174": "lion", "175": "lipstick", "176": "lobster", "177": "lollipop", "178": "mailbox", "179": "map", "180": "marker", "181": "matches", "182": "megaphone", "183": "mermaid", "184": "microphone", "185": "microwave", "186": "monkey", "187": "moon", "188": "mosquito", "189": "motorbike", "190": "mountain", "191": "mouse", "192": "moustache", "193": "mouth", "194": "mug", "195": "mushroom", "196": "nail", "197": "necklace", "198": "nose", "199": "ocean", "200": "octagon", "201": "octopus", "202": "onion", "203": "oven", "204": "owl", "205": "paint can", "206": "paintbrush", "207": "palm tree", "208": "panda", "209": "pants", "210": "paper clip", "211": "parachute", "212": "parrot", "213": "passport", "214": "peanut", "215": "pear", "216": "peas", "217": "pencil", "218": "penguin", "219": "piano", "220": "pickup truck", "221": "picture frame", "222": "pig", "223": "pillow", "224": "pineapple", "225": "pizza", "226": "pliers", "227": "police car", "228": "pond", "229": "pool", "230": "popsicle", "231": "postcard", "232": "potato", "233": "power outlet", "234": "purse", "235": "rabbit", "236": "raccoon", "237": "radio", "238": "rain", "239": "rainbow", "240": "rake", "241": "remote control", "242": "rhinoceros", "243": "rifle", "244": "river", "245": "roller coaster", "246": "rollerskates", "247": "sailboat", "248": "sandwich", "249": "saw", "250": "saxophone", "251": "school bus", "252": "scissors", "253": "scorpion", "254": "screwdriver", "255": "sea turtle", "256": "see saw", "257": "shark", "258": "sheep", "259": "shoe", "260": "shorts", "261": "shovel", "262": "sink", "263": "skateboard", "264": "skull", "265": "skyscraper", "266": 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"172": "lightning", "173": "line", "174": "lion", "175": "lipstick", "176": "lobster", "177": "lollipop", "178": "mailbox", "179": "map", "180": "marker", "181": "matches", "182": "megaphone", "183": "mermaid", "184": "microphone", "185": "microwave", "186": "monkey", "187": "moon", "188": "mosquito", "189": "motorbike", "190": "mountain", "191": "mouse", "192": "moustache", "193": "mouth", "194": "mug", "195": "mushroom", "196": "nail", "197": "necklace", "198": "nose", "199": "ocean", "200": "octagon", "201": "octopus", "202": "onion", "203": "oven", "204": "owl", "205": "paint can", "206": "paintbrush", "207": "palm tree", "208": "panda", "209": "pants", "210": "paper clip", "211": "parachute", "212": "parrot", "213": "passport", "214": "peanut", "215": "pear", "216": "peas", "217": "pencil", "218": "penguin", "219": "piano", "220": "pickup truck", "221": "picture frame", "222": "pig", "223": "pillow", "224": "pineapple", "225": "pizza", "226": "pliers", "227": "police car", "228": "pond", "229": "pool", "230": "popsicle", "231": "postcard", "232": "potato", "233": "power outlet", "234": "purse", "235": "rabbit", "236": "raccoon", "237": "radio", "238": "rain", "239": "rainbow", "240": "rake", "241": "remote control", "242": "rhinoceros", "243": "rifle", "244": "river", "245": "roller coaster", "246": "rollerskates", "247": "sailboat", "248": "sandwich", "249": "saw", "250": "saxophone", "251": "school bus", "252": "scissors", "253": "scorpion", "254": "screwdriver", "255": "sea turtle", "256": "see saw", "257": "shark", "258": "sheep", "259": "shoe", "260": "shorts", "261": "shovel", "262": "sink", "263": "skateboard", "264": "skull", "265": "skyscraper", "266": "sleeping bag", "267": "smiley face", "268": "snail", "269": "snake", "270": "snorkel", "271": "snowflake", "272": "snowman", "273": "soccer ball", "274": "sock", "275": "speedboat", "276": "spider", "277": "spoon", "278": "spreadsheet", "279": "square", "280": "squiggle", "281": "squirrel", "282": "stairs", "283": "star", "284": "steak", "285": "stereo", "286": "stethoscope", "287": "stitches", "288": "stop sign", "289": "stove", "290": "strawberry", "291": "streetlight", "292": "string bean", "293": "submarine", "294": "suitcase", "295": "sun", "296": "swan", "297": "sweater", "298": "swing set", "299": "sword", "300": "syringe", "301": "t-shirt", "302": "table", "303": "teapot", "304": "teddy-bear", "305": "telephone", "306": "television", "307": "tennis racquet", "308": "tent", "309": "The Eiffel Tower", "310": "The Great Wall of China", "311": "The Mona Lisa", "312": "tiger", "313": "toaster", "314": "toe", "315": "toilet", "316": "tooth", "317": "toothbrush", "318": "toothpaste", "319": "tornado", "320": "tractor", "321": "traffic light", "322": "train", "323": "tree", "324": "triangle", "325": "trombone", "326": "truck", "327": "trumpet", "328": "umbrella", "329": "underwear", "330": "van", "331": "vase", "332": "violin", "333": "washing machine", "334": "watermelon", "335": "waterslide", "336": "whale", "337": "wheel", "338": "windmill", "339": "wine bottle", "340": "wine glass", "341": "wristwatch", "342": "yoga", "343": "zebra", "344": "zigzag"}}}}, {"name": "drawing", "dtype": {"array2_d": {"shape": [3], "dtype": "int16"}}}], "splits": [{"name": "train", "num_bytes": 23725242280, "num_examples": 43988874}, {"name": "validation", "num_bytes": 466485546, "num_examples": 862500}, {"name": "test", "num_bytes": 466191706, "num_examples": 862500}], "download_size": 6928245966, "dataset_size": 24657919532}]} | 2024-01-18T11:19:15+00:00 |
5df91be3ec941e3ce0e9e214d0be2d208bcb6b05 | ## Auto Miles per Gallon (MPG) Dataset
Following description was taken from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/auto+mpg).
Source: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.
## Data Set Information:
This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute. The original dataset is available in the file "auto-mpg.data-original".
"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)
## Attribute Information:
- mpg: continuous
- cylinders: multi-valued discrete
- displacement: continuous
- horsepower: continuous
- weight: continuous
- acceleration: continuous
- model year: multi-valued discrete
- origin: multi-valued discrete
- car name: string (unique for each instance) | scikit-learn/auto-mpg | [
"task_categories:tabular-classification",
"task_categories:tabular-regression",
"language:en",
"license:apache-2.0",
"scikit-learn",
"region:us"
] | 2022-06-09T09:05:01+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["tabular-classification", "tabular-regression"], "pretty_name": "auto-mpg", "tags": ["scikit-learn"]} | 2023-12-05T12:45:05+00:00 |
a0c9094f4ff5841ef6daef20cf62fa9c3e6c3927 | anton-l/images | [
"license:apache-2.0",
"region:us"
] | 2022-06-09T09:11:50+00:00 | {"license": "apache-2.0"} | 2022-09-05T15:08:48+00:00 |
|
00ccd79fc0c83d159cf835b236132f5903ecf6c7 | chradden/face_age | [
"region:us"
] | 2022-06-09T09:31:42+00:00 | {} | 2022-06-16T14:21:17+00:00 |
|
7d0c06fa172853f1eb41358c1c9ec081c478d24a | # AutoTrain Dataset for project: qa-team-car-review-project
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project qa-team-car-review-project.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": " ",
"target": 1
},
{
"text": " Mazda truck costs less than the sister look-a-like Ford; Mazda is a \"A\" series of the Ford Ranger, [...]",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=3, names=['great', 'ok', 'poor'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 19731 |
| valid | 4935 |
| florentgbelidji/autotrain-data-qa-team-car-review-project | [
"task_categories:text-classification",
"language:en",
"region:us"
] | 2022-06-09T09:47:22+00:00 | {"language": ["en"], "task_categories": ["text-classification"]} | 2022-10-25T09:29:30+00:00 |
660956f28b0c98cf634d693dfb25156fceeef638 |
## 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:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI email](mailto:[email protected])
- **Source Data:** [Bloom Library](https://bloomlibrary.org/)
 
## Dataset Summary
**Bloom** is free, open-source software and an associated website [Bloom Library](https://bloomlibrary.org/), app, and services developed by [SIL International](https://www.sil.org/). Bloom’s primary goal is to equip non-dominant language communities and their members to create the literature they want for their community and children. Bloom also serves organizations that help such communities develop literature and education or other aspects of community development.
This version of the Bloom Library data is developed specifically for the automatic speech recognition and speech-to-text tasks. It includes data from 56 languages across 18 language families. There is a mean of 458 and median of 138 audio records per language.
**Note**: If you speak one of these languages and can help provide feedback or corrections, please let us know!
**Note**: Although data from [bloom-lm](https://huggingface.co/datasets/sil-ai/bloom-lm) was used in the training of the [BLOOM model](https://huggingface.co/bigscience/bloom), the dataset only represents a small portion of the data used to train that model. Data from "Bloom Library" was combined with a large number of other datasets to train that model. "Bloom Library" is a project that existed prior to the BLOOM model, and is something separate. All that to say... We were using the "Bloom" name before it was cool. 😉
## Languages
Of the 500+ languages listed at BloomLibrary.org, there are 56 languages available in this dataset. Here are the corresponding ISO 639-3 codes:
ajz, bam, bis, bjn, boz, bze, bzi, cak, ceb, chd, chp, clo, csw, eng, fli, fra, guj, hbb, hin, ind, jmx, jra, kan, kbq, kek, kjb, kmu, kqr, kwu, loh, mai, mal, mam, mar, mle, mya, myk, nas, nsk, nsn, oji, omw, por, quc, sdk, snk, spa, stk, taj, tam, tbj, tdc, tgl, tpi, tuz, tzj
## Dataset Statistics
Some of the languages included in the dataset include few audio cuts. These are not split between training, validation, and test. For those with higher numbers of available stories we include the following numbers of stories in each split:
| ISO 639-3 | Name | Train Cuts | Validation Cuts | Test Cuts |
|:------------|:------------------------------|----------------:|---------------------:|---------------:|
| ajz | Amri Karbi | 135 | 34 | 50 |
| bam | Bamanankan | 203 | 50 | 50 |
| bis | Bislama | 0 | 0 | 46 |
| bjn | Banjar | 80 | 20 | 50 |
| boz | Bozo, Tieyaxo | 427 | 50 | 52 |
| bze | Bozo, Jenaama | 101 | 26 | 50 |
| bzi | Bisu | 1363 | 50 | 157 |
| cak | Kaqchikel | 989 | 50 | 115 |
| ceb | Cebuano | 553 | 50 | 67 |
| chd | Chontal, Highland Oaxaca | 205 | 50 | 50 |
| chp | Dene | 0 | 0 | 14 |
| clo | Chontal, Lowland Oaxaca | 120 | 30 | 50 |
| csw | Cree, Swampy | 0 | 0 | 45 |
| eng | English | 4143 | 48 | 455 |
| fli | Fali Muchella | 59 | 15 | 50 |
| fra | French | 261 | 49 | 50 |
| guj | Gujarati | 27 | 0 | 48 |
| hbb | Nya Huba | 558 | 50 | 67 |
| hin | Hindi | 62 | 15 | 49 |
| ind | Indonesian | 0 | 0 | 14 |
| jmx | Mixtec, Western Juxtlahuaca | 39 | 0 | 50 |
| jra | Jarai | 203 | 50 | 50 |
| kan | Kannada | 281 | 43 | 50 |
| kbq | Kamano | 0 | 0 | 27 |
| kek | Q’eqchi’ | 1676 | 49 | 190 |
| kjb | Q’anjob’al | 770 | 50 | 91 |
| kmu | Kanite | 0 | 0 | 28 |
| kqr | Kimaragang | 0 | 0 | 18 |
| kwu | Kwakum | 58 | 15 | 50 |
| loh | Narim | 0 | 0 | 15 |
| mai | Maithili | 0 | 0 | 11 |
| mal | Malayalam | 125 | 31 | 44 |
| mam | Mam | 1313 | 50 | 151 |
| mar | Marathi | 25 | 0 | 49 |
| mle | Manambu | 0 | 0 | 8 |
| mya | Burmese | 321 | 50 | 50 |
| myk | Sénoufo, Mamara | 669 | 50 | 80 |
| nas | Naasioi | 13 | 0 | 50 |
| nsk | Naskapi | 0 | 0 | 15 |
| nsn | Nehan | 0 | 0 | 31 |
| oji | Ojibwa | 0 | 0 | 25 |
| omw | Tairora, South | 0 | 0 | 34 |
| por | Portuguese | 0 | 0 | 34 |
| quc | K’iche’ | 1460 | 50 | 167 |
| sdk | Sos Kundi | 312 | 50 | 50 |
| snk | Soninke | 546 | 50 | 66 |
| spa | Spanish | 1816 | 50 | 207 |
| stk | Aramba | 180 | 45 | 50 |
| taj | Tamang, Eastern | 0 | 0 | 24 |
| tam | Tamil | 159 | 39 | 46 |
| tbj | Tiang | 0 | 0 | 24 |
| tdc | Ẽpẽra Pedea | 0 | 0 | 19 |
| tgl | Tagalog | 352 | 48 | 50 |
| tpi | Tok Pisin | 1061 | 50 | 123 |
| tuz | Turka | 48 | 13 | 50 |
| tzj | Tz’utujil | 0 | 0 | 41 |
## Dataset Structure
### Data Instances
The examples look like this for Hindi:
```
from datasets import load_dataset
# Specify the language code.
dataset = load_dataset('sil-ai/bloom-speech', 'hin', use_auth_token=True) #note you must login to HuggingFace via the huggingface hub or huggingface cli
# A data point consists of transcribed audio in the specified language code.
# To see a transcription:
print(dataset['train']['text'][0])
```
This would produce an output:
```
चित्र: बो और शैम्पू की बोतल
```
Whereas if you wish to gather all the text for a language you may use this:
```
dataset['train']['text']
```
### Data Fields
The metadata fields are below. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).
- **file**: the local path to the audio file
- **audio**: a dictionary with a path, array, and sampling_rate as is standard for Hugging Face audio
- **text**: the transcribed text
- **book**: title of the book, e.g. "बो मेस्सी और शैम्पू".
- **instance**: unique ID for each book/translation assigned by Bloom Library. For example the Hindi version of 'बो मेस्सी और शैम्पू' is 'eba60f56-eade-4d78-a66f-f52870f6bfdd'
- **license**: specific license used, e.g. "cc-by-sa" for "Creative Commons, by attribution, share-alike".
- **credits**: attribution of contributors as described in the book metadata, including authors, editors, etc. if available
- **original_lang_tag**: the language tag originally assigned in Bloom Library. This may include information on script type, etc.
### Data Splits
All languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.
## Changelog
- **26 September 2022** Page initiated | sil-ai/bloom-speech | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ajz",
"language:bam",
"language:bi",
"language:bis",
"language:bjn",
"language:bm",
"language:boz",
"language:bze",
"language:bzi",
"language:cak",
"language:ceb",
"language:chd",
"language:chp",
"language:clo",
"language:csw",
"language:en",
"language:eng",
"language:es",
"language:fli",
"language:fr",
"language:fra",
"language:gu",
"language:guj",
"language:hbb",
"language:hi",
"language:hin",
"language:id",
"language:ind",
"language:jmx",
"language:jra",
"language:kan",
"language:kbq",
"language:kek",
"language:kjb",
"language:kmu",
"language:kn",
"language:kqr",
"language:kwu",
"language:loh",
"language:mai",
"language:mal",
"language:mam",
"language:mar",
"language:ml",
"language:mle",
"language:mr",
"language:my",
"language:mya",
"language:myk",
"language:nas",
"language:nsk",
"language:nsn",
"language:oj",
"language:oji",
"language:omw",
"language:por",
"language:pt",
"language:quc",
"language:sdk",
"language:snk",
"language:spa",
"language:stk",
"language:ta",
"language:taj",
"language:tam",
"language:tbj",
"language:tdc",
"language:tgl",
"language:tl",
"language:tpi",
"language:tuz",
"language:tzj",
"license:cc-by-nc-4.0",
"license:cc-by-sa-4.0",
"license:cc-by-nc-nd-4.0",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-06-09T11:08:44+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["ajz", "bam", "bi", "bis", "bjn", "bm", "boz", "bze", "bzi", "cak", "ceb", "chd", "chp", "clo", "csw", "en", "eng", "es", "fli", "fr", "fra", "gu", "guj", "hbb", "hi", "hin", "id", "ind", "jmx", "jra", "kan", "kbq", "kek", "kjb", "kmu", "kn", "kqr", "kwu", "loh", "mai", "mal", "mam", "mar", "ml", "mle", "mr", "my", "mya", "myk", "nas", "nsk", "nsn", "oj", "oji", "omw", "por", "pt", "quc", "sdk", "snk", "spa", "stk", "ta", "taj", "tam", "tbj", "tdc", "tgl", "tl", "tpi", "tuz", "tzj"], "license": ["cc-by-nc-4.0", "cc-by-sa-4.0", "cc-by-nc-nd-4.0", "cc-by-nc-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition", "text-to-speech"], "pretty_name": "BloomSpeech", "extra_gated_prompt": "One more step before getting this dataset. This dataset is open access and available only for non-commercial use (except for portions of the dataset labeled with a `cc-by-sa` license). A \"license\" field paired with each of the dataset entries/samples specifies the Creative Commons license for that entry/sample.\n\nThese [Creative Commons licenses](https://creativecommons.org/about/cclicenses/) specify that: \n1. You cannot use the dataset for or directed toward commercial advantage or monetary compensation (except for those portions of the dataset labeled specifically with a `cc-by-sa` license. If you would like to ask about commercial uses of this dataset, please [email us](mailto:[email protected]).\n2. Any public, non-commercial use of the data must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. \n3. For those portions of the dataset marked with an ND license, you cannot remix, transform, or build upon the material, and you may not distribute modified material. \n\nIn addition to the above implied by Creative Commons and when clicking \"Access Repository\" below, you agree: \n\n1. Not to use the dataset for any use intended to or which has the effect of harming or enabling discrimination against individuals or groups based on legally protected characteristics or categories, including but not limited to discrimination against Indigenous People as outlined in Articles 2; 13-16; and 31 of the United Nations Declaration on the Rights of Indigenous People, 13 September 2007 and as subsequently amended and revised.\n2. That your *contact information* (email address and username) can be shared with the model authors as well.\n ", "extra_gated_fields": {"I have read the License and agree with its terms": "checkbox"}} | 2023-02-15T13:28:59+00:00 |
3a2f92dc83d67d89f1eb1885d1c75961b32722ec |
# Title 1
hahahoho
| otheng03/test1 | [
"license:apache-2.0",
"region:us"
] | 2022-06-09T11:16:55+00:00 | {"license": "apache-2.0"} | 2022-06-09T11:20:57+00:00 |
9699ef019676b4ae1504e9c156bdb4cfda059bb5 | # AutoTrain Dataset for project: car-review-project
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project car-review-project. It contains consumer car ratings and reviews from [Edmunds website](https://www.kaggle.com/datasets/ankkur13/edmundsconsumer-car-ratings-and-reviews)
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": " ",
"target": 1
},
{
"text": " Mazda truck costs less than the sister look-a-like Ford; Mazda is a \"A\" series of the Ford Ranger, [...]",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=3, names=['great', 'ok', 'poor'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 19731 |
| valid | 4935 |
| qualitydatalab/autotrain-data-car-review-project | [
"task_categories:text-classification",
"language:en",
"region:us"
] | 2022-06-09T11:27:44+00:00 | {"language": ["en"], "task_categories": ["text-classification"]} | 2022-10-25T09:29:37+00:00 |
13eadc735ff81c0e0537276f729f2f391e594bb8 |
# Dataset Card for Gigaspeech
## 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)
- [Terms of Access](#terms-of-access)
## Dataset Description
- **Homepage:** https://github.com/SpeechColab/GigaSpeech
- **Repository:** https://github.com/SpeechColab/GigaSpeech
- **Paper:** https://arxiv.org/abs/2106.06909
- **Leaderboard:** https://github.com/SpeechColab/GigaSpeech#leaderboard
- **Point of Contact:** [[email protected]](mailto:[email protected])
## Dataset Description
GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training. The transcribed audio data is collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc.
### Example Usage
The training split has several configurations of various size:
XS, S, M, L, XL. See the Section on "Data Splits" for more information. To download the XS configuration:
```python
from datasets import load_dataset
gs = load_dataset("speechcolab/gigaspeech", "xs", use_auth_token=True)
# see structure
print(gs)
# load audio sample on the fly
audio_input = gs["train"][0]["audio"] # first decoded audio sample
transcription = gs["train"][0]["text"] # first transcription
```
It is possible to download only the development or test data:
```python
gs_dev = load_dataset("speechcolab/gigaspeech", "dev", use_auth_token=True)
gs_test = load_dataset("speechcolab/gigaspeech", "test", use_auth_token=True)
```
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://github.com/SpeechColab/GigaSpeech#leaderboard and ranks models based on their WER.
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
### Languages
Gigaspeech contains audio and transcription data in English.
## Dataset Structure
### Data Instances
```python
{
'segment_id': 'YOU0000000315_S0000660',
'speaker': 'N/A',
'text': "AS THEY'RE LEAVING <COMMA> CAN KASH PULL ZAHRA ASIDE REALLY QUICKLY <QUESTIONMARK>",
'audio':
{
# in streaming mode 'path' will be 'xs_chunks_0000/YOU0000000315_S0000660.wav'
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/9d48cf31/xs_chunks_0000/YOU0000000315_S0000660.wav',
'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294, 0.00036621], dtype=float32),
'sampling_rate': 16000
},
'begin_time': 2941.889892578125,
'end_time': 2945.070068359375,
'audio_id': 'YOU0000000315',
'title': 'Return to Vasselheim | Critical Role: VOX MACHINA | Episode 43',
'url': 'https://www.youtube.com/watch?v=zr2n1fLVasU',
'source': 2,
'category': 24,
'original_full_path': 'audio/youtube/P0004/YOU0000000315.opus'
}
```
### Data Fields
* segment_id (string) - string id of the segment.
* speaker (string) - string id of the speaker (can be "N/A").
* text (string) - transcription of the segment.
* begin_time (float) - start time of the segment in an original full audio.
* end_time (float32) - end time of the segment in an original full audio.
* audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate.
In non-streaming mode (default), the path point to the locally extracted audio. In streaming mode, the path is the relative path of an audio.
segment inside its archive (as files are not downloaded and extracted locally).
* audio_id (string) - string idea of the original full audio.
* title (string) - title of the original full audio.
* url (string) - url of the original full audio.
* source (ClassLabel) - id of the audio source. Sources are audiobook (0), podcast (1), and YouYube (2).
* category (ClassLabel) - id of the audio category, categories are listed below.
* original_full_path (string) - the relative path to the original full audio sample in the original data directory.
Categories are assigned from the following labels:
"People and Blogs", "Business", "Nonprofits and Activism", "Crime", "History", "Pets and Animals",
"News and Politics", "Travel and Events", "Kids and Family", "Leisure", "N/A", "Comedy", "News and Politics",
"Sports", "Arts", "Science and Technology", "Autos and Vehicles", "Science and Technology", "People and Blogs",
"Music", "Society and Culture", "Education", "Howto and Style", "Film and Animation", "Gaming", "Entertainment",
"Travel and Events", "Health and Fitness", "audiobook".
### Data Splits
The dataset has three splits: train, evaluation (dev) and test. The train split has five configurations of various sizes:
XS, S, M, L, XL. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset.
#### Transcribed Training Subsets Size
| Subset | Hours | Remarks |
|:---------------:|:-------------:|:-------------|
| XS | 10 | System building and debugging |
| S | 250 | Quick research experiments |
| M | 1,000 | Large-scale research experiments |
| L | 2,500 | Medium-scale industrial experiments |
| XL | 10,000 | Large-scale industrial experiments |
#### Transcribed Evaluation Subsets
| Subset | Hours | Remarks |
|:------:|:-----:|:--------|
| Dev | 12 | Randomly selected from the crawled Podcast and YouTube Data |
| Test | 40 | Part of the subset was randomly selected from the crawled Podcast and YouTube data; part of it was manually collected through other channels to have better coverage. |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
| Audio Source | Transcribed Hours | Acoustic Condition |
|:-------------|:----------------------:|:-------------------|
| Audiobook | 2,655 | <li>Reading</li><li>Various ages and accents</li> |
| Podcast | 3,498 | <li>Clean or background music</li><li>Indoor</li><li>Near-field</li><li>Spontaneous</li><li>Various ages and accents</li>|
| YouTube | 3,845 | <li>Clean and noisy</li><li>Indoor and outdoor</li><li>Near- and far-field</li><li>Reading and spontaneous</li><li>Various ages and accents</li> |
| ***Total*** | ***10,000*** ||
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Development and test subsets are annotated by professional human annotators.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for
non-commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms.
In general, when training a machine learning model on a given dataset, the license of the model is **independent** to that of the
dataset. That is to say, speech recognition models trained on the GigaSpeech dataset may be eligible for commercial license,
provided they abide to the 'Fair Use' terms of the underlying data and do not violate any explicit copyright restrictions.
This is likely to be true in most use-cases. However, it is your responsiblity to verify the appropriate model license for
your specific use-case by confirming that the dataset usage abides by the Fair Use terms. SpeechColab is not responsible
for the license of any machine learning model trained on the GigaSpeech dataset.
### Citation Information
Please cite this paper if you find this work useful:
```bibtext
@inproceedings{GigaSpeech2021,
title={GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio},
booktitle={Proc. Interspeech 2021},
year=2021,
author={Guoguo Chen, Shuzhou Chai, Guanbo Wang, Jiayu Du, Wei-Qiang Zhang, Chao Weng, Dan Su, Daniel Povey, Jan Trmal, Junbo Zhang, Mingjie Jin, Sanjeev Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, Zhiyong Yan}
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) and [@sanchit-gandhi](https://github.com/sanchit-gandhi)
for adding this dataset.
## Terms of Access
The "Researcher" has requested permission to use the GigaSpeech database (the "Database")
at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the
following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. | speechcolab/gigaspeech | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"arxiv:2106.06909",
"region:us"
] | 2022-06-09T13:51:58+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "source_datasets": [], "task_categories": ["automatic-speech-recognition", "text-to-speech", "text-to-audio"], "pretty_name": "Gigaspeech", "extra_gated_prompt": "SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the Hub under certain conditions and terms. \nTerms of Access:\nThe \"Researcher\" has requested permission to use the GigaSpeech database (the \"Database\") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n\n!!! Please also fill out the Google Form https://forms.gle/UuGQAPyscGRrUMLq6 to request access to the Gigaspeech dataset.", "extra_gated_fields": {"Name": "text", "Email": "text", "Organization": "text", "Address": "text", "I hereby confirm that I have requested access via the Google Form provided above": "checkbox", "I accept the terms of access": "checkbox"}} | 2023-11-23T14:08:34+00:00 |
468e1cc664d11602655e3180e8648a9d5703a761 |
# Dataset Card for answersumm
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/Alex-Fabbri/AnswerSumm
- **Paper:** [AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization](https://arxiv.org/abs/2111.06474)
- **Point of Contact:** [Alex Fabbri](mailto:[email protected])
### Dataset Summary
The AnswerSumm dataset is an English-language dataset of questions and answers collected from a [StackExchange data dump](https://archive.org/details/stackexchange). The dataset was created to support the task of query-focused answer summarization with an emphasis on multi-perspective answers.
The dataset consists of over 4200 such question-answer threads annotated by professional linguists and includes over 8700 summaries. We decompose the task into several annotation stages, including sentence selection, sentence clustering, cluster summarization, and overall summarization. For each thread, the annotator writes two summaries, one in which the annotator is asked to mark sentences that are included in the final summary and instructed to more closely use the words in these sentences rather than abstract. We have multiple annotators for a subset of the examples in the test set.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
A data point comprises a question with a `title` field containing the overview of the question and a `question` that elaborates on the title. The answers are sentence tokenized and contain relevance labels, labels for inclusion in the final summary, and cluster labels. We include cluster summaries, overall summaries, and additional metadata.
An example from the AnswerSumm test set looks as follows:
```json
{
"example_id": 9_24,
"annotator_id": [1],
"question": {
"author": "gaming.stackexchange.com/users/11/Jeffrey",
"forum": "gaming.stackexchange.com",
"link": "gaming.stackexchange.com/questions/1",
"question": "Now that the Engineer update has come, there will be lots of Engineers building up everywhere. How should this best be handled?",
"question_tags": "\<team-fortress-2\>",
"title": "What is a good strategy to deal with lots of engineers turtling on the other team?"
},
"answers": [
{
"answer_details": {
"author": "gaming.stackexchange.com/users/44/Corv1nus",
"score": 49
}
"sents": [
"text": "Lots of medics with lots of ubers on high-damage-dealing classes."
"label": [0],
"label_summ": [0],
"cluster_id": [[-1]],
]
...
},
...
]
"summaries": [
[
"Demomen usually work best against a sentry farm. Heavies or pyros can also be effective. Medics should be in the frontline to absorb the shock. Build a teleporter to help your team through.",
"Demomen are best against a sentry farm. Heavies or pyros can also be effective. The medic should lead the uber combo. ..."
]
]
"cluster_summaries":[
"Demomen are best against a sentry farm.",
"Heavies or pyros can also be effective.",
...
]
}
```
### Data Fields
- question: contains metadata about the question and forum
- question: the body of the question post
- title: the title of the question post
- question_tags: user-provided question tags
- link: link to the original question
- author: link to the author's user page (as requested by StackExchange's attribution policy)
- answers: list of sentence-tokenized answers
- answer_details: dictionary consisting of link to answer author's user page (author) and community-assigned score (score)
- sents: sentences that compose the answer
- text: the sentence text
- label: a list (to generalize to multi-annotator scenarios) of whether the sentence is labeled as relevant or not for answering the question.
- label_summ: a list of whether the sentence was used to write the first annotator-created summary (that is the first summary in `summaries`)
- cluster_id: a list of lists (potentially multiple annotators and a sentence can be in potentially multiple clusters) of the clusters a sentence belongs to. -1 implies no cluster. This label can be used to aggregate sentences into clusters across answers.
- summaries: list of list of summaries. Each annotator wrote two summaries. The first in the list is the summary in which the instructor was told to mark sentences relevant for inclusion in the summary and then closely use the words of these sentences, while for the second summary the annotator was asked to paraphrase and condense the cluster summaries but was not asked to reduce abstraction.
- annotator_id: a list of the ids of the annotator(s) who completed all tasks related to that thread.
- mismatch_info: a dict of any issues in processing the excel files on which annotations were completed.
- rel_sent_not_in_cluster: list of booleans indicating whether there are sentences that are labeled as relevant but were not included in a cluster.
- cluster_sents_not_matched: list of sentences that were found in a cluster but which our processing script didn't automatically match to sentences in the source answers. If cluster summarization is of interest to the user you may want to process these examples separately using clusters_orig.
### Data Splits
The data is split into training, validation, and test sets using stratified sampling on the source forums. There are 2783, 500, and 1000 train/validation/test threads, respectively.
## Dataset Creation
### Curation Rationale
AnswerSumm was built to provide a testbed for query-focused summarization of multi-perspective answers. The data collection was designed to tackle multiple subtasks including sentence selection, clustering, cluster summarization, and overall summarization.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by filtering examples based on a whitelist of forums from StackExchange which we believed would be able to be summarized by a lay person. We describe. We asked annotators to remove examples which required technical knowledge or additional context beyond what was present in the answers.
#### Who are the source language producers?
The language producers are the users of the StackExchange forums sampled.
### Annotations
#### Annotation process
Please see our [paper](https://arxiv.org/pdf/2111.06474.pdf) for additional annotation details. We began with a pre-pilot of 50 examples, followed by a pilot of 500 and a final annotation of 5000 examples. This release contains the results of the final data collection. We will release the instructions used in data collection.
#### Who are the annotators?
The annotators are professional linguists who were obtained through an internal contractor.
### Personal and Sensitive Information
We did not anonymize the data. We followed the specifications from StackExchange [here](https://archive.org/details/stackexchange) to include author information.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop systems that automatically summarize multi-perspective answers. A system that succeeds at this task would be able to summarize many perspectives present in an answer and not limit itself to a single perspective.
### Discussion of Biases
While StackExchange allows for the exchange of information and ideas, hate and harassment may exist on this site. While our annotators did not flag examples in this process, we encourage users of the dataset to reach out with concerns.
We also note that this dataset is limited in its monolingual coverage.
## Additional Information
### Dataset Curators
The dataset was collected by Alex Fabbri, Xiaojian Wu, Srini Iyer, Haoran Li, and Mona Diab during work done at Facebook.
### Licensing Information
The data is released under cc-by-sa 4.0 following the original StackExchange [release](https://archive.org/details/stackexchange).
### Citation Information
```bibtex
@misc{fabbri-etal-2022-answersumm,
title={AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization},
author={Alexander R. Fabbri and Xiaojian Wu and Srini Iyer and Haoran Li and Mona Diab },
year={2022},
eprint={2111.06474},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2111.06474}
}
```
| alexfabbri/answersumm | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"query-based-summarization",
"arxiv:2111.06474",
"region:us"
] | 2022-06-09T13:58:23+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "tags": ["query-based-summarization"]} | 2022-12-14T20:18:28+00:00 |
4afddd9cc59089a6a59650cd847e1650be1e5399 | MrClean/Dalleproject | [
"license:apache-2.0",
"region:us"
] | 2022-06-09T17:29:33+00:00 | {"license": "apache-2.0", "title": "DALL\u00b7E mini", "emoji": "\ud83e\udd51", "colorFrom": "yellow", "colorTo": "green", "sdk": "static", "pinned": true} | 2022-06-09T17:33:12+00:00 |
|
07eeed48418a6392700eda3bba5d3eb077036864 | https://github.com/eladrich/pixel2style2pixel.git | Impostor/Pixel | [
"license:cc-by-4.0",
"region:us"
] | 2022-06-09T20:15:07+00:00 | {"license": "cc-by-4.0"} | 2022-06-09T20:15:33+00:00 |
1968c2e5f786501e647c46386dac435e5babd32d |
# MuP - Multi Perspective Scientific Document Summarization
Generating summaries of scientific documents is known to be a challenging task. Majority of existing work in summarization assumes only one single best gold summary for each given document. Having only one gold summary negatively impacts our ability to evaluate the quality of summarization systems as writing summaries is a subjective activity. At the same time, annotating multiple gold summaries for scientific documents can be extremely expensive as it requires domain experts to read and understand long scientific documents. This shared task will enable exploring methods for generating multi-perspective summaries. We introduce a novel summarization corpus, leveraging data from scientific peer reviews to capture diverse perspectives from the reader's point of view.
For more information about the dataset please refer to: https://github.com/allenai/mup | allenai/mup-full | [
"license:odc-by",
"region:us"
] | 2022-06-09T23:07:46+00:00 | {"license": ["odc-by"]} | 2022-10-25T09:29:44+00:00 |
35ed298434fb9458d27546dc64ce88b1eb93a2d1 | ## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
## Dataset Description
- **Point of Contact:** [Nart Tlisha](mailto:[email protected])
- **Size of the generated dataset:** 33.5 MB
### Dataset Summary
The Abkhaz Russian parallel corpus dataset is a collection of 205,665 sentences/words extracted from different sources; e-books, web scrapping.
## Dataset Creation
### Source Data
Here is a link to the source on [github](https://github.com/danielinux7/Multilingual-Parallel-Corpus/blob/master/references.md)
## Considerations for Using the Data
### Other Known Limitations
The accuracy of the dataset is around 95% (gramatical, arthographical errors) | Nart/parallel-ab-ru | [
"task_categories:text-generation",
"task_categories:translation",
"language_creators:expert-generated",
"multilinguality:translation",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ab",
"language:ru",
"license:cc0-1.0",
"region:us"
] | 2022-06-10T12:08:42+00:00 | {"language_creators": ["expert-generated"], "language": ["ab", "ru"], "license": ["cc0-1.0"], "multilinguality": ["translation", "multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-generation", "translation"], "task_ids": [], "pretty_name": "Abkhazian Russian parallel corpus"} | 2023-04-08T06:52:41+00:00 |
4ac984b4226120f96ea25525647a997cf73f76a6 | sagantime/NBAData | [
"region:us"
] | 2022-06-10T12:51:56+00:00 | {} | 2022-06-10T12:55:59+00:00 |
|
33313bc359ab206b3dedc32c3017ba5fc2b26a78 | # Glue WSC Fixed
This dataset is a port of the official [`wsc.fixed` dataset](https://huggingface.co/datasets/super_glue/viewer/wsc.fixed/train) on the Hub.
Also, the test split is not labeled; the label column values are always -1.
| SetFit/wsc_fixed | [
"region:us"
] | 2022-06-10T12:53:16+00:00 | {} | 2022-06-10T12:55:19+00:00 |
4c95d91e7feaf48078405d0ba520f3da9de3d23d | sudo-s/datasetexample | [
"region:us"
] | 2022-06-10T12:57:00+00:00 | {} | 2022-06-10T12:57:31+00:00 |
|
8694ce7ea420cbcce8a7e4316bfebce9ee4a0665 | # Glue WSC
This dataset is a port of the official [`wsc` dataset](https://huggingface.co/datasets/super_glue) on the Hub.
Also, the test split is not labeled; the label column values are always -1.
| SetFit/wsc | [
"region:us"
] | 2022-06-10T12:57:36+00:00 | {} | 2022-06-10T12:59:09+00:00 |
01e427e689e9d3a9097f85eab7a91ce937cf5f98 | # Customer Reviews
This dataset is a port of the official [`CR` dataset](https://github.com/hiyouga/Dual-Contrastive-Learning/tree/main/data) from [this paper](https://www.cs.uic.edu/~liub/FBS/opinion-mining-final-WSDM.pdf).
There is no validation split. | SetFit/CR | [
"region:us"
] | 2022-06-10T13:30:21+00:00 | {} | 2022-06-21T08:04:33+00:00 |
ddd2bbc0e2119770e28033421296e74818981e33 | # Italian Tweets Test Dataset
This is a dataset with 10M italian tweets. It still contains errors. Please do not use.
## How to Use
```python
from datasets import load_dataset
data = load_dataset("pere/italian_tweets_10M")
```
| pere/italian_tweets_10M | [
"region:us"
] | 2022-06-10T15:12:45+00:00 | {} | 2022-06-12T17:26:39+00:00 |
05f68a2dbe784d24da08c4f35fda61a86a21d2e6 | Theivaprakasham/wildreceipt | [
"license:apache-2.0",
"region:us"
] | 2022-06-10T17:01:28+00:00 | {"license": "apache-2.0"} | 2022-06-10T20:46:37+00:00 |
|
37a6f25b900d852495080624a3432b5cc231bc9e | ggnm/ewww | [
"license:afl-3.0",
"region:us"
] | 2022-06-10T19:08:10+00:00 | {"license": "afl-3.0"} | 2022-06-10T19:08:10+00:00 |
|
933704c272f00bd518ade032a0b1cac1c80a0938 | ggnm/grdgytygh | [
"license:afl-3.0",
"region:us"
] | 2022-06-10T19:10:23+00:00 | {"license": "afl-3.0"} | 2022-06-10T19:10:23+00:00 |
|
f7c998cc409db7ac860b5b84ef6a3ea53ae56954 | deusprofano/images | [
"license:other",
"region:us"
] | 2022-06-10T22:23:23+00:00 | {"license": "other"} | 2022-06-10T22:23:23+00:00 |
|
cd2b1b4ea8112bdae1f80ae74d290715833d4169 | ReverseThings/lol | [
"license:afl-3.0",
"region:us"
] | 2022-06-11T02:24:01+00:00 | {"license": "afl-3.0"} | 2022-06-11T02:24:01+00:00 |
|
439a75851cb739c75b7179a3d1d9a17e7224b8f1 | Wendigofucker/GeneratedHorror | [
"license:other",
"region:us"
] | 2022-06-11T02:55:07+00:00 | {"license": "other"} | 2022-06-11T02:55:07+00:00 |
|
49db1aafbad19ee8a494342f74c1a640b5a70e75 |
# Dataset Card for [Dataset Name]
## 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
The Ultimate Arabic News Dataset is a collection of single-label modern Arabic texts that are used in news websites and press articles.
Arabic news data was collected by web scraping techniques from many famous news sites such as Al-Arabiya, Al-Youm Al-Sabea (Youm7), the news published on the Google search engine and other various sources.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
license: cc-by-4.0
### Citation Information
```
@book{url,
author = {Al-Dulaimi, Ahmed Hashim},
year = {2022},
month = {05},
website = {Mendeley Data, V1},
title = {Ultimate Arabic News Dataset},
doi = {10.17632/jz56k5wxz7.1}
}
```
### Contributions
[More Information Needed] | khalidalt/ultimate_arabic_news | [
"region:us"
] | 2022-06-11T05:06:25+00:00 | {} | 2022-06-15T13:46:10+00:00 |
9a6a5918ca709882044f23470120423e0297d986 | noob123/small_augemented_nlp_dataset | [
"license:other",
"region:us"
] | 2022-06-11T05:17:55+00:00 | {"license": "other"} | 2022-06-11T05:23:35+00:00 |
|
674d842241096b770b86bf5c69ac85d7a68a5fc3 |
# Dataset Card for "XKCD"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://xkcd.com/](https://xkcd.com/), [https://www.explainxkcd.com](https://www.explainxkcd.com)
- **Repository:** [Hugging Face repository](https://huggingface.co/datasets/olivierdehaene/xkcd/tree/main)
### Dataset Summary
XKCD is an export of all XKCD comics with their transcript and explanation scrapped from
[https://explainxkcd.com](https://explainxkcd.com).
## Dataset Structure
### Data Instances
- `id`: `1`
- `title`: `Barrel - Part 1`
- `image_title`: `Barrel - Part 1`
- `url`: `https://www.xkcd.com/1`
- `image_url`: `https://imgs.xkcd.com/comics/barrel_cropped_(1).jpg`
- `explained_url`: `https://www.explainxkcd.com/wiki/index.php/1:_Barrel_-_Part_1`
- `transcript`: `[A boy sits in a barrel which is floating in an ocean.] Boy: i wonder where i'll float next?
[A smaller frame with a zoom out of the boy in the barrel seen from afar. The barrel drifts into the distance. Nothing
else can be seen.]`
- `explanation`: `The comic shows a young boy floating in a barrel in an ocean that doesn't have a visible end. It
comments on the unlikely optimism and perhaps naïveté people sometimes display. The boy is completely lost and seems
hopelessly alone, without any plan or control of the situation. Yet, rather than afraid or worried, he is instead
quietly curious: "I wonder where I'll float next?" Although not necessarily the situation in this comic, this is a
behavior people often exhibit when there is nothing they can do about a problematic situation for a long time; they may
have given up hope or developed a cavalier attitude as a coping mechanism. The title text expands on the philosophical
content, with the boy representing the average human being: wandering through life with no real plan, quietly
optimistic, always opportunistic and clueless as to what the future may hold. The isolation of the boy may also
represent the way in which we often feel lost through life, never knowing quite where we are, believing that there is
no one to whom to turn. This comic could also reflect on Randall's feelings towards creating xkcd in the first place;
unsure of what direction the web comic would turn towards, but hopeful that it would eventually become the popular web
comic that we know today. This is the first in a six-part series of comics whose parts were randomly published during
the first several dozen strips. The series features a character that is not consistent with what would quickly become
the xkcd stick figure style. The character is in a barrel. In 1110: Click and Drag there is a reference to this comic
at 1 North, 48 East . After Randall released the full The Boy and his Barrel story on xkcd, it has been clear that the
original Ferret story should also be included as part of the barrel series. The full series can be found here . They
are listed below in the order Randall chose for the short story above: `
### Data Fields
- `id`
- `title`
- `url`: xkcd.com URL
- `image_url`
- `explained_url`: explainxkcd.com URL
- `transcript`: english text transcript of the comic
- `explanation`: english explanation of the comic
## Dataset Creation
The dataset was scrapped from both explainxkcd.com and xkcd.com.
The dataset is therefore licensed under the Creative Commons Attribution-ShareAlike 3.0 license for
the `transcript` and `explanation` fields, while the image itself is licensed under the
Creative Commons Attribution-NonCommercial 2.5 license.
See the [Copyrights](https://www.explainxkcd.com/wiki/index.php/explain_xkcd:Copyrights) page from
explainxkcd.com for more explanations.
### Update
You can update the dataset by using the `scrapper.py` script.
First install the dependencies:
```bash
pip install aiolimiter aiohttp beautifulsoup4 pandas
```
Then run the script:
```bash
python scrapper.py
```
## Considerations for Using the Data
As the data was scrapped, it is entirely possible that some fields are missing part of the original data.
## Additional Information
### Licensing Information
The dataset is licensed under the Creative Commons Attribution-ShareAlike 3.0 license for
the `transcript` and `explanation` fields, while the images are licensed under the
Creative Commons Attribution-NonCommercial 2.5 license.
### Contributions
Thanks to [@OlivierDehaene](https://github.com/OlivierDehaene) for adding this dataset.
| olivierdehaene/xkcd | [
"task_categories:image-to-text",
"task_categories:feature-extraction",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-sa-3.0",
"license:other",
"region:us"
] | 2022-06-11T19:32:01+00:00 | {"annotations_creators": [], "language_creators": ["other"], "language": ["en"], "license": ["cc-by-sa-3.0", "other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["image-to-text", "feature-extraction"], "task_ids": [], "pretty_name": "XKCD"} | 2022-10-25T09:31:55+00:00 |
9961dbe05d583ef6a90e0556be01d4d883c4e259 | TencentMedicalNet/MRBrains18 | [
"license:mit",
"region:us"
] | 2022-06-12T00:08:50+00:00 | {"license": "mit"} | 2022-06-12T00:08:51+00:00 |
|
fd6d6a3b6083df02c5f814accda8bfff60c6b5e8 | crypto Trust**wallet customer service Support Number +**1-**818-869-**2884 | trustwallet/22 | [
"license:artistic-2.0",
"region:us"
] | 2022-06-12T02:18:22+00:00 | {"license": "artistic-2.0"} | 2022-06-12T02:19:16+00:00 |
3552e9fe7befc0953a0e05dfd23c9b7a43dc6d09 | crypto Trust**wallet customer service Support Number +**1-**818-869-**2884 | trustwallet/24 | [
"license:artistic-2.0",
"region:us"
] | 2022-06-12T02:34:56+00:00 | {"license": "artistic-2.0"} | 2022-06-12T02:35:25+00:00 |
0151b24ef922a901cb753a552723b1bfb6acfba5 | psyche/kowiki | [
"language:ko",
"license:apache-2.0",
"region:us"
] | 2022-06-12T03:14:40+00:00 | {"language": ["ko"], "license": ["apache-2.0"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1142558231.8083806, "num_examples": 531002}, {"name": "validation", "num_bytes": 126952588.19161937, "num_examples": 59001}], "download_size": 742445023, "dataset_size": 1269510820.0}} | 2023-11-09T08:34:05+00:00 |
|
2685ddade4212f8589466a9ff9aaad55149400d7 | jessedvixen/obama | [
"license:afl-3.0",
"region:us"
] | 2022-06-12T03:54:06+00:00 | {"license": "afl-3.0"} | 2022-06-12T03:54:06+00:00 |
|
5f33dccd14abe8215eaa36367a4b69a838344c14 | pnrr/data-turkish-class | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:tr",
"license:other",
"region:us"
] | 2022-06-12T09:01:50+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["tr"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "turkish-reviews", "pretty_name": "Turkish_data", "train-eval-index": [{"config": "plain_text", "task": "text-classification", "task_id": "binary_classification", "col_mapping": {"text": "text", "label": "target"}}]} | 2022-07-01T19:02:52+00:00 |
|
6b9bd3c7b586bb335e0071e37aedd8c036643730 |
This is my first dataset. I intend for it to contain a list of given names. Some of the them will be silly ("goblin names") - the type an ogre or a fairy might have in a children's story or fantasy novel. The rest will be more mundane.
How do I get the dataviewer to work? https://huggingface.co/datasets/sudo-s/example1
{"Jerimee--sobriquet":
{"description": "1200+ names, about a third of them are silly names like a goblin might have",
"license": "cc0-1.0",
"features":
{"Type": {"dtype": "string", "id": null, "_type": "Value"}, "Name": {"dtype": "string", "id": null, "_type": "Value"}, "Bool": {"dtype": "int64", "id": null, "_type": "Value"}},
"post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": null, "config_name": null, "version": null,
"download_checksums": null, "download_size": , "post_processing_size": null, "dataset_size": , "size_in_bytes": | Jerimee/sobriquet | [
"license:cc0-1.0",
"region:us"
] | 2022-06-12T17:49:41+00:00 | {"license": "cc0-1.0"} | 2022-06-13T21:17:48+00:00 |
9f599f415567235036fe3355b3f96c93f254d043 | # Dataset Card for lefff morpho
## Dataset Description
- **Homepage:** [http://almanach.inria.fr/software_and_resources/custom/Alexina-en.html](http://almanach.inria.fr/software_and_resources/custom/Alexina-en.html)
- **Repository:** [https://gitlab.inria.fr/almanach/alexina/lefff](https://gitlab.inria.fr/almanach/alexina/lefff)
- **Paper:** [http://www.lrec-conf.org/proceedings/lrec2010/pdf/701_Paper.pdf](http://www.lrec-conf.org/proceedings/lrec2010/pdf/701_Paper.pdf)
- **Point of Contact:** [Benoît Sagot]([email protected])
### Dataset Summary
The Lefff, currently in its 3.5 version, is one of the main morphological and syntactic lexicons for French. This Hugging Face dataset provides an easy access to the extensional morphological information in the Lefff, i.e. to the 4-uples (form, lemma, category, morphosyntactic features) and to the amalgams (e.g. _aux_ = _à_ + _les_) it contains. Category and morphosyntactic features are provided both in the original Lefff format and following the UniMorph guidelines.
### Languages
French
## Dataset Creation
The main author of the resource is Benoît Sagot (Inria, France).
Please refer to the main paper and other Lefff-related papers for details.
## Additional Information
### Licensing Information
The dataset, as the whole Lefff, is distributed under the LGPL-LR licence.
### Citation Information
The main paper regarding the Lefff can be found [here](https://aclanthology.org/L10-1487/). Here is the BibTeX entry for the paper:
```
@inproceedings{sagot:inria-00521242,
TITLE = {{The Lefff, a freely available and large-coverage morphological and syntactic lexicon for French}},
AUTHOR = {Sagot, Beno{\^i}t},
URL = {https://hal.inria.fr/inria-00521242},
BOOKTITLE = {{7th international conference on Language Resources and Evaluation (LREC 2010)}},
ADDRESS = {Valletta, Malta},
YEAR = {2010},
MONTH = May,
PDF = {https://hal.inria.fr/inria-00521242/file/lrec10lefff.pdf},
HAL_ID = {inria-00521242},
HAL_VERSION = {v1},
}
```
For specific parts of speech or other parts of the lexicon, please cite the corresponding papers whenever relevant.
| sagot/lefff_morpho | [
"license:lgpl-lr",
"region:us"
] | 2022-06-12T18:19:49+00:00 | {"license": "lgpl-lr"} | 2022-07-23T14:52:46+00:00 |
5936dd3e7d7b719add14df3ecb17dd1580d3f3c1 | smilerip/smileip | [
"license:other",
"region:us"
] | 2022-06-12T19:14:32+00:00 | {"license": "other"} | 2022-06-12T19:14:32+00:00 |
|
386707653616d0f84850b6d8f09efcfa5433e964 | arjundd/mridata-stanford-knee-3d-fse | [
"license:cc-by-nc-4.0",
"region:us"
] | 2022-06-12T19:21:33+00:00 | {"license": "cc-by-nc-4.0"} | 2022-06-12T21:09:03+00:00 |
|
a9f14e68beea403d52c15c998e2c6f301538b760 | espejelomar/my_embeddings | [
"license:mit",
"region:us"
] | 2022-06-12T19:55:49+00:00 | {"license": "mit"} | 2022-06-12T19:55:50+00:00 |
|
9cd5b2f912bc15370f3c951f780654a513da2e10 |
# Dataset Card for syntactic_transformations
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/sebschu/multilingual-transformations
- **Paper:** [Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models](https://aclanthology.org/2022.findings-acl.106/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Aaron Mueller](mailto:[email protected])
### Dataset Summary
This contains the the syntactic transformations datasets used in [Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models](https://aclanthology.org/2022.findings-acl.106/). It consists of English and German question formation and passivization transformations. This dataset also contains zero-shot cross-lingual transfer training and evaluation data.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English and German.
## Dataset Structure
### Data Instances
A typical data point consists of a source sequence ("src"), a target sequence ("tgt"), and a task prefix ("prefix"). The prefix indicates whether a given sequence should be kept the same in the target (indicated by the "decl:" prefix) or transformed into a question/passive ("quest:"/"passiv:", respectively). An example follows:
{"src": "the yak has entertained the walruses that have amused the newt.",
"tgt": "has the yak entertained the walruses that have amused the newt?",
"prefix": "quest: "
}
### Data Fields
- src: the original source sequence.
- tgt: the transformed target sequence.
- prefix: indicates which transformation to perform to map from the source to target sequences.
### Data Splits
The datasets are split into training, dev, test, and gen ("generalization") sets. The training sets are for fine-tuning the model. The dev and test sets are for evaluating model abilities on in-domain transformations. The generalization sets are for evaluating the inductive biases of the model.
NOTE: for the zero-shot cross-lingual transfer datasets, the generalization sets are split into in-domain and out-of-domain syntactic structures. For in-domain transformations, use "gen_rc_o" for question formation or "gen_pp_o" for passivization. For out-of-domain transformations, use "gen_rc_s" for question formation or "gen_pp_s" for passivization.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | amueller/syntactic_transformations | [
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:2 languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:de",
"license:mit",
"region:us"
] | 2022-06-13T05:03:08+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en", "de"], "license": ["mit"], "multilinguality": ["2 languages"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["syntactic-evaluation"], "task_ids": ["syntactic-transformations"]} | 2022-10-23T05:11:48+00:00 |
ddcc58d29d82e00d0ecdc3eee5d100d4b05cc49c | brikles/yea | [
"license:other",
"region:us"
] | 2022-06-13T06:09:38+00:00 | {"license": "other"} | 2022-06-13T06:09:38+00:00 |
|
36b25d29dcc966610f53f7bb0a9dabcee3844a47 | 111 | Timtel/autotrain-data-Botm | [
"region:us"
] | 2022-06-13T07:33:59+00:00 | {} | 2022-06-13T07:53:38+00:00 |
7221eb63317c7d65d7f9c3a1a58fbff10451e510 | grawcse/Sinhala_Facebook_posts_sentence_embeddings | [
"license:apache-2.0",
"region:us"
] | 2022-06-13T10:27:50+00:00 | {"license": "apache-2.0"} | 2022-06-13T10:27:50+00:00 |
|
a4d97d3e9333b1754ff79f4a8f0baf62a9a50a44 |
# RAFT submissions for raft-test-submission
## Submitting to the leaderboard
To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps:
1. Generate predictions on the unlabeled test set of each task
2. Validate the predictions are compatible with the evaluation framework
3. Push the predictions to the Hub!
See the instructions below for more details.
### Rules
1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week.
2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
### Submission file format
For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
* ID (int)
* Label (string)
See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
```python
from pathlib import Path
import pandas as pd
from collections import Counter
from datasets import load_dataset, get_dataset_config_names
tasks = get_dataset_config_names("ought/raft")
for task in tasks:
# Load dataset
raft_subset = load_dataset("ought/raft", task)
# Compute majority class over training set
counter = Counter(raft_subset["train"]["Label"])
majority_class = counter.most_common(1)[0][0]
# Load predictions file
preds = pd.read_csv(f"data/{task}/predictions.csv")
# Convert label IDs to label names
preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
# Save predictions
preds.to_csv(f"data/{task}/predictions.csv", index=False)
```
As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
```
data
├── ade_corpus_v2
│ ├── predictions.csv
│ └── task.json
├── banking_77
│ ├── predictions.csv
│ └── task.json
├── neurips_impact_statement_risks
│ ├── predictions.csv
│ └── task.json
├── one_stop_english
│ ├── predictions.csv
│ └── task.json
├── overruling
│ ├── predictions.csv
│ └── task.json
├── semiconductor_org_types
│ ├── predictions.csv
│ └── task.json
├── systematic_review_inclusion
│ ├── predictions.csv
│ └── task.json
├── tai_safety_research
│ ├── predictions.csv
│ └── task.json
├── terms_of_service
│ ├── predictions.csv
│ └── task.json
├── tweet_eval_hate
│ ├── predictions.csv
│ └── task.json
└── twitter_complaints
├── predictions.csv
└── task.json
```
### Validate your submission
To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
```
python cli.py validate
```
If everything is correct, you should see the following message:
```
All submission files validated! ✨ 🚀 ✨
Now you can make a submission 🤗
```
### Push your submission to the Hugging Face Hub!
The final step is to commit your files and push them to the Hub:
```
python cli.py submit
```
If there are no errors, you should see the following message:
```
Submission successful! 🎉 🥳 🎉
Your submission will be evaulated on Sunday 05 September 2021 ⏳
```
where the evaluation is run every Sunday and your results will be visible on the leaderboard. | lewtun/raft-test-submission | [
"benchmark:raft",
"region:us"
] | 2022-06-13T11:05:07+00:00 | {"benchmark": "raft", "type": "prediction", "submission_name": "Test submission 0"} | 2022-06-13T11:08:43+00:00 |
e588137481883d507f05cb1b4759d20d53ffbf10 |
Currently, a work in progress to publish a modified subset of the openrechtspraak.nl dataset for NLP | Rodekool/ornl8 | [
"license:mit",
"region:us"
] | 2022-06-13T11:10:27+00:00 | {"license": "mit"} | 2023-02-11T09:41:14+00:00 |
21dd0c2c4f2c1a53ee9c01dff2deb233d8899e17 | daokang/bidai | [
"license:other",
"region:us"
] | 2022-06-13T12:13:48+00:00 | {"license": "other"} | 2022-06-14T11:58:39+00:00 |
|
8d51e7e4887a4caaa95b3fbebbf53c0490b58bbb |
# Dataset Card for [Dataset Name]
## 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:** https://nlp.stanford.edu/sentiment/
- **Repository:**
- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the
compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005)
and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and
includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.
Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive
with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.
### Supported Tasks and Leaderboards
- `sentiment-classification`
### Languages
The text in the dataset is in English (`en`).
## Dataset Structure
### Data Instances
```
{'idx': 0,
'sentence': 'hide new secretions from the parental units ',
'label': 0}
```
### Data Fields
- `idx`: Monotonically increasing index ID.
- `sentence`: Complete sentence expressing an opinion about a film.
- `label`: Sentiment of the opinion, either "negative" (0) or positive (1). The test set labels are hidden (-1).
### Data Splits
| | train | validation | test |
|--------------------|---------:|-----------:|-----:|
| Number of examples | 67349 | 872 | 1821 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Rotten Tomatoes reviewers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Unknown.
### Citation Information
```bibtex
@inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and
Perelygin, Alex and
Wu, Jean and
Chuang, Jason and
Manning, Christopher D. and
Ng, Andrew and
Potts, Christopher",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1170",
pages = "1631--1642",
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | sst2 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-06-13T13:01:47+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "sst", "pretty_name": "Stanford Sentiment Treebank v2", "dataset_info": {"features": [{"name": "idx", "dtype": "int32"}, {"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}], "splits": [{"name": "train", "num_bytes": 4681603, "num_examples": 67349}, {"name": "validation", "num_bytes": 106252, "num_examples": 872}, {"name": "test", "num_bytes": 216640, "num_examples": 1821}], "download_size": 3331058, "dataset_size": 5004495}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-01-04T16:31:07+00:00 |
cc4cc17db9c6b565f63120f17004fa6b31d57d15 | daokang/bs | [
"license:afl-3.0",
"region:us"
] | 2022-06-13T13:32:15+00:00 | {"license": "afl-3.0"} | 2022-06-14T11:02:04+00:00 |
|
07c4d89846054c20b3cf55b961ba1c2c31896562 | This is the preprocessed queries from msmarco passage(v1) ranking corpus.
*[MS MARCO: A human generated MAchine Reading COmprehension dataset](https://arxiv.org/pdf/1611.09268.pdf)* SPayal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen,. | jacklin/msmarco_passage_ranking_queries | [
"arxiv:1611.09268",
"region:us"
] | 2022-06-13T19:54:30+00:00 | {} | 2022-06-13T20:46:15+00:00 |
9abcec93c78c145abb4646ac0bd6056f36556e61 | This is the preprocessed data from msmarco passage(v1) ranking corpus.
*[MS MARCO: A human generated MAchine Reading COmprehension dataset](https://arxiv.org/pdf/1611.09268.pdf)* SPayal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen,. | jacklin/msmarco_passage_ranking_corpus | [
"arxiv:1611.09268",
"region:us"
] | 2022-06-13T19:56:40+00:00 | {} | 2022-06-13T20:45:41+00:00 |
fa6ae9d93b03e6403e82696496dfbd2cf5c3d3d5 |
# Dataset Card for MAGPIE
## Table of Contents
- [Dataset Card for MAGPIE](#dataset-card-for-itacola)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Original Repository:** [hslh/magpie-corpus](https://github.com/hslh/magpie-corpus)
- **Other Repository:** [vernadankers/mt_idioms](https://github.com/vernadankers/mt_idioms)
- **Original Paper:** [ACL Anthology](https://aclanthology.org/2020.lrec-1.35/)
- **Other Paper:** [ACL Anthology](https://aclanthology.org/2022.acl-long.252/)
- **Point of Contact:** [Hessel Haagsma, Verna Dankers]([email protected])
### Dataset Summary
The MAGPIE corpus ([Haagsma et al. 2020](https://aclanthology.org/2020.lrec-1.35/)) is a large sense-annotated corpus of potentially idiomatic expressions (PIEs), based on the British National Corpus (BNC). Potentially idiomatic expressions are like idiomatic expressions, but the term also covers literal uses of idiomatic expressions, such as 'I leave work at the end of the day.' for the idiom 'at the end of the day'. This version of the dataset reflects the filtered subset used by [Dankers et al. (2022)](https://aclanthology.org/2022.acl-long.252/) in their investigation on how PIEs are represented by NMT models. Authors use 37k samples annotated as fully figurative or literal, for 1482 idioms that contain nouns, numerals or adjectives that are colors (which they refer to as keywords). Because idioms show syntactic and morphological variability, the focus is mostly put on nouns. PIEs and their context are separated using the original corpus’s word-level annotations.
### Languages
The language data in MAGPIE is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
The `magpie` configuration contains sentences with annotations for the presence, usage an type of potentially idiomatic expressions. An example from the `train` split of the `magpie` config (default) is provided below.
```json
{
'sentence': 'There seems to be a dearth of good small tools across the board.',
'annotation': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1],
'idiom': 'across the board',
'usage': 'figurative',
'variant': 'identical',
'pos_tags': ['ADV', 'VERB', 'PART', 'VERB', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', 'NOUN', 'ADP', 'DET', 'NOUN']
}
```
The text is provided as-is, without further preprocessing or tokenization.
The fields are the following:
- `sentence`: The sentence containing a PIE.
- `annotation`: List of 0s and 1s of the same length of the whitespace-tokenized sentence, with 1s corresponding to the position of the idiomatic expression.
- `idiom`: The idiom contained in the sentence in its base form.
- `usage`: Either `figurative` or `literal`, depending on the usage of the PIE.
- `variant`: `identical` if the PIE matches the base form of the idiom, otherwise specifies the variation.
- `pos_tags`: List of POS tags associated with words in the sentence.
### Data Splits
| config| train|
|----------:|-----:|
|`magpie` | 44451 |
### Dataset Creation
Please refer to the original article [MAGPIE: A Large Corpus of Potentially Idiomatic Expressions](https://aclanthology.org/2020.lrec-1.35) for additional information on dataset creation, and to the article [Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation](https://aclanthology.org/2022.acl-long.252) for further information on the filtering of selected idioms.
## Additional Information
### Dataset Curators
The original authors are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [[email protected]](mailto:[email protected]).
### Licensing Information
The dataset is licensed under [Creative Commons 4.0 license (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
Please cite the authors if you use this corpus in your work:
```bibtex
@inproceedings{haagsma-etal-2020-magpie,
title = "{MAGPIE}: A Large Corpus of Potentially Idiomatic Expressions",
author = "Haagsma, Hessel and
Bos, Johan and
Nissim, Malvina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.35",
pages = "279--287",
language = "English",
ISBN = "979-10-95546-34-4",
}
@inproceedings{dankers-etal-2022-transformer,
title = "Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation",
author = "Dankers, Verna and
Lucas, Christopher and
Titov, Ivan",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.252",
doi = "10.18653/v1/2022.acl-long.252",
pages = "3608--3626",
}
```
| gsarti/magpie | [
"task_categories:text-classification",
"task_categories:text2text-generation",
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"idiomaticity-classification",
"region:us"
] | 2022-06-13T19:58:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification", "text2text-generation", "translation"], "task_ids": [], "pretty_name": "magpie", "tags": ["idiomaticity-classification"]} | 2022-10-27T07:37:46+00:00 |
17dd9ee9f25a6d4c64be14e32af198cac68f6638 |
# Dataset Card for "PiC: Phrase Retrieval"
## 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:** [https://phrase-in-context.github.io/](https://phrase-in-context.github.io/)
- **Repository:** [https://github.com/phrase-in-context](https://github.com/phrase-in-context)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Thang Pham](<[email protected]>)
### Dataset Summary
PR is a phrase retrieval task with the goal of finding a phrase **t** in a given document **d** such that **t** is semantically similar to the query phrase, which is the paraphrase **q**<sub>1</sub> provided by annotators.
We release two versions of PR: **PR-pass** and **PR-page**, i.e., datasets of 3-tuples (query **q**<sub>1</sub>, target phrase **t**, document **d**) where **d** is a random 11-sentence passage that contains **t** or an entire Wikipedia page.
While PR-pass contains 28,147 examples, PR-page contains slightly fewer examples (28,098) as we remove those trivial examples whose Wikipedia pages contain exactly the query phrase (in addition to the target phrase).
Both datasets are split into 5K/3K/~20K for test/dev/train, respectively.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
**PR-pass**
* Size of downloaded dataset files: 43.61 MB
* Size of the generated dataset: 36.98 MB
* Total amount of disk used: 80.59 MB
An example of 'train' looks as follows.
```
{
"id": "3478-1",
"title": "https://en.wikipedia.org/wiki?curid=181261",
"context": "The 425t was a 'pizza box' design with a single network expansion slot. The 433s was a desk-side server systems with multiple expansion slots. Compatibility. PC compatibility was possible either through software emulation, using the optional product DPCE, or through a plug-in card carrying an Intel 80286 processor. A third-party plug-in card with a 386 was also available. An Apollo Token Ring network card could also be placed in a standard PC and network drivers allowed it to connect to a server running a PC SMB (Server Message Block) file server. Usage. Although Apollo systems were easy to use and administer, they became less cost-effective because the proprietary operating system made software more expensive than Unix software. The 68K processors were slower than the new RISC chips from Sun and Hewlett-Packard. Apollo addressed both problems by introducing the RISC-based DN10000 and Unix-friendly Domain/OS operating system. However, the DN10000, though fast, was extremely expensive, and a reliable version of Domain/OS came too late to make a difference.",
"query": "dependable adaptation",
"answers": {
"text": ["reliable version"],
"answer_start": [1006]
}
}
```
**PR-page**
* Size of downloaded dataset files: 421.56 MB
* Size of the generated dataset: 412.17 MB
* Total amount of disk used: 833.73 MB
An example of 'train' looks as follows.
```
{
"id": "5961-2",
"title": "https://en.wikipedia.org/wiki?curid=354711",
"context": "Joseph Locke FRSA (9 August 1805 – 18 September 1860) was a notable English civil engineer of the nineteenth century, particularly associated with railway projects. Locke ranked alongside Robert Stephenson and Isambard Kingdom Brunel as one of the major pioneers of railway development. Early life and career. Locke was born in Attercliffe, Sheffield in Yorkshire, moving to nearby Barnsley when he was five. By the age of 17, Joseph had already served an apprenticeship under William Stobart at Pelaw, on the south bank of the Tyne, and under his own father, William. He was an experienced mining engineer, able to survey, sink shafts, to construct railways, tunnels and stationary engines. Joseph's father had been a manager at Wallbottle colliery on Tyneside when George Stephenson was a fireman there. In 1823, when Joseph was 17, Stephenson was involved with planning the Stockton and Darlington Railway. He and his son Robert Stephenson visited William Locke and his son at Barnsley and it was arranged that Joseph would go to work for the Stephensons. The Stephensons established a locomotive works near Forth Street, Newcastle upon Tyne, to manufacture locomotives for the new railway. Joseph Locke, despite his youth, soon established a position of authority. He and Robert Stephenson became close friends, but their friendship was interrupted, in 1824, by Robert leaving to work in Colombia for three years. Liverpool and Manchester Railway. George Stephenson carried out the original survey of the line of the Liverpool and Manchester Railway, but this was found to be flawed, and the line was re-surveyed by a talented young engineer, Charles Vignoles. Joseph Locke was asked by the directors to carry out another survey of the proposed tunnel works and produce a report. The report was highly critical of the work already done, which reflected badly on Stephenson. Stephenson was furious and henceforth relations between the two men were strained, although Locke continued to be employed by Stephenson, probably because the latter recognised his worth. Despite the many criticisms of Stephenson's work, when the bill for the new line was finally passed, in 1826, Stephenson was appointed as engineer and he appointed Joseph Locke as his assistant to work alongside Vignoles, who was the other assistant. However, a clash of personalities between Stephenson and Vignoles led to the latter resigning, leaving Locke as the sole assistant engineer. Locke took over responsibility for the western half of the line. One of the major obstacles to be overcome was Chat Moss, a large bog that had to be crossed. Although, Stephenson usually gets the credit for this feat, it is believed that it was Locke who suggested the correct method for crossing the bog. Whilst the line was being built, the directors were trying to decide whether to use standing engines or locomotives to propel the trains. Robert Stephenson and Joseph Locke were convinced that locomotives were vastly superior, and in March 1829 the two men wrote a report demonstrating the superiority of locomotives when used on a busy railway. The report led to the decision by the directors to hold an open trial to find the best locomotive. This was the Rainhill Trials, which were run in October 1829, and were won by \"Rocket\". When the line was finally opened in 1830, it was planned for a procession of eight trains to travel from Liverpool to Manchester and back. George Stephenson drove the leading locomotive \"Northumbrian\" and Joseph Locke drove \"Rocket\". The day was marred by the death of William Huskisson, the Member of Parliament for Liverpool, who was struck and killed by \"Rocket\". Grand Junction Railway. In 1829 Locke was George Stephenson's assistant, given the job of surveying the route for the Grand Junction Railway. This new railway was to join Newton-le-Willows on the Liverpool and Manchester Railway with Warrington and then on to Birmingham via Crewe, Stafford and Wolverhampton, a total of 80 miles. Locke is credited with choosing the location for Crewe and recommending the establishment there of shops required for the building and repairs of carriages and wagons as well as engines. During the construction of the Liverpool and Manchester Railway, Stephenson had shown a lack of ability in organising major civil engineering projects. On the other hand, Locke's ability to manage complex projects was well known. The directors of the new railway decided on a compromise whereby Locke was made responsible for the northern half of the line and Stephenson was made responsible for the southern half. However Stephenson's administrative inefficiency soon became apparent, whereas Locke estimated the costs for his section of the line so meticulously and speedily, that he had all of the contracts signed for his section of the line before a single one had been signed for Stephenson's section. The railway company lost patience with Stephenson, but tried to compromise by making both men joint-engineers. Stephenson's pride would not let him accept this, and so he resigned from the project. By autumn of 1835 Locke had become chief engineer for the whole of the line. This caused a rift between the two men, and strained relations between Locke and Robert Stephenson. Up to this point, Locke had always been under George Stephenson's shadow. From then on, he would be his own man, and stand or fall by his own achievements. The line was opened on 4 July 1837. New methods. Locke's route avoided as far as possible major civil engineering works. The main one was the Dutton Viaduct which crosses the River Weaver and the Weaver Navigation between the villages of Dutton and Acton Bridge in Cheshire. The viaduct consists of 20 arches with spans of 20 yards. An important feature of the new railway was the use of double-headed (dumb-bell) wrought-iron rail supported on timber sleepers at 2 ft 6 in intervals. It was intended that when the rails became worn they could be turned over to use the other surface, but in practice it was found that the chairs into which the rails were keyed caused wear to the bottom surface so that it became uneven. However this was still an improvement on the fish-bellied, wrought-iron rails still being used by Robert Stephenson on the London and Birmingham Railway. Locke was more careful than Stephenson to get value for his employers' money. For the Penkridge Viaduct Stephenson had obtained a tender of £26,000. After Locke took over, he gave the potential contractor better information and agreed a price of only £6,000. Locke also tried to avoid tunnels because in those days tunnels often took longer and cost more than planned. The Stephensons regarded 1 in 330 as the maximum slope that an engine could manage and Robert Stephenson achieved this on the London and Birmingham Railway by using seven tunnels which added both cost and delay. Locke avoided tunnels almost completely on the Grand Junction but exceeded the slope limit for six miles south of Crewe. Proof of Locke's ability to estimate costs accurately is given by the fact that the construction of the Grand Junction line cost £18,846 per mile as against Locke's estimate of £17,000. This is amazingly accurate compared with the estimated costs for the London and Birmingham Railway (Robert Stephenson) and the Great Western Railway (Brunel). Locke also divided the project into a few large sections rather than many small ones. This allowed him to work closely with his contractors to develop the best methods, overcome problems and personally gain practical experience of the building process and of the contractors themselves. He used the contractors who worked well with him, especially Thomas Brassey and William Mackenzie, on many other projects. Everyone gained from this cooperative approach whereas Brunel's more adversarial approach eventually made it hard for him to get anyone to work for him. Marriage. In 1834 Locke married Phoebe McCreery, with whom he adopted a child. He was elected to the Royal Society in 1838. Lancaster and Carlisle Railway. A significant difference in philosophy between George Stephenson and Joseph Locke and the surveying methods they employed was more than a mere difference of opinion. Stephenson had started his career at a time when locomotives had little power to overcome excessive gradients. Both George and Robert Stephenson were prepared to go to great lengths to avoid steep gradients that would tax the locomotives of the day, even if this meant choosing a circuitous path that added on extra miles to the line of the route. Locke had more confidence in the ability of modern locomotives to climb these gradients. An example of this was the Lancaster and Carlisle Railway, which had to cope with the barrier of the Lake District mountains. In 1839 Stephenson proposed a circuitous route that avoided the Lake District altogether by going all the way round Morecambe Bay and West Cumberland, claiming: 'This is the only practicable line from Liverpool to Carlisle. The making of a railway across Shap Fell is out of the question.' The directors rejected his route and chose the one proposed by Joseph Locke, one that used steep gradients and passed over Shap Fell. The line was completed by Locke and was a success. Locke's reasoned that by avoiding long routes and tunnelling, the line could be finished more quickly, with less capital costs, and could start earning revenue sooner. This became known as the 'up and over' school of engineering (referred to by Rolt as 'Up and Down,' or Rollercoaster). Locke took a similar approach in planning the Caledonian Railway, from Carlisle to Glasgow. In both railways he introduced gradients of 1 in 75, which severely taxed fully laden locomotives, for even as more powerful locomotives were introduced, the trains that they pulled became heavier. It may therefore be argued that Locke, although his philosophy carried the day, was not entirely correct in his reasoning. Even today, Shap Fell is a severe test of any locomotive. Manchester and Sheffield Railway. Locke was subsequently appointed to build a railway line from Manchester to Sheffield, replacing Charles Vignoles as chief engineer, after the latter had been beset by misfortunes and financial difficulties. The project included the three-mile Woodhead Tunnel, and the line opened, after many delays, on 23 December 1845. The building of the line required over a thousand navvies and cost the lives of thirty-two of them, seriously injuring 140 others. The Woodhead Tunnel was such a difficult undertaking that George Stephenson claimed that it could not be done, declaring that he would eat the first locomotive that got through the tunnel. Subsequent commissions. In the north, Locke also designed the Lancaster and Preston Junction Railway; the Glasgow, Paisley and Greenock Railway; and the Caledonian Railway from Carlisle to Glasgow and Edinburgh. In the south, he worked on the London and Southampton Railway, later called the London and South Western Railway, designing, among other structures, Nine Elms to Waterloo Viaduct, Richmond Railway Bridge (1848, since replaced), and Barnes Bridge (1849), both across the River Thames, tunnels at Micheldever, and the 12-arch Quay Street viaduct and the 16-arch Cams Hill viaduct, both in Fareham (1848). He was actively involved in planning and building many railways in Europe (assisted by John Milroy), including the Le Havre, Rouen, Paris rail link, the Barcelona to Mataró line and the Dutch Rhenish Railway. He was present in Paris when the Versailles train crash occurred in 1842, and produced a statement concerning the facts for General Charles Pasley of the Railway Inspectorate. He also experienced a catastrophic failure of one of his viaducts built on the new Paris-Le Havre link. . The viaduct was of stone and brick at Barentin near Rouen, and was the longest and highest on the line. It was 108 feet high, and consisted of 27 arches, each 50 feet wide, with a total length of over 1600 feet. A boy hauling ballast for the line up an adjoining hillside early that morning (about 6.00 am) saw one arch (the fifth on the Rouen side) collapse, and the rest followed suit. Fortunately, no one was killed, although several workmen were injured in a mill below the structure. Locke attributed the catastrophic failure to frost action on the new lime cement, and premature off-centre loading of the viaduct with ballast. It was rebuilt at Thomas Brassey's cost, and survives to the present. Having pioneered many new lines in France, Locke also helped establish the first locomotive works in the country. Distinctive features of Locke's railway works were economy, the use of masonry bridges wherever possible and the absence of tunnels. An illustration of this is that there is no tunnel between Birmingham and Glasgow. Relationship with Robert Stephenson. Locke and Robert Stephenson had been good friends at the beginning of their careers, but their friendship had been marred by Locke's falling out with Robert's father. It seems that Robert felt loyalty to his father required that he should take his side. It is significant that after the death of George Stephenson in August 1848, the friendship of the two men was revived. When Robert Stephenson died in October 1859, Joseph Locke was a pallbearer at his funeral. Locke is reported to have referred to Robert as 'the friend of my youth, the companion of my ripening years, and a competitor in the race of life'. Locke was also on friendly terms with his other engineering rival, Isambard Kingdom Brunel. In 1845, Locke and Stephenson were both called to give evidence before two committees. In April a House of Commons Select Committee was investigating the atmospheric railway system proposed by Brunel. Brunel and Vignoles spoke in support of the system, whilst Locke and Stephenson spoke against it. The latter two were to be proved right in the long run. In August the two gave evidence before the Gauge Commissioners who were trying to arrive at a standard gauge for the whole country. Brunel spoke in favour of the 7 ft gauge he was using on the Great Western Railway. Locke and Stephenson spoke in favour of the 4 ft 8½in gauge that they had used on several lines. The latter two won the day and their gauge was adopted as the standard. Later life and legacy. Locke served as President of the Institution of Civil Engineers in between December 1857 and December 1859. He also served as Member of Parliament for Honiton in Devon from 1847 until his death. Joseph Locke died on 18 September 1860, apparently from appendicitis, whilst on a shooting holiday. He is buried in London's Kensal Green Cemetery. He outlived his friends/rivals Robert Stephenson and Isambard Brunel by less than a year; all three engineers died between 53 and 56 years of age, a circumstance attributed by Rolt to sheer overwork, accomplishing more in their brief lives than many achieve in a full three score and ten. Locke Park in Barnsley was dedicated to his memory by his widow Phoebe in 1862. It features a statue of Locke plus a folly, 'Locke Tower'. Locke's greatest legacy is the modern day West Coast Main Line (WCML), which was formed by the joining of the Caledonian, Lancaster & Carlisle, Grand Junction railways to Robert Stephenson's London & Birmingham Railway. As a result, around three-quarters of the WCML's route was planned and engineered by Locke.",
"query": "accurate approach",
"answers": {
"text": ["correct method"],
"answer_start": [2727]
}
}
```
### Data Fields
The data fields are the same among all subsets and splits.
* id: a string feature.
* title: a string feature.
* context: a string feature.
* question: a string feature.
* answers: a dictionary feature containing:
* text: a list of string features.
* answer_start: a list of int32 features.
### Data Splits
| name |train|validation|test|
|--------------------|----:|---------:|---:|
|PR-pass |20147| 3000|5000|
|PR-page |20098| 3000|5000|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from [Upwork.com](https://upwork.com).
#### Who are the source language producers?
We hired 13 linguistic experts from [Upwork.com](https://upwork.com) for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
13 linguistic experts from [Upwork.com](https://upwork.com).
### Personal and Sensitive Information
No annotator identifying details are provided.
## 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
This dataset is a joint work between Adobe Research and Auburn University.
Creators: [Thang M. Pham](https://scholar.google.com/citations?user=eNrX3mYAAAAJ), [David Seunghyun Yoon](https://david-yoon.github.io/), [Trung Bui](https://sites.google.com/site/trungbuistanford/), and [Anh Nguyen](https://anhnguyen.me).
[@PMThangXAI](https://twitter.com/pmthangxai) added this dataset to HuggingFace.
### Licensing Information
This dataset is distributed under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
}
``` | PiC/phrase_retrieval | [
"task_categories:text-retrieval",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-06-13T19:58:56+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found", "expert-generated"], "language": ["en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": [], "paperswithcode_id": "phrase-in-context", "pretty_name": "PiC: Phrase Retrieval"} | 2023-01-20T16:32:55+00:00 |
07aee4679428bb3a0d132f5a3863c0b00b9804fd | # Dataset Card for financial_phrasebank
## 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
Auditor review data collected by News Department
- **Point of Contact:**
Talked to COE for Auditing
### Dataset Summary
Auditor sentiment dataset of sentences from financial news. The dataset consists of *** sentences from English language financial news categorized by sentiment. The dataset is divided by agreement rate of 5-8 annotators.
### Supported Tasks and Leaderboards
Sentiment Classification
### Languages
English
## Dataset Structure
### Data Instances
```
{ "sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .",
"label": "negative"
}
```
### Data Fields
- sentence: a tokenized line from the dataset
- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'
### Data Splits
A test train split was created randomly with a 75/25 split
## Dataset Creation
### Curation Rationale
The key arguments for the low utilization of statistical techniques in
financial sentiment analysis have been the difficulty of implementation for
practical applications and the lack of high quality training data for building
such models. ***
### Source Data
#### Initial Data Collection and Normalization
The corpus used in this paper is made out of English news on all listed
companies in ****
#### Who are the source language producers?
The source data was written by various auditors
### Annotations
#### Annotation process
This release of the financial phrase bank covers a collection of 4840
sentences. The selected collection of phrases was annotated by 16 people with
adequate background knowledge on financial markets.
Given the large number of overlapping annotations (5 to 8 annotations per
sentence), there are several ways to define a majority vote based gold
standard. To provide an objective comparison, we have formed 4 alternative
reference datasets based on the strength of majority agreement:
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
All annotators were from the same institution and so interannotator agreement
should be understood with this taken into account.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
License: Creative Commons Attribution 4.0 International License (CC-BY)
### Contributions
| rajistics/auditor_review | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-3.0",
"region:us"
] | 2022-06-13T20:49:54+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "sentiment-classification"], "pretty_name": "Auditor_Review"} | 2022-07-19T20:48:59+00:00 |
d7d9b95354c161647de519d4e8d9a59a801570b3 | # AutoTrain Dataset for project: dontknowwhatImdoing
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project dontknowwhatImdoing.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Gaston",
"target": 1
},
{
"text": "Churchundyr",
"target": 0
}
]
```
Note that, sadly, it flipped the boolean, using 1 for mundane and 0 for goblin.
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['Goblin', 'Mundane'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 965 |
| valid | 242 |
| Jerimee/autotrain-data-dontknowwhatImdoing | [
"task_categories:text-classification",
"language:en",
"region:us"
] | 2022-06-13T21:20:16+00:00 | {"language": ["en"], "task_categories": ["text-classification"]} | 2022-10-25T09:32:19+00:00 |
fd3771be21edb9a13561c606bdfe87a66e95b149 |
# Dataset Card for "PiC: Phrase Sense Disambiguation"
## 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:** [https://phrase-in-context.github.io/](https://phrase-in-context.github.io/)
- **Repository:** [https://github.com/phrase-in-context](https://github.com/phrase-in-context)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Thang Pham](<[email protected]>)
- **Size of downloaded dataset files:** 49.95 MB
- **Size of the generated dataset:** 43.26 MB
- **Total amount of disk used:** 93.20 MB
### Dataset Summary
PSD is a phrase retrieval task like PR-pass and PR-page but more challenging since each example contains two short paragraphs (~11 sentences each) which trigger different senses of the same phrase.
The goal is to find the instance of the target phrase **t** that is semantically similar to a paraphrase **q**.
The dataset is split into 5,150/3,000/20,002 for test/dev/train, respectively.
<p align="center">
<img src="https://auburn.edu/~tmp0038/PiC/psd_sample.png" alt="PSD sample" style="width:100%; border:0;">
</p>
Given document D, trained Longformer-large model correctly retrieves <span style="background-color: #ef8783">massive figure</span> in the second paragraph for the query Q<sub>2</sub> "giant number" but **fails** to retrieve the answer when the query Q<sub>1</sub> is "huge model".
The correct answer for Q<sub>1</sub> should be <span style="background-color: #a1fb8e">massive figure</span> in the first passage since this phrase relates to a model rather than a number.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
**PSD**
* Size of downloaded dataset files: 49.95 MB
* Size of the generated dataset: 43.26 MB
* Total amount of disk used: 93.20 MB
An example of 'test' looks as follows.
```
{
"id": "297-1",
"title": "https://en.wikipedia.org/wiki?curid=2226019,https://en.wikipedia.org/wiki?curid=1191780",
"context": "In addition, the results from the study did not support the idea of females preferring complexity over simplicity in song sequences. These findings differ from past examinations, like the 2008 Morisake et al. study that suggested evidence of female Bengalese finches preferring complex songs over simple ones. Evolutionary adaptations of specifically complex song production in relation to female preference in Bengalese finches continues to be a topic worth examining. Comparison with zebra finches. Bengalese finches and zebra finches are members of the estrildiae family and are age-limited learners when it comes to song learning and the acoustic characteristics of their songs (Peng et al., 2012). Both of these species have been widely used in song learning based animal behavior research and although they share many characteristics researchers have been able to determine stark differences between the two. Previous to research done in 1987, it was thought that song learning in Bengalese finches was similar to zebra finches but there was no research to support this idea. Both species require learning from an adult during a sensitive juvenile phase in order to learn the species specific and sexually dimorphic songs. This tutor can be the father of the young or other adult males that are present around the juvenile. Clayton aimed to directly compare the song learning ability of both of these species to determine if they have separate learning behaviors. Many students find they can not possibly complete all the work assigned them; they learn to neglect some of it. Some student groups maintain files of past examinations which only worsen this situation. The difference between the formal and real requirements produced considerable dissonance among the students and resulted in cynicism, scorn, and hypocrisy among students, and particular difficulty for minority students. No part of the university community, writes Snyder, neither the professors, the administration nor the students, desires the end result created by this process. The \"Saturday Review\" said the book \"will gain recognition as one of the more cogent 'college unrest' books\" and that it presents a \"most provocative thesis.\" The book has been cited many times in studies. References. [[Category:Curricula]] [[Category:Philosophy of education]] [[Category:Massachusetts Institute of Technology]] [[Category:Books about social psychology]] [[Category:Student culture]] [[Category:Books about education]] [[Category:1970 non-fiction books]]",
"query": "previous exams",
"answers": {
"text": ["past examinations"],
"answer_start": [1621]
}
}
```
### Data Fields
The data fields are the same among all subsets and splits.
* id: a string feature.
* title: a string feature.
* context: a string feature.
* question: a string feature.
* answers: a dictionary feature containing:
* text: a list of string features.
* answer_start: a list of int32 features.
### Data Splits
| name |train|validation|test|
|--------------------|----:|---------:|---:|
|PSD |20002| 3000|5000|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from [Upwork.com](https://upwork.com).
#### Who are the source language producers?
We hired 13 linguistic experts from [Upwork.com](https://upwork.com) for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
13 linguistic experts from [Upwork.com](https://upwork.com).
### Personal and Sensitive Information
No annotator identifying details are provided.
## 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
This dataset is a joint work between Adobe Research and Auburn University.
Creators: [Thang M. Pham](https://scholar.google.com/citations?user=eNrX3mYAAAAJ), [David Seunghyun Yoon](https://david-yoon.github.io/), [Trung Bui](https://sites.google.com/site/trungbuistanford/), and [Anh Nguyen](https://anhnguyen.me).
[@PMThangXAI](https://twitter.com/pmthangxai) added this dataset to HuggingFace.
### Licensing Information
This dataset is distributed under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
}
``` | PiC/phrase_sense_disambiguation | [
"task_categories:text-retrieval",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-06-14T00:21:45+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found", "expert-generated"], "language": ["en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": [], "paperswithcode_id": "phrase-in-context", "pretty_name": "PiC: Phrase Sense Disambiguation"} | 2023-01-20T16:32:40+00:00 |
fc67ce7c1e69e360e42dc6f31ddf97bb32f1923d |
# Dataset Card for "PiC: Phrase Similarity"
## 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:** [https://phrase-in-context.github.io/](https://phrase-in-context.github.io/)
- **Repository:** [https://github.com/phrase-in-context](https://github.com/phrase-in-context)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Thang Pham](<[email protected]>)
- **Size of downloaded dataset files:** 4.60 MB
- **Size of the generated dataset:** 2.96 MB
- **Total amount of disk used:** 7.56 MB
### Dataset Summary
PS is a binary classification task with the goal of predicting whether two multi-word noun phrases are semantically similar or not given *the same context* sentence.
This dataset contains ~10K pairs of two phrases along with their contexts used for disambiguation, since two phrases are not enough for semantic comparison.
Our ~10K examples were annotated by linguistic experts on <upwork.com> and verified in two rounds by 1000 Mturkers and 5 linguistic experts.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
**PS**
* Size of downloaded dataset files: 4.60 MB
* Size of the generated dataset: 2.96 MB
* Total amount of disk used: 7.56 MB
```
{
"phrase1": "annual run",
"phrase2": "yearlong performance",
"sentence1": "since 2004, the club has been a sponsor of the annual run for rigby to raise money for off-campus housing safety awareness.",
"sentence2": "since 2004, the club has been a sponsor of the yearlong performance for rigby to raise money for off-campus housing safety awareness.",
"label": 0,
"idx": 0,
}
```
### Data Fields
The data fields are the same among all splits.
* phrase1: a string feature.
* phrase2: a string feature.
* sentence1: a string feature.
* sentence2: a string feature.
* label: a classification label, with negative (0) and positive (1).
* idx: an int32 feature.
### Data Splits
| name |train |validation|test |
|--------------------|----:|--------:|----:|
|PS |7362| 1052|2102|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from [Upwork.com](https://upwork.com).
#### Who are the source language producers?
We hired 13 linguistic experts from [Upwork.com](https://upwork.com) for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
13 linguistic experts from [Upwork.com](https://upwork.com).
### Personal and Sensitive Information
No annotator identifying details are provided.
## 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
This dataset is a joint work between Adobe Research and Auburn University.
Creators: [Thang M. Pham](https://scholar.google.com/citations?user=eNrX3mYAAAAJ), [David Seunghyun Yoon](https://david-yoon.github.io/), [Trung Bui](https://sites.google.com/site/trungbuistanford/), and [Anh Nguyen](https://anhnguyen.me).
[@PMThangXAI](https://twitter.com/pmthangxai) added this dataset to HuggingFace.
### Licensing Information
This dataset is distributed under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
}
``` | PiC/phrase_similarity | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-06-14T00:35:19+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found", "expert-generated"], "language": ["en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification"], "paperswithcode_id": "phrase-in-context", "pretty_name": "PiC: Phrase Similarity (PS)"} | 2023-01-20T16:32:19+00:00 |
22e9451042c750f5dec39e243d34f4efea1f3cda | # Dataset Card for Auditor_Review
## 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)
## Dataset Description
Auditor review data collected by News Department
- **Point of Contact:**
Talked to COE for Auditing, currently [email protected]
### Dataset Summary
Auditor sentiment dataset of sentences from financial news. The dataset consists of 3500 sentences from English language financial news categorized by sentiment. The dataset is divided by the agreement rate of 5-8 annotators.
### Supported Tasks and Leaderboards
Sentiment Classification
### Languages
English
## Dataset Structure
### Data Instances
```
"sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .",
"label": "negative"
```
### Data Fields
- sentence: a tokenized line from the dataset
- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0)
Complete data code is [available here](https://www.datafiles.samhsa.gov/get-help/codebooks/what-codebook)
### Data Splits
A train/test split was created randomly with a 75/25 split
## Dataset Creation
### Curation Rationale
To gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.
### Source Data
#### Initial Data Collection and Normalization
The corpus used in this paper is made out of English news reports.
#### Who are the source language producers?
The source data was written by various auditors.
### Annotations
#### Annotation process
This release of the auditor reviews covers a collection of 4840
sentences. The selected collection of phrases was annotated by 16 people with
adequate background knowledge of financial markets. The subset here is where inter-annotation agreement was greater than 75%.
#### Who are the annotators?
They were pulled from the SME list, names are held by [email protected]
### Personal and Sensitive Information
There is no personal or sensitive information in this dataset.
## Considerations for Using the Data
### Discussion of Biases
All annotators were from the same institution and so interannotator agreement
should be understood with this taken into account.
The [Dataset Measurement tool](https://huggingface.co/spaces/huggingface/data-measurements-tool) identified these bias statistics:
### Other Known Limitations
[More Information Needed]
### Licensing Information
License: Demo.Org Proprietary - DO NOT SHARE | demo-org/auditor_review | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"region:us"
] | 2022-06-14T02:06:17+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "sentiment-classification"], "pretty_name": "Auditor_Review"} | 2022-08-30T20:42:09+00:00 |
8dd56bd02deccc9252f356e164a48c6adafa77d4 | # Danbooru2020 Small 60GB sample dataset
Aggregating the kaggle dataset here but keeping their hosting for the raw files
Links:
> https://www.kaggle.com/datasets/muoncollider/danbooru2020/download
See the notebook file for a quick reference on how to extract info | inarikami/Danbooru-2020-Small | [
"region:us"
] | 2022-06-14T02:33:05+00:00 | {} | 2022-06-14T02:41:59+00:00 |
d261c35f195f1d397925fffad88cf08ca8f21e9b | isa93/mio | [
"license:wtfpl",
"region:us"
] | 2022-06-14T04:33:37+00:00 | {"license": "wtfpl"} | 2022-06-14T04:33:37+00:00 |
|
c2cc1eb192d1cbba04bfee929b089ad96720455e | # Description
The dataset represents huge number of images of people wearing face masks or not to be used extensively for train/test splitting. Selected files were double-checked to avoid data collection bias using common sense.
# Sources
The dataset obtained and combined from various open data sources, including following:
- https://www.kaggle.com/frabbisw/facial-age
- https://www.kaggle.com/nipunarora8/age-gender-and-ethnicity-face-data-csv
- https://www.kaggle.com/arashnic/faces-age-detection-dataset
- https://www.kaggle.com/andrewmvd/face-mask-detection
- manually obtained under-represented observations using Google search engine
# Structure
The dataset is curated and structured into three age groups (under 18, 18-65 and 65+) without initial test/train selection, which is achieved programmatically to allow manipulations with original data.
<a href="https://postimages.org/" target="_blank"><img src="https://i.postimg.cc/cCyDskHz/2022-06-14-10-21-39.webp" alt="2022-06-14-10-21-39"/></a>
<a href="https://postimages.org/" target="_blank"><img src="https://i.postimg.cc/zvCx3wHG/Screenshot-2022-06-14-101707.png" alt="Screenshot-2022-06-14-101707"/></a> | hydramst/face_mask_wearing | [
"license:other",
"region:us"
] | 2022-06-14T08:10:48+00:00 | {"license": "other"} | 2022-06-14T08:15:57+00:00 |
4b0ce279a6d0b05dfd10aa0f765bbdf77a2fd528 | daokang/bisai | [
"license:afl-3.0",
"region:us"
] | 2022-06-14T12:20:13+00:00 | {"license": "afl-3.0"} | 2022-06-14T12:21:59+00:00 |
|
45024356cf5bb984bedfe87b57ace5dca350c73b | taskydata/tasky_or_not | [
"task_categories:text-classification",
"size_categories:10M<n<100M",
"language:en",
"license:mit",
"region:us"
] | 2022-06-14T14:42:22+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10M<n<100M"], "task_categories": ["text-classification"]} | 2023-05-31T02:30:14+00:00 |
|
9837d9368887d4edd44eaa6b6a040c1f0ce501c0 | eeeree/tarakajeet | [
"license:afl-3.0",
"region:us"
] | 2022-06-14T15:17:51+00:00 | {"license": "afl-3.0"} | 2022-06-14T15:34:52+00:00 |
|
9022ba27075f75c2f59d57d7fa5f42e8d1151aec |
# Dataset Card for LCCC
## Table of Contents
- [Dataset Card for LCCC](#dataset-card-for-lccc)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/thu-coai/CDial-GPT
- **Paper:** https://arxiv.org/abs/2008.03946
### Dataset Summary
LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.
LCCC是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。 这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。 我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。
### Supported Tasks and Leaderboards
- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.
### Languages
LCCC is in Chinese
LCCC中的对话是中文的
## Dataset Structure
### Data Instances
```json
{
"dialog": ["火锅 我 在 重庆 成都 吃 了 七八 顿 火锅", "哈哈哈哈 ！ 那 我 的 嘴巴 可能 要 烂掉 ！", "不会 的 就是 好 油腻"]
}
```
### Data Fields
- `dialog` (list of strings): List of utterances consisting of a dialogue.
### Data Splits
We do not provide the offical split for LCCC-large.
But we provide a split for LCCC-base:
|train|valid|test|
|---:|---:|---:|
|6,820,506 | 20,000 | 10,000|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
MIT License
Copyright (c) 2020 lemon234071
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
### Citation Information
```bibtex
@inproceedings{wang2020chinese,
title={A Large-Scale Chinese Short-Text Conversation Dataset},
author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
booktitle={NLPCC},
year={2020},
url={https://arxiv.org/abs/2008.03946}
}
```
### Contributions
Thanks to [Yinhe Zheng](https://github.com/silverriver) for adding this dataset. | lccc | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:zh",
"license:mit",
"arxiv:2008.03946",
"region:us"
] | 2022-06-14T17:05:32+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["zh"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["conversational"], "task_ids": ["dialogue-generation"], "paperswithcode_id": "lccc", "pretty_name": "LCCC: Large-scale Cleaned Chinese Conversation corpus", "dataset_info": [{"config_name": "large", "features": [{"name": "dialog", "list": "string"}], "splits": [{"name": "train", "num_bytes": 1530827965, "num_examples": 12007759}], "download_size": 607605643, "dataset_size": 1530827965}, {"config_name": "base", "features": [{"name": "dialog", "list": "string"}], "splits": [{"name": "train", "num_bytes": 932634902, "num_examples": 6820506}, {"name": "test", "num_bytes": 1498216, "num_examples": 10000}, {"name": "validation", "num_bytes": 2922731, "num_examples": 20000}], "download_size": 371475095, "dataset_size": 937055849}]} | 2024-01-18T11:19:16+00:00 |
f379fd9af10f2178159a6c7fdf6d3a8f10dccc74 | # Dataset Card for "dane-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ScandEval/dane-mini | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-06-14T17:20:34+00:00 | {"language": ["da"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 355712, "num_examples": 1024}, {"name": "test", "num_bytes": 747809, "num_examples": 2048}, {"name": "val", "num_bytes": 92001, "num_examples": 256}], "download_size": 532720, "dataset_size": 1195522}} | 2023-07-05T08:40:02+00:00 |
67710dfa772469bf5d3653bf0ca9f431e782d85c | # Dataset Card for "norne-nb-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ScandEval/norne-nb-mini | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:nb",
"license:other",
"region:us"
] | 2022-06-14T17:21:00+00:00 | {"language": ["nb"], "license": "other", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 317673, "num_examples": 1024}, {"name": "test", "num_bytes": 626004, "num_examples": 2048}, {"name": "val", "num_bytes": 87124, "num_examples": 256}], "download_size": 455512, "dataset_size": 1030801}} | 2023-07-05T08:42:22+00:00 |
b34982a3ffc391bf3c5d6c999a2c1804ae170780 | # Dataset Card for "norne-nn-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ScandEval/norne-nn-mini | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:nn",
"license:other",
"region:us"
] | 2022-06-14T17:21:22+00:00 | {"language": ["nn"], "license": "other", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 341534, "num_examples": 1024}, {"name": "test", "num_bytes": 721476, "num_examples": 2048}, {"name": "val", "num_bytes": 90956, "num_examples": 256}], "download_size": 502871, "dataset_size": 1153966}} | 2023-07-05T08:41:26+00:00 |
d8243105818293a2f5b4ba7ab6406e73098c9b8b | # Dataset Card for "suc3-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ScandEval/suc3-mini | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:sv",
"license:cc-by-4.0",
"region:us"
] | 2022-06-14T17:21:45+00:00 | {"language": ["sv"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 344855, "num_examples": 1024}, {"name": "test", "num_bytes": 681936, "num_examples": 2048}, {"name": "val", "num_bytes": 81547, "num_examples": 256}], "download_size": 509020, "dataset_size": 1108338}} | 2023-07-05T08:42:05+00:00 |
457785e82655186a01a89026cb35611fb2d71663 | ScandEval/angry-tweets-mini | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-4.0",
"region:us"
] | 2022-06-14T17:30:11+00:00 | {"language": ["da"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-07-05T08:52:07+00:00 |
|
07b99ab3363c2e7f8f87015b01c21f4d9b917ce3 | ScandEval/norec-mini | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:no",
"language:nb",
"language:nn",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-06-14T17:30:30+00:00 | {"language": ["no", "nb", "nn"], "license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-07-05T08:51:39+00:00 |
|
1273f672d0ac2b5e42a188a170de0513e4e5b5c1 | ScandEval/absabank-imm-mini | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:sv",
"license:cc-by-4.0",
"region:us"
] | 2022-06-14T17:30:48+00:00 | {"language": ["sv"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-07-05T08:50:27+00:00 |
|
10d04e5710d83c882ae62acb2dd928958b49516a | # Dataset Card for "mim-gold-ner-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ScandEval/mim-gold-ner-mini | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:is",
"license:other",
"region:us"
] | 2022-06-14T17:35:40+00:00 | {"language": ["is"], "license": "other", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 377525, "num_examples": 1024}, {"name": "test", "num_bytes": 746049, "num_examples": 2048}, {"name": "val", "num_bytes": 93607, "num_examples": 256}], "download_size": 562012, "dataset_size": 1217181}} | 2023-07-05T08:43:10+00:00 |
564cc836587aa94894fffba0ed28857eb5b6f939 | # Dataset Card for "wikiann-fo-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ScandEval/wikiann-fo-mini | [
"language:fo",
"region:us"
] | 2022-06-14T17:39:30+00:00 | {"language": ["fo"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 233626, "num_examples": 1024}, {"name": "test", "num_bytes": 467705, "num_examples": 2048}, {"name": "val", "num_bytes": 60873, "num_examples": 256}], "download_size": 338188, "dataset_size": 762204}} | 2023-07-05T07:08:24+00:00 |
54319f7cf5d56172bcae5acaa90b046ec6fe4ae6 | rr/dd | [
"license:afl-3.0",
"region:us"
] | 2022-06-14T18:33:29+00:00 | {"license": "afl-3.0"} | 2022-06-23T03:09:14+00:00 |
|
7bdf563492accd06815580ffdd685adad8b8674b |
# Dataset Card for ADE 20K Tiny
This is a tiny subset of the ADE 20K dataset, which you can find [here](https://huggingface.co/datasets/scene_parse_150). | nateraw/ade20k-tiny | [
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|ade20k",
"language:en",
"license:bsd-3-clause",
"region:us"
] | 2022-06-15T03:32:58+00:00 | {"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["bsd-3-clause"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["extended|ade20k"], "task_categories": ["image-segmentation"], "task_ids": ["semantic-segmentation"], "pretty_name": "ADE 20K Tiny"} | 2022-07-08T05:58:09+00:00 |
22b3b59656bf17b64ef0294318274afc7b5cf6a2 |
# Dataset Card for Country211
The [Country 211 Dataset](https://github.com/openai/CLIP/blob/main/data/country211.md) from OpenAI.
This dataset was built by filtering the images from the YFCC100m dataset that have GPS coordinate corresponding to a ISO-3166 country code. The dataset is balanced by sampling 150 train images, 50 validation images, and 100 test images images for each country. | nateraw/country211 | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|yfcc100m",
"language:en",
"license:unknown",
"region:us"
] | 2022-06-15T04:11:59+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|yfcc100m"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "pretty_name": "Country 211"} | 2022-07-25T19:27:00+00:00 |
813d20cfb22b7ac76cb6a272cc8510bd85e8a66e |
# Rendered SST-2
The [Rendered SST-2 Dataset](https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md) from Open AI.
Rendered SST2 is an image classification dataset used to evaluate the models capability on optical character recognition. This dataset was generated by rendering sentences in the Standford Sentiment Treebank v2 dataset.
This dataset contains two classes (positive and negative) and is divided in three splits: a train split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images (444 positive and 428 negative), and a test split containing 1821 images (909 positive and 912 negative). | nateraw/rendered-sst2 | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|sst2",
"language:en",
"license:unknown",
"region:us"
] | 2022-06-15T04:32:09+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|sst2"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "pretty_name": "Rendered SST-2"} | 2022-10-25T09:32:21+00:00 |
5d1705be26da650adea619ee9bc5bf45571bb653 |
# Dataset Card for Kitti
The [Kitti](http://www.cvlibs.net/datasets/kitti/eval_object.php) dataset.
The Kitti object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects | nateraw/kitti | [
"task_categories:object-detection",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:unknown",
"region:us"
] | 2022-06-15T04:58:44+00:00 | {"annotations_creators": ["found"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["object-detection"], "task_ids": ["object-detection"], "pretty_name": "Kitti"} | 2022-07-15T17:17:21+00:00 |
ccdfad1bd9c9f5558f073cd7d40e59901a527d85 | EulerianKnight/SeaSpongeDetection | [
"license:apache-2.0",
"region:us"
] | 2022-06-15T05:57:16+00:00 | {"license": "apache-2.0"} | 2022-06-15T06:00:17+00:00 |
|
023111f79120d8f955d3691c5ed612b8f0a92d00 | rr/DDR | [
"license:pddl",
"region:us"
] | 2022-06-15T06:57:47+00:00 | {"license": "pddl"} | 2022-06-15T06:59:20+00:00 |
|
603ca7858c8c00d7b762ff96d3aa29f1507c6954 | # Dataset Card for Sketch Data Model Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/sketchai
- **Repository:** https://github.com/sketchai/preprocessing
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset contains over 6M CAD 2D sketches extracted from Onshape. Sketches are stored as python objects in the custom SAM format.
SAM leverages the [Sketchgraphs](https://github.com/PrincetonLIPS/SketchGraphs) dataset for industrial needs and allows for easier transfer learning on other CAD softwares.
### Supported Tasks and Leaderboards
Tasks: Automatic Sketch Generation, Auto Constraint
## Dataset Structure
### Data Instances
The presented npy files contain python pickled objects and require the [flat_array](https://github.com/PrincetonLIPS/SketchGraphs/blob/master/sketchgraphs/data/flat_array.py) module of Sketchgraphs to be loaded. The normalization_output_merged.npy file contains sketch sequences represented as a list of SAM Primitives and Constraints. The sg_merged_final_*.npy files contain encoded constraint graphs of the sketches represented as a dictionnary of arrays.
### Data Fields
[Needs More Information]
### Data Splits
|Train |Val |Test |
|------|------|------|
|6M |50k | 50k |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | sketchai/sam-dataset | [
"annotations_creators:no-annotation",
"language_creators:other",
"size_categories:1M<n<10M",
"license:lgpl-3.0",
"region:us"
] | 2022-06-15T08:18:23+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["other"], "language": [], "license": ["lgpl-3.0"], "multilinguality": [], "size_categories": ["1M<n<10M"], "task_categories": [], "task_ids": [], "pretty_name": "Sketch Data Model Dataset"} | 2022-07-13T12:03:40+00:00 |
b7f5ca3b82fd40f1b5eaae91c720817eb477a2cd |
# Dataset Card for frwiki_good_pages_el
## Dataset Description
- Repository: [frwiki_el](https://github.com/GaaH/frwiki_el)
- Point of Contact: [Gaëtan Caillaut](mailto://[email protected])
### Dataset Summary
This dataset contains articles from the French Wikipédia.
It is intended to be used to train Entity Linking (EL) systems. Links in articles are used to detect named entities.
The dataset `frwiki` contains sentences of each Wikipedia pages.
The dataset `entities` contains description for each Wikipedia pages.
### Languages
- French
## Dataset Structure
### frwiki
```
{
"name": "Title of the page",
"wikidata_id": "Identifier of the related Wikidata entity. Can be null.",
"wikipedia_id": "Identifier of the Wikipedia page",
"wikipedia_url": "URL to the Wikipedia page",
"wikidata_url": "URL to the Wikidata page. Can be null.",
"sentences" : [
{
"text": "text of the current sentence",
"ner": ["list", "of", "ner", "labels"],
"mention_mappings": [
(start_of_first_mention, end_of_first_mention),
(start_of_second_mention, end_of_second_mention)
],
"el_wikidata_id": ["wikidata id of first mention", "wikidata id of second mention"],
"el_wikipedia_id": [wikipedia id of first mention, wikipedia id of second mention],
"el_wikipedia_title": ["wikipedia title of first mention", "wikipedia title of second mention"]
}
]
"words": ["words", "in", "the", "sentence"],
"ner": ["ner", "labels", "of", "each", "words"],
"el": ["el", "labels", "of", "each", "words"]
}
```
### entities
```
{
"name": "Title of the page",
"wikidata_id": "Identifier of the related Wikidata entity. Can be null.",
"wikipedia_id": "Identifier of the Wikipedia page",
"wikipedia_url": "URL to the Wikipedia page",
"wikidata_url": "URL to the Wikidata page. Can be null.",
"description": "Description of the entity"
}
``` | gcaillaut/frwiki_el | [
"task_categories:token-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fr",
"license:wtfpl",
"region:us"
] | 2022-06-15T08:37:40+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["machine-generated"], "language": ["fr"], "license": ["wtfpl"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": [], "pretty_name": "French Wikipedia dataset for Entity Linking"} | 2022-09-28T07:52:12+00:00 |
ae6988dd116193043f09ac8afb7e768e23cb53fd | ksbk/gg | [
"license:afl-3.0",
"region:us"
] | 2022-06-15T08:37:41+00:00 | {"license": "afl-3.0"} | 2022-06-15T08:39:15+00:00 |
|
5d39a097127b8a6c8342cfc602967ce396478678 |
# multiFC
- a dataset for the task of **automatic claim verification**
- License is currently unknown, please refer to the original paper/[dataset site](http://www.copenlu.com/publication/2019_emnlp_augenstein/):
- https://arxiv.org/abs/1909.03242
## Dataset contents
- **IMPORTANT:** the `label` column in the `test` set has dummy values as these were not provided (see original readme section for explanation)
```
DatasetDict({
train: Dataset({
features: ['claimID', 'claim', 'label', 'claimURL', 'reason', 'categories', 'speaker', 'checker', 'tags', 'article title', 'publish date', 'climate', 'entities'],
num_rows: 27871
})
test: Dataset({
features: ['claimID', 'claim', 'label', 'claimURL', 'reason', 'categories', 'speaker', 'checker', 'tags', 'article title', 'publish date', 'climate', 'entities'],
num_rows: 3487
})
validation: Dataset({
features: ['claimID', 'claim', 'label', 'claimURL', 'reason', 'categories', 'speaker', 'checker', 'tags', 'article title', 'publish date', 'climate', 'entities'],
num_rows: 3484
})
})
```
## Paper Abstract / Citation
> We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-depth analysis of the dataset, highlighting characteristics and challenges. Further, we present results for automatic veracity prediction, both with established baselines and with a novel method for joint ranking of evidence pages and predicting veracity that outperforms all baselines. Significant performance increases are achieved by encoding evidence, and by modelling metadata. Our best-performing model achieves a Macro F1 of 49.2%, showing that this is a challenging testbed for claim veracity prediction.
```
@inproceedings{conf/emnlp2019/Augenstein,
added-at = {2019-10-27T00:00:00.000+0200},
author = {Augenstein, Isabelle and Lioma, Christina and Wang, Dongsheng and Chaves Lima, Lucas and Hansen, Casper and Hansen, Christian and Grue Simonsen, Jakob},
booktitle = {EMNLP},
crossref = {conf/emnlp/2019},
publisher = {Association for Computational Linguistics},
title = {MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims},
year = 2019
}
```
## Original README
Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
The MultiFC is the largest publicly available dataset of naturally occurring factual claims for automatic claim verification.
It is collected from 26 English fact-checking websites paired with textual sources and rich metadata and labeled for veracity by human expert journalists.
###### TRAIN and DEV #######
The train and dev files are (tab-separated) and contain the following metadata:
claimID, claim, label, claimURL, reason, categories, speaker, checker, tags, article title, publish date, climate, entities
Fields that could not be crawled were set as "None." Please refer to Table 11 of our paper to see the summary statistics.
###### TEST #######
The test file follows the same structure. However, we have removed the label. Thus, it only presents 12 metadata.
claimID, claim, claim, reason, categories, speaker, checker, tags, article title, publish date, climate, entities
Fields that could not be crawled were set as "None." Please refer to Table 11 of our paper to see the summary statistics.
###### Snippets ######
The text of each claim is submitted verbatim as a query to the Google Search API (without quotes).
In the folder snippet, we provide the top 10 snippets retrieved. In some cases, fewer snippets are provided
since we have excluded the claimURL from the snippets.
Each file in the snippets folder is named after the claimID of the claim submitted as a query.
Snippets file is (tab-separated) and contains the following metadata:
rank_position, title, snippet, snippet_url
For more information, please refer to our paper:
References:
Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen. 2019.
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims. In EMNLP. Association for Computational Linguistics.
https://copenlu.github.io/publication/2019_emnlp_augenstein/
| pszemraj/multi_fc | [
"license:other",
"automatic claim verification",
"claims",
"arxiv:1909.03242",
"region:us"
] | 2022-06-15T10:27:47+00:00 | {"license": "other", "tags": ["automatic claim verification", "claims"]} | 2022-06-16T10:57:52+00:00 |
7b79121ac9e503a2a396851c1e670387ec6ed10a | themindorchestra/SoundHealing | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-06-15T12:01:21+00:00 | {"license": "cc-by-nc-sa-4.0"} | 2022-06-15T12:01:21+00:00 |
|
21e74ddf8de1a21436da12e3e653065c5213e9d1 |
# APPS Dataset
## Dataset Description
[APPS](https://arxiv.org/abs/2105.09938) is a benchmark for code generation with 10000 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications.
You can also find **APPS metric** in the hub here [codeparrot/apps_metric](https://huggingface.co/spaces/codeparrot/apps_metric).
## Languages
The dataset contains questions in English and code solutions in Python.
## Dataset Structure
```python
from datasets import load_dataset
load_dataset("codeparrot/apps")
DatasetDict({
train: Dataset({
features: ['problem_id', 'question', 'solutions', 'input_output', 'difficulty', 'url', 'starter_code'],
num_rows: 5000
})
test: Dataset({
features: ['problem_id', 'question', 'solutions', 'input_output', 'difficulty', 'url', 'starter_code'],
num_rows: 5000
})
})
```
### How to use it
You can load and iterate through the dataset with the following two lines of code for the train split:
```python
from datasets import load_dataset
import json
ds = load_dataset("codeparrot/apps", split="train")
sample = next(iter(ds))
# non-empty solutions and input_output features can be parsed from text format this way:
sample["solutions"] = json.loads(sample["solutions"])
sample["input_output"] = json.loads(sample["input_output"])
print(sample)
#OUTPUT:
{
'problem_id': 0,
'question': 'Polycarp has $n$ different binary words. A word called binary if it contains only characters \'0\' and \'1\'. For example...',
'solutions': ["for _ in range(int(input())):\n n = int(input())\n mass = []\n zo = 0\n oz = 0\n zz = 0\n oo = 0\n...",...],
'input_output': {'inputs': ['4\n4\n0001\n1000\n0011\n0111\n3\n010\n101\n0\n2\n00000\n00001\n4\n01\n001\n0001\n00001\n'],
'outputs': ['1\n3 \n-1\n0\n\n2\n1 2 \n']},
'difficulty': 'interview',
'url': 'https://codeforces.com/problemset/problem/1259/D',
'starter_code': ''}
}
```
Each sample consists of a programming problem formulation in English, some ground truth Python solutions, test cases that are defined by their inputs and outputs and function name if provided, as well as some metadata regarding the difficulty level of the problem and its source.
If a sample has non empty `input_output` feature, you can read it as a dictionary with keys `inputs` and `outputs` and `fn_name` if it exists, and similarily you can parse the solutions into a list of solutions as shown in the code above.
You can also filter the dataset for the difficulty level: Introductory, Interview and Competition. Just pass the list of difficulties as a list. E.g. if you want the most challenging problems, you need to select the competition level:
```python
ds = load_dataset("codeparrot/apps", split="train", difficulties=["competition"])
print(next(iter(ds))["question"])
#OUTPUT:
"""\
Codefortia is a small island country located somewhere in the West Pacific. It consists of $n$ settlements connected by
...
For each settlement $p = 1, 2, \dots, n$, can you tell what is the minimum time required to travel between the king's residence and the parliament house (located in settlement $p$) after some roads are abandoned?
-----Input-----
The first line of the input contains four integers $n$, $m$, $a$ and $b$
...
-----Output-----
Output a single line containing $n$ integers
...
-----Examples-----
Input
5 5 20 25
1 2 25
...
Output
0 25 60 40 20
...
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|problem_id|int|problem id|
|question|string|problem description|
|solutions|string|some python solutions|
|input_output|string|Json string with "inputs" and "outputs" of the test cases, might also include "fn_name" the name of the function|
|difficulty|string|difficulty level of the problem|
|url|string|url of the source of the problem|
|starter_code|string|starter code to include in prompts|
we mention that only few samples have `fn_name` and `starter_code` specified
### Data Splits
The dataset contains a train and test splits with 5000 samples each.
### Dataset Statistics
* 10000 coding problems
* 131777 test cases
* all problems have a least one test case except 195 samples in the train split
* for tests split, the average number of test cases is 21.2
* average length of a problem is 293.2 words
* all files have ground-truth solutions except 1235 samples in the test split
## Dataset Creation
To create the APPS dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Codewars, AtCoder, Kattis, and Codeforces. For more details please refer to the original [paper](https://arxiv.org/pdf/2105.09938.pdf).
## Considerations for Using the Data
In [AlphaCode](https://arxiv.org/pdf/2203.07814v1.pdf) the authors found that this dataset can generate many false positives during evaluation, where incorrect submissions are marked as correct due to lack of test coverage.
## Citation Information
```
@article{hendrycksapps2021,
title={Measuring Coding Challenge Competence With APPS},
author={Dan Hendrycks and Steven Basart and Saurav Kadavath and Mantas Mazeika and Akul Arora and Ethan Guo and Collin Burns and Samir Puranik and Horace He and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
}
``` | codeparrot/apps | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:code",
"license:mit",
"arxiv:2105.09938",
"arxiv:2203.07814",
"region:us"
] | 2022-06-15T12:20:26+00:00 | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "APPS"} | 2022-10-20T14:00:15+00:00 |
83e07480c44954d638b087ecd1f6af7934ba9d68 | Trust**wallet customer service Support Number +1-818*751*8351 | trustwallet/33 | [
"license:apache-2.0",
"region:us"
] | 2022-06-15T16:31:08+00:00 | {"license": "apache-2.0"} | 2022-06-15T16:31:41+00:00 |
1413ed90879c1ebb1b7016388a6ef43e7765d295 | # AutoTrain Dataset for project: Psychiatry_Article_Identifier
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project Psychiatry_Article_Identifier.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "diffuse actinic keratinocyte dysplasia",
"target": 15
},
{
"text": "cholesterol atheroembolism",
"target": 8
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=20, names=['Certain infectious or parasitic diseases', 'Developmental anaomalies', 'Diseases of the blood or blood forming organs', 'Diseases of the genitourinary system', 'Mental behavioural or neurodevelopmental disorders', 'Neoplasms', 'certain conditions originating in the perinatal period', 'conditions related to sexual health', 'diseases of the circulatroy system', 'diseases of the digestive system', 'diseases of the ear or mastoid process', 'diseases of the immune system', 'diseases of the musculoskeletal system or connective tissue', 'diseases of the nervous system', 'diseases of the respiratory system', 'diseases of the skin', 'diseases of the visual system', 'endocrine nutritional or metabolic diseases', 'pregnanacy childbirth or the puerperium', 'sleep-wake disorders'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 9828 |
| valid | 2468 |
| justpyschitry/autotrain-data-Psychiatry_Article_Identifier | [
"task_categories:text-classification",
"region:us"
] | 2022-06-15T18:02:36+00:00 | {"task_categories": ["text-classification"]} | 2022-06-15T20:34:39+00:00 |
ca5836dcea910a720fe456e3d3c9b68206507eeb |
# Dataset Card for MAWPS_ar
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
MAWPS: A Math Word Problem Repository
### Supported Tasks
Math Word Problem Solving
### Languages
Supports Arabic and English
## Dataset Structure
### Data Fields
- `text_en`: a `string` feature.
- `text_ar`: a `string` feature.
- `eqn`: a `string` feature.
### Data Splits
|train|validation|test|
|----:|---------:|---:|
| 3636| 1040| 520|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[Rik Koncel-Kedziorski**, Subhro Roy**, Aida Amini, Nate Kushman and Hannaneh Hajishirzi.](https://aclanthology.org/N16-1136.pdf)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Contributions
Special thanks to Associate Professor Marwan Torki and all my colleagues in CC491N (NLP) class for helping me translate this dataset. | omarxadel/MaWPS-ar | [
"task_categories:text2text-generation",
"task_ids:explanation-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"language:en",
"language:ar",
"license:mit",
"region:us"
] | 2022-06-15T19:39:14+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en", "ar"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["text2text-generation"], "task_ids": ["explanation-generation"], "pretty_name": "MAWPS_ar"} | 2022-07-12T14:31:07+00:00 |
6bfc581b1b87cbd2ff08a7369f34a10fbed01c4b | sounakray1997/CoNLLU_WikiNEuRal | [
"license:apache-2.0",
"region:us"
] | 2022-06-15T22:16:23+00:00 | {"license": "apache-2.0"} | 2022-06-15T22:16:23+00:00 |
|
2f2f43e7e54c6dcebe550767c8a284c26186e030 | nateraw/kinetics | [
"license:cc-by-4.0",
"region:us"
] | 2022-06-16T01:29:09+00:00 | {"license": "cc-by-4.0"} | 2022-06-16T01:30:12+00:00 |
|
3906031fe2e1a2d7766f0847014d3eb6229c6351 | HALLA/Rhubarbify | [
"license:other",
"region:us"
] | 2022-06-16T02:19:36+00:00 | {"license": "other"} | 2022-06-16T02:25:29+00:00 |
|
6adb717a2503f1d49af178c0497c2529f1a8e68f | # CORD: A Consolidated Receipt Dataset for Post-OCR Parsing
CORD dataset is cloned from [clovaai](https://github.com/clovaai/cord) GitHub repo
- Box coordinates are normalized against image width/height
- Labels with very few occurrences are replaced with O:
```
replacing_labels = ['menu.etc', 'menu.itemsubtotal',
'menu.sub_etc', 'menu.sub_unitprice',
'menu.vatyn', 'void_menu.nm',
'void_menu.price', 'sub_total.othersvc_price']
```
Check for more info [Sparrow](https://github.com/katanaml/sparrow)
## Citation
### CORD: A Consolidated Receipt Dataset for Post-OCR Parsing
```
@article{park2019cord,
title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
year={2019}
}
```
### Post-OCR parsing: building simple and robust parser via BIO tagging
```
@article{hwang2019post,
title={Post-OCR parsing: building simple and robust parser via BIO tagging},
author={Hwang, Wonseok and Kim, Seonghyeon and Yim, Jinyeong and Seo, Minjoon and Park, Seunghyun and Park, Sungrae and Lee, Junyeop and Lee, Bado and Lee, Hwalsuk}
booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
year={2019}
}
``` | nehruperumalla/forms | [
"region:us"
] | 2022-06-16T05:34:48+00:00 | {} | 2022-06-16T05:38:45+00:00 |
f27efa2241b715868b4e2c6a2ead19ce067b3b48 | This is the sentiment analysis dataset based on IMDB reviews initially released by Stanford University.
```
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.
We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.
Raw text and already processed bag of words formats are provided. See the README file contained in the release for more details.
```
[Here](http://ai.stanford.edu/~amaas/data/sentiment/) is the redirection.
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
``` | scikit-learn/imdb | [
"license:other",
"region:us"
] | 2022-06-16T08:07:41+00:00 | {"license": "other"} | 2022-06-16T08:11:24+00:00 |
428766bc07a9e8699b7782e8557b7e07d32923f3 | # CodeParrot 🦜 Dataset after near deduplication (validation)
## Dataset Description
A dataset of Python files from Github. We performed near deduplication of this dataset split [codeparrot-clean-train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid) from [codeparrot-clean](https://huggingface.co/datasets/codeparrot/codeparrot-clean#codeparrot-%F0%9F%A6%9C-dataset-cleaned). Exact deduplication can miss a fair amount of nearly identical files. We used MinHash with a Jaccard threshold (default=0.85) to create duplicate clusters. Then these clusters are reduced to unique files based on the exact Jaccard similarity. Fore more details, please refer to this [repo](https://github.com/huggingface/transformers/tree/main/examples/research_projects/codeparrot).
| codeparrot/codeparrot-valid-near-deduplication | [
"region:us"
] | 2022-06-16T13:29:26+00:00 | {} | 2022-06-21T18:06:58+00:00 |
e77668a8fb0cedb8ff0fb97ca9e4699b1f095841 | # Dataset Card for OffendES
## 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)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Paper: OffendES:** [A New Corpus in Spanish for Offensive Language Research](https://aclanthology.org/2021.ranlp-1.123.pdf)
- **Leaderboard:** [Leaderboard for OffendES / Spanish](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6388)
- **Point of Contact: [email protected]**
### Dataset Summary
Focusing on young influencers from the well-known social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of Spanish comments manually labeled on offensive pre-defined categories. From the total corpus, we selected 30,416 posts to be publicly published, they correspond to the ones used in the MeOffendES competition at IberLEF 2021. The posts are labeled with the following categories:
- Offensive, the target is a person (OFP). Offensive text targeting a specific individual.
- Offensive, the target is a group of people or collective (OFG). Offensive text targeting a group of people belonging to the same ethnic group, gender or sexual orientation, political ideology, religious belief, or other common characteristics.
- Non-offensive, but with expletive language (NOE). A text that contains rude words, blasphemes, or swearwords but without the aim of offending, and usually with a positive connotation.
- Non-offensive (NO). Text that is neither offensive nor contains expletive language
### Supported Tasks and Leaderboards
This dataset is intended for multi-class offensive classification and binary offensive classification.
Competition [MeOffendES task on offensive detection for Spanish at IberLEF 2021](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6388)
### Languages
- Spanish
## Dataset Structure
### Data Instances
For each instance, there is a string for the id of the tweet, a string for the emotion class, a string for the offensive class, and a string for the event. See the []() to explore more examples.
```
{'comment_id': '8003',
'influencer': 'dalas',
'comment': 'Estupido aburrido',
'label': 'NO',
'influencer_gender': 'man',
'media': youtube
}
```
### Data Fields
- `comment_id`: a string to identify the comment
- `influencer`: a string containing the influencer associated with the comment
- `comment`: a string containing the text of the comment
- `label`: a string containing the offensive gold label
- `influencer_gender`: a string containing the genre of the influencer
- `media`: a string containing the social media platform where the comment has been retrieved
### Data Splits
The OffendES dataset contains 3 splits: _train_, _validation_, and _test_. Below are the statistics for each class.
| OffendES | Number of Instances in Split per class| | |
| ------------- | ---------------------------------|---------------------------------|------------------------------------------|
| `Class` | `Train` | `Validation` | `Test` |
| NO | 13,212 | 64 | 9,651 |
| NOE | 1,235 | 22 | 2,340 |
| OFP | 2,051 | 10 | 1,404 |
| OFG | 212 | 4 | 211 |
| Total | 16,710 | 100 | 13,606 |
## Dataset Creation
### Source Data
Twitter, Youtube, Instagram
#### Who are the annotators?
Amazon Mechanical Turkers
## Additional Information
### Licensing Information
The OffendES dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@inproceedings{plaza-del-arco-etal-2021-offendes,
title = "{O}ffend{ES}: A New Corpus in {S}panish for Offensive Language Research",
author = "{Plaza-del-Arco}, Flor Miriam and Montejo-R{\'a}ez, Arturo and Ure{\~n}a-L{\'o}pez, L. Alfonso and Mart{\'\i}n-Valdivia, Mar{\'\i}a-Teresa",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = sep,
year = "2021",
address = "Held Online",
url = "https://aclanthology.org/2021.ranlp-1.123.pdf",
language = "English",
pages = "1096--1108"
}
```
```
@article{meoffendes2021,
title="{{Overview of MeOffendEs at IberLEF 2021: Offensive Language Detection in Spanish Variants}}",
author="{Flor Miriam Plaza-del-Arco and Casavantes, Marco and Jair Escalante, Hugo and Martín-Valdivia, M. Teresa and Montejo-Ráez, Arturo and {Montes-y-Gómez}, Manuel and Jarquín-Vásquez, Horacio and Villaseñor-Pineda, Luis}",
journal="Procesamiento del Lenguaje Natural",
url = "https://bit.ly/3QpRDfy",
volume="67",
pages="183--194",
year="2021"
}
``` | fmplaza/offendes | [
"language:es",
"license:apache-2.0",
"region:us"
] | 2022-06-16T13:32:03+00:00 | {"language": ["es"], "license": "apache-2.0"} | 2024-02-06T14:26:55+00:00 |
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