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63215ab73d1b4993ec61768e0246dc03c12cb567 | # Dataset Card for "wikitext-2__llama__block-size-1024"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | carlosejimenez/wikitext-2__llama__block-size-1024 | [
"region:us"
]
| 2023-03-21T00:56:02+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 4467438, "num_examples": 331}, {"name": "train", "num_bytes": 1864955854, "num_examples": 137385}, {"name": "validation", "num_bytes": 3955329, "num_examples": 291}], "download_size": 553105401, "dataset_size": 1873378621}} | 2023-03-21T01:15:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikitext-2__llama__block-size-1024"
More Information needed | [
"# Dataset Card for \"wikitext-2__llama__block-size-1024\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikitext-2__llama__block-size-1024\"\n\nMore Information needed"
]
| [
6,
22
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikitext-2__llama__block-size-1024\"\n\nMore Information needed"
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|
88c38d5a4fb8951d338f72f881e71dd23bd415d6 | # Dataset Card for "wikitext-103__llama__block-size-1024"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | carlosejimenez/wikitext-103__llama__block-size-1024 | [
"region:us"
]
| 2023-03-21T01:17:53+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 4367920, "num_examples": 321}, {"name": "train", "num_bytes": 1828055797, "num_examples": 133529}, {"name": "validation", "num_bytes": 3866529, "num_examples": 282}], "download_size": 551236184, "dataset_size": 1836290246}} | 2023-03-21T02:56:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikitext-103__llama__block-size-1024"
More Information needed | [
"# Dataset Card for \"wikitext-103__llama__block-size-1024\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikitext-103__llama__block-size-1024\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikitext-103__llama__block-size-1024\"\n\nMore Information needed"
]
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|
cad1d1df8fd915fbfa09eb02d862515796f7b8e9 | # Dataset Card for "face_synthetics_spiga_captioned"
This is a copy of the [Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations](https://huggingface.co/datasets/pcuenq/face_synthetics_spiga), and additional BLIP-generated captions.
For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to [pcuenq/face_synthetics](https://huggingface.co/datasets/pcuenq/face_synthetics).
Here is the code for parsing the dataset and generating the BLIP captions:
```py
from transformers import pipeline
dataset_name = "pcuenq/face_synthetics_spiga"
faces = load_dataset(dataset_name)
faces = faces["train"]
captioner = pipeline("image-to-text",model="Salesforce/blip-image-captioning-large", device=0)
def caption_image_data(example):
image = example["image"]
image_caption = captioner(image)[0]['generated_text']
example['image_caption'] = image_caption
return example
faces_proc = faces.map(caption_image_data)
faces_proc.push_to_hub(f"multimodalart/face_synthetics_spiga_captioned")
```
| multimodalart/facesyntheticsspigacaptioned | [
"region:us"
]
| 2023-03-21T02:37:14+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_seg", "dtype": "image"}, {"name": "landmarks", "dtype": "string"}, {"name": "spiga", "sequence": {"sequence": "float64"}}, {"name": "spiga_seg", "dtype": "image"}, {"name": "image_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31087489990.0, "num_examples": 100000}], "download_size": 31011261945, "dataset_size": 31087489990.0}} | 2023-03-23T14:56:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "face_synthetics_spiga_captioned"
This is a copy of the Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations, and additional BLIP-generated captions.
For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.
Here is the code for parsing the dataset and generating the BLIP captions:
| [
"# Dataset Card for \"face_synthetics_spiga_captioned\"\n\nThis is a copy of the Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations, and additional BLIP-generated captions. \nFor a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.\n\nHere is the code for parsing the dataset and generating the BLIP captions:"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"face_synthetics_spiga_captioned\"\n\nThis is a copy of the Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations, and additional BLIP-generated captions. \nFor a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.\n\nHere is the code for parsing the dataset and generating the BLIP captions:"
]
| [
6,
109
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"face_synthetics_spiga_captioned\"\n\nThis is a copy of the Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations, and additional BLIP-generated captions. \nFor a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.\n\nHere is the code for parsing the dataset and generating the BLIP captions:"
]
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|
76d569c5763bb660521459bd740d1d4c6a86f364 | # AutoTrain Dataset for project: t5baseparaphrase
## Dataset Description
This dataset has been automatically processed by AutoTrain for project t5baseparaphrase.
### 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
[
{
"feat_Unnamed: 0": 69,
"text": "1\uba85 - \uc5f0 15\ub9cc \uc6d0\n2\uba85 - \uc5f0 30\ub9cc \uc6d0\n3\uba85 \uc774\uc0c1 - \uc5f0 30\ub9cc \uc6d0 + 3\ubc88\uc9f8 \uc774\ud6c4 \uc790\ub140 1\uba85\ub2f9 30\ub9cc \uc6d0\n\uc790\ub140 \uc138\uc561\uacf5\uc81c\uc561\uc740 1\uba85\ub2f9 15\ub9cc \uc6d0\uc774\uae30 \ub54c\ubb38\uc5d0 \ub0a8\ud3b8\uc774 1\uba85, \uc544\ub0b4\uac00 1\uba85\uc5d0 \ub300\ud574 \uc790\ub140 \uc138\uc561\uacf5\uc81c\ub97c \ubc1b\uc544\ub3c4 \ucd1d \uacf5\uc81c\uc561\uc740 \uac19\uc544\uc694.\n\ub2e4\ub9cc, \uc790\ub140\uac00 3\uba85\uc774 \ub2e4\ub465\uc774 \ubd80\ubd80\ub77c\uba74 \uc544\ube60\ub098 \uc5c4\ub9c8 \ud55c\ucabd\uc5d0 \ubab0\uc544\uc11c \uc138\uc561\uacf5\uc81c\ub97c \ubc1b\ub294 \uac8c \ud6e8\uc52c \uc720\ub9ac\ud574\uc694.\n\ub0a8\ud3b8\uc774 \uc790\ub140 2\uba85, \uc544\ub0b4\uac00 \uc790\ub140 1\uba85\uc744 \uae30\ubcf8 \uacf5\uc81c \ub300\uc0c1\uc790\ub85c \uc62c\ub9ac\uba74 \ub0a8\ud3b8\uc740 \uc790\ub140 \uc138\uc561\uacf5\uc81c 30\ub9cc \uc6d0, \uc544\ub0b4\ub294 \uc790\ub140 \uc138\uc561\uacf5\uc81c 10\ub9cc \uc6d0\uc744 \ubc1b\uac8c \ub418\uc8e0.\n\uadf8\ub7f0\ub370, \ud55c \uba85\uc5d0\uac8c \ubab0\uc544\uc8fc\uba74 3\uba85\uc758 \uc790\ub140\uc5d0 \ub300\ud55c \ucd1d 60\ub9cc \uc6d0\uc758 \uc138\uc561\uacf5\uc81c\ub97c \ubc1b\uc744 \uc218 \uc788\uc5b4\uc694.\n\uadf8\ub798\uc11c 3\uba85 \uc774\uc0c1\uc758 \uc790\ub140\uac00 \uc788\ub294 \ub9de\ubc8c\uc774 \ubd80\ubd80\ub77c\uba74 \uc18c\ub4dd\uc774 \ub9ce\uc740 \ucabd\uc5d0 \ubab0\uc544\uc8fc\ub294 \uac8c \uc808\uc138 \uce21\uba74\uc5d0\uc120 \ub354 \ud6a8\uacfc\uc801\uc774\ub78d\ub2c8\ub2e4.\n\ub2e4\uc12f. \ubcf4\ud5d8\ub8cc \uc138\uc561\uacf5\uc81c\ub294 \u2018\uba85\uc758\uc790\u2019\uac00 \uc911\uc694\ud558\ub2e4\n\ubcf8\uc778\uacfc \ubd80\uc591\uac00\uc871\uc744 \uc704\ud574 \uc9c0\ucd9c\ud55c \ubcf4\ud5d8\ub8cc\uc5d0 \ub300\ud55c \uc138\uc561\uacf5\uc81c\ub294 \uc5f0\uac04 \ud55c\ub3c4 1\ubc31\ub9cc \uc6d0\uae4c\uc9c0 \ub0a9\uc785\uc561\uc758 12%\ub97c \ub3cc\ub824\uc8fc\ub294\ub370\uc694.\n\uc8fc\uc758\ud574\uc57c \ud560 \uc810\uc740 \ud53c\ubcf4\ud5d8\uc790\uc640 \uacc4\uc57d\uc790\uac00 \uc77c\uce58\ud574\uc57c \uacf5\uc81c\uac00 \uac00\ub2a5\ud558\ub2e8 \uac70\uc608\uc694.\n\ud53c\ubcf4\ud5d8\uc790\uac00 \uacc4\uc57d\ud55c \ubcf8\uc778\uc774 \uc544\ub2cc \ub2e4\ub978 \ubc30\uc6b0\uc790\ub85c \uc9c0\uc815\ub418\uc5b4 \uc788\ub2e4\uba74 \uacf5\uc81c\ub97c \ubc1b\uc744 \uc218 \uc5c6\uc5b4\uc694.\n\uac00\ub839 \ub0a8\ud3b8\uc774 \uc0dd\uba85\ubcf4\ud5d8\uc5d0 \uac00\uc785\ud588\ub294\ub370, \ud53c\ubcf4\ud5d8\uc790\uac00 \uc544\ub0b4\ub77c\uba74 \uacf5\uc81c\ubc1b\uc744 \uc218 \uc5c6\ub294 \uc148\uc774\uc8e0.\n\ub2e8, \uacc4\uc57d\uc790\uac00 \ub0a8\ud3b8\uc774\uace0 \ud53c\ubcf4\ud5d8\uc790\uac00 \ubd80\ubd80 \uacf5\ub3d9\uc77c \ub54c\ub294 \ub0a8\ud3b8\uc774 \uacf5\uc81c\ubc1b\uc744 \uc218 \uc788\uc5b4\uc694.\n\ub610\ud55c, \uc790\ub140 \ubcf4\ud5d8\ub8cc \uacf5\uc81c\ub97c \ubc1b\uc73c\ub824\uba74, \uc790\ub140\ub97c \uae30\ubcf8 \uacf5\uc81c \ub300\uc0c1\uc790\ub85c \uc2e0\uccad\ud55c \ubd84\uc774 \uc9c1\uc811 \uacc4\uc57d\ud574 \ub0a9\uc785\ud55c \ubcf4\ud5d8\ub8cc\uc5d0 \ub300\ud574 \uacf5\uc81c\ubc1b\uc744 \uc218 \uc788\ub294\ub370\uc694. \ub0a8\ud3b8\uc774 \uc790\ub140 \uc778\uc801\uacf5\uc81c\ub97c \ubc1b\uc558\ub294\ub370, \uc544\ub0b4\uac00 \uc790\ub140\uc758 \ubcf4\ud5d8\ub8cc\ub97c \uacc4\uc57d\ud558\uace0 \ub0a9\uc785\ud558\uace0 \uc788\ub294 \uc0c1\ud669\uc774\ub77c\uba74 \uacf5\uc81c\uac00 \uc5b4\ub824\uc6cc\uc694.\n\uadf8\ub7f0\ub370 \ubcf4\ud5d8\ub8cc \uc138\uc561\uacf5\uc81c\ub294 \uc5f0\uac04 \ud55c\ub3c4 1\ubc31\ub9cc \uc6d0\uc774\uc5b4\uc11c \uc790\ub3d9\ucc28\ubcf4\ud5d8\uc774\ub098 \uc2e4\ube44\ub9cc\uc73c\ub85c \uacf5\uc81c \ud55c\ub3c4\uac00 \ucc44\uc6cc\uc9c0\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc544\uc694. \uadf8\ub798\uc11c \uad73\uc774 \ubc88\uac70\ub86d\uac8c \ubaa8\ub4e0 \uacc4\uc57d\uc744 \ubc14\uafc0 \ud544\uc694\ub294 \uc5c6\uc5b4\uc694.",
"target": "1\uba85 - \uc5f0 15\ub9cc \uc6d0\n2\uba85 - \uc5f0 30\ub9cc \uc6d0\n3\uba85 \uc774\uc0c1 - \uc5f0 30\ub9cc \uc6d0 + 3\ubc88\uc9f8 \uc774\ud6c4 \uc790\ub140 1\uba85\ub2f9 30\ub9cc \uc6d0\n\uc790\ub140 \uc138\uc561\uacf5\uc81c\uc561\uc740 1\uba85\ub2f9 15\ub9cc \uc6d0\uc774\uae30 \ub54c\ubb38\uc5d0 \ub0a8\ud3b8\uc774 1\uba85, \uc544\ub0b4\uac00 1\uba85\uc5d0 \ub300\ud574 \uc790\ub140 \uc138\uc561\uacf5\uc81c\ub97c \ubc1b\uc544\ub3c4 \ucd1d \uacf5\uc81c\uc561\uc740 \uac19\uc544\uc694.\n\ub2e4\ub9cc, \uc790\ub140\uac00 3\uba85\uc774 \ub2e4\ub465\uc774 \ubd80\ubd80\ub77c\uba74 \uc544\ube60\ub098 \uc5c4\ub9c8 \ud55c\ucabd\uc5d0 \ubab0\uc544\uc11c \uc138\uc561\uacf5\uc81c\ub97c \ubc1b\ub294 \uac8c \ud6e8\uc52c \uc720\ub9ac\ud574\uc694.\n\ub0a8\ud3b8\uc774 \uc790\ub140 2\uba85, \uc544\ub0b4\uac00 \uc790\ub140 1\uba85\uc744 \uae30\ubcf8 \uacf5\uc81c \ub300\uc0c1\uc790\ub85c \uc62c\ub9ac\uba74 \ub0a8\ud3b8\uc740 \uc790\ub140 \uc138\uc561\uacf5\uc81c 30\ub9cc \uc6d0, \uc544\ub0b4\ub294 \uc790\ub140 \uc138\uc561\uacf5\uc81c 10\ub9cc \uc6d0\uc744 \ubc1b\uac8c \ub418\uc8e0.\n\uadf8\ub7f0\ub370, \ud55c \uba85\uc5d0\uac8c \ubab0\uc544\uc8fc\uba74 3\uba85\uc758 \uc790\ub140\uc5d0 \ub300\ud55c \ucd1d 60\ub9cc \uc6d0\uc758 \uc138\uc561\uacf5\uc81c\ub97c \ubc1b\uc744 \uc218 \uc788\uc5b4\uc694.\n\uadf8\ub798\uc11c 3\uba85 \uc774\uc0c1\uc758 \uc790\ub140\uac00 \uc788\ub294 \ub9de\ubc8c\uc774 \ubd80\ubd80\ub77c\uba74 \uc18c\ub4dd\uc774 \ub9ce\uc740 \ucabd\uc5d0 \ubab0\uc544\uc8fc\ub294 \uac8c \uc808\uc138 \uce21\uba74\uc5d0\uc120 \ub354 \ud6a8\uacfc\uc801\uc774\ub78d\ub2c8\ub2e4.\n\ub2e4\uc12f. \ubcf4\ud5d8\ub8cc \uc138\uc561\uacf5\uc81c\ub294 \u2018\uba85\uc758\uc790\u2019\uac00 \uc911\uc694\ud558\ub2e4\n\ubcf8\uc778\uacfc \ubd80\uc591\uac00\uc871\uc744 \uc704\ud574 \uc9c0\ucd9c\ud55c \ubcf4\ud5d8\ub8cc\uc5d0 \ub300\ud55c \uc138\uc561\uacf5\uc81c\ub294 \uc5f0\uac04 \ud55c\ub3c4 1\ubc31\ub9cc \uc6d0\uae4c\uc9c0 \ub0a9\uc785\uc561\uc758 12%\ub97c \ub3cc\ub824\uc8fc\ub294\ub370\uc694.\n\uc8fc\uc758\ud574\uc57c \ud560 \uc810\uc740 \ud53c\ubcf4\ud5d8\uc790\uc640 \uacc4\uc57d\uc790\uac00 \uc77c\uce58\ud574\uc57c \uacf5\uc81c\uac00 \uac00\ub2a5\ud558\ub2e8 \uac70\uc608\uc694.\n\ud53c\ubcf4\ud5d8\uc790\uac00 \uacc4\uc57d\ud55c \ubcf8\uc778\uc774 \uc544\ub2cc \ub2e4\ub978 \ubc30\uc6b0\uc790\ub85c \uc9c0\uc815\ub418\uc5b4 \uc788\ub2e4\uba74 \uacf5\uc81c\ub97c \ubc1b\uc744 \uc218 \uc5c6\uc5b4\uc694.\n\uac00\ub839 \ub0a8\ud3b8\uc774 \uc0dd\uba85\ubcf4\ud5d8\uc5d0 \uac00\uc785\ud588\ub294\ub370, \ud53c\ubcf4\ud5d8\uc790\uac00 \uc544\ub0b4\ub77c\uba74 \uacf5\uc81c\ubc1b\uc744 \uc218 \uc5c6\ub294 \uc148\uc774\uc8e0.\n\ub2e8, \uacc4\uc57d\uc790\uac00 \ub0a8\ud3b8\uc774\uace0 \ud53c\ubcf4\ud5d8\uc790\uac00 \ubd80\ubd80 \uacf5\ub3d9\uc77c \ub54c\ub294 \ub0a8\ud3b8\uc774 \uacf5\uc81c\ubc1b\uc744 \uc218 \uc788\uc5b4\uc694.\n\ub610\ud55c, \uc790\ub140 \ubcf4\ud5d8\ub8cc \uacf5\uc81c\ub97c \ubc1b\uc73c\ub824\uba74, \uc790\ub140\ub97c \uae30\ubcf8 \uacf5\uc81c \ub300\uc0c1\uc790\ub85c \uc2e0\uccad\ud55c \ubd84\uc774 \uc9c1\uc811 \uacc4\uc57d\ud574 \ub0a9\uc785\ud55c \ubcf4\ud5d8\ub8cc\uc5d0 \ub300\ud574 \uacf5\uc81c\ubc1b\uc744 \uc218 \uc788\ub294\ub370\uc694. \ub0a8\ud3b8\uc774 \uc790\ub140 \uc778\uc801\uacf5\uc81c\ub97c \ubc1b\uc558\ub294\ub370, \uc544\ub0b4\uac00 \uc790\ub140\uc758 \ubcf4\ud5d8\ub8cc\ub97c \uacc4\uc57d\ud558\uace0 \ub0a9\uc785\ud558\uace0 \uc788\ub294 \uc0c1\ud669\uc774\ub77c\uba74 \uacf5\uc81c\uac00 \uc5b4\ub824\uc6cc\uc694.\n\uadf8\ub7f0\ub370 \ubcf4\ud5d8\ub8cc \uc138\uc561\uacf5\uc81c\ub294 \uc5f0\uac04 \ud55c\ub3c4 1\ubc31\ub9cc \uc6d0\uc774\uc5b4\uc11c \uc790\ub3d9\ucc28\ubcf4\ud5d8\uc774\ub098 \uc2e4\ube44\ub9cc\uc73c\ub85c \uacf5\uc81c \ud55c\ub3c4\uac00 \ucc44\uc6cc\uc9c0\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc544\uc694. \uadf8\ub798\uc11c \uad73\uc774 \ubc88\uac70\ub86d\uac8c \ubaa8\ub4e0 \uacc4\uc57d\uc744 \ubc14\uafc0 \ud544\uc694\ub294 \uc5c6\uc5b4\uc694."
},
{
"feat_Unnamed: 0": 67,
"text": "\ub9de\ubc8c\uc774 \ubd80\ubd80\uc758 \uc5f0\ub9d0\uc815\uc0b0 \uacf5\uc81c \ucd5c\uc801\ud654\n\uccab\uc9f8. \ubd80\uc591\uac00\uc871 \uacf5\uc81c\ub294 \uc18c\ub4dd\uc774 \ub9ce\uc740 \ucabd\uc5d0 \ubab0\uc544\uc8fc\uc790\n\ub9ce\uc740 \uc0ac\ub78c\uc774 \ubc30\uc6b0\uc790 \uc911 \uc18c\ub4dd\uc774 \ub192\uc740 \ucabd\uc5d0 \uacf5\uc81c\ub97c \ubab0\uc544\uc8fc\ub294 \uac8c \uc720\ub9ac\ud558\ub2e4\uace0 \uc54c\uace0 \uc788\ub294\ub370\uc694. \ub9de\ub294 \ub9d0\uc774\uae30\ub3c4 \ud558\uace0, \ud2c0\ub9b0 \ub9d0\uc774\uae30\ub3c4 \ud574\uc694. \uc885\ud569\uc18c\ub4dd\uc138\ub294 \ub9ce\uc774 \ubc8c\uc218\ub85d \ub9ce\uc740 \uc18c\ub4dd\uc138\ub97c \ub0b4\uc57c \ud558\ub294 \ub204\uc9c4\uc138\uc728 \uad6c\uc870\ub85c \ub418\uc5b4 \uc788\uc5b4\uc694. \uadf8\ub798\uc11c \ub9de\ubc8c\uc774 \ubd80\ubd80 \uc5f0\ub9d0\uc815\uc0b0\uc5d0\uc11c\ub294 \uc18c\ub4dd\uc774 \ub192\uc740 \ucabd\uc73c\ub85c \uacf5\uc81c\ub97c \ubc1b\ub294 \uac8c \uc138\uc561 \uc0c1 \uc720\ub9ac\ud55c \ubd80\ubd84\uc774 \uc788\uc8e0.\n\uac00\uc7a5 \ub300\ud45c\uc801\uc73c\ub85c \ubd80\uc591\uac00\uc871 \uacf5\uc81c\uac00 \uc788\ub294\ub370\uc694.\n\ubd80\uc591\uac00\uc871 \uacf5\uc81c\ub780 \uc9c1\uacc4\uc874\uc18d(\ub9cc 60\uc138 \uc774\uc0c1), \uc9c1\uacc4\ube44\uc18d(\ub9cc 20\uc138 \uc774\ud558), \ud615\uc81c\uc790\ub9e4(\ub9cc 20\uc138 \uc774\ud558, \ub9cc 60\uc138 \uc774\uc0c1) \ub4f1\uc744 \ubd80\uc591\ud558\ub294 \uacbd\uc6b0 1\uc778\ub2f9 150\ub9cc \uc6d0\uc758 \uae30\ubcf8 \uc18c\ub4dd\uacf5\uc81c\ub97c \ud574\uc8fc\ub294 \uac78 \ub9d0\ud574\uc694.\n\uc5ec\uae30\uc5d0 70\uc138 \uc774\uc0c1 \uace0\ub839\uc790\uc5d0 \ub300\ud574 \uacbd\ub85c\uc6b0\ub300\uacf5\uc81c 100\ub9cc \uc6d0, \uc7a5\uc560\uc778 \uacf5\uc81c 200\ub9cc \uc6d0 \ub4f1\uc774 \ub354\ud574\uc838\uc694.\n\uc774\ub807\uac8c \ubd80\uc591\uac00\uc871\uc774 \uc788\ub294 \uacbd\uc6b0 \ubd80\ubd80 \uc911 \ud55c \uba85\uc774 \uacf5\uc81c\ubc1b\uc744 \uc218 \uc788\uc8e0.\n\ub9de\ubc8c\uc774 \ubd80\ubd80\uac00 \ubd80\uc591\uac00\uc871\uc73c\ub85c 100\ub9cc \uc6d0\uc744 \uacf5\uc81c\ubc1b\ub294\ub2e4\uace0 \ud574\ubcfc\uac8c\uc694. \uacfc\uc138\ud45c\uc900\uc774 35% \uad6c\uac04\uc5d0 \ud574\ub2f9\ud558\ub294 \ubc30\uc6b0\uc790\ub77c\uba74 35\ub9cc \uc6d0, \uacfc\uc138\ud45c\uc900\uc774 24% \uad6c\uac04\uc5d0 \ud574\ub2f9\ud558\ub294 \ubc30\uc6b0\uc790\ub77c\uba74 24\ub9cc \uc6d0\uc744 \uc904\uc774\ub294 \ud6a8\uacfc\uac00 \ubc1c\uc0dd\ud574\uc694. \uadf8\ub7ec\ubbc0\ub85c \ubd80\uc591\uac00\uc871 \uae30\ubcf8\uacf5\uc81c\ub294 \uc18c\ub4dd\uc774 \ub192\uc740 \ubc30\uc6b0\uc790\uc5d0\uac8c \ubab0\uc544\uc8fc\ub294 \uac8c \uc720\ub9ac\ud574\uc694.\n\ub458\uc9f8. \uc758\ub8cc\ube44 \uc18c\ub4dd\uc774 \uc801\uc740 \ubc30\uc6b0\uc790\uc5d0\uac8c \ubab0\uc544\uc8fc\uc790\n\uc758\ub8cc\ube44\ub294 \uc18c\ub4dd\uc774 \uc801\uc740 \ubc30\uc6b0\uc790\uc5d0\uac8c \ubab0\uc544\uc8fc\ub294 \uac8c \uc720\ub9ac\ud558\ub2f5\ub2c8\ub2e4.",
"target": "\ub9de\ubc8c\uc774 \ubd80\ubd80\uc758 \uc5f0\ub9d0\uc815\uc0b0 \uacf5\uc81c \ucd5c\uc801\ud654\n\uccab\uc9f8. \ubd80\uc591\uac00\uc871 \uacf5\uc81c\ub294 \uc18c\ub4dd\uc774 \ub9ce\uc740 \ucabd\uc5d0 \ubab0\uc544\uc8fc\uc790\n\ub9ce\uc740 \uc0ac\ub78c\uc774 \ubc30\uc6b0\uc790 \uc911 \uc18c\ub4dd\uc774 \ub192\uc740 \ucabd\uc5d0 \uacf5\uc81c\ub97c \ubab0\uc544\uc8fc\ub294 \uac8c \uc720\ub9ac\ud558\ub2e4\uace0 \uc54c\uace0 \uc788\ub294\ub370\uc694. \ub9de\ub294 \ub9d0\uc774\uae30\ub3c4 \ud558\uace0, \ud2c0\ub9b0 \ub9d0\uc774\uae30\ub3c4 \ud574\uc694. \uc885\ud569\uc18c\ub4dd\uc138\ub294 \ub9ce\uc774 \ubc8c\uc218\ub85d \ub9ce\uc740 \uc18c\ub4dd\uc138\ub97c \ub0b4\uc57c \ud558\ub294 \ub204\uc9c4\uc138\uc728 \uad6c\uc870\ub85c \ub418\uc5b4 \uc788\uc5b4\uc694. \uadf8\ub798\uc11c \ub9de\ubc8c\uc774 \ubd80\ubd80 \uc5f0\ub9d0\uc815\uc0b0\uc5d0\uc11c\ub294 \uc18c\ub4dd\uc774 \ub192\uc740 \ucabd\uc73c\ub85c \uacf5\uc81c\ub97c \ubc1b\ub294 \uac8c \uc138\uc561 \uc0c1 \uc720\ub9ac\ud55c \ubd80\ubd84\uc774 \uc788\uc8e0.\n\uac00\uc7a5 \ub300\ud45c\uc801\uc73c\ub85c \ubd80\uc591\uac00\uc871 \uacf5\uc81c\uac00 \uc788\ub294\ub370\uc694.\n\ubd80\uc591\uac00\uc871 \uacf5\uc81c\ub780 \uc9c1\uacc4\uc874\uc18d(\ub9cc 60\uc138 \uc774\uc0c1), \uc9c1\uacc4\ube44\uc18d(\ub9cc 20\uc138 \uc774\ud558), \ud615\uc81c\uc790\ub9e4(\ub9cc 20\uc138 \uc774\ud558, \ub9cc 60\uc138 \uc774\uc0c1) \ub4f1\uc744 \ubd80\uc591\ud558\ub294 \uacbd\uc6b0 1\uc778\ub2f9 150\ub9cc \uc6d0\uc758 \uae30\ubcf8 \uc18c\ub4dd\uacf5\uc81c\ub97c \ud574\uc8fc\ub294 \uac78 \ub9d0\ud574\uc694.\n\uc5ec\uae30\uc5d0 70\uc138 \uc774\uc0c1 \uace0\ub839\uc790\uc5d0 \ub300\ud574 \uacbd\ub85c\uc6b0\ub300\uacf5\uc81c 100\ub9cc \uc6d0, \uc7a5\uc560\uc778 \uacf5\uc81c 200\ub9cc \uc6d0 \ub4f1\uc774 \ub354\ud574\uc838\uc694.\n\uc774\ub807\uac8c \ubd80\uc591\uac00\uc871\uc774 \uc788\ub294 \uacbd\uc6b0 \ubd80\ubd80 \uc911 \ud55c \uba85\uc774 \uacf5\uc81c\ubc1b\uc744 \uc218 \uc788\uc8e0.\n\ub9de\ubc8c\uc774 \ubd80\ubd80\uac00 \ubd80\uc591\uac00\uc871\uc73c\ub85c 100\ub9cc \uc6d0\uc744 \uacf5\uc81c\ubc1b\ub294\ub2e4\uace0 \ud574\ubcfc\uac8c\uc694. \uacfc\uc138\ud45c\uc900\uc774 35% \uad6c\uac04\uc5d0 \ud574\ub2f9\ud558\ub294 \ubc30\uc6b0\uc790\ub77c\uba74 35\ub9cc \uc6d0, \uacfc\uc138\ud45c\uc900\uc774 24% \uad6c\uac04\uc5d0 \ud574\ub2f9\ud558\ub294 \ubc30\uc6b0\uc790\ub77c\uba74 24\ub9cc \uc6d0\uc744 \uc904\uc774\ub294 \ud6a8\uacfc\uac00 \ubc1c\uc0dd\ud574\uc694. \uadf8\ub7ec\ubbc0\ub85c \ubd80\uc591\uac00\uc871 \uae30\ubcf8\uacf5\uc81c\ub294 \uc18c\ub4dd\uc774 \ub192\uc740 \ubc30\uc6b0\uc790\uc5d0\uac8c \ubab0\uc544\uc8fc\ub294 \uac8c \uc720\ub9ac\ud574\uc694.\n\ub458\uc9f8. \uc758\ub8cc\ube44 \uc18c\ub4dd\uc774 \uc801\uc740 \ubc30\uc6b0\uc790\uc5d0\uac8c \ubab0\uc544\uc8fc\uc790\n\uc758\ub8cc\ube44\ub294 \uc18c\ub4dd\uc774 \uc801\uc740 \ubc30\uc6b0\uc790\uc5d0\uac8c \ubab0\uc544\uc8fc\ub294 \uac8c \uc720\ub9ac\ud558\ub2f5\ub2c8\ub2e4."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_Unnamed: 0": "Value(dtype='int64', id=None)",
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', 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 | 159 |
| valid | 40 |
| sieu-n/autotrain-data-t5baseparaphrase | [
"task_categories:summarization",
"region:us"
]
| 2023-03-21T03:28:45+00:00 | {"task_categories": ["summarization"]} | 2023-03-21T03:29:31+00:00 | []
| []
| TAGS
#task_categories-summarization #region-us
| AutoTrain Dataset for project: t5baseparaphrase
===============================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project t5baseparaphrase.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-summarization #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
16,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-summarization #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
c01766f98ca32964d7e3808b89d2f28da2fb4b53 |
This dataset contains a single file full of network security questions in Chinese.
Could be used as good initial sources for scrapers, though not good as your browsing history.
| James4Ever0/network_security_questions | [
"license:wtfpl",
"region:us"
]
| 2023-03-21T05:20:43+00:00 | {"license": "wtfpl"} | 2024-01-29T16:25:21+00:00 | []
| []
| TAGS
#license-wtfpl #region-us
|
This dataset contains a single file full of network security questions in Chinese.
Could be used as good initial sources for scrapers, though not good as your browsing history.
| []
| [
"TAGS\n#license-wtfpl #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-wtfpl #region-us \n"
]
| [
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|
097357c7c76141cb1b2b5b3f311fe9439886f1ce | # Dataset Card for "sts17-crosslingual"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lingjzhu/sts17-crosslingual | [
"region:us"
]
| 2023-03-21T05:43:21+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "score", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 742173, "num_examples": 5346}], "download_size": 429499, "dataset_size": 742173}} | 2023-03-21T05:43:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sts17-crosslingual"
More Information needed | [
"# Dataset Card for \"sts17-crosslingual\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sts17-crosslingual\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sts17-crosslingual\"\n\nMore Information needed"
]
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|
f06b35f784489ca50bb1ff8ca804d7d6f40c1e69 | # AnyTXT Searcher
------
AnyTXT Searcher is a powerful local data full-text search engine, just like a local disk Google search engine. It is your free Google Desktop Search alternative.
AnyTXT Searcher has a powerful document parsing engine built in, which extracts the text of commonly used documents/images(OCR) without installing any other software, and combines the built-in high-speed indexing system to store the metadata of the text. You can quickly find any text that exists on your computer with the AnyTXT Searcher. It works on Windows 11, 10, 8, 7, Vista, XP(below 1.2.540), 2003, 2008, 2012, 2016, 2022 ...
### [Download Installer](https://anytxt.net/download/)
### [More ... ](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=57&ved=2ahUKEwiqx4jT-JvmAhWDJjQIHUJhB6Y4MhAWMAZ6BAgIEAE&url=https%3A%2F%2Fanytxt.net%2F&usg=AOvVaw22wtPNrBgzzwvh2hRvZm9I)
### Formats Supported
> * Plain Text Format (txt, cpp, html etc.)
> * Microsoft Word (doc, docx)
> * Microsoft Excel (xls, xlsx)
> * Microsoft PowerPoint (ppt, pptx)
> * Microsoft OneNote (one)
> * Portable Document Format (pdf)
> * eBook Format (epub, mobi, djvu, chm, fb2, azw(3) etc.)
> * WPS Word Format (wps)
> * WPS Excel Format (et)
> * WPS PowerPoint Format (dps)
> * Open Document Format (OpenOffice, LibreOffice etc.)
> * Mind Map Format (lighten, mmap, mm, xmind etc.)
> * Open Fixed-layout Document Format (ofd)
> * Edraw Max Format (eddx)
> * WizNote Format (ziw)
> * Image Format (jpg, png, bmp, gif etc.)
> * Binary File (exe, dll, so)
> * More Document Types please let us know
### More Features
> * Microsoft Office (doc, xls, ppt) Full Text Index Supported
> * Microsoft Office 2007 (docx, xlsx, pptx, docm, xlsm, docm) Full Text Index Supported
> * Multi-language(include Chinese,Japanese,Korean) Document Full Text Index Supported
> * WPS Office (wps, et, dps) Full Text Index Supported
> * Image text (png, jpg, bmp etc.) Full Text Index Supported
> * Fast Full-Text Index
> * Fast Full-Text Search, almost in 0.1 second
> * Keyword View
> * SSD Optimization
> * HTTP Search Service (Beta)
> * Realtime Sync Full-Text Index (Beta)
> * Hight DPI Supported
> * Startup at Boot
> * Scanned PDF text search
### Changelog
#### 2023-3-4 [Version 1.3.1071](https://anytxt.net/download)
1. Added the feature of online search at raw text preview window;
2. Fixed the issue that [indexing never completes](https://anytxt.net/forums/topic/indexing-never-completes/);
3. Fixed some other known issues;
#### 2023-2-1 Version 1.3.1043
1. Added the feature of starting Anytxt at boot;
2. Added the feature of scanned PDF text indexing and search;
3. Added the feature of changing Anytxt font;
4. Fixed the issue that USB device occupation;
5. Fixed the issue that some [azw3 file encodes detect incorrectly](https://anytxt.net/forums/topic/some-of-the-azw3-files-are-not-rendered-properly/);
6. Fixed some other known issues;
#### 2022-11-26 Version 1.3.1019
1. Added support for binary file (exe, so, dll) full-text indexing and search;
2. Added the shortcut key Atl+G to the preview window to search for global content;
3. Added the shortcut key Atl+S to the preview window to search the content of this file;
4. Add the snippet to the search result item in the preview window;
5. Fixed the issue of characters in Japanese, Korean, Chinese, and other local encodings of chm files;
6. Fixed the issue that the wrong file name was displayed in the preview window after the result list item was clicked;
7. Fixed some other known issues;
#### 2022-10-30 Version 1.2.993
1. Added support for Edraw Max full-text indexing and search;
2. Added support for WizNote full-text indexing and search;
3. Added support for text in image (OCR) full-text indexing and search(Beta);
4. Added the feature of exporting the search result list;
5. Added the feature of show search terms snippets;
6. Added the feature of the multi-preview window;
7. Fixed the issue of google translation not working in China;
8. Fixed the issue of crash on some computers at ver1.2.941;
9. Fixed some other known issues;
#### 2022-9-3 Version 1.2.941
1. Added the feature of the line number;
2. Added the feature of the line match-tags in the text preview window scrollbar;
3. Added the feature of regex search in the preview window;
4. Fixed the issue that the indexing service works abnormally;
6. Try to fix the issue of freezing on some computers;
7. Fixed some other known issues;
#### 2022-7-30 Version 1.2.901
1. Added support for full-text indexing and searching in mind map formats: lighten, mmap, mm, xmind, etc.;
2. Added support for djvu and azw(3) full-text indexing and search;
3. Added support for ofd full-text indexing and search;
4. Add 64-bit program support;
5. Speed up indexing and searching;
6. Add right-click search in the text preview window;
7. Fixed the unclear fonts issue;
8. Added 한국어 language, thanks to VenusGirl – 비너스걸❤;
9. Fixed some known issues;
#### 2022-4-17 Version 1.2.715 (Beta)
1. Added the feature of syncing the index of specified folders;
#### 2022-4-10 Version 1.2.703 (Beta)
1. Added the feature of advanced search syntax (beta), you can use &|!"() to do some advanced search;
2. Added the feature of theme, Anytxt comes with 3 themes: default, light, dark;
3. Added the feature of HTTP search service (Beta), the fixed http server listen port is 9921, you can change the port in coming version;
4. Added the feature of editing multiple file types' inclusion and exclusion rules for indexing;
5. Fixed the issue that Anytxt may crash on some Windows 11;
6. Added Polski language, thanks to Dmocha;
7. Added עברית language, thanks to Yeshurun Kubi;
#### 2021-7-16 Version 1.2.540
1. Added the feature of custom hotkeys;
#### 2021-6-15 Version 1.2.532
1. Added Nederlands language, thanks to Atze;
2. Added Українська language, thanks to Helly;
3. Added the feature of rebuilding the index;
#### 2021-5-31 [Version 1.2.523]
1. Added online translation feature, supporting Google Translate, Bing Translator, and Yandex Translate. This feature requires the Internet;
2. Added toolbar;
3. Added forward and back feature for file text preview;
4. Added Microsoft OneNote file to default index file types;
5. Added Русский language, thanks to Антон Мырзин aka Paperdaemon;
6. Fix some issues;
#### 2021-4-23 [Version 1.2.483]
1. You can turn on/off the feature of displaying Anytxt in the system tray;
2. Added the feature of displaying Anytxt to the system context menu. You can start Anytxt directly in the system context menu;
3. Added the feature of filtering search results by directory;
4. Added the feature of filtering search results by multiple file types;
5. Fix some issues;
#### 2021-4-2 [Version 1.2.445]
1. Added the feature of that closing Anytxt to the system tray;
2. Added the feature of that word segmentation by space for Chinese, Japanese, Korean, and Vietnamese;
3. Added the feature of that results are default sorted by match degree and the relevance of documents to a given search query;
4. Optimized search speed, almost in 0.5 sec;
5. Fixed the issue that no files can be scanned on the FAT file system;
6. Fixed the issue that the full-text index engine could not work on some network devices;
7. Fixed some other known issues;
#### 2021-1-15 [Version 1.2.394]
1. Added the zoom-in/out feature;
2. Added the word wrap feature;
3. Added support for full-text indexing and searching in WPS Office formats .wps .et and .dps;
4. Improved performance when updating index data;
5. Fixed some other known issues;
6. Added multi-language support based on [google translation](https://translate.google.com/) for [www.anytxt.net](https://anytxt.net). It may not works fine in Mainland China;
#### 2020-12-4
1. Added the feature of right-click opening the contain file folder(s);
2. Added the feature of right-click copying the full path of the file(s);
#### 2020-12-1
1. Try to fix the index database corruption issue;
2. Added support for German, provided by dhu. Thank you very much;
3. Fixed some other known issues;
#### 2020-11-12
1. Added the feature of setting index database store path;
2. Fixed the issue that AnyTXT Searcher would block when starting;
3. Fixed some other known issues;
#### 2020-10-2
1. Added the feature of keyword browsing;
#### 2020-8-31
1. Added support for NAS devices. NAS storage based on Microsoft SMB protocol and CIFS protocol has been tested, and it works perfect. Other types of remote storage have not been tested;
#### 2020-6-13
1. Added support for full-text indexing and searching in e-book format epub;
2. Added support for full-text indexing and searching in e-book format mobi;
3. Added support for full-text indexing and searching in format chm;
4. Added support for full-text indexing and searching in e-book format fb2;
5. Added real-time displaying of the index status;
6. Added support for High DPI;
#### 2020-4-12
1. Added support for updating the full-text index database manually;
2. Added support for setting automatic update full-text index cycle;
3. Added support for starting and stopping full-text indexing service;
#### 2020-2-27
1. Added the command line;
2. Fixed known issues;
#### 2019-11-29
1. Try to fix 100% CPU usage issue;
2. Fix the issue of re-indexing during file update;
#### 2019-11-9
1. Added fuzzy matching search;
2. Added whole matching search(Beta);
3. Added Multi-language support. Currently, the Chinese language has been added due to there are many Chinese users;
You are welcome to translate it(English.ini) into your local language, I will integrate it into the installation package;
4. Fix some issues;
#### 2019-10-4
1. Added removal of the index;
2. Added the index rule feature;
3. Added the Ctrl+C feature;
4. Added the Ctrl+X feature;
5. Added the Delete feature;
6. Added the automatic detection of the new version feature;
7. Fix some issues;
#### 2019-6-24
1. Added snippets to the search results;
#### 2019-6-11
1. Added an icon to the search button;
#### 2019-6-8
1. Added support for the none-NTFS file system;
2. Speed up file traversal;
3. Reduced computer resource consumption;
4. Fixed some issues;
#### 2019-6-2
1. Added a community link to the help menu to get users' request;
2. Added support dragging for the search results list;
#### 2019-5-23:
1. Optimized indexing speed;
2. Optimized support for Arabic language based on user feedback;
3. Optimized support for Chinese language based on user feedback;
4. Optimized support for Korean language based on user feedback;
5. Optimized support for Japanese language based on user feedback;
6. Optimized the loading interface when the program starts; | anytxt/release | [
"region:us"
]
| 2023-03-21T05:56:52+00:00 | {} | 2023-03-22T07:13:35+00:00 | []
| []
| TAGS
#region-us
| # AnyTXT Searcher
------
AnyTXT Searcher is a powerful local data full-text search engine, just like a local disk Google search engine. It is your free Google Desktop Search alternative.
AnyTXT Searcher has a powerful document parsing engine built in, which extracts the text of commonly used documents/images(OCR) without installing any other software, and combines the built-in high-speed indexing system to store the metadata of the text. You can quickly find any text that exists on your computer with the AnyTXT Searcher. It works on Windows 11, 10, 8, 7, Vista, XP(below 1.2.540), 2003, 2008, 2012, 2016, 2022 ...
### Download Installer
### More ...
### Formats Supported
> * Plain Text Format (txt, cpp, html etc.)
> * Microsoft Word (doc, docx)
> * Microsoft Excel (xls, xlsx)
> * Microsoft PowerPoint (ppt, pptx)
> * Microsoft OneNote (one)
> * Portable Document Format (pdf)
> * eBook Format (epub, mobi, djvu, chm, fb2, azw(3) etc.)
> * WPS Word Format (wps)
> * WPS Excel Format (et)
> * WPS PowerPoint Format (dps)
> * Open Document Format (OpenOffice, LibreOffice etc.)
> * Mind Map Format (lighten, mmap, mm, xmind etc.)
> * Open Fixed-layout Document Format (ofd)
> * Edraw Max Format (eddx)
> * WizNote Format (ziw)
> * Image Format (jpg, png, bmp, gif etc.)
> * Binary File (exe, dll, so)
> * More Document Types please let us know
### More Features
> * Microsoft Office (doc, xls, ppt) Full Text Index Supported
> * Microsoft Office 2007 (docx, xlsx, pptx, docm, xlsm, docm) Full Text Index Supported
> * Multi-language(include Chinese,Japanese,Korean) Document Full Text Index Supported
> * WPS Office (wps, et, dps) Full Text Index Supported
> * Image text (png, jpg, bmp etc.) Full Text Index Supported
> * Fast Full-Text Index
> * Fast Full-Text Search, almost in 0.1 second
> * Keyword View
> * SSD Optimization
> * HTTP Search Service (Beta)
> * Realtime Sync Full-Text Index (Beta)
> * Hight DPI Supported
> * Startup at Boot
> * Scanned PDF text search
### Changelog
#### 2023-3-4 Version 1.3.1071
1. Added the feature of online search at raw text preview window;
2. Fixed the issue that indexing never completes;
3. Fixed some other known issues;
#### 2023-2-1 Version 1.3.1043
1. Added the feature of starting Anytxt at boot;
2. Added the feature of scanned PDF text indexing and search;
3. Added the feature of changing Anytxt font;
4. Fixed the issue that USB device occupation;
5. Fixed the issue that some azw3 file encodes detect incorrectly;
6. Fixed some other known issues;
#### 2022-11-26 Version 1.3.1019
1. Added support for binary file (exe, so, dll) full-text indexing and search;
2. Added the shortcut key Atl+G to the preview window to search for global content;
3. Added the shortcut key Atl+S to the preview window to search the content of this file;
4. Add the snippet to the search result item in the preview window;
5. Fixed the issue of characters in Japanese, Korean, Chinese, and other local encodings of chm files;
6. Fixed the issue that the wrong file name was displayed in the preview window after the result list item was clicked;
7. Fixed some other known issues;
#### 2022-10-30 Version 1.2.993
1. Added support for Edraw Max full-text indexing and search;
2. Added support for WizNote full-text indexing and search;
3. Added support for text in image (OCR) full-text indexing and search(Beta);
4. Added the feature of exporting the search result list;
5. Added the feature of show search terms snippets;
6. Added the feature of the multi-preview window;
7. Fixed the issue of google translation not working in China;
8. Fixed the issue of crash on some computers at ver1.2.941;
9. Fixed some other known issues;
#### 2022-9-3 Version 1.2.941
1. Added the feature of the line number;
2. Added the feature of the line match-tags in the text preview window scrollbar;
3. Added the feature of regex search in the preview window;
4. Fixed the issue that the indexing service works abnormally;
6. Try to fix the issue of freezing on some computers;
7. Fixed some other known issues;
#### 2022-7-30 Version 1.2.901
1. Added support for full-text indexing and searching in mind map formats: lighten, mmap, mm, xmind, etc.;
2. Added support for djvu and azw(3) full-text indexing and search;
3. Added support for ofd full-text indexing and search;
4. Add 64-bit program support;
5. Speed up indexing and searching;
6. Add right-click search in the text preview window;
7. Fixed the unclear fonts issue;
8. Added 한국어 language, thanks to VenusGirl – 비너스걸;
9. Fixed some known issues;
#### 2022-4-17 Version 1.2.715 (Beta)
1. Added the feature of syncing the index of specified folders;
#### 2022-4-10 Version 1.2.703 (Beta)
1. Added the feature of advanced search syntax (beta), you can use &|!"() to do some advanced search;
2. Added the feature of theme, Anytxt comes with 3 themes: default, light, dark;
3. Added the feature of HTTP search service (Beta), the fixed http server listen port is 9921, you can change the port in coming version;
4. Added the feature of editing multiple file types' inclusion and exclusion rules for indexing;
5. Fixed the issue that Anytxt may crash on some Windows 11;
6. Added Polski language, thanks to Dmocha;
7. Added עברית language, thanks to Yeshurun Kubi;
#### 2021-7-16 Version 1.2.540
1. Added the feature of custom hotkeys;
#### 2021-6-15 Version 1.2.532
1. Added Nederlands language, thanks to Atze;
2. Added Українська language, thanks to Helly;
3. Added the feature of rebuilding the index;
#### 2021-5-31 [Version 1.2.523]
1. Added online translation feature, supporting Google Translate, Bing Translator, and Yandex Translate. This feature requires the Internet;
2. Added toolbar;
3. Added forward and back feature for file text preview;
4. Added Microsoft OneNote file to default index file types;
5. Added Русский language, thanks to Антон Мырзин aka Paperdaemon;
6. Fix some issues;
#### 2021-4-23 [Version 1.2.483]
1. You can turn on/off the feature of displaying Anytxt in the system tray;
2. Added the feature of displaying Anytxt to the system context menu. You can start Anytxt directly in the system context menu;
3. Added the feature of filtering search results by directory;
4. Added the feature of filtering search results by multiple file types;
5. Fix some issues;
#### 2021-4-2 [Version 1.2.445]
1. Added the feature of that closing Anytxt to the system tray;
2. Added the feature of that word segmentation by space for Chinese, Japanese, Korean, and Vietnamese;
3. Added the feature of that results are default sorted by match degree and the relevance of documents to a given search query;
4. Optimized search speed, almost in 0.5 sec;
5. Fixed the issue that no files can be scanned on the FAT file system;
6. Fixed the issue that the full-text index engine could not work on some network devices;
7. Fixed some other known issues;
#### 2021-1-15 [Version 1.2.394]
1. Added the zoom-in/out feature;
2. Added the word wrap feature;
3. Added support for full-text indexing and searching in WPS Office formats .wps .et and .dps;
4. Improved performance when updating index data;
5. Fixed some other known issues;
6. Added multi-language support based on google translation for URL. It may not works fine in Mainland China;
#### 2020-12-4
1. Added the feature of right-click opening the contain file folder(s);
2. Added the feature of right-click copying the full path of the file(s);
#### 2020-12-1
1. Try to fix the index database corruption issue;
2. Added support for German, provided by dhu. Thank you very much;
3. Fixed some other known issues;
#### 2020-11-12
1. Added the feature of setting index database store path;
2. Fixed the issue that AnyTXT Searcher would block when starting;
3. Fixed some other known issues;
#### 2020-10-2
1. Added the feature of keyword browsing;
#### 2020-8-31
1. Added support for NAS devices. NAS storage based on Microsoft SMB protocol and CIFS protocol has been tested, and it works perfect. Other types of remote storage have not been tested;
#### 2020-6-13
1. Added support for full-text indexing and searching in e-book format epub;
2. Added support for full-text indexing and searching in e-book format mobi;
3. Added support for full-text indexing and searching in format chm;
4. Added support for full-text indexing and searching in e-book format fb2;
5. Added real-time displaying of the index status;
6. Added support for High DPI;
#### 2020-4-12
1. Added support for updating the full-text index database manually;
2. Added support for setting automatic update full-text index cycle;
3. Added support for starting and stopping full-text indexing service;
#### 2020-2-27
1. Added the command line;
2. Fixed known issues;
#### 2019-11-29
1. Try to fix 100% CPU usage issue;
2. Fix the issue of re-indexing during file update;
#### 2019-11-9
1. Added fuzzy matching search;
2. Added whole matching search(Beta);
3. Added Multi-language support. Currently, the Chinese language has been added due to there are many Chinese users;
You are welcome to translate it(URL) into your local language, I will integrate it into the installation package;
4. Fix some issues;
#### 2019-10-4
1. Added removal of the index;
2. Added the index rule feature;
3. Added the Ctrl+C feature;
4. Added the Ctrl+X feature;
5. Added the Delete feature;
6. Added the automatic detection of the new version feature;
7. Fix some issues;
#### 2019-6-24
1. Added snippets to the search results;
#### 2019-6-11
1. Added an icon to the search button;
#### 2019-6-8
1. Added support for the none-NTFS file system;
2. Speed up file traversal;
3. Reduced computer resource consumption;
4. Fixed some issues;
#### 2019-6-2
1. Added a community link to the help menu to get users' request;
2. Added support dragging for the search results list;
#### 2019-5-23:
1. Optimized indexing speed;
2. Optimized support for Arabic language based on user feedback;
3. Optimized support for Chinese language based on user feedback;
4. Optimized support for Korean language based on user feedback;
5. Optimized support for Japanese language based on user feedback;
6. Optimized the loading interface when the program starts; | [
"# AnyTXT Searcher\n\n------\n\nAnyTXT Searcher is a powerful local data full-text search engine, just like a local disk Google search engine. It is your free Google Desktop Search alternative.\nAnyTXT Searcher has a powerful document parsing engine built in, which extracts the text of commonly used documents/images(OCR) without installing any other software, and combines the built-in high-speed indexing system to store the metadata of the text. You can quickly find any text that exists on your computer with the AnyTXT Searcher. It works on Windows 11, 10, 8, 7, Vista, XP(below 1.2.540), 2003, 2008, 2012, 2016, 2022 ...",
"### Download Installer",
"### More ...",
"### Formats Supported\n> * Plain Text Format (txt, cpp, html etc.)\n> * Microsoft Word (doc, docx)\n> * Microsoft Excel (xls, xlsx)\n> * Microsoft PowerPoint (ppt, pptx)\n> * Microsoft OneNote (one)\n> * Portable Document Format (pdf)\n> * eBook Format (epub, mobi, djvu, chm, fb2, azw(3) etc.)\n> * WPS Word Format (wps)\n> * WPS Excel Format (et)\n> * WPS PowerPoint Format (dps)\n> * Open Document Format (OpenOffice, LibreOffice etc.)\n> * Mind Map Format (lighten, mmap, mm, xmind etc.)\n> * Open Fixed-layout Document Format (ofd)\n> * Edraw Max Format (eddx)\n> * WizNote Format (ziw)\n> * Image Format (jpg, png, bmp, gif etc.)\n> * Binary File (exe, dll, so)\n> * More Document Types please let us know",
"### More Features\n> * Microsoft Office (doc, xls, ppt) Full Text Index Supported\n> * Microsoft Office 2007 (docx, xlsx, pptx, docm, xlsm, docm) Full Text Index Supported\n> * Multi-language(include Chinese,Japanese,Korean) Document Full Text Index Supported\n> * WPS Office (wps, et, dps) Full Text Index Supported\n> * Image text (png, jpg, bmp etc.) Full Text Index Supported\n> * Fast Full-Text Index\n> * Fast Full-Text Search, almost in 0.1 second\n> * Keyword View\n> * SSD Optimization\n> * HTTP Search Service (Beta)\n> * Realtime Sync Full-Text Index (Beta)\n> * Hight DPI Supported\n> * Startup at Boot\n> * Scanned PDF text search",
"### Changelog",
"#### 2023-3-4 Version 1.3.1071\n 1. Added the feature of online search at raw text preview window; \n 2. Fixed the issue that indexing never completes; \n 3. Fixed some other known issues;",
"#### 2023-2-1 Version 1.3.1043\n 1. Added the feature of starting Anytxt at boot; \n 2. Added the feature of scanned PDF text indexing and search; \n 3. Added the feature of changing Anytxt font; \n 4. Fixed the issue that USB device occupation; \n 5. Fixed the issue that some azw3 file encodes detect incorrectly;\n 6. Fixed some other known issues;",
"#### 2022-11-26 Version 1.3.1019\n 1. Added support for binary file (exe, so, dll) full-text indexing and search;\n 2. Added the shortcut key Atl+G to the preview window to search for global content;\n 3. Added the shortcut key Atl+S to the preview window to search the content of this file;\n 4. Add the snippet to the search result item in the preview window;\n 5. Fixed the issue of characters in Japanese, Korean, Chinese, and other local encodings of chm files;\n 6. Fixed the issue that the wrong file name was displayed in the preview window after the result list item was clicked;\n 7. Fixed some other known issues;",
"#### 2022-10-30 Version 1.2.993\n 1. Added support for Edraw Max full-text indexing and search;\n 2. Added support for WizNote full-text indexing and search;\n 3. Added support for text in image (OCR) full-text indexing and search(Beta);\n 4. Added the feature of exporting the search result list;\n 5. Added the feature of show search terms snippets;\n 6. Added the feature of the multi-preview window;\n 7. Fixed the issue of google translation not working in China;\n 8. Fixed the issue of crash on some computers at ver1.2.941;\n 9. Fixed some other known issues;",
"#### 2022-9-3 Version 1.2.941\n 1. Added the feature of the line number;\n 2. Added the feature of the line match-tags in the text preview window scrollbar;\n 3. Added the feature of regex search in the preview window;\n 4. Fixed the issue that the indexing service works abnormally;\n 6. Try to fix the issue of freezing on some computers;\n 7. Fixed some other known issues;",
"#### 2022-7-30 Version 1.2.901\n 1. Added support for full-text indexing and searching in mind map formats: lighten, mmap, mm, xmind, etc.;\n 2. Added support for djvu and azw(3) full-text indexing and search;\n 3. Added support for ofd full-text indexing and search;\n 4. Add 64-bit program support;\n 5. Speed up indexing and searching;\n 6. Add right-click search in the text preview window;\n 7. Fixed the unclear fonts issue;\n 8. Added 한국어 language, thanks to VenusGirl – 비너스걸;\n 9. Fixed some known issues;",
"#### 2022-4-17 Version 1.2.715 (Beta)\n 1. Added the feature of syncing the index of specified folders;",
"#### 2022-4-10 Version 1.2.703 (Beta)\n 1. Added the feature of advanced search syntax (beta), you can use &|!\"() to do some advanced search;\n 2. Added the feature of theme, Anytxt comes with 3 themes: default, light, dark;\n 3. Added the feature of HTTP search service (Beta), the fixed http server listen port is 9921, you can change the port in coming version;\n 4. Added the feature of editing multiple file types' inclusion and exclusion rules for indexing;\n 5. Fixed the issue that Anytxt may crash on some Windows 11; \n 6. Added Polski language, thanks to Dmocha;\n 7. Added עברית language, thanks to Yeshurun Kubi;",
"#### 2021-7-16 Version 1.2.540\n 1. Added the feature of custom hotkeys;",
"#### 2021-6-15 Version 1.2.532\n 1. Added Nederlands language, thanks to Atze;\n 2. Added Українська language, thanks to Helly;\n 3. Added the feature of rebuilding the index;",
"#### 2021-5-31 [Version 1.2.523]\n 1. Added online translation feature, supporting Google Translate, Bing Translator, and Yandex Translate. This feature requires the Internet;\n 2. Added toolbar;\n 3. Added forward and back feature for file text preview;\n 4. Added Microsoft OneNote file to default index file types;\n 5. Added Русский language, thanks to Антон Мырзин aka Paperdaemon;\n 6. Fix some issues;",
"#### 2021-4-23 [Version 1.2.483]\n 1. You can turn on/off the feature of displaying Anytxt in the system tray;\n 2. Added the feature of displaying Anytxt to the system context menu. You can start Anytxt directly in the system context menu;\n 3. Added the feature of filtering search results by directory;\n 4. Added the feature of filtering search results by multiple file types;\n 5. Fix some issues;",
"#### 2021-4-2 [Version 1.2.445]\n 1. Added the feature of that closing Anytxt to the system tray;\n 2. Added the feature of that word segmentation by space for Chinese, Japanese, Korean, and Vietnamese;\n 3. Added the feature of that results are default sorted by match degree and the relevance of documents to a given search query;\n 4. Optimized search speed, almost in 0.5 sec;\n 5. Fixed the issue that no files can be scanned on the FAT file system;\n 6. Fixed the issue that the full-text index engine could not work on some network devices;\n 7. Fixed some other known issues;",
"#### 2021-1-15 [Version 1.2.394]\n 1. Added the zoom-in/out feature;\n 2. Added the word wrap feature;\n 3. Added support for full-text indexing and searching in WPS Office formats .wps .et and .dps;\n 4. Improved performance when updating index data;\n 5. Fixed some other known issues;\n 6. Added multi-language support based on google translation for URL. It may not works fine in Mainland China;",
"#### 2020-12-4\n 1. Added the feature of right-click opening the contain file folder(s);\n 2. Added the feature of right-click copying the full path of the file(s);",
"#### 2020-12-1\n 1. Try to fix the index database corruption issue;\n 2. Added support for German, provided by dhu. Thank you very much;\n 3. Fixed some other known issues;",
"#### 2020-11-12\n 1. Added the feature of setting index database store path;\n 2. Fixed the issue that AnyTXT Searcher would block when starting;\n 3. Fixed some other known issues;",
"#### 2020-10-2\n 1. Added the feature of keyword browsing;",
"#### 2020-8-31\n 1. Added support for NAS devices. NAS storage based on Microsoft SMB protocol and CIFS protocol has been tested, and it works perfect. Other types of remote storage have not been tested;",
"#### 2020-6-13\n 1. Added support for full-text indexing and searching in e-book format epub;\n 2. Added support for full-text indexing and searching in e-book format mobi;\n 3. Added support for full-text indexing and searching in format chm;\n 4. Added support for full-text indexing and searching in e-book format fb2;\n 5. Added real-time displaying of the index status;\n 6. Added support for High DPI;",
"#### 2020-4-12\n 1. Added support for updating the full-text index database manually;\n 2. Added support for setting automatic update full-text index cycle;\n 3. Added support for starting and stopping full-text indexing service;",
"#### 2020-2-27\n 1. Added the command line;\n 2. Fixed known issues;",
"#### 2019-11-29\n 1. Try to fix 100% CPU usage issue;\n 2. Fix the issue of re-indexing during file update;",
"#### 2019-11-9\n 1. Added fuzzy matching search;\n 2. Added whole matching search(Beta);\n 3. Added Multi-language support. Currently, the Chinese language has been added due to there are many Chinese users;\n You are welcome to translate it(URL) into your local language, I will integrate it into the installation package;\n 4. Fix some issues;",
"#### 2019-10-4\n 1. Added removal of the index;\n 2. Added the index rule feature;\n 3. Added the Ctrl+C feature;\n 4. Added the Ctrl+X feature;\n 5. Added the Delete feature;\n 6. Added the automatic detection of the new version feature;\n 7. Fix some issues;",
"#### 2019-6-24\n 1. Added snippets to the search results;",
"#### 2019-6-11\n 1. Added an icon to the search button;",
"#### 2019-6-8\n 1. Added support for the none-NTFS file system;\n 2. Speed up file traversal;\n 3. Reduced computer resource consumption;\n 4. Fixed some issues;",
"#### 2019-6-2\n 1. Added a community link to the help menu to get users' request;\n 2. Added support dragging for the search results list;",
"#### 2019-5-23:\n 1. Optimized indexing speed;\n 2. Optimized support for Arabic language based on user feedback;\n 3. Optimized support for Chinese language based on user feedback;\n 4. Optimized support for Korean language based on user feedback;\n 5. Optimized support for Japanese language based on user feedback;\n 6. Optimized the loading interface when the program starts;"
]
| [
"TAGS\n#region-us \n",
"# AnyTXT Searcher\n\n------\n\nAnyTXT Searcher is a powerful local data full-text search engine, just like a local disk Google search engine. It is your free Google Desktop Search alternative.\nAnyTXT Searcher has a powerful document parsing engine built in, which extracts the text of commonly used documents/images(OCR) without installing any other software, and combines the built-in high-speed indexing system to store the metadata of the text. You can quickly find any text that exists on your computer with the AnyTXT Searcher. It works on Windows 11, 10, 8, 7, Vista, XP(below 1.2.540), 2003, 2008, 2012, 2016, 2022 ...",
"### Download Installer",
"### More ...",
"### Formats Supported\n> * Plain Text Format (txt, cpp, html etc.)\n> * Microsoft Word (doc, docx)\n> * Microsoft Excel (xls, xlsx)\n> * Microsoft PowerPoint (ppt, pptx)\n> * Microsoft OneNote (one)\n> * Portable Document Format (pdf)\n> * eBook Format (epub, mobi, djvu, chm, fb2, azw(3) etc.)\n> * WPS Word Format (wps)\n> * WPS Excel Format (et)\n> * WPS PowerPoint Format (dps)\n> * Open Document Format (OpenOffice, LibreOffice etc.)\n> * Mind Map Format (lighten, mmap, mm, xmind etc.)\n> * Open Fixed-layout Document Format (ofd)\n> * Edraw Max Format (eddx)\n> * WizNote Format (ziw)\n> * Image Format (jpg, png, bmp, gif etc.)\n> * Binary File (exe, dll, so)\n> * More Document Types please let us know",
"### More Features\n> * Microsoft Office (doc, xls, ppt) Full Text Index Supported\n> * Microsoft Office 2007 (docx, xlsx, pptx, docm, xlsm, docm) Full Text Index Supported\n> * Multi-language(include Chinese,Japanese,Korean) Document Full Text Index Supported\n> * WPS Office (wps, et, dps) Full Text Index Supported\n> * Image text (png, jpg, bmp etc.) Full Text Index Supported\n> * Fast Full-Text Index\n> * Fast Full-Text Search, almost in 0.1 second\n> * Keyword View\n> * SSD Optimization\n> * HTTP Search Service (Beta)\n> * Realtime Sync Full-Text Index (Beta)\n> * Hight DPI Supported\n> * Startup at Boot\n> * Scanned PDF text search",
"### Changelog",
"#### 2023-3-4 Version 1.3.1071\n 1. Added the feature of online search at raw text preview window; \n 2. Fixed the issue that indexing never completes; \n 3. Fixed some other known issues;",
"#### 2023-2-1 Version 1.3.1043\n 1. Added the feature of starting Anytxt at boot; \n 2. Added the feature of scanned PDF text indexing and search; \n 3. Added the feature of changing Anytxt font; \n 4. Fixed the issue that USB device occupation; \n 5. Fixed the issue that some azw3 file encodes detect incorrectly;\n 6. Fixed some other known issues;",
"#### 2022-11-26 Version 1.3.1019\n 1. Added support for binary file (exe, so, dll) full-text indexing and search;\n 2. Added the shortcut key Atl+G to the preview window to search for global content;\n 3. Added the shortcut key Atl+S to the preview window to search the content of this file;\n 4. Add the snippet to the search result item in the preview window;\n 5. Fixed the issue of characters in Japanese, Korean, Chinese, and other local encodings of chm files;\n 6. Fixed the issue that the wrong file name was displayed in the preview window after the result list item was clicked;\n 7. Fixed some other known issues;",
"#### 2022-10-30 Version 1.2.993\n 1. Added support for Edraw Max full-text indexing and search;\n 2. Added support for WizNote full-text indexing and search;\n 3. Added support for text in image (OCR) full-text indexing and search(Beta);\n 4. Added the feature of exporting the search result list;\n 5. Added the feature of show search terms snippets;\n 6. Added the feature of the multi-preview window;\n 7. Fixed the issue of google translation not working in China;\n 8. Fixed the issue of crash on some computers at ver1.2.941;\n 9. Fixed some other known issues;",
"#### 2022-9-3 Version 1.2.941\n 1. Added the feature of the line number;\n 2. Added the feature of the line match-tags in the text preview window scrollbar;\n 3. Added the feature of regex search in the preview window;\n 4. Fixed the issue that the indexing service works abnormally;\n 6. Try to fix the issue of freezing on some computers;\n 7. Fixed some other known issues;",
"#### 2022-7-30 Version 1.2.901\n 1. Added support for full-text indexing and searching in mind map formats: lighten, mmap, mm, xmind, etc.;\n 2. Added support for djvu and azw(3) full-text indexing and search;\n 3. Added support for ofd full-text indexing and search;\n 4. Add 64-bit program support;\n 5. Speed up indexing and searching;\n 6. Add right-click search in the text preview window;\n 7. Fixed the unclear fonts issue;\n 8. Added 한국어 language, thanks to VenusGirl – 비너스걸;\n 9. Fixed some known issues;",
"#### 2022-4-17 Version 1.2.715 (Beta)\n 1. Added the feature of syncing the index of specified folders;",
"#### 2022-4-10 Version 1.2.703 (Beta)\n 1. Added the feature of advanced search syntax (beta), you can use &|!\"() to do some advanced search;\n 2. Added the feature of theme, Anytxt comes with 3 themes: default, light, dark;\n 3. Added the feature of HTTP search service (Beta), the fixed http server listen port is 9921, you can change the port in coming version;\n 4. Added the feature of editing multiple file types' inclusion and exclusion rules for indexing;\n 5. Fixed the issue that Anytxt may crash on some Windows 11; \n 6. Added Polski language, thanks to Dmocha;\n 7. Added עברית language, thanks to Yeshurun Kubi;",
"#### 2021-7-16 Version 1.2.540\n 1. Added the feature of custom hotkeys;",
"#### 2021-6-15 Version 1.2.532\n 1. Added Nederlands language, thanks to Atze;\n 2. Added Українська language, thanks to Helly;\n 3. Added the feature of rebuilding the index;",
"#### 2021-5-31 [Version 1.2.523]\n 1. Added online translation feature, supporting Google Translate, Bing Translator, and Yandex Translate. This feature requires the Internet;\n 2. Added toolbar;\n 3. Added forward and back feature for file text preview;\n 4. Added Microsoft OneNote file to default index file types;\n 5. Added Русский language, thanks to Антон Мырзин aka Paperdaemon;\n 6. Fix some issues;",
"#### 2021-4-23 [Version 1.2.483]\n 1. You can turn on/off the feature of displaying Anytxt in the system tray;\n 2. Added the feature of displaying Anytxt to the system context menu. You can start Anytxt directly in the system context menu;\n 3. Added the feature of filtering search results by directory;\n 4. Added the feature of filtering search results by multiple file types;\n 5. Fix some issues;",
"#### 2021-4-2 [Version 1.2.445]\n 1. Added the feature of that closing Anytxt to the system tray;\n 2. Added the feature of that word segmentation by space for Chinese, Japanese, Korean, and Vietnamese;\n 3. Added the feature of that results are default sorted by match degree and the relevance of documents to a given search query;\n 4. Optimized search speed, almost in 0.5 sec;\n 5. Fixed the issue that no files can be scanned on the FAT file system;\n 6. Fixed the issue that the full-text index engine could not work on some network devices;\n 7. Fixed some other known issues;",
"#### 2021-1-15 [Version 1.2.394]\n 1. Added the zoom-in/out feature;\n 2. Added the word wrap feature;\n 3. Added support for full-text indexing and searching in WPS Office formats .wps .et and .dps;\n 4. Improved performance when updating index data;\n 5. Fixed some other known issues;\n 6. Added multi-language support based on google translation for URL. It may not works fine in Mainland China;",
"#### 2020-12-4\n 1. Added the feature of right-click opening the contain file folder(s);\n 2. Added the feature of right-click copying the full path of the file(s);",
"#### 2020-12-1\n 1. Try to fix the index database corruption issue;\n 2. Added support for German, provided by dhu. Thank you very much;\n 3. Fixed some other known issues;",
"#### 2020-11-12\n 1. Added the feature of setting index database store path;\n 2. Fixed the issue that AnyTXT Searcher would block when starting;\n 3. Fixed some other known issues;",
"#### 2020-10-2\n 1. Added the feature of keyword browsing;",
"#### 2020-8-31\n 1. Added support for NAS devices. NAS storage based on Microsoft SMB protocol and CIFS protocol has been tested, and it works perfect. Other types of remote storage have not been tested;",
"#### 2020-6-13\n 1. Added support for full-text indexing and searching in e-book format epub;\n 2. Added support for full-text indexing and searching in e-book format mobi;\n 3. Added support for full-text indexing and searching in format chm;\n 4. Added support for full-text indexing and searching in e-book format fb2;\n 5. Added real-time displaying of the index status;\n 6. Added support for High DPI;",
"#### 2020-4-12\n 1. Added support for updating the full-text index database manually;\n 2. Added support for setting automatic update full-text index cycle;\n 3. Added support for starting and stopping full-text indexing service;",
"#### 2020-2-27\n 1. Added the command line;\n 2. Fixed known issues;",
"#### 2019-11-29\n 1. Try to fix 100% CPU usage issue;\n 2. Fix the issue of re-indexing during file update;",
"#### 2019-11-9\n 1. Added fuzzy matching search;\n 2. Added whole matching search(Beta);\n 3. Added Multi-language support. Currently, the Chinese language has been added due to there are many Chinese users;\n You are welcome to translate it(URL) into your local language, I will integrate it into the installation package;\n 4. Fix some issues;",
"#### 2019-10-4\n 1. Added removal of the index;\n 2. Added the index rule feature;\n 3. Added the Ctrl+C feature;\n 4. Added the Ctrl+X feature;\n 5. Added the Delete feature;\n 6. Added the automatic detection of the new version feature;\n 7. Fix some issues;",
"#### 2019-6-24\n 1. Added snippets to the search results;",
"#### 2019-6-11\n 1. Added an icon to the search button;",
"#### 2019-6-8\n 1. Added support for the none-NTFS file system;\n 2. Speed up file traversal;\n 3. Reduced computer resource consumption;\n 4. Fixed some issues;",
"#### 2019-6-2\n 1. Added a community link to the help menu to get users' request;\n 2. Added support dragging for the search results list;",
"#### 2019-5-23:\n 1. Optimized indexing speed;\n 2. Optimized support for Arabic language based on user feedback;\n 3. Optimized support for Chinese language based on user feedback;\n 4. Optimized support for Korean language based on user feedback;\n 5. Optimized support for Japanese language based on user feedback;\n 6. Optimized the loading interface when the program starts;"
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| [
"passage: TAGS\n#region-us \n# AnyTXT Searcher\n\n------\n\nAnyTXT Searcher is a powerful local data full-text search engine, just like a local disk Google search engine. It is your free Google Desktop Search alternative.\nAnyTXT Searcher has a powerful document parsing engine built in, which extracts the text of commonly used documents/images(OCR) without installing any other software, and combines the built-in high-speed indexing system to store the metadata of the text. You can quickly find any text that exists on your computer with the AnyTXT Searcher. It works on Windows 11, 10, 8, 7, Vista, XP(below 1.2.540), 2003, 2008, 2012, 2016, 2022 ...### Download Installer### More ...### Formats Supported\n> * Plain Text Format (txt, cpp, html etc.)\n> * Microsoft Word (doc, docx)\n> * Microsoft Excel (xls, xlsx)\n> * Microsoft PowerPoint (ppt, pptx)\n> * Microsoft OneNote (one)\n> * Portable Document Format (pdf)\n> * eBook Format (epub, mobi, djvu, chm, fb2, azw(3) etc.)\n> * WPS Word Format (wps)\n> * WPS Excel Format (et)\n> * WPS PowerPoint Format (dps)\n> * Open Document Format (OpenOffice, LibreOffice etc.)\n> * Mind Map Format (lighten, mmap, mm, xmind etc.)\n> * Open Fixed-layout Document Format (ofd)\n> * Edraw Max Format (eddx)\n> * WizNote Format (ziw)\n> * Image Format (jpg, png, bmp, gif etc.)\n> * Binary File (exe, dll, so)\n> * More Document Types please let us know",
"passage: ### More Features\n> * Microsoft Office (doc, xls, ppt) Full Text Index Supported\n> * Microsoft Office 2007 (docx, xlsx, pptx, docm, xlsm, docm) Full Text Index Supported\n> * Multi-language(include Chinese,Japanese,Korean) Document Full Text Index Supported\n> * WPS Office (wps, et, dps) Full Text Index Supported\n> * Image text (png, jpg, bmp etc.) Full Text Index Supported\n> * Fast Full-Text Index\n> * Fast Full-Text Search, almost in 0.1 second\n> * Keyword View\n> * SSD Optimization\n> * HTTP Search Service (Beta)\n> * Realtime Sync Full-Text Index (Beta)\n> * Hight DPI Supported\n> * Startup at Boot\n> * Scanned PDF text search### Changelog#### 2023-3-4 Version 1.3.1071\n 1. Added the feature of online search at raw text preview window; \n 2. Fixed the issue that indexing never completes; \n 3. Fixed some other known issues;#### 2023-2-1 Version 1.3.1043\n 1. Added the feature of starting Anytxt at boot; \n 2. Added the feature of scanned PDF text indexing and search; \n 3. Added the feature of changing Anytxt font; \n 4. Fixed the issue that USB device occupation; \n 5. Fixed the issue that some azw3 file encodes detect incorrectly;\n 6. Fixed some other known issues;#### 2022-11-26 Version 1.3.1019\n 1. Added support for binary file (exe, so, dll) full-text indexing and search;\n 2. Added the shortcut key Atl+G to the preview window to search for global content;\n 3. Added the shortcut key Atl+S to the preview window to search the content of this file;\n 4. Add the snippet to the search result item in the preview window;\n 5. Fixed the issue of characters in Japanese, Korean, Chinese, and other local encodings of chm files;\n 6. Fixed the issue that the wrong file name was displayed in the preview window after the result list item was clicked;\n 7. Fixed some other known issues;#### 2022-10-30 Version 1.2.993\n 1. Added support for Edraw Max full-text indexing and search;\n 2. Added support for WizNote full-text indexing and search;\n 3. Added support for text in image (OCR) full-text indexing and search(Beta);\n 4. Added the feature of exporting the search result list;\n 5. Added the feature of show search terms snippets;\n 6. Added the feature of the multi-preview window;\n 7. Fixed the issue of google translation not working in China;\n 8. Fixed the issue of crash on some computers at ver1.2.941;\n 9. Fixed some other known issues;",
"passage: #### 2022-9-3 Version 1.2.941\n 1. Added the feature of the line number;\n 2. Added the feature of the line match-tags in the text preview window scrollbar;\n 3. Added the feature of regex search in the preview window;\n 4. Fixed the issue that the indexing service works abnormally;\n 6. Try to fix the issue of freezing on some computers;\n 7. Fixed some other known issues;#### 2022-7-30 Version 1.2.901\n 1. Added support for full-text indexing and searching in mind map formats: lighten, mmap, mm, xmind, etc.;\n 2. Added support for djvu and azw(3) full-text indexing and search;\n 3. Added support for ofd full-text indexing and search;\n 4. Add 64-bit program support;\n 5. Speed up indexing and searching;\n 6. Add right-click search in the text preview window;\n 7. Fixed the unclear fonts issue;\n 8. Added 한국어 language, thanks to VenusGirl – 비너스걸;\n 9. Fixed some known issues;#### 2022-4-17 Version 1.2.715 (Beta)\n 1. Added the feature of syncing the index of specified folders;#### 2022-4-10 Version 1.2.703 (Beta)\n 1. Added the feature of advanced search syntax (beta), you can use &|!\"() to do some advanced search;\n 2. Added the feature of theme, Anytxt comes with 3 themes: default, light, dark;\n 3. Added the feature of HTTP search service (Beta), the fixed http server listen port is 9921, you can change the port in coming version;\n 4. Added the feature of editing multiple file types' inclusion and exclusion rules for indexing;\n 5. Fixed the issue that Anytxt may crash on some Windows 11; \n 6. Added Polski language, thanks to Dmocha;\n 7. Added עברית language, thanks to Yeshurun Kubi;#### 2021-7-16 Version 1.2.540\n 1. Added the feature of custom hotkeys;#### 2021-6-15 Version 1.2.532\n 1. Added Nederlands language, thanks to Atze;\n 2. Added Українська language, thanks to Helly;\n 3. Added the feature of rebuilding the index;#### 2021-5-31 [Version 1.2.523]\n 1. Added online translation feature, supporting Google Translate, Bing Translator, and Yandex Translate. This feature requires the Internet;\n 2. Added toolbar;\n 3. Added forward and back feature for file text preview;\n 4. Added Microsoft OneNote file to default index file types;\n 5. Added Русский language, thanks to Антон Мырзин aka Paperdaemon;\n 6. Fix some issues;",
"passage: #### 2021-4-23 [Version 1.2.483]\n 1. You can turn on/off the feature of displaying Anytxt in the system tray;\n 2. Added the feature of displaying Anytxt to the system context menu. You can start Anytxt directly in the system context menu;\n 3. Added the feature of filtering search results by directory;\n 4. Added the feature of filtering search results by multiple file types;\n 5. Fix some issues;#### 2021-4-2 [Version 1.2.445]\n 1. Added the feature of that closing Anytxt to the system tray;\n 2. Added the feature of that word segmentation by space for Chinese, Japanese, Korean, and Vietnamese;\n 3. Added the feature of that results are default sorted by match degree and the relevance of documents to a given search query;\n 4. Optimized search speed, almost in 0.5 sec;\n 5. Fixed the issue that no files can be scanned on the FAT file system;\n 6. Fixed the issue that the full-text index engine could not work on some network devices;\n 7. Fixed some other known issues;#### 2021-1-15 [Version 1.2.394]\n 1. Added the zoom-in/out feature;\n 2. Added the word wrap feature;\n 3. Added support for full-text indexing and searching in WPS Office formats .wps .et and .dps;\n 4. Improved performance when updating index data;\n 5. Fixed some other known issues;\n 6. Added multi-language support based on google translation for URL. It may not works fine in Mainland China;#### 2020-12-4\n 1. Added the feature of right-click opening the contain file folder(s);\n 2. Added the feature of right-click copying the full path of the file(s);#### 2020-12-1\n 1. Try to fix the index database corruption issue;\n 2. Added support for German, provided by dhu. Thank you very much;\n 3. Fixed some other known issues;#### 2020-11-12\n 1. Added the feature of setting index database store path;\n 2. Fixed the issue that AnyTXT Searcher would block when starting;\n 3. Fixed some other known issues;#### 2020-10-2\n 1. Added the feature of keyword browsing;#### 2020-8-31\n 1. Added support for NAS devices. NAS storage based on Microsoft SMB protocol and CIFS protocol has been tested, and it works perfect. Other types of remote storage have not been tested;"
]
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|
c4f60b8def4104aab588c81c214b4b7670bf7ada | # Dataset Card for "UN Sitemap Multilingual HTML Corpus"
## Update Time +8:00 2023-3-23 17:25:20
## Dataset Summary
此数据集是从联合国网站提供的的sitemap中爬取的，包含了各种语言的HTML文件，并按照语言进行分类。数据集包含了不同语言的文章、新闻等联合国文本。数据集旨在为研究人员、学者和语言技术开发人员提供一个多语言文本集，可用于各种自然语言处理任务和应用。
数据集包括以下语言：汉语（zh）、英语（en）、西班牙语（ar）、俄语（ru）、西班牙语（es）、法语（fr）。
## Dataset Structure
### Data Instances
- **数据集文件大小:** 约 14 GB
一个 'zh' 的例子如下：
```
{
'uuid': 'a154688c-b385-4d2a-bec7-f239f1397d21',
'url': 'https://news.un.org/zh/gallery/287612',
'title': '印度尼西亚承诺到2022年消除一切形式的童工现象'，
'html_content': '<!DOCTYPE html> <html lang="zh-hans" ...'
}
```
# How to use
```python
from datasets import load_dataset
import datasets
dataset = load_dataset('ranWang/UN_Sitemap_Multilingual_HTML_Corpus')
# lang_list = ['zh', 'en', 'fr', 'es', 'ru', 'ar']
for lang in dataset:
for colum in dataset[lang]:
# colum.keys = ['uuid', 'url', 'title', 'html_content']
# code...
OR
# you want to specify the language
dataset = load_dataset('ranWang/UN_Sitemap_Multilingual_HTML_Corpus', split={lang})
for colum in dataset:
# colum.keys = ['uuid', 'url', 'title', 'html_content']
# code...
``` | ranWang/UN_Sitemap_Multilingual_HTML_Corpus | [
"region:us"
]
| 2023-03-21T06:28:43+00:00 | {"dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "html_content", "dtype": "string"}], "splits": [{"name": "zh", "num_bytes": 4667272633, "num_examples": 39850}, {"name": "en", "num_bytes": 8180560380, "num_examples": 67374}, {"name": "ar", "num_bytes": 4456751663, "num_examples": 35807}, {"name": "ru", "num_bytes": 4311781034, "num_examples": 34774}, {"name": "es", "num_bytes": 5336518150, "num_examples": 44877}, {"name": "fr", "num_bytes": 5709424711, "num_examples": 46756}], "download_size": 0, "dataset_size": 32662308571}} | 2023-06-15T10:57:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "UN Sitemap Multilingual HTML Corpus"
## Update Time +8:00 2023-3-23 17:25:20
## Dataset Summary
此数据集是从联合国网站提供的的sitemap中爬取的，包含了各种语言的HTML文件，并按照语言进行分类。数据集包含了不同语言的文章、新闻等联合国文本。数据集旨在为研究人员、学者和语言技术开发人员提供一个多语言文本集，可用于各种自然语言处理任务和应用。
数据集包括以下语言：汉语（zh）、英语（en）、西班牙语（ar）、俄语（ru）、西班牙语（es）、法语（fr）。
## Dataset Structure
### Data Instances
- 数据集文件大小: 约 14 GB
一个 'zh' 的例子如下：
# How to use
| [
"# Dataset Card for \"UN Sitemap Multilingual HTML Corpus\"",
"## Update Time +8:00 2023-3-23 17:25:20",
"## Dataset Summary\n\n此数据集是从联合国网站提供的的sitemap中爬取的，包含了各种语言的HTML文件，并按照语言进行分类。数据集包含了不同语言的文章、新闻等联合国文本。数据集旨在为研究人员、学者和语言技术开发人员提供一个多语言文本集，可用于各种自然语言处理任务和应用。\n\n数据集包括以下语言：汉语（zh）、英语（en）、西班牙语（ar）、俄语（ru）、西班牙语（es）、法语（fr）。",
"## Dataset Structure",
"### Data Instances\n\n- 数据集文件大小: 约 14 GB\n\n\n一个 'zh' 的例子如下：",
"# How to use"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"UN Sitemap Multilingual HTML Corpus\"",
"## Update Time +8:00 2023-3-23 17:25:20",
"## Dataset Summary\n\n此数据集是从联合国网站提供的的sitemap中爬取的，包含了各种语言的HTML文件，并按照语言进行分类。数据集包含了不同语言的文章、新闻等联合国文本。数据集旨在为研究人员、学者和语言技术开发人员提供一个多语言文本集，可用于各种自然语言处理任务和应用。\n\n数据集包括以下语言：汉语（zh）、英语（en）、西班牙语（ar）、俄语（ru）、西班牙语（es）、法语（fr）。",
"## Dataset Structure",
"### Data Instances\n\n- 数据集文件大小: 约 14 GB\n\n\n一个 'zh' 的例子如下：",
"# How to use"
]
| [
6,
14,
12,
116,
6,
27,
4
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"UN Sitemap Multilingual HTML Corpus\"## Update Time +8:00 2023-3-23 17:25:20## Dataset Summary\n\n此数据集是从联合国网站提供的的sitemap中爬取的，包含了各种语言的HTML文件，并按照语言进行分类。数据集包含了不同语言的文章、新闻等联合国文本。数据集旨在为研究人员、学者和语言技术开发人员提供一个多语言文本集，可用于各种自然语言处理任务和应用。\n\n数据集包括以下语言：汉语（zh）、英语（en）、西班牙语（ar）、俄语（ru）、西班牙语（es）、法语（fr）。## Dataset Structure### Data Instances\n\n- 数据集文件大小: 约 14 GB\n\n\n一个 'zh' 的例子如下：# How to use"
]
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|
248355764dfab017a3d0bf4fa4f29d5b517f7ecd | # Dataset Card for "har_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hazardous/har_processed | [
"region:us"
]
| 2023-03-21T06:29:06+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "calling", "1": "clapping", "2": "cycling", "3": "dancing", "4": "drinking", "5": "eating", "6": "fighting", "7": "hugging", "8": "laughing", "9": "listening_to_music", "10": "running", "11": "sitting", "12": "sleeping", "13": "texting"}}}}], "splits": [{"name": "train", "num_bytes": 135868615.36, "num_examples": 11760}], "download_size": 138276406, "dataset_size": 135868615.36}} | 2023-03-21T06:41:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "har_processed"
More Information needed | [
"# Dataset Card for \"har_processed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"har_processed\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"har_processed\"\n\nMore Information needed"
]
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|
6af67d7d80ddbafb22dc002349d4519f159fcf4e | # Dataset Card for "patched_test_p_150_f_UCH_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_150_f_UCH_v4 | [
"region:us"
]
| 2023-03-21T07:06:54+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 35036323, "num_examples": 75442}], "download_size": 3105585, "dataset_size": 35036323}} | 2023-03-21T07:07:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_150_f_UCH_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_150_f_UCH_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_150_f_UCH_v4\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_150_f_UCH_v4\"\n\nMore Information needed"
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d74802f11e1ada15e0318ecf6ff3908ed1d98aa3 | # Dataset Card for "patched_test_p_150_f_UCH_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_150_f_UCH_m1_predictions | [
"region:us"
]
| 2023-03-21T07:32:18+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 35338091, "num_examples": 75442}], "download_size": 3370181, "dataset_size": 35338091}} | 2023-03-21T07:32:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_150_f_UCH_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_150_f_UCH_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_150_f_UCH_m1_predictions\"\n\nMore Information needed"
]
| [
6,
29
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_150_f_UCH_m1_predictions\"\n\nMore Information needed"
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|
e1d57f13a9a49a4df20fe151eb8d8e1b7407b6a5 | # Dataset Card for "muti-language-tatoeba"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bigpang/muti-language-tatoeba | [
"region:us"
]
| 2023-03-21T08:02:05+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16672765, "num_examples": 280000}, {"name": "test", "num_bytes": 2092587, "num_examples": 35000}, {"name": "valid", "num_bytes": 2087357, "num_examples": 35000}], "download_size": 11758606, "dataset_size": 20852709}} | 2023-04-14T06:11:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "muti-language-tatoeba"
More Information needed | [
"# Dataset Card for \"muti-language-tatoeba\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"muti-language-tatoeba\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"muti-language-tatoeba\"\n\nMore Information needed"
]
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|
3955a2541c55aaad850793fb5b1f70ea5bc2eed1 | # Dataset Card for "un_corpus_seed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hayesyang/un_corpus_seed | [
"region:us"
]
| 2023-03-21T08:08:46+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 258559, "num_examples": 3733}], "download_size": 93162, "dataset_size": 258559}} | 2023-03-21T10:39:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "un_corpus_seed"
More Information needed | [
"# Dataset Card for \"un_corpus_seed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"un_corpus_seed\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"un_corpus_seed\"\n\nMore Information needed"
]
| [
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|
2bfb453809a30bea88d321ccb70fe4d68941b061 | # Dataset Card for "un_corpus_content"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hayesyang/un_corpus_content | [
"region:us"
]
| 2023-03-21T08:09:07+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "url", "dtype": "string"}, {"name": "status", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "hash", "dtype": "string"}, {"name": "is_duplicate", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 125504913, "num_examples": 2140}], "download_size": 39366870, "dataset_size": 125504913}} | 2023-03-21T10:39:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "un_corpus_content"
More Information needed | [
"# Dataset Card for \"un_corpus_content\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"un_corpus_content\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"un_corpus_content\"\n\nMore Information needed"
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|
dfc0d8b21321f1b56b90349ca9c600fff946d1dc | # Dataset Card for "piqa-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nayohan/piqa-ko | [
"region:us"
]
| 2023-03-21T08:26:30+00:00 | {"dataset_info": {"features": [{"name": "goal", "dtype": "string"}, {"name": "sol1", "dtype": "string"}, {"name": "sol2", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4835603, "num_examples": 16113}, {"name": "valid", "num_bytes": 548051, "num_examples": 1838}], "download_size": 3256746, "dataset_size": 5383654}} | 2023-03-21T08:26:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "piqa-ko"
More Information needed | [
"# Dataset Card for \"piqa-ko\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"piqa-ko\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"piqa-ko\"\n\nMore Information needed"
]
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|
6a9c9a3a237b52f183d3a2d11c0bddc314c05298 | # Dataset Card for "siqa-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nayohan/siqa-ko | [
"region:us"
]
| 2023-03-21T08:26:53+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answerA", "dtype": "string"}, {"name": "answerB", "dtype": "string"}, {"name": "answerC", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8358726, "num_examples": 33410}, {"name": "valid", "num_bytes": 490857, "num_examples": 1954}], "download_size": 4320202, "dataset_size": 8849583}} | 2023-03-21T08:27:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "siqa-ko"
More Information needed | [
"# Dataset Card for \"siqa-ko\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"siqa-ko\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"siqa-ko\"\n\nMore Information needed"
]
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|
6adb0750abaaa888f294d26e14411d91d74edf63 | # Dataset Card for "openai_summarize_tldr_human_eval_ppo_result"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pvduy/openai_summarize_tldr_human_eval_ppo_result | [
"region:us"
]
| 2023-03-21T08:41:27+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "pvduy/pythia-125M-ppo-summarize-tldr", "dtype": "string"}, {"name": "pvduy/pythia-1B-ppo-summarize-tldr", "dtype": "string"}, {"name": "pvduy/pythia-6B-ppo-summarize-tldr", "dtype": "string"}, {"name": "pvduy/pythia-20B-ppo-summarize-tldr", "dtype": "string"}, {"name": "pvduy/pythia-125M-sft-summarize-tldr", "dtype": "string"}, {"name": "pvduy/pythia-1B-sft-summarize-tldr", "dtype": "string"}, {"name": "pvduy/pythia-6B-sft-summarize-tldr", "dtype": "string"}, {"name": "pvduy/pythia-20B-sft-summarize-tldr", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 258676, "num_examples": 100}], "download_size": 180261, "dataset_size": 258676}} | 2023-03-23T01:01:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "openai_summarize_tldr_human_eval_ppo_result"
More Information needed | [
"# Dataset Card for \"openai_summarize_tldr_human_eval_ppo_result\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"openai_summarize_tldr_human_eval_ppo_result\"\n\nMore Information needed"
]
| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"openai_summarize_tldr_human_eval_ppo_result\"\n\nMore Information needed"
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|
9bcdff850229f910d4c69ffc6a2f75f796e31d81 | # Dataset Card for mini_cleaned_diachronic_swe
The Swedish Diachronic Corpus is a project funded by [Swe-Clarin](https://sweclarin.se/eng) and provides a corpus of texts covering the time period from Old Swedish.
The dataset has been preprocessed and can be recreated from here: [Src_code](https://github.com/Borg93/kbuhist2/tree/main).
## Dataset Summary
The dataset has been filtered with the metadata:
- Manueally transcribed or post-ocr correction
- No scrambled sentences
- Year of origin: 15-19th centuary
### Data Splits
**This will be further extended!**
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 352137 |
| Test | 7187 |
## Acknowledgements
We gratefully acknowledge [SWE-clarin](https://sweclarin.se/) for the datasets.
## Citation Information
Eva Pettersson and Lars Borin (2022)
Swedish Diachronic Corpus
In Darja Fišer & Andreas Witt (eds.), CLARIN. The Infrastructure for Language Resources. Berlin: deGruyter. https://degruyter.com/document/doi/10.1515/9783110767377-022/html | Riksarkivet/mini_cleaned_diachronic_swe | [
"size_categories:1M<n<10M",
"language:sv",
"license:mit",
"historical",
"WIP",
"region:us"
]
| 2023-03-21T08:47:21+00:00 | {"language": ["sv"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "Kbuhist2", "dataset_info": {"features": [{"name": "chunked_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 14891546.825140134, "num_examples": 8410}, {"name": "train", "num_bytes": 729669858.1748599, "num_examples": 412081}], "download_size": 480496204, "dataset_size": 744561405.0}, "tags": ["historical", "WIP"]} | 2023-03-21T11:36:45+00:00 | []
| [
"sv"
]
| TAGS
#size_categories-1M<n<10M #language-Swedish #license-mit #historical #WIP #region-us
| Dataset Card for mini\_cleaned\_diachronic\_swe
===============================================
The Swedish Diachronic Corpus is a project funded by Swe-Clarin and provides a corpus of texts covering the time period from Old Swedish.
The dataset has been preprocessed and can be recreated from here: Src\_code.
Dataset Summary
---------------
The dataset has been filtered with the metadata:
* Manueally transcribed or post-ocr correction
* No scrambled sentences
* Year of origin: 15-19th centuary
### Data Splits
This will be further extended!
Acknowledgements
----------------
We gratefully acknowledge SWE-clarin for the datasets.
Eva Pettersson and Lars Borin (2022)
Swedish Diachronic Corpus
In Darja Fišer & Andreas Witt (eds.), CLARIN. The Infrastructure for Language Resources. Berlin: deGruyter. URL
| [
"### Data Splits\n\n\nThis will be further extended!\n\n\n\nAcknowledgements\n----------------\n\n\nWe gratefully acknowledge SWE-clarin for the datasets.\n\n\nEva Pettersson and Lars Borin (2022)\nSwedish Diachronic Corpus\nIn Darja Fišer & Andreas Witt (eds.), CLARIN. The Infrastructure for Language Resources. Berlin: deGruyter. URL"
]
| [
"TAGS\n#size_categories-1M<n<10M #language-Swedish #license-mit #historical #WIP #region-us \n",
"### Data Splits\n\n\nThis will be further extended!\n\n\n\nAcknowledgements\n----------------\n\n\nWe gratefully acknowledge SWE-clarin for the datasets.\n\n\nEva Pettersson and Lars Borin (2022)\nSwedish Diachronic Corpus\nIn Darja Fišer & Andreas Witt (eds.), CLARIN. The Infrastructure for Language Resources. Berlin: deGruyter. URL"
]
| [
35,
82
]
| [
"passage: TAGS\n#size_categories-1M<n<10M #language-Swedish #license-mit #historical #WIP #region-us \n### Data Splits\n\n\nThis will be further extended!\n\n\n\nAcknowledgements\n----------------\n\n\nWe gratefully acknowledge SWE-clarin for the datasets.\n\n\nEva Pettersson and Lars Borin (2022)\nSwedish Diachronic Corpus\nIn Darja Fišer & Andreas Witt (eds.), CLARIN. The Infrastructure for Language Resources. Berlin: deGruyter. URL"
]
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]
|
57646ff6814084fec0591d71b1d5ac887f2b42d5 |
# Dataset Card for chinese_chatgpt_corpus
## 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
- **Size of downloaded dataset files:** 5.05 GB
- **Size of the generated dataset:** 0 GB
- **Total amount of disk used:** 5.05 GB
### Dataset Summary
This repo collects chinese corpus for Supervised Finetuning (SFT) and Reinforcement Learning From Human Feedback (RLHF).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
Chinese
## Dataset Structure
### Data Instances
#### train_data_external_v1.jsonl
- **Size of downloaded dataset files:** 5.04 GB
- **Size of the generated dataset:** 0 GB
- **Total amount of disk used:** 5.04 GB
An example looks as follows:
```
{
"prompt": "问题：有没有给未成年贷款的有的联系",
"answers":
[
{
"answer": "若通过招行办理，我行规定，贷款人年龄需年满18岁，且年龄加贷款年限不得超过70岁。如果您持有我行信用卡附属卡，可尝试办理预借现金。",
"score": 1
}
],
"prefix": "回答："
}
```
#### dev_data_external_v1.jsonl
- **Size of downloaded dataset files:** 9.55 MB
- **Size of the generated dataset:** 0 MB
- **Total amount of disk used:** 9.55 MB
An example looks as follows:
```
{
"prompt": "初学纹发现1/2\"的管螺纹并不是1\"的一半。不知道其中的原因，请各位指点。",
"answers":
[
{
"answer": "管螺纹的名义尺寸是“管子”的孔（内）径，而管子的壁厚不是两倍。所以，1/2\"的管螺纹并不是1\"的一半，",
"score": 1
}
],
"prefix": "回答："
}
```
### Data Fields
The data fields are the same among all splits.
#### train_data_external_v1.jsonl
- `prompt`: prompt, `string`
- `answers`: list of answers
- `answer`: answer, `string`
- `score`: score of answer, `int`
- `prefix`: prefix to the answer, `string`
#### dev_data_external_v1.jsonl
- `prompt`: prompt, `string`
- `answers`: list of answers
- `answer`: answer, `string`
- `score`: score of answer, `int`
- `prefix`: prefix to the answer, `string`
### Data Splits
| name | train |
|----------|-------:|
|train_data_external_v1.jsonl|5477982|
|dev_data_external_v1.jsonl|10000|
## Dataset Creation
### Curation Rationale
Link to github: [data_prepare](https://github.com/sunzeyeah/RLHF/blob/master/src/data_prepare.py)
### Source Data
#### Initial Data Collection and Normalization
- [百科](https://github.com/brightmart/nlp_chinese_corpus)
- [知道问答](https://github.com/SophonPlus/ChineseNlpCorpus)
- [对联](https://github.com/wb14123/couplet-dataset/releases/download/1.0/couplet.tar.gz)
- [古文](https://github.com/NiuTrans/Classical-Modern)
- [古诗词](https://github.com/chinese-poetry/chinese-poetry)
- 微博新闻评论
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
| sunzeyeah/chinese_chatgpt_corpus | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"task_categories:reinforcement-learning",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:5M<n<10M",
"language:zh",
"license:unknown",
"region:us"
]
| 2023-03-21T09:16:21+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["unknown"], "language": ["zh"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["5M<n<10M"], "task_categories": ["text-generation", "text2text-generation", "question-answering", "reinforcement-learning"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "Chinese-ChatGPT-Corpus"} | 2023-03-23T16:53:47+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text-generation #task_categories-text2text-generation #task_categories-question-answering #task_categories-reinforcement-learning #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-unknown #multilinguality-monolingual #size_categories-5M<n<10M #language-Chinese #license-unknown #region-us
| Dataset Card for chinese\_chatgpt\_corpus
=========================================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Size of downloaded dataset files: 5.05 GB
* Size of the generated dataset: 0 GB
* Total amount of disk used: 5.05 GB
### Dataset Summary
This repo collects chinese corpus for Supervised Finetuning (SFT) and Reinforcement Learning From Human Feedback (RLHF).
### Supported Tasks and Leaderboards
### Languages
Chinese
Dataset Structure
-----------------
### Data Instances
#### train\_data\_external\_v1.jsonl
* Size of downloaded dataset files: 5.04 GB
* Size of the generated dataset: 0 GB
* Total amount of disk used: 5.04 GB
An example looks as follows:
#### dev\_data\_external\_v1.jsonl
* Size of downloaded dataset files: 9.55 MB
* Size of the generated dataset: 0 MB
* Total amount of disk used: 9.55 MB
An example looks as follows:
### Data Fields
The data fields are the same among all splits.
#### train\_data\_external\_v1.jsonl
* 'prompt': prompt, 'string'
* 'answers': list of answers
+ 'answer': answer, 'string'
+ 'score': score of answer, 'int'
* 'prefix': prefix to the answer, 'string'
#### dev\_data\_external\_v1.jsonl
* 'prompt': prompt, 'string'
* 'answers': list of answers
+ 'answer': answer, 'string'
+ 'score': score of answer, 'int'
* 'prefix': prefix to the answer, 'string'
### Data Splits
Dataset Creation
----------------
### Curation Rationale
Link to github: data\_prepare
### Source Data
#### Initial Data Collection and Normalization
* 百科
* 知道问答
* 对联
* 古文
* 古诗词
* 微博新闻评论
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
| [
"### Dataset Summary\n\n\nThis repo collects chinese corpus for Supervised Finetuning (SFT) and Reinforcement Learning From Human Feedback (RLHF).",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nChinese\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### train\\_data\\_external\\_v1.jsonl\n\n\n* Size of downloaded dataset files: 5.04 GB\n* Size of the generated dataset: 0 GB\n* Total amount of disk used: 5.04 GB\n\n\nAn example looks as follows:",
"#### dev\\_data\\_external\\_v1.jsonl\n\n\n* Size of downloaded dataset files: 9.55 MB\n* Size of the generated dataset: 0 MB\n* Total amount of disk used: 9.55 MB\n\n\nAn example looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits.",
"#### train\\_data\\_external\\_v1.jsonl\n\n\n* 'prompt': prompt, 'string'\n* 'answers': list of answers\n\t+ 'answer': answer, 'string'\n\t+ 'score': score of answer, 'int'\n* 'prefix': prefix to the answer, 'string'",
"#### dev\\_data\\_external\\_v1.jsonl\n\n\n* 'prompt': prompt, 'string'\n* 'answers': list of answers\n\t+ 'answer': answer, 'string'\n\t+ 'score': score of answer, 'int'\n* 'prefix': prefix to the answer, 'string'",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nLink to github: data\\_prepare",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\n* 百科\n* 知道问答\n* 对联\n* 古文\n* 古诗词\n* 微博新闻评论",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_categories-question-answering #task_categories-reinforcement-learning #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-unknown #multilinguality-monolingual #size_categories-5M<n<10M #language-Chinese #license-unknown #region-us \n",
"### Dataset Summary\n\n\nThis repo collects chinese corpus for Supervised Finetuning (SFT) and Reinforcement Learning From Human Feedback (RLHF).",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nChinese\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### train\\_data\\_external\\_v1.jsonl\n\n\n* Size of downloaded dataset files: 5.04 GB\n* Size of the generated dataset: 0 GB\n* Total amount of disk used: 5.04 GB\n\n\nAn example looks as follows:",
"#### dev\\_data\\_external\\_v1.jsonl\n\n\n* Size of downloaded dataset files: 9.55 MB\n* Size of the generated dataset: 0 MB\n* Total amount of disk used: 9.55 MB\n\n\nAn example looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits.",
"#### train\\_data\\_external\\_v1.jsonl\n\n\n* 'prompt': prompt, 'string'\n* 'answers': list of answers\n\t+ 'answer': answer, 'string'\n\t+ 'score': score of answer, 'int'\n* 'prefix': prefix to the answer, 'string'",
"#### dev\\_data\\_external\\_v1.jsonl\n\n\n* 'prompt': prompt, 'string'\n* 'answers': list of answers\n\t+ 'answer': answer, 'string'\n\t+ 'score': score of answer, 'int'\n* 'prefix': prefix to the answer, 'string'",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nLink to github: data\\_prepare",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\n* 百科\n* 知道问答\n* 对联\n* 古文\n* 古诗词\n* 微博新闻评论",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information"
]
| [
132,
36,
10,
12,
6,
59,
59,
17,
79,
79,
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17,
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| [
"passage: TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_categories-question-answering #task_categories-reinforcement-learning #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-unknown #multilinguality-monolingual #size_categories-5M<n<10M #language-Chinese #license-unknown #region-us \n### Dataset Summary\n\n\nThis repo collects chinese corpus for Supervised Finetuning (SFT) and Reinforcement Learning From Human Feedback (RLHF).### Supported Tasks and Leaderboards### Languages\n\n\nChinese\n\n\nDataset Structure\n-----------------### Data Instances#### train\\_data\\_external\\_v1.jsonl\n\n\n* Size of downloaded dataset files: 5.04 GB\n* Size of the generated dataset: 0 GB\n* Total amount of disk used: 5.04 GB\n\n\nAn example looks as follows:#### dev\\_data\\_external\\_v1.jsonl\n\n\n* Size of downloaded dataset files: 9.55 MB\n* Size of the generated dataset: 0 MB\n* Total amount of disk used: 9.55 MB\n\n\nAn example looks as follows:### Data Fields\n\n\nThe data fields are the same among all splits.#### train\\_data\\_external\\_v1.jsonl\n\n\n* 'prompt': prompt, 'string'\n* 'answers': list of answers\n\t+ 'answer': answer, 'string'\n\t+ 'score': score of answer, 'int'\n* 'prefix': prefix to the answer, 'string'#### dev\\_data\\_external\\_v1.jsonl\n\n\n* 'prompt': prompt, 'string'\n* 'answers': list of answers\n\t+ 'answer': answer, 'string'\n\t+ 'score': score of answer, 'int'\n* 'prefix': prefix to the answer, 'string'### Data Splits\n\n\n\nDataset Creation\n----------------"
]
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|
2958b1345150fec263717302b819969e8e1c980f |
# SlovAlapca dataset
This dataset was created using machine translation (DeepL) of the original Alpaca dataset published here: https://github.com/tatsu-lab/stanford_alpaca
Here is an example of the first record...
```json
[
{
"instruction": "Uveďte tri tipy, ako si udržať zdravie.",
"input": "",
"output": "1.Jedzte vyváženú stravu a dbajte na to, aby obsahovala dostatok ovocia a zeleniny. \n2. Pravidelne cvičte, aby ste udržali svoje telo aktívne a silné. \n3. Doprajte si dostatok spánku a dodržiavajte dôsledný spánkový režim."
},
]
```
| blip-solutions/SlovAlpaca | [
"task_categories:text-generation",
"language:sk",
"license:other",
"region:us"
]
| 2023-03-21T09:23:57+00:00 | {"language": ["sk"], "license": "other", "task_categories": ["text-generation"]} | 2023-03-21T09:35:15+00:00 | []
| [
"sk"
]
| TAGS
#task_categories-text-generation #language-Slovak #license-other #region-us
|
# SlovAlapca dataset
This dataset was created using machine translation (DeepL) of the original Alpaca dataset published here: URL
Here is an example of the first record...
| [
"# SlovAlapca dataset\n\nThis dataset was created using machine translation (DeepL) of the original Alpaca dataset published here: URL\n\n\nHere is an example of the first record..."
]
| [
"TAGS\n#task_categories-text-generation #language-Slovak #license-other #region-us \n",
"# SlovAlapca dataset\n\nThis dataset was created using machine translation (DeepL) of the original Alpaca dataset published here: URL\n\n\nHere is an example of the first record..."
]
| [
28,
40
]
| [
"passage: TAGS\n#task_categories-text-generation #language-Slovak #license-other #region-us \n# SlovAlapca dataset\n\nThis dataset was created using machine translation (DeepL) of the original Alpaca dataset published here: URL\n\n\nHere is an example of the first record..."
]
| [
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|
1c3594dc23119256e2cc41224e8f01d91f9577ac | # Dataset Card for "Noise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammedNasri/Noise | [
"region:us"
]
| 2023-03-21T09:44:20+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5151937713.875, "num_examples": 38481}, {"name": "test", "num_bytes": 986222326.0, "num_examples": 38481}], "download_size": 5532021982, "dataset_size": 6138160039.875}} | 2023-03-21T09:47:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Noise"
More Information needed | [
"# Dataset Card for \"Noise\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Noise\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Noise\"\n\nMore Information needed"
]
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|
2866d8c71392e7e705d6a72df7754bb0ca5afad7 |
Dataset generated using handwritten fonts
=========================================
Number of images: 300000
Sources:
* [Handwriting generation code](https://github.com/NastyBoget/HandwritingGeneration)
The code was executed with `hkr` option (with fewer augmentations) | nastyboget/synthetic_hkr | [
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-21T09:53:26+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"]} | 2023-03-23T18:43:05+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us
|
Dataset generated using handwritten fonts
=========================================
Number of images: 300000
Sources:
* Handwriting generation code
The code was executed with 'hkr' option (with fewer augmentations) | []
| [
"TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
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1a79069c3f44e32a591290963684974ecc3aafee | # Dataset Card for "openai_summarize_tldr_human_eval_ilql_result"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pvduy/openai_summarize_tldr_human_eval_ilql_result | [
"region:us"
]
| 2023-03-21T10:03:34+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "ILQL_125M", "dtype": "string"}, {"name": "ILQL_1B", "dtype": "string"}, {"name": "ILQL_6B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 176617, "num_examples": 100}], "download_size": 122501, "dataset_size": 176617}} | 2023-03-22T02:57:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "openai_summarize_tldr_human_eval_ilql_result"
More Information needed | [
"# Dataset Card for \"openai_summarize_tldr_human_eval_ilql_result\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"openai_summarize_tldr_human_eval_ilql_result\"\n\nMore Information needed"
]
| [
6,
31
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|
11d3c04afd63178e04abec78f8da323cc9427a7d | # Dataset Card for "yelp_short_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | guangyil/yelp_short_v2 | [
"region:us"
]
| 2023-03-21T10:10:00+00:00 | {"dataset_info": {"features": [{"name": "bert_token", "sequence": "int64"}, {"name": "gpt2_token", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 89578672.0, "num_examples": 447259}, {"name": "test", "num_bytes": 222800.0, "num_examples": 1000}], "download_size": 21476776, "dataset_size": 89801472.0}} | 2023-03-21T10:10:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "yelp_short_v2"
More Information needed | [
"# Dataset Card for \"yelp_short_v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"yelp_short_v2\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"yelp_short_v2\"\n\nMore Information needed"
]
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|
ad612da1a26b53cfb960c8bff98a069a2bd1b0fb |
# Coco dataset loader based on tensorflow dataset coco
## Object Detection
```python
import os
from datasets import load_dataset
from PIL import Image, ImageFont, ImageDraw, ImageColor
def calc_lum(rgb):
return (0.2126*rgb[0] + 0.7152*rgb[1] + 0.0722*rgb[2])
COLOR_MAP = [ImageColor.getrgb(code) for name, code in ImageColor.colormap.items()]
def get_text_bbox(bb, tbb, margin, im_w, im_h, anchor="leftBottom"):
m = margin
l, t, r, b = bb
tl, tt, tr, tb = tbb
bbw, bbh = r - l, b - t
tbbw, tbbh = tr - tl, tb - tt
# bbox (left-top)
if anchor == "leftTop":
ax, ay = l, t
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-bottom)
x1, y1 = max(ax, 0), max(ay - tb - 2*m, 0)
x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-top)
x1, y1 = max(ax, 0), max(ay, 0)
x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h)
return (( x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "rightTop":
ax, ay = r, t
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-bottom)
x2, y1 = max(ax, 0), max(ay - tb - 2*m, 0)
x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-top)
x2, y1 = max(ax, 0), max(ay, 0)
x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "rightBottom":
ax, ay = r, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x2, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h)
x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x2, y2 = min(ax, im_w), max(ay, 0)
x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "leftBottom":
ax, ay = l, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x1, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x1, y2 = min(ax, im_w), max(ay, 0)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "centerBottom":
ax, ay = (l+r)//2, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x1, y2 = min(ax - tr//2 - m, im_w), min(ay + tb + 2*m, im_h)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x1, y2 = min(ax - tr//2 - m, im_w), max(ay, 0)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
def draw_bbox(image, objects, out_path, label_names=None, font="Roboto-Bold.ttf", fontsize=15, fill=True, opacity=60, width=2, margin=3, anchor="leftBottom"):
fnt = ImageFont.truetype(font, fontsize)
im_w, im_h = image.size
img = image.convert("RGBA")
overlay = Image.new('RGBA', img.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
for bb, lbl_id in zip(objects["bbox"], objects["label"]):
c = COLOR_MAP[min(lbl_id, len(COLOR_MAP)-1)]
fill_c = c + (opacity, ) if fill else None
draw.rectangle((bb[0], bb[1], bb[2], bb[3]), outline=c, fill=fill_c, width=width)
text = ""
if label_names is not None:
text = label_names[lbl_id]
tbb = fnt.getbbox(text)
btn_bbox, text_pos = get_text_bbox(bb, tbb, margin, im_w, im_h, anchor)
fc = (0, 0, 0) if calc_lum(c) > 150 else (255, 255, 255)
draw.rectangle(btn_bbox, outline=c, fill=c + (255, ))
draw.text(text_pos, text, font=fnt, fill=fc + (255, ))
img = Image.alpha_composite(img, overlay)
overlay = Image.new('RGBA', img.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
img = img.convert("RGB")
img.save(out_path)
raw_datasets = load_dataset(
"coco.py",
"2017",
cache_dir="./huggingface_datasets",
)
train_dataset = raw_datasets["train"]
label_list = raw_datasets["train"].features["objects"].feature['label'].names
for idx, item in zip(range(10), train_dataset):
draw_bbox(item["image"], item["objects"], item["image/filename"], label_list)
```
## Panoptic segmentation
```python
import numpy as np
from datasets import load_dataset
from PIL import Image, ImageFont, ImageDraw, ImageColor
from transformers.image_transforms import (
rgb_to_id,
)
def calc_lum(rgb):
return (0.2126*rgb[0] + 0.7152*rgb[1] + 0.0722*rgb[2])
COLOR_MAP = [ImageColor.getrgb(code) for name, code in ImageColor.colormap.items()]
def get_text_bbox(bb, tbb, margin, im_w, im_h, anchor="leftBottom"):
m = margin
l, t, r, b = bb
tl, tt, tr, tb = tbb
bbw, bbh = r - l, b - t
tbbw, tbbh = tr - tl, tb - tt
# bbox (left-top)
if anchor == "leftTop":
ax, ay = l, t
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-bottom)
x1, y1 = max(ax, 0), max(ay - tb - 2*m, 0)
x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-top)
x1, y1 = max(ax, 0), max(ay, 0)
x2, y2 = min(x1 + tr + 2*m, im_w), min(y1 + tb + 2*m, im_h)
return (( x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "rightTop":
ax, ay = r, t
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-bottom)
x2, y1 = max(ax, 0), max(ay - tb - 2*m, 0)
x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-top)
x2, y1 = max(ax, 0), max(ay, 0)
x1, y2 = max(x2 - tr - 2*m, 0), min(y1 + tb + 2*m, im_h)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "rightBottom":
ax, ay = r, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x2, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h)
x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x2, y2 = min(ax, im_w), max(ay, 0)
x1, y1 = max(x2 - tr - 2*m, 0), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "leftBottom":
ax, ay = l, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x1, y2 = min(ax, im_w), min(ay + tb + 2*m, im_h)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x1, y2 = min(ax, im_w), max(ay, 0)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
elif anchor == "centerBottom":
ax, ay = (l+r)//2, b
if tbbw*3 > bbw or tbbh*4 > bbh:
# align (text box: left-top)
x1, y2 = min(ax - tr//2 - m, im_w), min(ay + tb + 2*m, im_h)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
else:
# align (text box: left-bottom)
x1, y2 = min(ax - tr//2 - m, im_w), max(ay, 0)
x2, y1 = min(x1 + tr + 2*m, im_w), max(y2 - tb - 2*m, 0)
return ((x1, y1, x2, y2), (max(x1+m, 0), max(y1+m, 0)))
# Copied from transformers.models.detr.image_processing_detr.masks_to_boxes
def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
"""
Compute the bounding boxes around the provided panoptic segmentation masks.
Args:
masks: masks in format `[number_masks, height, width]` where N is the number of masks
Returns:
boxes: bounding boxes in format `[number_masks, 4]` in xyxy format
"""
if masks.size == 0:
return np.zeros((0, 4))
h, w = masks.shape[-2:]
y = np.arange(0, h, dtype=np.float32)
x = np.arange(0, w, dtype=np.float32)
# see https://github.com/pytorch/pytorch/issues/50276
y, x = np.meshgrid(y, x, indexing="ij")
x_mask = masks * np.expand_dims(x, axis=0)
x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
x_min = x.filled(fill_value=1e8)
x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
y_mask = masks * np.expand_dims(y, axis=0)
y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
y_min = y.filled(fill_value=1e8)
y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
return np.stack([x_min, y_min, x_max, y_max], 1)
def draw_seg(image, panoptic_image, oids, labels, out_path, label_names=None, font="Roboto-Bold.ttf", fontsize=15, opacity=160, anchor="leftBottom"):
fnt = ImageFont.truetype(font, fontsize)
im_w, im_h = image.size
masks = np.asarray(panoptic_image, dtype=np.uint32)
masks = rgb_to_id(masks)
oids = np.array(oids, dtype=np.uint32)
masks = masks == oids[:, None, None]
masks = masks.astype(np.uint8)
bboxes = masks_to_boxes(masks)
img = image.convert("RGBA")
for label, mask, bbox in zip(labels, masks, bboxes):
c = COLOR_MAP[min(label, len(COLOR_MAP)-1)]
cf = np.array(c + (opacity, )).astype(np.uint8)
cmask = mask[:, :, None] * cf[None, None, :]
cmask = Image.fromarray(cmask)
img = Image.alpha_composite(img, cmask)
if label_names is not None:
text = label_names[label]
tbb = fnt.getbbox(text)
btn_bbox, text_pos = get_text_bbox(bbox, tbb, 3, im_w, im_h, anchor=anchor)
overlay = Image.new('RGBA', img.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
fc = (0, 0, 0) if calc_lum(c) > 150 else (255, 255, 255)
draw.rectangle(btn_bbox, outline=c, fill=c + (255, ))
draw.text(text_pos, text, font=fnt, fill=fc + (255, ))
img = Image.alpha_composite(img, overlay)
img = img.convert("RGB")
img.save(out_path)
raw_datasets = load_dataset(
"coco.py",
"2017_panoptic",
cache_dir="./huggingface_datasets",
# data_dir="./data",
)
train_dataset = raw_datasets["train"]
label_list = raw_datasets["train"].features["panoptic_objects"].feature['label'].names
for idx, item in zip(range(10), train_dataset):
draw_seg(
item["image"],
item["panoptic_image"],
item["panoptic_objects"]["id"],
item["panoptic_objects"]["label"],
"panoptic_" + item["image/filename"],
label_list)
```
| KETI-AIR/coco | [
"task_categories:object-detection",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-21T11:05:35+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["object-detection"], "pretty_name": "Coco"} | 2023-03-22T11:45:13+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us
|
# Coco dataset loader based on tensorflow dataset coco
## Object Detection
!sample1
!sample2
## Panoptic segmentation
!sample1
!sample2
| [
"# Coco dataset loader based on tensorflow dataset coco",
"## Object Detection\n\n\n\n!sample1\n!sample2",
"## Panoptic segmentation\n\n\n\n!sample1\n!sample2"
]
| [
"TAGS\n#task_categories-object-detection #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n",
"# Coco dataset loader based on tensorflow dataset coco",
"## Object Detection\n\n\n\n!sample1\n!sample2",
"## Panoptic segmentation\n\n\n\n!sample1\n!sample2"
]
| [
41,
14,
12,
13
]
| [
"passage: TAGS\n#task_categories-object-detection #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n# Coco dataset loader based on tensorflow dataset coco## Object Detection\n\n\n\n!sample1\n!sample2## Panoptic segmentation\n\n\n\n!sample1\n!sample2"
]
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|
16555b5a36fa8af69f894931fe57235dcdda140a | # Dataset Card for "wikiart-resized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/wikiart-resized | [
"size_categories:10K<n<100K",
"art",
"lam ",
"region:us"
]
| 2023-03-21T11:05:48+00:00 | {"size_categories": ["10K<n<100K"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "artist", "dtype": {"class_label": {"names": {"0": "Unknown Artist", "1": "boris-kustodiev", "2": "camille-pissarro", "3": "childe-hassam", "4": "claude-monet", "5": "edgar-degas", "6": "eugene-boudin", "7": "gustave-dore", "8": "ilya-repin", "9": "ivan-aivazovsky", "10": "ivan-shishkin", "11": "john-singer-sargent", "12": "marc-chagall", "13": "martiros-saryan", "14": "nicholas-roerich", "15": "pablo-picasso", "16": "paul-cezanne", "17": "pierre-auguste-renoir", "18": "pyotr-konchalovsky", "19": "raphael-kirchner", "20": "rembrandt", "21": "salvador-dali", "22": "vincent-van-gogh", "23": "hieronymus-bosch", "24": "leonardo-da-vinci", "25": "albrecht-durer", "26": "edouard-cortes", "27": "sam-francis", "28": "juan-gris", "29": "lucas-cranach-the-elder", "30": "paul-gauguin", "31": "konstantin-makovsky", "32": "egon-schiele", "33": "thomas-eakins", "34": "gustave-moreau", "35": "francisco-goya", "36": "edvard-munch", "37": "henri-matisse", "38": "fra-angelico", "39": "maxime-maufra", "40": "jan-matejko", "41": "mstislav-dobuzhinsky", "42": "alfred-sisley", "43": "mary-cassatt", "44": "gustave-loiseau", "45": "fernando-botero", "46": "zinaida-serebriakova", "47": "georges-seurat", "48": "isaac-levitan", "49": "joaqu\u00e3\u00adn-sorolla", "50": "jacek-malczewski", "51": "berthe-morisot", "52": "andy-warhol", "53": "arkhip-kuindzhi", "54": "niko-pirosmani", "55": "james-tissot", "56": "vasily-polenov", "57": "valentin-serov", "58": "pietro-perugino", "59": "pierre-bonnard", "60": "ferdinand-hodler", "61": "bartolome-esteban-murillo", "62": "giovanni-boldini", "63": "henri-martin", "64": "gustav-klimt", "65": "vasily-perov", "66": "odilon-redon", "67": "tintoretto", "68": "gene-davis", "69": "raphael", "70": "john-henry-twachtman", "71": "henri-de-toulouse-lautrec", "72": "antoine-blanchard", "73": "david-burliuk", "74": "camille-corot", "75": "konstantin-korovin", "76": "ivan-bilibin", "77": "titian", "78": "maurice-prendergast", "79": "edouard-manet", "80": "peter-paul-rubens", "81": "aubrey-beardsley", "82": "paolo-veronese", "83": "joshua-reynolds", "84": "kuzma-petrov-vodkin", "85": "gustave-caillebotte", "86": "lucian-freud", "87": "michelangelo", "88": "dante-gabriel-rossetti", "89": "felix-vallotton", "90": "nikolay-bogdanov-belsky", "91": "georges-braque", "92": "vasily-surikov", "93": "fernand-leger", "94": "konstantin-somov", "95": "katsushika-hokusai", "96": "sir-lawrence-alma-tadema", "97": "vasily-vereshchagin", "98": "ernst-ludwig-kirchner", "99": "mikhail-vrubel", "100": "orest-kiprensky", "101": "william-merritt-chase", "102": "aleksey-savrasov", "103": "hans-memling", "104": "amedeo-modigliani", "105": "ivan-kramskoy", "106": "utagawa-kuniyoshi", "107": "gustave-courbet", "108": "william-turner", "109": "theo-van-rysselberghe", "110": "joseph-wright", "111": "edward-burne-jones", "112": "koloman-moser", "113": "viktor-vasnetsov", "114": "anthony-van-dyck", "115": "raoul-dufy", "116": "frans-hals", "117": "hans-holbein-the-younger", "118": "ilya-mashkov", "119": "henri-fantin-latour", "120": "m.c.-escher", "121": "el-greco", "122": "mikalojus-ciurlionis", "123": "james-mcneill-whistler", "124": "karl-bryullov", "125": "jacob-jordaens", "126": "thomas-gainsborough", "127": "eugene-delacroix", "128": "canaletto"}}}}, {"name": "genre", "dtype": {"class_label": {"names": {"0": "abstract_painting", "1": "cityscape", "2": "genre_painting", "3": "illustration", "4": "landscape", "5": "nude_painting", "6": "portrait", "7": "religious_painting", "8": "sketch_and_study", "9": "still_life", "10": "Unknown Genre"}}}}, {"name": "style", "dtype": {"class_label": {"names": {"0": "Abstract_Expressionism", "1": "Action_painting", "2": "Analytical_Cubism", "3": "Art_Nouveau", "4": "Baroque", "5": "Color_Field_Painting", "6": "Contemporary_Realism", "7": "Cubism", "8": "Early_Renaissance", "9": "Expressionism", "10": "Fauvism", "11": "High_Renaissance", "12": "Impressionism", "13": "Mannerism_Late_Renaissance", "14": "Minimalism", "15": "Naive_Art_Primitivism", "16": "New_Realism", "17": "Northern_Renaissance", "18": "Pointillism", "19": "Pop_Art", "20": "Post_Impressionism", "21": "Realism", "22": "Rococo", "23": "Romanticism", "24": "Symbolism", "25": "Synthetic_Cubism", "26": "Ukiyo_e"}}}}], "splits": [{"name": "train", "num_bytes": 5066964513.5, "num_examples": 81444}], "download_size": 5065060725, "dataset_size": 5066964513.5}, "tags": ["art", "lam "]} | 2023-03-21T13:27:06+00:00 | []
| []
| TAGS
#size_categories-10K<n<100K #art #lam #region-us
| # Dataset Card for "wikiart-resized"
More Information needed | [
"# Dataset Card for \"wikiart-resized\"\n\nMore Information needed"
]
| [
"TAGS\n#size_categories-10K<n<100K #art #lam #region-us \n",
"# Dataset Card for \"wikiart-resized\"\n\nMore Information needed"
]
| [
22,
15
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #art #lam #region-us \n# Dataset Card for \"wikiart-resized\"\n\nMore Information needed"
]
| [
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|
b6f1bc1d9c1ef80404b3517e1ff822652c92a9bc | # Dataset Card for "tweets_for_labelling"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dvilasuero/tweets_for_labelling | [
"region:us"
]
| 2023-03-21T12:29:05+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "neutral", "2": "positive"}}}}], "splits": [{"name": "train", "num_bytes": 3480.269230769231, "num_examples": 41}, {"name": "test", "num_bytes": 933.7307692307693, "num_examples": 11}], "download_size": 7108, "dataset_size": 4414.0}} | 2023-03-21T12:29:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tweets_for_labelling"
More Information needed | [
"# Dataset Card for \"tweets_for_labelling\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tweets_for_labelling\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tweets_for_labelling\"\n\nMore Information needed"
]
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|
eb952c9415af6729354790c9dc47400586459285 | # PII dataset
## Dataset description
This is an annotated dataset for Personal Identifiable Information (PII) in code. The target entities are: Names, Usernames, Emails, IP addresses, Keys, Passwords, and IDs.
The annotation process involved 1,399 crowd-workers from 35 countries with [Toloka](https://toloka.ai/).
It consists of **12,099** samples of
~50 lines of code in 31 programming languages. You can also find a PII detection model that we trained on this dataset at [bigcode-pii-model](https://huggingface.co/loubnabnl/bigcode-pii-model).
## Dataset Structure
You can load the dataset with:
```python
from datasets import load_dataset
ds = load_dataset("bigcode/bigcode-pii-dataset", use_auth_token=True)
ds
```
````
DatasetDict({
test: Dataset({
features: ['text', 'type', 'language', 'fragments', 'id'],
num_rows: 12099
})
})
````
It has the following data fields:
- text: the code snippet
- type: indicated if the data was pre-filtered with regexes (before annotation we selected 7100 files that were pre-filtered as positive for PII with regexes, and selected 5199 randomly)
- language: programming language
- fragments: detected secrets and their positions and categories
- category: PII category
- position: start and end
- value: PII value
## Statistics
Figure below shows the distribution of programming languages in the dataset:
<img src="https://huggingface.co/datasets/bigcode/admin/resolve/main/pii_lang_dist.png" width="50%">
The following table shows the distribution of PII in all classes, as well as annotation quality after manual inspection of 300 diverse files from the dataset:
| Entity | Count | Precision | Recall |
| ---------------- | ----- | --------- | ------ |
| IP\_ADDRESS | 2526 | 85% | 97% |
| KEY | 308 | 91% | 78% |
| PASSWORD | 598 | 91% | 86% |
| ID | 1702 | 53% | 51% |
| EMAIL | 5470 | 99% | 97% |
| EMAIL\_EXAMPLE | 1407 | | |
| EMAIL\_LICENSE | 3141 | | |
| NAME | 2477 | 89% | 94% |
| NAME\_EXAMPLE | 318 | | |
| NAME\_LICENSE | 3105 | | |
| USERNAME | 780 | 74% | 86% |
| USERNAME\_EXAMPLE| 328 | | |
| USERNAME\_LICENSE| 503 | | |
| AMBIGUOUS | 287 | | |
`AMBIGUOUS` and `ID` were not used in our [NER model](https://huggingface.co/loubnabnl/bigcode-pii-model) training for PII detection.
# Dataset Creation
We selected the annotation samples from [The Stack](https://huggingface.co/datasets/bigcode/the-stack) dataset after deduplication,
a collection of code from open permissively licensed repositories on GitHub.
To increase the representation of rare PII types, such as keys and IP addresses, we pre-filtered 7100 files from a larger sample.
This pre-filtering was carried out using the [detect-secrets](https://github.com/Yelp/detect-secrets) tool with all default plugins activated,
in addition to the regular expressions to detect emails, IPv4 and IPv6 addresses. To avoid introducing bias, the remaining 5100 files were randomly sampled from the dataset without pre-filtering.
We then annotated the dataset through [Toloka Platform](https://toloka.ai/) with 1,399 crowd-workers from 35 countries. To ensure that crowd-workers received fair compensation, we established an hourly pay rate of \$7.30, taking into consideration different minimum wage rates across countries and their corresponding purchasing power.
We limited annotation eligibility to countries where the hourly pay rate of \$7.30 was equivalent to the highest minimum wage in the US (\$16.50) in terms of purchasing power parity.
# Considerations for Using the Data
When using this dataset, please be mindful of the data governance risks that come with handling personally identifiable information (PII). Despite sourcing the data from open, permissive GitHub repositories and having it annotated by fairly paid crowd-workers, it does contain sensitive details such as names, usernames, keys, emails, passwords, and IP addresses. To ensure responsible use for research within the open-source community, access to the dataset will be provided through a gated mechanism.
We expect researchers and developers working with the dataset to adhere to the highest ethical standards and employ robust data protection measures.
To assist users in effectively detecting and masking PII, we've also released a PII model trained on this dataset.
Our goal in providing access to both the dataset and the PII model is to foster the development of privacy-preserving AI technologies while minimizing potential risks related to handling PII. | bigcode/bigcode-pii-dataset | [
"task_categories:token-classification",
"language:code",
"region:us"
]
| 2023-03-21T12:57:14+00:00 | {"language": ["code"], "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "fragments", "list": [{"name": "category", "dtype": "string"}, {"name": "position", "sequence": "int64"}, {"name": "value", "dtype": "string"}]}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 22496122, "num_examples": 12099}], "download_size": 9152605, "dataset_size": 22496122}, "extra_gated_prompt": "## Terms of Use for the dataset\n\nThis is an annotated dataset for Personal Identifiable Information (PII) in code. We ask that you read and agree to the following Terms of Use before using the dataset and fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSfiWKyBB8-PxOCLo-KMsLlYNyQNJEzxJw0gcUAUHT3UY848qA/viewform):\n**Incomplete answers to the form will result in the request for access being ignored, with no follow-up actions by BigCode.**\n1. You agree that you will not use the PII dataset for any purpose other than training or evaluating models for PII removal from datasets.\n2. You agree that you will not share the PII dataset or any modified versions for whatever purpose.\n3. Unless required by applicable law or agreed to in writing, the dataset is provided on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using the dataset, and assume any risks associated with your exercise of permissions under these Terms of Use.\n4. 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 DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}} | 2023-05-15T09:07:10+00:00 | []
| [
"code"
]
| TAGS
#task_categories-token-classification #language-code #region-us
| PII dataset
===========
Dataset description
-------------------
This is an annotated dataset for Personal Identifiable Information (PII) in code. The target entities are: Names, Usernames, Emails, IP addresses, Keys, Passwords, and IDs.
The annotation process involved 1,399 crowd-workers from 35 countries with Toloka.
It consists of 12,099 samples of
~50 lines of code in 31 programming languages. You can also find a PII detection model that we trained on this dataset at bigcode-pii-model.
Dataset Structure
-----------------
You can load the dataset with:
'
It has the following data fields:
* text: the code snippet
* type: indicated if the data was pre-filtered with regexes (before annotation we selected 7100 files that were pre-filtered as positive for PII with regexes, and selected 5199 randomly)
* language: programming language
* fragments: detected secrets and their positions and categories
+ category: PII category
+ position: start and end
+ value: PII value
Statistics
----------
Figure below shows the distribution of programming languages in the dataset:
<img src="URL width="50%">
The following table shows the distribution of PII in all classes, as well as annotation quality after manual inspection of 300 diverse files from the dataset:
'AMBIGUOUS' and 'ID' were not used in our NER model training for PII detection.
Dataset Creation
================
We selected the annotation samples from The Stack dataset after deduplication,
a collection of code from open permissively licensed repositories on GitHub.
To increase the representation of rare PII types, such as keys and IP addresses, we pre-filtered 7100 files from a larger sample.
This pre-filtering was carried out using the detect-secrets tool with all default plugins activated,
in addition to the regular expressions to detect emails, IPv4 and IPv6 addresses. To avoid introducing bias, the remaining 5100 files were randomly sampled from the dataset without pre-filtering.
We then annotated the dataset through Toloka Platform with 1,399 crowd-workers from 35 countries. To ensure that crowd-workers received fair compensation, we established an hourly pay rate of $7.30, taking into consideration different minimum wage rates across countries and their corresponding purchasing power.
We limited annotation eligibility to countries where the hourly pay rate of $7.30 was equivalent to the highest minimum wage in the US ($16.50) in terms of purchasing power parity.
Considerations for Using the Data
=================================
When using this dataset, please be mindful of the data governance risks that come with handling personally identifiable information (PII). Despite sourcing the data from open, permissive GitHub repositories and having it annotated by fairly paid crowd-workers, it does contain sensitive details such as names, usernames, keys, emails, passwords, and IP addresses. To ensure responsible use for research within the open-source community, access to the dataset will be provided through a gated mechanism.
We expect researchers and developers working with the dataset to adhere to the highest ethical standards and employ robust data protection measures.
To assist users in effectively detecting and masking PII, we've also released a PII model trained on this dataset.
Our goal in providing access to both the dataset and the PII model is to foster the development of privacy-preserving AI technologies while minimizing potential risks related to handling PII.
| []
| [
"TAGS\n#task_categories-token-classification #language-code #region-us \n"
]
| [
22
]
| [
"passage: TAGS\n#task_categories-token-classification #language-code #region-us \n"
]
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|
15f16eb89cf4c4633be57a10be1b57772437c74a | # Dataset Card for "preprocessed_birdclef_2023_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Syoy/preprocessed_birdclef_2023_train | [
"region:us"
]
| 2023-03-21T13:37:04+00:00 | {"dataset_info": {"features": [{"name": "primary_label", "dtype": {"class_label": {"names": {"0": "yetgre1", "1": "moccha1", "2": "rostur1", "3": "walsta1", "4": "ratcis1", "5": "norfis1", "6": "macshr1", "7": "brrwhe3", "8": "crefra2", "9": "pabspa1", "10": "sltnig1", "11": "cabgre1", "12": "equaka1", "13": "sobfly1", "14": "rindov", "15": "wlwwar", "16": "brwwar1", "17": "gnbcam2", "18": "carcha1", "19": "abethr1", "20": "yertin1", "21": "spewea1", "22": "varsun2", "23": "yebduc1", "24": "eubeat1", "25": "hadibi1", "26": "brcale1", "27": "litwea1", "28": "sincis1", "29": "whbcro2", "30": "thrnig1", "31": "bubwar2", "32": "kvbsun1", "33": "blbpuf2", "34": "blakit1", "35": "colsun2", "36": "bltapa1", "37": "gycwar3", "38": "joygre1", "39": "greegr", "40": "vibsta2", "41": "wtbeat1", "42": "afrgos1", "43": "rebfir2", "44": "yebgre1", "45": "comsan", "46": "pygbat1", "47": "meypar1", "48": "yelbis1", "49": "norbro1", "50": "ndcsun2", "51": "gybfis1", "52": 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"dataset_size": 8951693048}} | 2023-03-21T13:42:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "preprocessed_birdclef_2023_train"
More Information needed | [
"# Dataset Card for \"preprocessed_birdclef_2023_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"preprocessed_birdclef_2023_train\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"preprocessed_birdclef_2023_train\"\n\nMore Information needed"
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|
1c0bd603f6ab84f13d3434c19ba7443a37aaf85d | # Dataset Card for "birdclef_2023_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Syoy/birdclef_2023_train | [
"region:us"
]
| 2023-03-21T13:47:03+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "primary_label", "dtype": {"class_label": {"names": {"0": "yetgre1", "1": "moccha1", "2": "rostur1", "3": "walsta1", "4": "ratcis1", "5": "norfis1", "6": "macshr1", "7": "brrwhe3", "8": "crefra2", "9": "pabspa1", "10": "sltnig1", "11": "cabgre1", "12": "equaka1", "13": "sobfly1", "14": "rindov", "15": "wlwwar", "16": "brwwar1", "17": "gnbcam2", "18": "carcha1", "19": "abethr1", "20": "yertin1", "21": "spewea1", "22": "varsun2", "23": "yebduc1", "24": "eubeat1", "25": "hadibi1", "26": "brcale1", "27": "litwea1", "28": "sincis1", "29": "whbcro2", "30": "thrnig1", "31": "bubwar2", "32": "kvbsun1", "33": "blbpuf2", "34": "blakit1", "35": "colsun2", "36": "bltapa1", "37": "gycwar3", "38": "joygre1", "39": "greegr", "40": "vibsta2", "41": "wtbeat1", "42": "afrgos1", "43": "rebfir2", "44": "yebgre1", "45": "comsan", "46": "pygbat1", "47": "meypar1", "48": "yelbis1", "49": "norbro1", 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"dtype": "string"}, {"name": "common_name", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "rating", "dtype": "float64"}, {"name": "url", "dtype": "string"}, {"name": "embeddings", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 5388534029.882, "num_examples": 16941}], "download_size": 5367714895, "dataset_size": 5388534029.882}} | 2023-03-21T13:51:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "birdclef_2023_train"
More Information needed | [
"# Dataset Card for \"birdclef_2023_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"birdclef_2023_train\"\n\nMore Information needed"
]
| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"birdclef_2023_train\"\n\nMore Information needed"
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|
ad16b1c02813663d7df5211d176edd8133a454c2 |
# Dataset Card for the Juliet Test Suite 1.3
### Dataset Summary
This Datasets contains all test cases from the NIST's [Juliet test suite](https://samate.nist.gov/SARD/test-suites/112) for the C and C++ programming languages. The dataset contains a benign and a defective implementation of each sample, which have been extracting by means of the OMITGOOD and OMITBAD preprocessor macros of the Juliet test suite.
### Supported Tasks and Leaderboards
Software defect prediction, code clone detection.
### Languages
The C and C++ programming languages.
## Dataset Structure
### Data Instances
### Data Fields
| index | name | type | description |
| --- | --- | --- | --- |
| 0 | index | int | The index of each sample in the dataset. |
| 1 | filename | str | The path to the test case including the file name. |
| 2 | class | int | The class of the defect, i.e., the collection by CWE number from which the sample was taken. |
| 3 | good | str | The code of the benign implementation. |
| 4 | bad | str | The code of the defective implementation. |
### Data Splits
| type | size |
|------|------|
| train | 80706 cases |
| test | 20177 cases |
## Dataset Creation
### Curation Rationale
### Source Data
https://samate.nist.gov/SARD/test-suites/112
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
The Juliet test suite is a synthetic dataset, meaning that all samples have been manually crafted. Therefore they are not entirely representative of actual software defects found in the wild. A classifier trained on these samples may suffer from decreased predictive performance, leading to gross misclassifactions. Critical software defects may therefore be overlooked, when such model is applied in a realistic environment.
## Additional Information
### Dataset Curators
https://github.com/lorenz9314/
### Licensing Information
### Citation Information
### Contributions
| LorenzH/juliet_test_suite_c_1_3 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"license:cc0-1.0",
"region:us"
]
| 2023-03-21T13:49:04+00:00 | {"license": "cc0-1.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Juliet Test Suite 1.3"} | 2023-03-21T14:38:12+00:00 | []
| []
| TAGS
#task_categories-text-classification #size_categories-10K<n<100K #license-cc0-1.0 #region-us
| Dataset Card for the Juliet Test Suite 1.3
==========================================
### Dataset Summary
This Datasets contains all test cases from the NIST's Juliet test suite for the C and C++ programming languages. The dataset contains a benign and a defective implementation of each sample, which have been extracting by means of the OMITGOOD and OMITBAD preprocessor macros of the Juliet test suite.
### Supported Tasks and Leaderboards
Software defect prediction, code clone detection.
### Languages
The C and C++ programming languages.
Dataset Structure
-----------------
### Data Instances
### Data Fields
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
URL
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
The Juliet test suite is a synthetic dataset, meaning that all samples have been manually crafted. Therefore they are not entirely representative of actual software defects found in the wild. A classifier trained on these samples may suffer from decreased predictive performance, leading to gross misclassifactions. Critical software defects may therefore be overlooked, when such model is applied in a realistic environment.
Additional Information
----------------------
### Dataset Curators
URL
### Licensing Information
### Contributions
| [
"### Dataset Summary\n\n\nThis Datasets contains all test cases from the NIST's Juliet test suite for the C and C++ programming languages. The dataset contains a benign and a defective implementation of each sample, which have been extracting by means of the OMITGOOD and OMITBAD preprocessor macros of the Juliet test suite.",
"### Supported Tasks and Leaderboards\n\n\nSoftware defect prediction, code clone detection.",
"### Languages\n\n\nThe C and C++ programming languages.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data\n\n\nURL",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nThe Juliet test suite is a synthetic dataset, meaning that all samples have been manually crafted. Therefore they are not entirely representative of actual software defects found in the wild. A classifier trained on these samples may suffer from decreased predictive performance, leading to gross misclassifactions. Critical software defects may therefore be overlooked, when such model is applied in a realistic environment.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nURL",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #license-cc0-1.0 #region-us \n",
"### Dataset Summary\n\n\nThis Datasets contains all test cases from the NIST's Juliet test suite for the C and C++ programming languages. The dataset contains a benign and a defective implementation of each sample, which have been extracting by means of the OMITGOOD and OMITBAD preprocessor macros of the Juliet test suite.",
"### Supported Tasks and Leaderboards\n\n\nSoftware defect prediction, code clone detection.",
"### Languages\n\n\nThe C and C++ programming languages.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data\n\n\nURL",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nThe Juliet test suite is a synthetic dataset, meaning that all samples have been manually crafted. Therefore they are not entirely representative of actual software defects found in the wild. A classifier trained on these samples may suffer from decreased predictive performance, leading to gross misclassifactions. Critical software defects may therefore be overlooked, when such model is applied in a realistic environment.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nURL",
"### Licensing Information",
"### Contributions"
]
| [
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| [
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #license-cc0-1.0 #region-us \n### Dataset Summary\n\n\nThis Datasets contains all test cases from the NIST's Juliet test suite for the C and C++ programming languages. The dataset contains a benign and a defective implementation of each sample, which have been extracting by means of the OMITGOOD and OMITBAD preprocessor macros of the Juliet test suite.### Supported Tasks and Leaderboards\n\n\nSoftware defect prediction, code clone detection.### Languages\n\n\nThe C and C++ programming languages.\n\n\nDataset Structure\n-----------------### Data Instances### Data Fields### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data\n\n\nURL#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nThe Juliet test suite is a synthetic dataset, meaning that all samples have been manually crafted. Therefore they are not entirely representative of actual software defects found in the wild. A classifier trained on these samples may suffer from decreased predictive performance, leading to gross misclassifactions. Critical software defects may therefore be overlooked, when such model is applied in a realistic environment.\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\nURL### Licensing Information### Contributions"
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|
5d43db5d520f0b0ec7a3db194aad0ddcaa4a6092 | # Dataset Card for "wikiart-resized-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/wikiart-resized-sample | [
"region:us"
]
| 2023-03-21T14:04:35+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "artist", "dtype": {"class_label": {"names": {"0": "Unknown Artist", "1": "boris-kustodiev", "2": "camille-pissarro", "3": "childe-hassam", "4": "claude-monet", "5": "edgar-degas", "6": "eugene-boudin", "7": "gustave-dore", "8": "ilya-repin", "9": "ivan-aivazovsky", "10": "ivan-shishkin", "11": "john-singer-sargent", "12": "marc-chagall", "13": "martiros-saryan", "14": "nicholas-roerich", "15": "pablo-picasso", "16": "paul-cezanne", "17": "pierre-auguste-renoir", "18": "pyotr-konchalovsky", "19": "raphael-kirchner", "20": "rembrandt", "21": "salvador-dali", "22": "vincent-van-gogh", "23": "hieronymus-bosch", "24": "leonardo-da-vinci", "25": "albrecht-durer", "26": "edouard-cortes", "27": "sam-francis", "28": "juan-gris", "29": "lucas-cranach-the-elder", "30": "paul-gauguin", "31": "konstantin-makovsky", "32": "egon-schiele", "33": "thomas-eakins", "34": "gustave-moreau", "35": "francisco-goya", "36": "edvard-munch", "37": "henri-matisse", "38": "fra-angelico", "39": "maxime-maufra", "40": "jan-matejko", "41": "mstislav-dobuzhinsky", "42": "alfred-sisley", "43": "mary-cassatt", "44": "gustave-loiseau", "45": "fernando-botero", "46": "zinaida-serebriakova", "47": "georges-seurat", "48": "isaac-levitan", "49": "joaqu\u00e3\u00adn-sorolla", "50": "jacek-malczewski", "51": "berthe-morisot", "52": "andy-warhol", "53": "arkhip-kuindzhi", "54": "niko-pirosmani", "55": "james-tissot", "56": "vasily-polenov", "57": "valentin-serov", "58": "pietro-perugino", "59": "pierre-bonnard", "60": "ferdinand-hodler", "61": "bartolome-esteban-murillo", "62": "giovanni-boldini", "63": "henri-martin", "64": "gustav-klimt", "65": "vasily-perov", "66": "odilon-redon", "67": "tintoretto", "68": "gene-davis", "69": "raphael", "70": "john-henry-twachtman", "71": "henri-de-toulouse-lautrec", "72": "antoine-blanchard", "73": "david-burliuk", "74": "camille-corot", "75": "konstantin-korovin", "76": "ivan-bilibin", "77": "titian", "78": "maurice-prendergast", "79": "edouard-manet", "80": "peter-paul-rubens", "81": "aubrey-beardsley", "82": "paolo-veronese", "83": "joshua-reynolds", "84": "kuzma-petrov-vodkin", "85": "gustave-caillebotte", "86": "lucian-freud", "87": "michelangelo", "88": "dante-gabriel-rossetti", "89": "felix-vallotton", "90": "nikolay-bogdanov-belsky", "91": "georges-braque", "92": "vasily-surikov", "93": "fernand-leger", "94": "konstantin-somov", "95": "katsushika-hokusai", "96": "sir-lawrence-alma-tadema", "97": "vasily-vereshchagin", "98": "ernst-ludwig-kirchner", "99": "mikhail-vrubel", "100": "orest-kiprensky", "101": "william-merritt-chase", "102": "aleksey-savrasov", "103": "hans-memling", "104": "amedeo-modigliani", "105": "ivan-kramskoy", "106": "utagawa-kuniyoshi", "107": "gustave-courbet", "108": "william-turner", "109": "theo-van-rysselberghe", "110": "joseph-wright", "111": "edward-burne-jones", "112": "koloman-moser", "113": "viktor-vasnetsov", "114": "anthony-van-dyck", "115": "raoul-dufy", "116": "frans-hals", "117": "hans-holbein-the-younger", "118": "ilya-mashkov", "119": "henri-fantin-latour", "120": "m.c.-escher", "121": "el-greco", "122": "mikalojus-ciurlionis", "123": "james-mcneill-whistler", "124": "karl-bryullov", "125": "jacob-jordaens", "126": "thomas-gainsborough", "127": "eugene-delacroix", "128": "canaletto"}}}}, {"name": "genre", "dtype": {"class_label": {"names": {"0": "abstract_painting", "1": "cityscape", "2": "genre_painting", "3": "illustration", "4": "landscape", "5": "nude_painting", "6": "portrait", "7": "religious_painting", "8": "sketch_and_study", "9": "still_life", "10": "Unknown Genre"}}}}, {"name": "style", "dtype": {"class_label": {"names": {"0": "Abstract_Expressionism", "1": "Action_painting", "2": "Analytical_Cubism", "3": "Art_Nouveau", "4": "Baroque", "5": "Color_Field_Painting", "6": "Contemporary_Realism", "7": "Cubism", "8": "Early_Renaissance", "9": "Expressionism", "10": "Fauvism", "11": "High_Renaissance", "12": "Impressionism", "13": "Mannerism_Late_Renaissance", "14": "Minimalism", "15": "Naive_Art_Primitivism", "16": "New_Realism", "17": "Northern_Renaissance", "18": "Pointillism", "19": "Pop_Art", "20": "Post_Impressionism", "21": "Realism", "22": "Rococo", "23": "Romanticism", "24": "Symbolism", "25": "Synthetic_Cubism", "26": "Ukiyo_e"}}}}], "splits": [{"name": "train", "num_bytes": 3110660852.85595, "num_examples": 50000}], "download_size": 3114376026, "dataset_size": 3110660852.85595}} | 2023-03-21T20:09:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikiart-resized-sample"
More Information needed | [
"# Dataset Card for \"wikiart-resized-sample\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikiart-resized-sample\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikiart-resized-sample\"\n\nMore Information needed"
]
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|
30a5899284561651363efc827e85a37aacc5a080 | https://huggingface.c.o/datasets/Samuelcr8/Eva | Samuelcr8/Eva | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:aa",
"license:afl-3.0",
"biology",
"region:us"
]
| 2023-03-21T14:24:15+00:00 | {"language": ["aa"], "license": "afl-3.0", "size_categories": ["n<1K"], "task_categories": ["question-answering"], "tags": ["biology"]} | 2023-03-21T14:28:13+00:00 | []
| [
"aa"
]
| TAGS
#task_categories-question-answering #size_categories-n<1K #language-Afar #license-afl-3.0 #biology #region-us
| https://huggingface.c.o/datasets/Samuelcr8/Eva | []
| [
"TAGS\n#task_categories-question-answering #size_categories-n<1K #language-Afar #license-afl-3.0 #biology #region-us \n"
]
| [
44
]
| [
"passage: TAGS\n#task_categories-question-answering #size_categories-n<1K #language-Afar #license-afl-3.0 #biology #region-us \n"
]
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|
ee9f31b8f7b49af0612031f38a7cd93953552db5 | # Dataset Card for "x_thinks_y"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LRudL/x_thinks_y | [
"region:us"
]
| 2023-03-21T15:39:07+00:00 | {"dataset_info": {"features": [{"name": "type", "dtype": "string"}, {"name": "false_part", "dtype": "string"}, {"name": "true_version_of_part", "dtype": "string"}, {"name": "entire_statement", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25046, "num_examples": 96}], "download_size": 18252, "dataset_size": 25046}} | 2023-03-21T15:39:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "x_thinks_y"
More Information needed | [
"# Dataset Card for \"x_thinks_y\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"x_thinks_y\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"x_thinks_y\"\n\nMore Information needed"
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|
969d59b0b7373f625575476fd31b2c3c743d704e | # Dataset Card for "anthropic_hh_modified"
Total copy of [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) (all credit and rights to the authors of that), just with some modifications to the format so that it can be used with the [eleuther-elk](https://github.com/EleutherAI/elk/tree/main/elk) repository.
Changes:
- rename column "chosen" to "choice0" and "rejected" to "choice1"
- randomly flip the entry in column choice0 and choice1 for half of the entries
- create a ClassLabel column "label" that stores an integer 0 or 1, corresponding to which of choice0 or choice1 was preferred by the human.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LRudL/anthropic_hh_modified | [
"region:us"
]
| 2023-03-21T15:41:42+00:00 | {"dataset_info": {"features": [{"name": "choice0", "dtype": "string"}, {"name": "choice1", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 56635938, "num_examples": 42537}, {"name": "test", "num_bytes": 3195756, "num_examples": 2312}], "download_size": 33135346, "dataset_size": 59831694}} | 2023-03-21T15:45:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "anthropic_hh_modified"
Total copy of Anthropic/hh-rlhf (all credit and rights to the authors of that), just with some modifications to the format so that it can be used with the eleuther-elk repository.
Changes:
- rename column "chosen" to "choice0" and "rejected" to "choice1"
- randomly flip the entry in column choice0 and choice1 for half of the entries
- create a ClassLabel column "label" that stores an integer 0 or 1, corresponding to which of choice0 or choice1 was preferred by the human.
More Information needed | [
"# Dataset Card for \"anthropic_hh_modified\"\n\nTotal copy of Anthropic/hh-rlhf (all credit and rights to the authors of that), just with some modifications to the format so that it can be used with the eleuther-elk repository.\n\nChanges:\n- rename column \"chosen\" to \"choice0\" and \"rejected\" to \"choice1\"\n- randomly flip the entry in column choice0 and choice1 for half of the entries\n- create a ClassLabel column \"label\" that stores an integer 0 or 1, corresponding to which of choice0 or choice1 was preferred by the human.\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"anthropic_hh_modified\"\n\nTotal copy of Anthropic/hh-rlhf (all credit and rights to the authors of that), just with some modifications to the format so that it can be used with the eleuther-elk repository.\n\nChanges:\n- rename column \"chosen\" to \"choice0\" and \"rejected\" to \"choice1\"\n- randomly flip the entry in column choice0 and choice1 for half of the entries\n- create a ClassLabel column \"label\" that stores an integer 0 or 1, corresponding to which of choice0 or choice1 was preferred by the human.\n\nMore Information needed"
]
| [
6,
158
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"anthropic_hh_modified\"\n\nTotal copy of Anthropic/hh-rlhf (all credit and rights to the authors of that), just with some modifications to the format so that it can be used with the eleuther-elk repository.\n\nChanges:\n- rename column \"chosen\" to \"choice0\" and \"rejected\" to \"choice1\"\n- randomly flip the entry in column choice0 and choice1 for half of the entries\n- create a ClassLabel column \"label\" that stores an integer 0 or 1, corresponding to which of choice0 or choice1 was preferred by the human.\n\nMore Information needed"
]
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|
260a445d139c18787e5ecd00b1acd0f0547b6c08 | # Dataset Card for "hellenistic-greek-plaintext"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryderwishart/hellenistic-greek-plaintext | [
"region:us"
]
| 2023-03-21T15:43:36+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 73193787, "num_examples": 355703}, {"name": "test", "num_bytes": 9681763, "num_examples": 44463}, {"name": "eval", "num_bytes": 8999996, "num_examples": 44463}], "download_size": 45069557, "dataset_size": 91875546}} | 2023-03-21T16:43:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hellenistic-greek-plaintext"
More Information needed | [
"# Dataset Card for \"hellenistic-greek-plaintext\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hellenistic-greek-plaintext\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"hellenistic-greek-plaintext\"\n\nMore Information needed"
]
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|
4d662ed99a3e8ac8d1d2e5a5599ad825aef7da44 | # Dataset Card for "hellenistic-greek-lemmas"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryderwishart/hellenistic-greek-lemmas | [
"region:us"
]
| 2023-03-21T15:45:29+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 146758866, "num_examples": 1086734}, {"name": "test", "num_bytes": 14082903, "num_examples": 135842}, {"name": "eval", "num_bytes": 18754547, "num_examples": 135842}], "download_size": 6083954, "dataset_size": 179596316}} | 2023-03-21T16:45:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hellenistic-greek-lemmas"
More Information needed | [
"# Dataset Card for \"hellenistic-greek-lemmas\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hellenistic-greek-lemmas\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"hellenistic-greek-lemmas\"\n\nMore Information needed"
]
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|
3ef5968373535ff75324969314660af3d444385f | # Dataset Card for "portuguese_wikineural"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arubenruben/portuguese_wikineural | [
"region:us"
]
| 2023-03-21T16:07:03+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC", "7": "B-MISC", "8": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 33140600, "num_examples": 80560}, {"name": "test", "num_bytes": 4400517, "num_examples": 10160}, {"name": "validation", "num_bytes": 4384834, "num_examples": 10070}], "download_size": 10275737, "dataset_size": 41925951}} | 2023-04-10T12:45:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "portuguese_wikineural"
More Information needed | [
"# Dataset Card for \"portuguese_wikineural\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"portuguese_wikineural\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"portuguese_wikineural\"\n\nMore Information needed"
]
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|
89d9ace53b05723ed371e55e91bc1ec07afcc7ad | # Dataset Card for "lam_gender"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lamnt2008/lam_gender | [
"region:us"
]
| 2023-03-21T16:12:59+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "female", "1": "male"}}}}], "splits": [{"name": "train", "num_bytes": 886700538.492, "num_examples": 188402}, {"name": "validation", "num_bytes": 34511251.337, "num_examples": 10617}], "download_size": 1046144749, "dataset_size": 921211789.829}} | 2023-03-21T16:28:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "lam_gender"
More Information needed | [
"# Dataset Card for \"lam_gender\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"lam_gender\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"lam_gender\"\n\nMore Information needed"
]
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|
bd6b2838302f0ec211c6e1a40c67627736e0bee4 | # Dataset Card for "news-programmatic-labeling"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dvilasuero/news-programmatic-labeling | [
"region:us"
]
| 2023-03-21T16:14:53+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Business", "1": "Sci/Tech", "2": "Sports", "3": "World"}}}}], "splits": [{"name": "train", "num_bytes": 167543.4299287411, "num_examples": 673}, {"name": "test", "num_bytes": 42072.57007125891, "num_examples": 169}], "download_size": 146616, "dataset_size": 209616.0}} | 2023-03-21T16:14:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "news-programmatic-labeling"
More Information needed | [
"# Dataset Card for \"news-programmatic-labeling\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"news-programmatic-labeling\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"news-programmatic-labeling\"\n\nMore Information needed"
]
| [
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|
2fa911a56b9c5f19cb84a2934136e8745c7a0f25 | ```
@article{salvatore2019logical,
title={A logical-based corpus for cross-lingual evaluation},
author={Salvatore, Felipe and Finger, Marcelo and Hirata Jr, Roberto},
journal={arXiv preprint arXiv:1905.05704},
year={2019}
}
```
| metaeval/clcd-english | [
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-21T16:32:25+00:00 | {"language": ["en"], "license": "apache-2.0"} | 2023-06-22T13:07:47+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-apache-2.0 #region-us
| []
| [
"TAGS\n#language-English #license-apache-2.0 #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#language-English #license-apache-2.0 #region-us \n"
]
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|
|
625d605e9450371a66e23c98a3033a98fffcceb8 | # Dataset Card for "new-testament-syntax"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryderwishart/new-testament-syntax | [
"region:us"
]
| 2023-03-21T16:35:19+00:00 | {"dataset_info": {"features": [{"name": "morphgnt_id", "dtype": "string"}, {"name": "morphgnt_head_id", "dtype": "string"}, {"name": "function", "dtype": "string"}, {"name": "ref", "dtype": "string"}, {"name": "Cat", "dtype": "string"}, {"name": "Start", "dtype": "string"}, {"name": "End", "dtype": "string"}, {"name": "StrongNumber", "dtype": "string"}, {"name": "UnicodeLemma", "dtype": "string"}, {"name": "Gender", "dtype": "string"}, {"name": "Number", "dtype": "string"}, {"name": "FunctionalTag", "dtype": "string"}, {"name": "Type", "dtype": "string"}, {"name": "morphId", "dtype": "string"}, {"name": "NormalizedForm", "dtype": "string"}, {"name": "Case", "dtype": "string"}, {"name": "Unicode", "dtype": "string"}, {"name": "FormalTag", "dtype": "string"}, {"name": "nodeId", "dtype": "string"}, {"name": "Gloss", "dtype": "string"}, {"name": "LexDomain", "dtype": "string"}, {"name": "LN", "dtype": "string"}, {"name": "Mood", "dtype": "string"}, {"name": "Tense", "dtype": "string"}, {"name": "Voice", "dtype": "string"}, {"name": "SubjRef", "dtype": "string"}, {"name": "Frame", "dtype": "string"}, {"name": "Ref", "dtype": "string"}, {"name": "Person", "dtype": "string"}, {"name": "Degree", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38838442, "num_examples": 137779}], "download_size": 10423268, "dataset_size": 38838442}} | 2023-03-22T21:07:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "new-testament-syntax"
More Information needed | [
"# Dataset Card for \"new-testament-syntax\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"new-testament-syntax\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"new-testament-syntax\"\n\nMore Information needed"
]
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|
6bdcaf5faab615ed9d0299830143a2b777b2f295 | # Dataset Card for "Rettsavgjoerelser"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MasterThesisCBS/Rettsavgjoerelser | [
"region:us"
]
| 2023-03-21T16:36:53+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "sak", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "stikkord", "dtype": "string"}, {"name": "Prompt w/suffix", "dtype": "string"}, {"name": "Prompt w/o suffix", "dtype": "string"}, {"name": "prompt length", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 950306420, "num_examples": 8044}, {"name": "test", "num_bytes": 49390818, "num_examples": 424}], "download_size": 494719016, "dataset_size": 999697238}} | 2023-03-23T11:10:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Rettsavgjoerelser"
More Information needed | [
"# Dataset Card for \"Rettsavgjoerelser\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Rettsavgjoerelser\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Rettsavgjoerelser\"\n\nMore Information needed"
]
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|
04346ad8fef940ff7056879d78d40b9c136416b7 |
从小说以及其他来源提取的单/多轮对话语料。 | wybxc/books | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:odc-by",
"region:us"
]
| 2023-03-21T16:43:43+00:00 | {"language": ["zh"], "license": "odc-by", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"]} | 2023-04-03T11:47:08+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-odc-by #region-us
|
从小说以及其他来源提取的单/多轮对话语料。 | []
| [
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-odc-by #region-us \n"
]
| [
42
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-odc-by #region-us \n"
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|
546cba9a268d3c45a392baf1245ba39201f020db | # Dataset Card for "disc_cla_primera"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_primera | [
"region:us"
]
| 2023-03-21T17:51:14+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 60679317, "num_examples": 11713}], "download_size": 32426863, "dataset_size": 60679317}} | 2023-03-21T17:51:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_primera"
More Information needed | [
"# Dataset Card for \"disc_cla_primera\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_primera\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_primera\"\n\nMore Information needed"
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|
200e880a43c209b33599c64a6ca30dd15171d6cf | # Dataset Card for "disc_cla_segunda"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_segunda | [
"region:us"
]
| 2023-03-21T17:52:22+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 30333232, "num_examples": 7327}], "download_size": 15864012, "dataset_size": 30333232}} | 2023-03-21T17:52:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_segunda"
More Information needed | [
"# Dataset Card for \"disc_cla_segunda\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_segunda\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_segunda\"\n\nMore Information needed"
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|
fb3d69e52d48a6b9de217e9a29fd3253b559bed7 | # Dataset Card for "disc_cla_tercera"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_tercera | [
"region:us"
]
| 2023-03-21T17:53:12+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 15774540, "num_examples": 4913}], "download_size": 8277875, "dataset_size": 15774540}} | 2023-03-21T17:53:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_tercera"
More Information needed | [
"# Dataset Card for \"disc_cla_tercera\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_tercera\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_tercera\"\n\nMore Information needed"
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|
28c5eed0d7387e5b28c165303311bb3d6b20a0f0 | # Dataset Card for "disc_cla_cuarta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_cuarta | [
"region:us"
]
| 2023-03-21T17:54:41+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 14861447, "num_examples": 3349}], "download_size": 7807410, "dataset_size": 14861447}} | 2023-03-21T17:54:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_cuarta"
More Information needed | [
"# Dataset Card for \"disc_cla_cuarta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_cuarta\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_cuarta\"\n\nMore Information needed"
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|
c1152f5e2abbc360fb05e7efd7af7dd9124c5f27 | # Dataset Card for "disc_cla_quinta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_quinta | [
"region:us"
]
| 2023-03-21T17:56:25+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 38464874, "num_examples": 7507}], "download_size": 20620936, "dataset_size": 38464874}} | 2023-03-21T17:56:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_quinta"
More Information needed | [
"# Dataset Card for \"disc_cla_quinta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_quinta\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_quinta\"\n\nMore Information needed"
]
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|
2fd620e010ad25390b3296c23cb121b4e4bbefa7 | # Dataset Card for "disc_cla_sexta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_sexta | [
"region:us"
]
| 2023-03-21T17:58:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 27186116, "num_examples": 7591}], "download_size": 14208855, "dataset_size": 27186116}} | 2023-03-21T17:58:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_sexta"
More Information needed | [
"# Dataset Card for \"disc_cla_sexta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_sexta\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_sexta\"\n\nMore Information needed"
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|
abad78880a277d77cb825ce7686c4a7a427a8ff0 | # Dataset Card for "disc_cla_septima"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_septima | [
"region:us"
]
| 2023-03-21T18:00:04+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 39746420, "num_examples": 9432}], "download_size": 20745223, "dataset_size": 39746420}} | 2023-03-21T18:00:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_septima"
More Information needed | [
"# Dataset Card for \"disc_cla_septima\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_septima\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_septima\"\n\nMore Information needed"
]
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|
cf822853cceca59be7f32cec944b56ed630973dc | # Dataset Card for "nllb_lug_en_vigorous_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mekaneeky/nllb_lug_en_vigorous_clean | [
"region:us"
]
| 2023-03-21T18:02:39+00:00 | {"dataset_info": {"features": [{"name": "translation", "dtype": {"translation": {"languages": ["eng_Latn", "lug_Latn"]}}}, {"name": "laser_score", "dtype": "float32"}, {"name": "source_sentence_lid", "dtype": "float32"}, {"name": "target_sentence_lid", "dtype": "float32"}, {"name": "source_sentence_source", "dtype": "string"}, {"name": "source_sentence_url", "dtype": "string"}, {"name": "target_sentence_source", "dtype": "string"}, {"name": "target_sentence_url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 44321608.60547476, "num_examples": 94114}], "download_size": 21686104, "dataset_size": 44321608.60547476}} | 2023-03-21T18:03:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nllb_lug_en_vigorous_clean"
More Information needed | [
"# Dataset Card for \"nllb_lug_en_vigorous_clean\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nllb_lug_en_vigorous_clean\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nllb_lug_en_vigorous_clean\"\n\nMore Information needed"
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|
f37643e3c8bafb15cd9ceca03a3ee90a98088d83 | # Dataset Card for "split-imdb"
| Deysi/split-imdb | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"sentiment analysis",
"region:us"
]
| 2023-03-21T18:25:05+00:00 | {"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Split dataset for imdb film reviews", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 46538455.6, "num_examples": 35000}, {"name": "test", "num_bytes": 9972526.2, "num_examples": 7500}, {"name": "valid", "num_bytes": 9972526.2, "num_examples": 7500}], "download_size": 0, "dataset_size": 66483508}, "tags": ["sentiment analysis"]} | 2023-03-21T22:55:45+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #sentiment analysis #region-us
| # Dataset Card for "split-imdb"
| [
"# Dataset Card for \"split-imdb\""
]
| [
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #sentiment analysis #region-us \n",
"# Dataset Card for \"split-imdb\""
]
| [
37,
12
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #sentiment analysis #region-us \n# Dataset Card for \"split-imdb\""
]
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|
03a78b0ca3a248d3c1ac7bcad9913da84811989e |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Clone-detection-BigCloneBench in Semeru
# CodeXGLUE -- Clone Detection (BCB)
## Task Definition
Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
## Updates
2021-9-13: We have update the evaluater script. Since it's a binary classification, we use binary F1 score instead of "macro" F1 score.
## Dataset
The dataset we use is [BigCloneBench](https://www.cs.usask.ca/faculty/croy/papers/2014/SvajlenkoICSME2014BigERA.pdf) and filtered following the paper [Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree](https://arxiv.org/pdf/2002.08653.pdf).
### Data Format
1. dataset/data.jsonl is stored in jsonlines format. Each line in the uncompressed file represents one function. One row is illustrated below.
- **func:** the function
- **idx:** index of the example
2. train.txt/valid.txt/test.txt provide examples, stored in the following format: idx1 idx2 label
### Data Statistics
Data statistics of the dataset are shown in the below table:
| | #Examples |
| ----- | :-------: |
| Train | 901,028 |
| Dev | 415,416 |
| Test | 415,416 |
## Reference
<pre><code>@inproceedings{svajlenko2014towards,
title={Towards a big data curated benchmark of inter-project code clones},
author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
pages={476--480},
year={2014},
organization={IEEE}
}
@inproceedings{wang2020detecting,
title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
pages={261--271},
year={2020},
organization={IEEE}
}</code></pre>
| semeru/Code-Code-CloneDetection-BigCloneBench | [
"license:mit",
"arxiv:2002.08653",
"region:us"
]
| 2023-03-21T18:37:21+00:00 | {"license": "mit", "Programminglanguage": "Java", "version": "N/A", "Date": "2014 Big clone bench paper https://www.cs.usask.ca/faculty/croy/papers/2014/SvajlenkoICSME2014BigERA.pdf", "Contaminated": "Very Likely", "Size": "Standard Tokenizer"} | 2023-03-27T17:31:02+00:00 | [
"2002.08653"
]
| []
| TAGS
#license-mit #arxiv-2002.08653 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/Clone-detection-BigCloneBench in Semeru
CodeXGLUE -- Clone Detection (BCB)
==================================
Task Definition
---------------
Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
Updates
-------
2021-9-13: We have update the evaluater script. Since it's a binary classification, we use binary F1 score instead of "macro" F1 score.
Dataset
-------
The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.
### Data Format
1. dataset/URL is stored in jsonlines format. Each line in the uncompressed file represents one function. One row is illustrated below.
* func: the function
* idx: index of the example
2. URL provide examples, stored in the following format: idx1 idx2 label
### Data Statistics
Data statistics of the dataset are shown in the below table:
Reference
---------
```
@inproceedings{svajlenko2014towards,
title={Towards a big data curated benchmark of inter-project code clones},
author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
pages={476--480},
year={2014},
organization={IEEE}
}
@inproceedings{wang2020detecting,
title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
pages={261--271},
year={2020},
organization={IEEE}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Clone-detection-BigCloneBench in Semeru\n\n\nCodeXGLUE -- Clone Detection (BCB)\n==================================\n\n\nTask Definition\n---------------\n\n\nGiven two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.\n\n\nUpdates\n-------\n\n\n2021-9-13: We have update the evaluater script. Since it's a binary classification, we use binary F1 score instead of \"macro\" F1 score.\n\n\nDataset\n-------\n\n\nThe dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.",
"### Data Format\n\n\n1. dataset/URL is stored in jsonlines format. Each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n\t* func: the function\n\t* idx: index of the example\n2. URL provide examples, stored in the following format: idx1 idx2 label",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:\n\n\n\nReference\n---------\n\n\n\n```\n@inproceedings{svajlenko2014towards,\n title={Towards a big data curated benchmark of inter-project code clones},\n author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},\n booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},\n pages={476--480},\n year={2014},\n organization={IEEE}\n}\n\n@inproceedings{wang2020detecting,\n title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},\n author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},\n booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},\n pages={261--271},\n year={2020},\n organization={IEEE}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-2002.08653 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Clone-detection-BigCloneBench in Semeru\n\n\nCodeXGLUE -- Clone Detection (BCB)\n==================================\n\n\nTask Definition\n---------------\n\n\nGiven two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.\n\n\nUpdates\n-------\n\n\n2021-9-13: We have update the evaluater script. Since it's a binary classification, we use binary F1 score instead of \"macro\" F1 score.\n\n\nDataset\n-------\n\n\nThe dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.",
"### Data Format\n\n\n1. dataset/URL is stored in jsonlines format. Each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n\t* func: the function\n\t* idx: index of the example\n2. URL provide examples, stored in the following format: idx1 idx2 label",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:\n\n\n\nReference\n---------\n\n\n\n```\n@inproceedings{svajlenko2014towards,\n title={Towards a big data curated benchmark of inter-project code clones},\n author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},\n booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},\n pages={476--480},\n year={2014},\n organization={IEEE}\n}\n\n@inproceedings{wang2020detecting,\n title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},\n author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},\n booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},\n pages={261--271},\n year={2020},\n organization={IEEE}\n}\n```"
]
| [
20,
238,
75,
257
]
| [
"passage: TAGS\n#license-mit #arxiv-2002.08653 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Clone-detection-BigCloneBench in Semeru\n\n\nCodeXGLUE -- Clone Detection (BCB)\n==================================\n\n\nTask Definition\n---------------\n\n\nGiven two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.\n\n\nUpdates\n-------\n\n\n2021-9-13: We have update the evaluater script. Since it's a binary classification, we use binary F1 score instead of \"macro\" F1 score.\n\n\nDataset\n-------\n\n\nThe dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.### Data Format\n\n\n1. dataset/URL is stored in jsonlines format. Each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n\t* func: the function\n\t* idx: index of the example\n2. URL provide examples, stored in the following format: idx1 idx2 label"
]
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|
218877649f258051ca35532e4ae8a52db7ff0143 | # Dataset Card for DeTexD: A Benchmark Dataset for Delicate Text Detection
## Dataset Description
- **Repository:** [DeTexD repository](https://github.com/grammarly/detexd)
- **Paper:** [DeTexD: A Benchmark Dataset for Delicate Text Detection](TODO)
### Dataset Summary
We define *delicate text* as any text that is emotionally charged or potentially triggering such that engaging with it has the potential to result in harm. This broad term covers a range of sensitive texts that vary across four major dimensions: 1) riskiness, 2) explicitness, 3) topic, and 4) target.
This dataset contains texts with fine-grained individual annotator labels from 0 to 5 (where 0 indicates no risk and 5 indicates high risk) and averaged binary labels. See paper for more details.
**Repository:** [DeTexD repository](https://github.com/grammarly/detexd) <br>
**Paper:** [DeTexD: A Benchmark Dataset for Delicate Text Detection](TODO)
## Dataset Structure
### Data Instances
```
{'text': '"He asked me and the club if we could give him a couple of days off just to clear up his mind and he will be back in the group, I suppose, next Monday, back for training and then be a regular part of the whole squad again," Rangnick said.',
'annotator_1': 0,
'annotator_2': 0,
'annotator_3': 0,
'label': 0}
```
### Data Fields
- `text`: Text to be classified
- `annotator_1`: Annotator 1 score (0-5)
- `annotator_2`: Annotator 2 score (0-5)
- `annotator_3`: Annotator 3 score (0-5)
- `label`: Averaged binary score (>=3), either "negative" (0) or positive (1)
### Data Splits
| | test |
|--------------------|-----:|
| Number of examples | 1023 |
### Citation Information
```
@inproceedings{chernodub-etal-2023-detexd,
title = "{D}e{T}ex{D}: A Benchmark Dataset for Delicate Text Detection",
author = "Yavnyi, Serhii and Sliusarenko, Oleksii and Razzaghi, Jade and Mo, Yichen and Hovakimyan, Knar and Chernodub, Artem",
booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.woah-1.2",
pages = "14--28",
abstract = "Over the past few years, much research has been conducted to identify and regulate toxic language. However, few studies have addressed a broader range of sensitive texts that are not necessarily overtly toxic. In this paper, we introduce and define a new category of sensitive text called {``}delicate text.{''} We provide the taxonomy of delicate text and present a detailed annotation scheme. We annotate DeTexD, the first benchmark dataset for delicate text detection. The significance of the difference in the definitions is highlighted by the relative performance deltas between models trained each definitions and corpora and evaluated on the other. We make publicly available the DeTexD Benchmark dataset, annotation guidelines, and baseline model for delicate text detection.",
}
``` | grammarly/detexd-benchmark | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-21T18:44:32+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "DeTexD: A Benchmark Dataset for Delicate Text Detection", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "annotator_1", "dtype": "int32"}, {"name": "annotator_2", "dtype": "int32"}, {"name": "annotator_3", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}], "splits": [{"name": "test", "num_examples": 1023}]}} | 2023-07-10T16:36:37+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-apache-2.0 #region-us
| Dataset Card for DeTexD: A Benchmark Dataset for Delicate Text Detection
========================================================================
Dataset Description
-------------------
* Repository: DeTexD repository
* Paper: DeTexD: A Benchmark Dataset for Delicate Text Detection
### Dataset Summary
We define *delicate text* as any text that is emotionally charged or potentially triggering such that engaging with it has the potential to result in harm. This broad term covers a range of sensitive texts that vary across four major dimensions: 1) riskiness, 2) explicitness, 3) topic, and 4) target.
This dataset contains texts with fine-grained individual annotator labels from 0 to 5 (where 0 indicates no risk and 5 indicates high risk) and averaged binary labels. See paper for more details.
Repository: DeTexD repository
Paper: DeTexD: A Benchmark Dataset for Delicate Text Detection
Dataset Structure
-----------------
### Data Instances
### Data Fields
* 'text': Text to be classified
* 'annotator\_1': Annotator 1 score (0-5)
* 'annotator\_2': Annotator 2 score (0-5)
* 'annotator\_3': Annotator 3 score (0-5)
* 'label': Averaged binary score (>=3), either "negative" (0) or positive (1)
### Data Splits
| [
"### Dataset Summary\n\n\nWe define *delicate text* as any text that is emotionally charged or potentially triggering such that engaging with it has the potential to result in harm. This broad term covers a range of sensitive texts that vary across four major dimensions: 1) riskiness, 2) explicitness, 3) topic, and 4) target.\n\n\nThis dataset contains texts with fine-grained individual annotator labels from 0 to 5 (where 0 indicates no risk and 5 indicates high risk) and averaged binary labels. See paper for more details.\n\n\nRepository: DeTexD repository \n\nPaper: DeTexD: A Benchmark Dataset for Delicate Text Detection\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\n* 'text': Text to be classified\n* 'annotator\\_1': Annotator 1 score (0-5)\n* 'annotator\\_2': Annotator 2 score (0-5)\n* 'annotator\\_3': Annotator 3 score (0-5)\n* 'label': Averaged binary score (>=3), either \"negative\" (0) or positive (1)",
"### Data Splits"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-apache-2.0 #region-us \n",
"### Dataset Summary\n\n\nWe define *delicate text* as any text that is emotionally charged or potentially triggering such that engaging with it has the potential to result in harm. This broad term covers a range of sensitive texts that vary across four major dimensions: 1) riskiness, 2) explicitness, 3) topic, and 4) target.\n\n\nThis dataset contains texts with fine-grained individual annotator labels from 0 to 5 (where 0 indicates no risk and 5 indicates high risk) and averaged binary labels. See paper for more details.\n\n\nRepository: DeTexD repository \n\nPaper: DeTexD: A Benchmark Dataset for Delicate Text Detection\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\n* 'text': Text to be classified\n* 'annotator\\_1': Annotator 1 score (0-5)\n* 'annotator\\_2': Annotator 2 score (0-5)\n* 'annotator\\_3': Annotator 3 score (0-5)\n* 'label': Averaged binary score (>=3), either \"negative\" (0) or positive (1)",
"### Data Splits"
]
| [
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163,
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92,
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]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-apache-2.0 #region-us \n### Dataset Summary\n\n\nWe define *delicate text* as any text that is emotionally charged or potentially triggering such that engaging with it has the potential to result in harm. This broad term covers a range of sensitive texts that vary across four major dimensions: 1) riskiness, 2) explicitness, 3) topic, and 4) target.\n\n\nThis dataset contains texts with fine-grained individual annotator labels from 0 to 5 (where 0 indicates no risk and 5 indicates high risk) and averaged binary labels. See paper for more details.\n\n\nRepository: DeTexD repository \n\nPaper: DeTexD: A Benchmark Dataset for Delicate Text Detection\n\n\nDataset Structure\n-----------------### Data Instances### Data Fields\n\n\n* 'text': Text to be classified\n* 'annotator\\_1': Annotator 1 score (0-5)\n* 'annotator\\_2': Annotator 2 score (0-5)\n* 'annotator\\_3': Annotator 3 score (0-5)\n* 'label': Averaged binary score (>=3), either \"negative\" (0) or positive (1)### Data Splits"
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|
0178d208f04726d0430236e7beba43d1c10c720a | # Dataset Card for "fungi_futures"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pimentooliver/fungifutures | [
"region:us"
]
| 2023-03-21T18:59:12+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3671136212.896, "num_examples": 4657}], "download_size": 3127940814, "dataset_size": 3671136212.896}} | 2023-03-21T20:20:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fungi_futures"
More Information needed | [
"# Dataset Card for \"fungi_futures\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fungi_futures\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fungi_futures\"\n\nMore Information needed"
]
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|
6dfe3ddea439c4ba66029d9b2284871e5917822f |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Clone-detection-POJ-104 in Semeru
# CodeXGLUE -- Clone Detection (POJ-104)
## Task Definition
Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP@R score. MAP@R is defined as the mean of average precision scores, each of which is evaluated for retrieving R most similar samples given a query. For a code (query), R is the number of other codes in the same class, i.e. R=499 in this dataset.
## Dataset
We use [POJ-104](https://arxiv.org/pdf/1409.5718.pdf) dataset on this task.
### Data Format
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **code:** the source code
- **label:** the number of problem that the source code solves
- **index:** the index of example
### Data Statistics
Data statistics of the dataset are shown in the below table:
| | #Problems | #Examples |
| ----- | --------- | :-------: |
| Train | 64 | 32,000 |
| Dev | 16 | 8,000 |
| Test | 24 | 12,000 |
## Reference
<pre><code>@inproceedings{mou2016convolutional,
title={Convolutional neural networks over tree structures for programming language processing},
author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
pages={1287--1293},
year={2016}
}</code></pre>
| semeru/Code-Code-CloneDetection-POJ104 | [
"license:mit",
"arxiv:1409.5718",
"region:us"
]
| 2023-03-21T19:44:24+00:00 | {"license": "mit", "Programminglanguage": "C", "version": "N/A", "Date": "2015 POJ dataset from paper: https://arxiv.org/pdf/1409.5718.pdf", "Contaminated": "Very Likely", "Size": "Standard Tokenizer"} | 2023-03-27T17:29:02+00:00 | [
"1409.5718"
]
| []
| TAGS
#license-mit #arxiv-1409.5718 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/Clone-detection-POJ-104 in Semeru
CodeXGLUE -- Clone Detection (POJ-104)
======================================
Task Definition
---------------
Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP@R score. MAP@R is defined as the mean of average precision scores, each of which is evaluated for retrieving R most similar samples given a query. For a code (query), R is the number of other codes in the same class, i.e. R=499 in this dataset.
Dataset
-------
We use POJ-104 dataset on this task.
### Data Format
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* code: the source code
* label: the number of problem that the source code solves
* index: the index of example
### Data Statistics
Data statistics of the dataset are shown in the below table:
Reference
---------
```
@inproceedings{mou2016convolutional,
title={Convolutional neural networks over tree structures for programming language processing},
author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
pages={1287--1293},
year={2016}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Clone-detection-POJ-104 in Semeru\n\n\nCodeXGLUE -- Clone Detection (POJ-104)\n======================================\n\n\nTask Definition\n---------------\n\n\nGiven a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP@R score. MAP@R is defined as the mean of average precision scores, each of which is evaluated for retrieving R most similar samples given a query. For a code (query), R is the number of other codes in the same class, i.e. R=499 in this dataset.\n\n\nDataset\n-------\n\n\nWe use POJ-104 dataset on this task.",
"### Data Format\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* code: the source code\n* label: the number of problem that the source code solves\n* index: the index of example",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:\n\n\n\nReference\n---------\n\n\n\n```\n@inproceedings{mou2016convolutional,\n title={Convolutional neural networks over tree structures for programming language processing},\n author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},\n booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},\n pages={1287--1293},\n year={2016}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1409.5718 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Clone-detection-POJ-104 in Semeru\n\n\nCodeXGLUE -- Clone Detection (POJ-104)\n======================================\n\n\nTask Definition\n---------------\n\n\nGiven a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP@R score. MAP@R is defined as the mean of average precision scores, each of which is evaluated for retrieving R most similar samples given a query. For a code (query), R is the number of other codes in the same class, i.e. R=499 in this dataset.\n\n\nDataset\n-------\n\n\nWe use POJ-104 dataset on this task.",
"### Data Format\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* code: the source code\n* label: the number of problem that the source code solves\n* index: the index of example",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:\n\n\n\nReference\n---------\n\n\n\n```\n@inproceedings{mou2016convolutional,\n title={Convolutional neural networks over tree structures for programming language processing},\n author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},\n booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},\n pages={1287--1293},\n year={2016}\n}\n```"
]
| [
19,
232,
56,
125
]
| [
"passage: TAGS\n#license-mit #arxiv-1409.5718 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Clone-detection-POJ-104 in Semeru\n\n\nCodeXGLUE -- Clone Detection (POJ-104)\n======================================\n\n\nTask Definition\n---------------\n\n\nGiven a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP@R score. MAP@R is defined as the mean of average precision scores, each of which is evaluated for retrieving R most similar samples given a query. For a code (query), R is the number of other codes in the same class, i.e. R=499 in this dataset.\n\n\nDataset\n-------\n\n\nWe use POJ-104 dataset on this task.### Data Format\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* code: the source code\n* label: the number of problem that the source code solves\n* index: the index of example### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:\n\n\n\nReference\n---------\n\n\n\n```\n@inproceedings{mou2016convolutional,\n title={Convolutional neural networks over tree structures for programming language processing},\n author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},\n booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},\n pages={1287--1293},\n year={2016}\n}\n```"
]
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|
d6861110e812a0a941db36943ee65371c1f46540 |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/code-refinement/data/medium in Semeru
## Task Definition
Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.
Models are evaluated by BLEU scores, accuracy (exactly match) and [CodeBLEU](https://github.com/microsoft/CodeXGLUE/blob/main/code-to-code-trans/CodeBLEU.MD).
## Dataset
We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one.
All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is medium.
### Data Statistics
Data statistics of this dataset are shown in the below table:
| | #Examples |
| ------- | :-------: |
| | Medium |
| Train | 52,364 |
| Valid | 6,545 |
| Test | 6,545 |
# Reference
<pre><code>@article{tufano2019empirical,
title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},
author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},
volume={28},
number={4},
pages={1--29},
year={2019},
publisher={ACM New York, NY, USA}
}</code></pre>
| semeru/code-code-CodeRefinement-Java-Medium | [
"license:mit",
"arxiv:1812.08693",
"region:us"
]
| 2023-03-21T19:57:57+00:00 | {"license": "mit", "Programminglanguage": "Java", "version": "N/A", "Date": "May 2019 paper release date for https://arxiv.org/pdf/1812.08693.pdf", "Contaminated": "Very Likely", "Size": "Standard Tokenizer "} | 2023-03-27T17:24:46+00:00 | [
"1812.08693"
]
| []
| TAGS
#license-mit #arxiv-1812.08693 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/code-refinement/data/medium in Semeru
Task Definition
---------------
Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.
Models are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.
Dataset
-------
We use the dataset released by this paper(URL The source side is a Java function with bugs and the target side is the refined one.
All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is medium.
### Data Statistics
Data statistics of this dataset are shown in the below table:
Reference
=========
```
@article{tufano2019empirical,
title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},
author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},
volume={28},
number={4},
pages={1--29},
year={2019},
publisher={ACM New York, NY, USA}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-refinement/data/medium in Semeru\n\n\nTask Definition\n---------------\n\n\nCode refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.\nIn CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.\nModels are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.\n\n\nDataset\n-------\n\n\nWe use the dataset released by this paper(URL The source side is a Java function with bugs and the target side is the refined one.\nAll the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is medium.",
"### Data Statistics\n\n\nData statistics of this dataset are shown in the below table:\n\n\n\nReference\n=========\n\n\n\n```\n@article{tufano2019empirical,\n title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},\n author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\n journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},\n volume={28},\n number={4},\n pages={1--29},\n year={2019},\n publisher={ACM New York, NY, USA}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1812.08693 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-refinement/data/medium in Semeru\n\n\nTask Definition\n---------------\n\n\nCode refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.\nIn CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.\nModels are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.\n\n\nDataset\n-------\n\n\nWe use the dataset released by this paper(URL The source side is a Java function with bugs and the target side is the refined one.\nAll the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is medium.",
"### Data Statistics\n\n\nData statistics of this dataset are shown in the below table:\n\n\n\nReference\n=========\n\n\n\n```\n@article{tufano2019empirical,\n title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},\n author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\n journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},\n volume={28},\n number={4},\n pages={1--29},\n year={2019},\n publisher={ACM New York, NY, USA}\n}\n```"
]
| [
20,
253,
164
]
| [
"passage: TAGS\n#license-mit #arxiv-1812.08693 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-refinement/data/medium in Semeru\n\n\nTask Definition\n---------------\n\n\nCode refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.\nIn CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.\nModels are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.\n\n\nDataset\n-------\n\n\nWe use the dataset released by this paper(URL The source side is a Java function with bugs and the target side is the refined one.\nAll the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is medium.### Data Statistics\n\n\nData statistics of this dataset are shown in the below table:\n\n\n\nReference\n=========\n\n\n\n```\n@article{tufano2019empirical,\n title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},\n author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\n journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},\n volume={28},\n number={4},\n pages={1--29},\n year={2019},\n publisher={ACM New York, NY, USA}\n}\n```"
]
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|
b7678eb87ffd22ddc934ad07b483893c30a6a7a1 |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/code-refinement/data/small in Semeru
## Task Definition
Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.
Models are evaluated by BLEU scores, accuracy (exactly match) and [CodeBLEU](https://github.com/microsoft/CodeXGLUE/blob/main/code-to-code-trans/CodeBLEU.MD).
## Dataset
We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one.
All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is small.
### Data Statistics
Data statistics of this dataset are shown in the below table:
| | #Examples |
| ------- | :-------: |
| | Small |
| Train | 46,680 |
| Valid | 5,835 |
| Test | 5,835 |
# Reference
<pre><code>@article{tufano2019empirical,
title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},
author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},
volume={28},
number={4},
pages={1--29},
year={2019},
publisher={ACM New York, NY, USA}
}</code></pre> | semeru/code-code-CodeRefinement-Java-Small | [
"license:mit",
"arxiv:1812.08693",
"region:us"
]
| 2023-03-21T20:03:09+00:00 | {"license": "mit", "Programminglanguage": "Java", "version": "N/A", "Date": "May 2019 paper release date for https://arxiv.org/pdf/1812.08693.pdf", "Contaminated": "Very Likely", "Size": "Standard Tokenizer"} | 2023-03-27T17:23:44+00:00 | [
"1812.08693"
]
| []
| TAGS
#license-mit #arxiv-1812.08693 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/code-refinement/data/small in Semeru
Task Definition
---------------
Code refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.
In CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.
Models are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.
Dataset
-------
We use the dataset released by this paper(URL The source side is a Java function with bugs and the target side is the refined one.
All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is small.
### Data Statistics
Data statistics of this dataset are shown in the below table:
Reference
=========
```
@article{tufano2019empirical,
title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},
author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},
volume={28},
number={4},
pages={1--29},
year={2019},
publisher={ACM New York, NY, USA}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-refinement/data/small in Semeru\n\n\nTask Definition\n---------------\n\n\nCode refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.\nIn CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.\nModels are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.\n\n\nDataset\n-------\n\n\nWe use the dataset released by this paper(URL The source side is a Java function with bugs and the target side is the refined one.\nAll the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is small.",
"### Data Statistics\n\n\nData statistics of this dataset are shown in the below table:\n\n\n\nReference\n=========\n\n\n\n```\n@article{tufano2019empirical,\n title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},\n author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\n journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},\n volume={28},\n number={4},\n pages={1--29},\n year={2019},\n publisher={ACM New York, NY, USA}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1812.08693 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-refinement/data/small in Semeru\n\n\nTask Definition\n---------------\n\n\nCode refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.\nIn CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.\nModels are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.\n\n\nDataset\n-------\n\n\nWe use the dataset released by this paper(URL The source side is a Java function with bugs and the target side is the refined one.\nAll the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is small.",
"### Data Statistics\n\n\nData statistics of this dataset are shown in the below table:\n\n\n\nReference\n=========\n\n\n\n```\n@article{tufano2019empirical,\n title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},\n author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\n journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},\n volume={28},\n number={4},\n pages={1--29},\n year={2019},\n publisher={ACM New York, NY, USA}\n}\n```"
]
| [
20,
253,
164
]
| [
"passage: TAGS\n#license-mit #arxiv-1812.08693 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-refinement/data/small in Semeru\n\n\nTask Definition\n---------------\n\n\nCode refinement aims to automatically fix bugs in the code, which can contribute to reducing the cost of bug-fixes for developers.\nIn CodeXGLUE, given a piece of Java code with bugs, the task is to remove the bugs to output the refined code.\nModels are evaluated by BLEU scores, accuracy (exactly match) and CodeBLEU.\n\n\nDataset\n-------\n\n\nWe use the dataset released by this paper(URL The source side is a Java function with bugs and the target side is the refined one.\nAll the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. This dataset is small.### Data Statistics\n\n\nData statistics of this dataset are shown in the below table:\n\n\n\nReference\n=========\n\n\n\n```\n@article{tufano2019empirical,\n title={An empirical study on learning bug-fixing patches in the wild via neural machine translation},\n author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},\n journal={ACM Transactions on Software Engineering and Methodology (TOSEM)},\n volume={28},\n number={4},\n pages={1--29},\n year={2019},\n publisher={ACM New York, NY, USA}\n}\n```"
]
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|
2eed561239d04b5c74a0c350b11849c8163f6f74 | # Dataset Card for "processed_bert_dataset-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Exqrch/processed_bert_dataset-test | [
"region:us"
]
| 2023-03-21T20:13:45+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 10029.0, "num_examples": 48}], "download_size": 6515, "dataset_size": 10029.0}} | 2023-03-22T13:30:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_bert_dataset-test"
More Information needed | [
"# Dataset Card for \"processed_bert_dataset-test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_bert_dataset-test\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_bert_dataset-test\"\n\nMore Information needed"
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|
d41bf942e2f2e7e151cef4fe0de22fdec763b921 |
Dataset generated from cyrillic train set using Stackmix
========================================================
Number of images: 300000
Sources:
* [Cyrillic dataset](https://www.kaggle.com/datasets/constantinwerner/cyrillic-handwriting-dataset)
* [Stackmix code](https://github.com/ai-forever/StackMix-OCR)
| nastyboget/stackmix_cyrillic | [
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-21T20:18:13+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"]} | 2023-03-23T18:44:41+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us
|
Dataset generated from cyrillic train set using Stackmix
========================================================
Number of images: 300000
Sources:
* Cyrillic dataset
* Stackmix code
| []
| [
"TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
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|
671500c2221f0033931d21a8264b9663813f4910 | # Dataset Card for "speeches-congre-clean-names"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/speeches-congre-clean-names | [
"region:us"
]
| 2023-03-21T20:35:46+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "comision", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 181327260, "num_examples": 94501}], "download_size": 92131968, "dataset_size": 181327260}} | 2023-03-21T20:38:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "speeches-congre-clean-names"
More Information needed | [
"# Dataset Card for \"speeches-congre-clean-names\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"speeches-congre-clean-names\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"speeches-congre-clean-names\"\n\nMore Information needed"
]
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|
2308814b089b8e663c4a68d7862a6e210ad9fe88 |
Dataset generated from HKR train set using ScrabbleGAN
======================================================
Number of images: 300000
Sources:
* [HKR dataset](https://github.com/abdoelsayed2016/HKR_Dataset)
* [ScrabbleGAN code](https://github.com/ai-forever/ScrabbleGAN) | nastyboget/gan_hkr | [
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-21T21:10:05+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"]} | 2023-03-23T18:43:53+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us
|
Dataset generated from HKR train set using ScrabbleGAN
======================================================
Number of images: 300000
Sources:
* HKR dataset
* ScrabbleGAN code | []
| [
"TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
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|
460b1f3312aa21ef774e916e532a9576f7938a0d | # RuTurboAlpaca
Dataset of ChatGPT-generated instructions in Russian.
<img src="https://cdn.midjourney.com/770a35fa-00c0-4214-bb88-727dbc7cfaf3/0_0.png" >
* Code: [rulm/self_instruct](https://github.com/IlyaGusev/rulm/tree/master/self_instruct)
* Code is based on [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [self-instruct](https://github.com/yizhongw/self-instruct/).
* 29822 examples
Preliminary evaluation by an expert based on 400 samples:
* 83% of samples contain correct instructions
* 63% of samples have correct instructions and outputs
Crowdsouring-based evaluation on 3500 samples:
* 90% of samples contain correct instructions
* 68% of samples have correct instructions and outputs
Prompt template:
```
Составь набор из {{num_tasks}} разных заданий для дообучения языковой модели:
1. Делай задания максимально непохожими друг на друга: по типу, по запрашиваемым действиям, по формулировке, по наличию входа.
2. Задания должны быть выполнимы языковой моделью, которая не умеет работать с картинками, видео, и аудио, и не имеет доступа ко внешнему миру.
3. Используй хороший грамотный русский язык.
4. Делай задания в одно или два предложения.
5. Генерируй подходящие реалистичные входные данные, не используй общие шаблоны типа \"Имя человека\" или [имя] вместо реального имени.
6. Задание может быть без входных данных, в таком случае используй токен <noinput> вместо них.
7. На выходе сгенерируй подходящий длинный ответ.
8. Следуй тому же шаблону, который приведен в примерах, разделяй задания с помощью ###. Это важно!
Примеры заданий:
{% for task in example_tasks %}
{{task.index}}. Задание: {{task.instruction}}
{{task.index}}. Вход: {{task.input}}
{{task.index}}. Выход: {{task.output}}
{{ "###" if not loop.last else "" }}
{% endfor %}
```
## Legal disclaimer
Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit for us developing models that compete with OpenAI. Not for you. | IlyaGusev/ru_turbo_alpaca | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"instruction-finetuning",
"instruction generation",
"alpaca",
"region:us"
]
| 2023-03-21T21:17:42+00:00 | {"language": ["ru"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text2text-generation"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "alternative_output", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "all_labels", "sequence": "string"}, {"name": "agreement", "dtype": "float32"}, {"name": "overlap", "dtype": "uint32"}], "splits": [{"name": "train", "num_bytes": 54774775, "num_examples": 29822}], "download_size": 14565995, "dataset_size": 54774775}, "tags": ["instruction-finetuning", "instruction generation", "alpaca"]} | 2023-05-25T18:45:14+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<100K #language-Russian #license-cc-by-4.0 #instruction-finetuning #instruction generation #alpaca #region-us
| # RuTurboAlpaca
Dataset of ChatGPT-generated instructions in Russian.
<img src="URL >
* Code: rulm/self_instruct
* Code is based on Stanford Alpaca and self-instruct.
* 29822 examples
Preliminary evaluation by an expert based on 400 samples:
* 83% of samples contain correct instructions
* 63% of samples have correct instructions and outputs
Crowdsouring-based evaluation on 3500 samples:
* 90% of samples contain correct instructions
* 68% of samples have correct instructions and outputs
Prompt template:
## Legal disclaimer
Data is based on OpenAI’s gpt-3.5-turbo, whose terms of use prohibit for us developing models that compete with OpenAI. Not for you. | [
"# RuTurboAlpaca\n\nDataset of ChatGPT-generated instructions in Russian.\n\n\n<img src=\"URL >\n\n* Code: rulm/self_instruct\n* Code is based on Stanford Alpaca and self-instruct.\n* 29822 examples\n\nPreliminary evaluation by an expert based on 400 samples:\n* 83% of samples contain correct instructions\n* 63% of samples have correct instructions and outputs\n\nCrowdsouring-based evaluation on 3500 samples:\n* 90% of samples contain correct instructions\n* 68% of samples have correct instructions and outputs\n\nPrompt template:",
"## Legal disclaimer\n\nData is based on OpenAI’s gpt-3.5-turbo, whose terms of use prohibit for us developing models that compete with OpenAI. Not for you."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<100K #language-Russian #license-cc-by-4.0 #instruction-finetuning #instruction generation #alpaca #region-us \n",
"# RuTurboAlpaca\n\nDataset of ChatGPT-generated instructions in Russian.\n\n\n<img src=\"URL >\n\n* Code: rulm/self_instruct\n* Code is based on Stanford Alpaca and self-instruct.\n* 29822 examples\n\nPreliminary evaluation by an expert based on 400 samples:\n* 83% of samples contain correct instructions\n* 63% of samples have correct instructions and outputs\n\nCrowdsouring-based evaluation on 3500 samples:\n* 90% of samples contain correct instructions\n* 68% of samples have correct instructions and outputs\n\nPrompt template:",
"## Legal disclaimer\n\nData is based on OpenAI’s gpt-3.5-turbo, whose terms of use prohibit for us developing models that compete with OpenAI. Not for you."
]
| [
68,
131,
41
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<100K #language-Russian #license-cc-by-4.0 #instruction-finetuning #instruction generation #alpaca #region-us \n# RuTurboAlpaca\n\nDataset of ChatGPT-generated instructions in Russian.\n\n\n<img src=\"URL >\n\n* Code: rulm/self_instruct\n* Code is based on Stanford Alpaca and self-instruct.\n* 29822 examples\n\nPreliminary evaluation by an expert based on 400 samples:\n* 83% of samples contain correct instructions\n* 63% of samples have correct instructions and outputs\n\nCrowdsouring-based evaluation on 3500 samples:\n* 90% of samples contain correct instructions\n* 68% of samples have correct instructions and outputs\n\nPrompt template:## Legal disclaimer\n\nData is based on OpenAI’s gpt-3.5-turbo, whose terms of use prohibit for us developing models that compete with OpenAI. Not for you."
]
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|
d4d7dcdd080358c03e77c026c1acada1c9bb4998 | # Dataset Card for "mayo_clinic_symptoms_and_diseases_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | celikmus/mayo_clinic_symptoms_and_diseases_v1 | [
"language:en",
"region:us"
]
| 2023-03-21T21:31:15+00:00 | {"language": "en", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1321926, "num_examples": 1058}], "download_size": 626009, "dataset_size": 1321926}} | 2023-07-16T18:37:52+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
| # Dataset Card for "mayo_clinic_symptoms_and_diseases_v1"
More Information needed | [
"# Dataset Card for \"mayo_clinic_symptoms_and_diseases_v1\"\n\nMore Information needed"
]
| [
"TAGS\n#language-English #region-us \n",
"# Dataset Card for \"mayo_clinic_symptoms_and_diseases_v1\"\n\nMore Information needed"
]
| [
10,
30
]
| [
"passage: TAGS\n#language-English #region-us \n# Dataset Card for \"mayo_clinic_symptoms_and_diseases_v1\"\n\nMore Information needed"
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|
5a78d0c3079b4d05de4c9151d4df188b691053f8 |
[ミツア](https://huggingface.co/Mitsua/mitsua-diffusion-one) 用 ネガティブ TI
## Test1
TI
- [badmitsua-test1-e10.pt](https://huggingface.co/datasets/p1atdev/badmitsua/blob/main/embeddings/badmitsua-test1-e10.pt)
データセット
mitsua-diffusion-one-base で生成した 150枚を使用
- [test1.zip](https://huggingface.co/datasets/p1atdev/badmitsua/blob/main/test1.zip)
| p1atdev/badmitsua | [
"license:cc0-1.0",
"region:us"
]
| 2023-03-21T21:51:49+00:00 | {"license": "cc0-1.0"} | 2023-03-21T22:24:10+00:00 | []
| []
| TAGS
#license-cc0-1.0 #region-us
|
ミツア 用 ネガティブ TI
## Test1
TI
- URL
データセット
mitsua-diffusion-one-base で生成した 150枚を使用
- URL
| [
"## Test1\n\nTI\n\n- URL\n\nデータセット\nmitsua-diffusion-one-base で生成した 150枚を使用\n\n- URL"
]
| [
"TAGS\n#license-cc0-1.0 #region-us \n",
"## Test1\n\nTI\n\n- URL\n\nデータセット\nmitsua-diffusion-one-base で生成した 150枚を使用\n\n- URL"
]
| [
14,
29
]
| [
"passage: TAGS\n#license-cc0-1.0 #region-us \n## Test1\n\nTI\n\n- URL\n\nデータセット\nmitsua-diffusion-one-base で生成した 150枚を使用\n\n- URL"
]
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|
91c0ea92482d3f91ba48d7554305d9c86339e6a3 | # Dataset Card for "sentences-and-emotions"
Recognizing Emotion Cause in Conversations. Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian, Pengfei Hong, Romila Ghosh, Abhinaba Roy, Niyati Chhaya, Alexander Gelbukh, Rada Mihalcea. Cognitive Computation (2021). | Deysi/sentences-and-emotions | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"region:us"
]
| 2023-03-21T22:23:47+00:00 | {"language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "pretty_name": "Sentences and emotions", "dataset_info": {"features": [{"name": "utterance", "dtype": "string"}, {"name": "emotion", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 62487, "num_examples": 816}, {"name": "valid", "num_bytes": 39971, "num_examples": 493}, {"name": "train", "num_bytes": 188423, "num_examples": 2405}], "download_size": 36170, "dataset_size": 290881}} | 2023-03-21T22:54:16+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-English #region-us
| # Dataset Card for "sentences-and-emotions"
Recognizing Emotion Cause in Conversations. Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian, Pengfei Hong, Romila Ghosh, Abhinaba Roy, Niyati Chhaya, Alexander Gelbukh, Rada Mihalcea. Cognitive Computation (2021). | [
"# Dataset Card for \"sentences-and-emotions\"\n\nRecognizing Emotion Cause in Conversations. Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian, Pengfei Hong, Romila Ghosh, Abhinaba Roy, Niyati Chhaya, Alexander Gelbukh, Rada Mihalcea. Cognitive Computation (2021)."
]
| [
"TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #region-us \n",
"# Dataset Card for \"sentences-and-emotions\"\n\nRecognizing Emotion Cause in Conversations. Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian, Pengfei Hong, Romila Ghosh, Abhinaba Roy, Niyati Chhaya, Alexander Gelbukh, Rada Mihalcea. Cognitive Computation (2021)."
]
| [
33,
103
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #region-us \n# Dataset Card for \"sentences-and-emotions\"\n\nRecognizing Emotion Cause in Conversations. Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian, Pengfei Hong, Romila Ghosh, Abhinaba Roy, Niyati Chhaya, Alexander Gelbukh, Rada Mihalcea. Cognitive Computation (2021)."
]
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|
c8647293d538fe3aeda3539a3f4274d1b5ea6762 | # Dataset Card for "GunDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pzalavad/HelmetDataset | [
"region:us"
]
| 2023-03-21T22:56:57+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Helmet", "1": "Helmet No helmet", "2": "No helmet"}}}}], "splits": [{"name": "train", "num_bytes": 29913013.721, "num_examples": 2193}, {"name": "test", "num_bytes": 1107623.0, "num_examples": 107}, {"name": "validation", "num_bytes": 2204328.0, "num_examples": 209}], "download_size": 34614447, "dataset_size": 33224964.721}} | 2023-03-25T00:49:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "GunDataset"
More Information needed | [
"# Dataset Card for \"GunDataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"GunDataset\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"GunDataset\"\n\nMore Information needed"
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|
8244f185697cf454a580255f2fbf24d5c13870be | # Codex Math QA
Solve math_qa using codex-davinci-002 via Python Programming.
[Since OpenAI decided to shut off code-davinci-002 behind Azure](https://mobile.twitter.com/Veqtor/status/1638081493099597824), this dataset aims to share the generation results for code-davinci-002 OpenAI's 176B code generation model.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|main|25065| 4133|2985|
|rational| - | 4151|2985|
1. Basic prompts : only question
```
dataset = load_dataset("theblackcat102/codex-math-qa", "main")
```
**Example :**
```
the average weight of 8 person ' s increases by 1.5 kg when a new person comes in place of one of them weighing 75 kg. what might be the weight of the new person ?
Write a short snippet of python function which solve this problem. No need to explain the answer, let the code output the answer.
```
output
```python
def average_weight(weights):
total_weight = sum(weights)
return total_weight / len(weights)
def new_person_weight(weights):
average_weight = average_weight(weights)
new_person_weight = average_weight + 1.5
return new_person_weight
weights = [75, 80, 85, 90, 95]
print(new_person_weight(weights))
```
2. Prompts with rationale : include the `Rationale` text which should helps the programming planinig
```
dataset = load_dataset("theblackcat102/codex-math-qa", "rational")
```
**Example :**
```
one copy machine can make 20 copies a minute, and a second copy machine makes 10 copies a minute. if the two copiers work together, how long would it take them to make 900 copies ?
Rationale: "total work done by both machines in a minute = 20 + 10 = 30 copies total number of copies required = 900 time = 900 / 30 = 30 mins answer d"
Write a short snippet of python function which solve this problem within 30 lines of code. Add comments in the function block for explaination.
```
output
```python
def copy_machine(copies, copy_speed):
"""
:param copies: number of copies required
:param copy_speed: speed of the copy machine
:return: time in minutes
"""
total_work = sum(copy_speed)
time = copies / total_work
return time
if __name__ == "__main__":
print(copy_machine(900, [20, 10]))
```
### Notes:
The generated results are unvalidated and are as what it is from the codex-davinci-002 outputs. So there's a majority of answers which is incorrect and code with syntax error. However, this is a work for a future study and the aim of this dataset was to provide a source or reference for code based math answering by codex-davinci-002.
## Dataset Creation
Dataset was sourced from [math_qa](https://huggingface.co/datasets/math_qa) and append prompts at the end of section for generating Python solutions for the answer. This is an aim for providing dataset for the work offload seem in galactica
The generation config for code-davinci-02 are as follows:
| name | value|
|-------|----:|
|max_tokens| 2048 |
|temperature| 0.5 |
|top_p| 0.7 |
### Citation Information
```
@inproceedings{amini-etal-2019-mathqa,
title = "{M}ath{QA}: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms",
author = "Amini, Aida and
Gabriel, Saadia and
Lin, Shanchuan and
Koncel-Kedziorski, Rik and
Choi, Yejin and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1245",
doi = "10.18653/v1/N19-1245",
pages = "2357--2367",
}
``` | theblackcat102/codex-math-qa | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"language:en",
"license:other",
"codex-generated",
"code",
"mathematic",
"region:us"
]
| 2023-03-22T00:56:14+00:00 | {"language": ["en"], "license": "other", "task_categories": ["text2text-generation", "text-generation"], "tags": ["codex-generated", "code", "mathematic"]} | 2023-03-26T00:04:18+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text2text-generation #task_categories-text-generation #language-English #license-other #codex-generated #code #mathematic #region-us
| Codex Math QA
=============
Solve math\_qa using codex-davinci-002 via Python Programming.
Since OpenAI decided to shut off code-davinci-002 behind Azure, this dataset aims to share the generation results for code-davinci-002 OpenAI's 176B code generation model.
### Data Splits
1. Basic prompts : only question
Example :
output
2. Prompts with rationale : include the 'Rationale' text which should helps the programming planinig
Example :
output
### Notes:
The generated results are unvalidated and are as what it is from the codex-davinci-002 outputs. So there's a majority of answers which is incorrect and code with syntax error. However, this is a work for a future study and the aim of this dataset was to provide a source or reference for code based math answering by codex-davinci-002.
Dataset Creation
----------------
Dataset was sourced from math\_qa and append prompts at the end of section for generating Python solutions for the answer. This is an aim for providing dataset for the work offload seem in galactica
The generation config for code-davinci-02 are as follows:
| [
"### Data Splits\n\n\n\n1. Basic prompts : only question\n\n\nExample :\n\n\noutput\n\n\n2. Prompts with rationale : include the 'Rationale' text which should helps the programming planinig\n\n\nExample :\n\n\noutput",
"### Notes:\n\n\nThe generated results are unvalidated and are as what it is from the codex-davinci-002 outputs. So there's a majority of answers which is incorrect and code with syntax error. However, this is a work for a future study and the aim of this dataset was to provide a source or reference for code based math answering by codex-davinci-002.\n\n\nDataset Creation\n----------------\n\n\nDataset was sourced from math\\_qa and append prompts at the end of section for generating Python solutions for the answer. This is an aim for providing dataset for the work offload seem in galactica\n\n\nThe generation config for code-davinci-02 are as follows:"
]
| [
"TAGS\n#task_categories-text2text-generation #task_categories-text-generation #language-English #license-other #codex-generated #code #mathematic #region-us \n",
"### Data Splits\n\n\n\n1. Basic prompts : only question\n\n\nExample :\n\n\noutput\n\n\n2. Prompts with rationale : include the 'Rationale' text which should helps the programming planinig\n\n\nExample :\n\n\noutput",
"### Notes:\n\n\nThe generated results are unvalidated and are as what it is from the codex-davinci-002 outputs. So there's a majority of answers which is incorrect and code with syntax error. However, this is a work for a future study and the aim of this dataset was to provide a source or reference for code based math answering by codex-davinci-002.\n\n\nDataset Creation\n----------------\n\n\nDataset was sourced from math\\_qa and append prompts at the end of section for generating Python solutions for the answer. This is an aim for providing dataset for the work offload seem in galactica\n\n\nThe generation config for code-davinci-02 are as follows:"
]
| [
51,
44,
160
]
| [
"passage: TAGS\n#task_categories-text2text-generation #task_categories-text-generation #language-English #license-other #codex-generated #code #mathematic #region-us \n### Data Splits\n\n\n\n1. Basic prompts : only question\n\n\nExample :\n\n\noutput\n\n\n2. Prompts with rationale : include the 'Rationale' text which should helps the programming planinig\n\n\nExample :\n\n\noutput### Notes:\n\n\nThe generated results are unvalidated and are as what it is from the codex-davinci-002 outputs. So there's a majority of answers which is incorrect and code with syntax error. However, this is a work for a future study and the aim of this dataset was to provide a source or reference for code based math answering by codex-davinci-002.\n\n\nDataset Creation\n----------------\n\n\nDataset was sourced from math\\_qa and append prompts at the end of section for generating Python solutions for the answer. This is an aim for providing dataset for the work offload seem in galactica\n\n\nThe generation config for code-davinci-02 are as follows:"
]
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|
e8cadd6bf716fba2e5d8d0e8dfb4327683a4261c | # Dataset Card for "Food101_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Food101_train | [
"region:us"
]
| 2023-03-22T01:07:54+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "apple pie", "1": "baby back ribs", "2": "baklava", "3": "beef carpaccio", "4": "beef tartare", "5": "beet salad", "6": "beignets", "7": "bibimbap", "8": "bread pudding", "9": "breakfast burrito", "10": "bruschetta", "11": "caesar salad", "12": "cannoli", "13": "caprese salad", "14": "carrot cake", "15": "ceviche", "16": "cheesecake", "17": "cheese plate", "18": "chicken curry", "19": "chicken quesadilla", "20": "chicken wings", "21": "chocolate cake", "22": "chocolate mousse", "23": "churros", "24": "clam chowder", "25": "club sandwich", "26": "crab cakes", "27": "creme brulee", "28": "croque madame", "29": "cup cakes", "30": "deviled eggs", "31": "donuts", "32": "dumplings", "33": "edamame", "34": "eggs benedict", "35": "escargots", "36": "falafel", "37": "filet mignon", "38": "fish and chips", "39": "foie gras", "40": "french fries", "41": "french onion soup", "42": "french toast", "43": "fried calamari", "44": "fried rice", "45": "frozen yogurt", "46": "garlic bread", "47": "gnocchi", "48": "greek salad", "49": "grilled cheese sandwich", "50": "grilled salmon", "51": "guacamole", "52": "gyoza", "53": "hamburger", "54": "hot and sour soup", "55": "hot dog", "56": "huevos rancheros", "57": "hummus", "58": "ice cream", "59": "lasagna", "60": "lobster bisque", "61": "lobster roll sandwich", "62": "macaroni and cheese", "63": "macarons", "64": "miso soup", "65": "mussels", "66": "nachos", "67": "omelette", "68": "onion rings", "69": "oysters", "70": "pad thai", "71": "paella", "72": "pancakes", "73": "panna cotta", "74": "peking duck", "75": "pho", "76": "pizza", "77": "pork chop", "78": "poutine", "79": "prime rib", "80": "pulled pork sandwich", "81": "ramen", "82": "ravioli", "83": "red velvet cake", "84": "risotto", "85": "samosa", "86": "sashimi", "87": "scallops", "88": "seaweed salad", "89": "shrimp and grits", "90": "spaghetti bolognese", "91": "spaghetti carbonara", "92": "spring rolls", "93": "steak", "94": "strawberry shortcake", "95": "sushi", "96": "tacos", "97": "takoyaki", "98": "tiramisu", "99": "tuna tartare", "100": "waffles"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3906035933.0, "num_examples": 75750}], "download_size": 3802666577, "dataset_size": 3906035933.0}} | 2023-05-04T05:17:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Food101_train"
More Information needed | [
"# Dataset Card for \"Food101_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Food101_train\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Food101_train\"\n\nMore Information needed"
]
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|
408419795277f050a1f9f07780bebb46ac162f85 | # Dataset Card for "Food101_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Food101_test | [
"region:us"
]
| 2023-03-22T01:14:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "apple pie", "1": "baby back ribs", "2": "baklava", "3": "beef carpaccio", "4": "beef tartare", "5": "beet salad", "6": "beignets", "7": "bibimbap", "8": "bread pudding", "9": "breakfast burrito", "10": "bruschetta", "11": "caesar salad", "12": "cannoli", "13": "caprese salad", "14": "carrot cake", "15": "ceviche", "16": "cheesecake", "17": "cheese plate", "18": "chicken curry", "19": "chicken quesadilla", "20": "chicken wings", "21": "chocolate cake", "22": "chocolate mousse", "23": "churros", "24": "clam chowder", "25": "club sandwich", "26": "crab cakes", "27": "creme brulee", "28": "croque madame", "29": "cup cakes", "30": "deviled eggs", "31": "donuts", "32": "dumplings", "33": "edamame", "34": "eggs benedict", "35": "escargots", "36": "falafel", "37": "filet mignon", "38": "fish and chips", "39": "foie gras", "40": "french fries", "41": "french onion soup", "42": "french toast", "43": "fried calamari", "44": "fried rice", "45": "frozen yogurt", "46": "garlic bread", "47": "gnocchi", "48": "greek salad", "49": "grilled cheese sandwich", "50": "grilled salmon", "51": "guacamole", "52": "gyoza", "53": "hamburger", "54": "hot and sour soup", "55": "hot dog", "56": "huevos rancheros", "57": "hummus", "58": "ice cream", "59": "lasagna", "60": "lobster bisque", "61": "lobster roll sandwich", "62": "macaroni and cheese", "63": "macarons", "64": "miso soup", "65": "mussels", "66": "nachos", "67": "omelette", "68": "onion rings", "69": "oysters", "70": "pad thai", "71": "paella", "72": "pancakes", "73": "panna cotta", "74": "peking duck", "75": "pho", "76": "pizza", "77": "pork chop", "78": "poutine", "79": "prime rib", "80": "pulled pork sandwich", "81": "ramen", "82": "ravioli", "83": "red velvet cake", "84": "risotto", "85": "samosa", "86": "sashimi", "87": "scallops", "88": "seaweed salad", "89": "shrimp and grits", "90": "spaghetti bolognese", "91": "spaghetti carbonara", "92": "spring rolls", "93": "steak", "94": "strawberry shortcake", "95": "sushi", "96": "tacos", "97": "takoyaki", "98": "tiramisu", "99": "tuna tartare", "100": "waffles"}}}}, {"name": "id", "dtype": "int64"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_food101", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1317820332.5, "num_examples": 25250}], "download_size": 1263803958, "dataset_size": 1317820332.5}} | 2023-05-04T05:23:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Food101_test"
More Information needed | [
"# Dataset Card for \"Food101_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Food101_test\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Food101_test\"\n\nMore Information needed"
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|
bd3988995e6db193427255abdebd522afe24ce76 | # Dataset Card for "CUB_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/CUB_train | [
"region:us"
]
| 2023-03-22T02:07:02+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "description", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Black footed Albatross", "1": "Laysan Albatross", "2": "Sooty Albatross", "3": "Groove billed Ani", "4": "Crested Auklet", "5": "Least Auklet", "6": "Parakeet Auklet", "7": "Rhinoceros Auklet", "8": "Brewer Blackbird", "9": "Red winged Blackbird", "10": "Rusty Blackbird", "11": "Yellow headed Blackbird", "12": "Bobolink", "13": "Indigo Bunting", "14": "Lazuli Bunting", "15": "Painted Bunting", "16": "Cardinal", "17": "Spotted Catbird", "18": "Gray Catbird", "19": "Yellow breasted Chat", "20": "Eastern Towhee", "21": "Chuck will Widow", "22": "Brandt Cormorant", "23": "Red faced Cormorant", "24": "Pelagic Cormorant", "25": "Bronzed Cowbird", "26": "Shiny Cowbird", "27": "Brown Creeper", "28": "American Crow", "29": "Fish Crow", "30": "Black billed Cuckoo", "31": "Mangrove Cuckoo", "32": "Yellow billed Cuckoo", "33": "Gray crowned Rosy Finch", "34": "Purple Finch", "35": "Northern Flicker", "36": "Acadian Flycatcher", "37": "Great Crested Flycatcher", "38": "Least Flycatcher", "39": "Olive sided Flycatcher", "40": "Scissor tailed Flycatcher", "41": "Vermilion Flycatcher", "42": "Yellow bellied Flycatcher", "43": "Frigatebird", "44": "Northern Fulmar", "45": "Gadwall", "46": "American Goldfinch", "47": "European Goldfinch", "48": "Boat tailed Grackle", "49": "Eared Grebe", "50": "Horned Grebe", "51": "Pied billed Grebe", "52": "Western Grebe", "53": "Blue Grosbeak", "54": "Evening Grosbeak", "55": "Pine Grosbeak", "56": "Rose breasted Grosbeak", "57": "Pigeon Guillemot", "58": "California Gull", "59": "Glaucous winged Gull", "60": "Heermann Gull", "61": "Herring Gull", "62": "Ivory Gull", "63": "Ring billed Gull", "64": "Slaty backed Gull", "65": "Western Gull", "66": "Anna Hummingbird", "67": "Ruby throated Hummingbird", "68": "Rufous Hummingbird", "69": "Green Violetear", "70": "Long tailed Jaeger", "71": "Pomarine Jaeger", "72": "Blue Jay", "73": "Florida Jay", "74": "Green Jay", "75": "Dark eyed Junco", "76": "Tropical Kingbird", "77": "Gray Kingbird", "78": "Belted Kingfisher", "79": "Green Kingfisher", "80": "Pied Kingfisher", "81": "Ringed Kingfisher", "82": "White breasted Kingfisher", "83": "Red legged Kittiwake", "84": "Horned Lark", "85": "Pacific Loon", "86": "Mallard", "87": "Western Meadowlark", "88": "Hooded Merganser", "89": "Red breasted Merganser", "90": "Mockingbird", "91": "Nighthawk", "92": "Clark Nutcracker", "93": "White breasted Nuthatch", "94": "Baltimore Oriole", "95": "Hooded Oriole", "96": "Orchard Oriole", "97": "Scott Oriole", "98": "Ovenbird", "99": "Brown Pelican", "100": "White Pelican", "101": "Western Wood Pewee", "102": "Sayornis", "103": "American Pipit", "104": "Whip poor Will", "105": "Horned Puffin", "106": "Common Raven", "107": "White necked Raven", "108": "American Redstart", "109": "Geococcyx", "110": "Loggerhead Shrike", "111": "Great Grey Shrike", "112": "Baird Sparrow", "113": "Black throated Sparrow", "114": "Brewer Sparrow", "115": "Chipping Sparrow", "116": "Clay colored Sparrow", "117": "House Sparrow", "118": "Field Sparrow", "119": "Fox Sparrow", "120": "Grasshopper Sparrow", "121": "Harris Sparrow", "122": "Henslow Sparrow", "123": "Le Conte Sparrow", "124": "Lincoln Sparrow", "125": "Nelson Sharp tailed Sparrow", "126": "Savannah Sparrow", "127": "Seaside Sparrow", "128": "Song Sparrow", "129": "Tree Sparrow", "130": "Vesper Sparrow", "131": "White crowned Sparrow", "132": "White throated Sparrow", "133": "Cape Glossy Starling", "134": "Bank Swallow", "135": "Barn Swallow", "136": "Cliff Swallow", "137": "Tree Swallow", "138": "Scarlet Tanager", "139": "Summer Tanager", "140": "Artic Tern", "141": "Black Tern", "142": "Caspian Tern", "143": "Common Tern", "144": "Elegant Tern", "145": "Forsters Tern", "146": "Least Tern", "147": "Green tailed Towhee", "148": "Brown Thrasher", "149": "Sage Thrasher", "150": "Black capped Vireo", "151": "Blue headed Vireo", "152": "Philadelphia Vireo", "153": "Red eyed Vireo", "154": "Warbling Vireo", "155": "White eyed Vireo", "156": "Yellow throated Vireo", "157": "Bay breasted Warbler", "158": "Black and white Warbler", "159": "Black throated Blue Warbler", "160": "Blue winged Warbler", "161": "Canada Warbler", "162": "Cape May Warbler", "163": "Cerulean Warbler", "164": "Chestnut sided Warbler", "165": "Golden winged Warbler", "166": "Hooded Warbler", "167": "Kentucky Warbler", "168": "Magnolia Warbler", "169": "Mourning Warbler", "170": "Myrtle Warbler", "171": "Nashville Warbler", "172": "Orange crowned Warbler", "173": "Palm Warbler", "174": "Pine Warbler", "175": "Prairie Warbler", "176": "Prothonotary Warbler", "177": "Swainson Warbler", "178": "Tennessee Warbler", "179": "Wilson Warbler", "180": "Worm eating Warbler", "181": "Yellow Warbler", "182": "Northern Waterthrush", "183": "Louisiana Waterthrush", "184": "Bohemian Waxwing", "185": "Cedar Waxwing", "186": "American Three toed Woodpecker", "187": "Pileated Woodpecker", "188": "Red bellied Woodpecker", "189": "Red cockaded Woodpecker", "190": "Red headed Woodpecker", "191": "Downy Woodpecker", "192": "Bewick Wren", "193": "Cactus Wren", "194": "Carolina Wren", "195": "House Wren", "196": "Marsh Wren", "197": "Rock Wren", "198": "Winter Wren", "199": "Common Yellowthroat"}}}}, {"name": "file_name", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 583337273.046, "num_examples": 5994}], "download_size": 583734869, "dataset_size": 583337273.046}} | 2023-03-22T02:08:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "CUB_train"
More Information needed | [
"# Dataset Card for \"CUB_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"CUB_train\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"CUB_train\"\n\nMore Information needed"
]
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|
82447a42c5e05c7fe21cd36accfdf399148e8668 | # Dataset Card for "CUB_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/CUB_test | [
"region:us"
]
| 2023-03-22T02:08:06+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "description", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Black footed Albatross", "1": "Laysan Albatross", "2": "Sooty Albatross", "3": "Groove billed Ani", "4": "Crested Auklet", "5": "Least Auklet", "6": "Parakeet Auklet", "7": "Rhinoceros Auklet", "8": "Brewer Blackbird", "9": "Red winged Blackbird", "10": "Rusty Blackbird", "11": "Yellow headed Blackbird", "12": "Bobolink", "13": "Indigo Bunting", "14": "Lazuli Bunting", "15": "Painted Bunting", "16": "Cardinal", "17": "Spotted Catbird", "18": "Gray Catbird", "19": "Yellow breasted Chat", "20": "Eastern Towhee", "21": "Chuck will Widow", "22": "Brandt Cormorant", "23": "Red faced Cormorant", "24": "Pelagic Cormorant", "25": "Bronzed Cowbird", "26": "Shiny Cowbird", "27": "Brown Creeper", "28": "American Crow", "29": "Fish Crow", "30": "Black billed Cuckoo", "31": "Mangrove Cuckoo", "32": "Yellow billed Cuckoo", "33": "Gray crowned Rosy Finch", "34": "Purple Finch", "35": "Northern Flicker", "36": "Acadian Flycatcher", "37": "Great Crested Flycatcher", "38": "Least Flycatcher", "39": "Olive sided Flycatcher", "40": "Scissor tailed Flycatcher", "41": "Vermilion Flycatcher", "42": "Yellow bellied Flycatcher", "43": "Frigatebird", "44": "Northern Fulmar", "45": "Gadwall", "46": "American Goldfinch", "47": "European Goldfinch", "48": "Boat tailed Grackle", "49": "Eared Grebe", "50": "Horned Grebe", "51": "Pied billed Grebe", "52": "Western Grebe", "53": "Blue Grosbeak", "54": "Evening Grosbeak", "55": "Pine Grosbeak", "56": "Rose breasted Grosbeak", "57": "Pigeon Guillemot", "58": "California Gull", "59": "Glaucous winged Gull", "60": "Heermann Gull", "61": "Herring Gull", "62": "Ivory Gull", "63": "Ring billed Gull", "64": "Slaty backed Gull", "65": "Western Gull", "66": "Anna Hummingbird", "67": "Ruby throated Hummingbird", "68": "Rufous Hummingbird", "69": "Green Violetear", "70": "Long tailed Jaeger", "71": "Pomarine Jaeger", "72": "Blue Jay", "73": "Florida Jay", "74": "Green Jay", "75": "Dark eyed Junco", "76": "Tropical Kingbird", "77": "Gray Kingbird", "78": "Belted Kingfisher", "79": "Green Kingfisher", "80": "Pied Kingfisher", "81": "Ringed Kingfisher", "82": "White breasted Kingfisher", "83": "Red legged Kittiwake", "84": "Horned Lark", "85": "Pacific Loon", "86": "Mallard", "87": "Western Meadowlark", "88": "Hooded Merganser", "89": "Red breasted Merganser", "90": "Mockingbird", "91": "Nighthawk", "92": "Clark Nutcracker", "93": "White breasted Nuthatch", "94": "Baltimore Oriole", "95": "Hooded Oriole", "96": "Orchard Oriole", "97": "Scott Oriole", "98": "Ovenbird", "99": "Brown Pelican", "100": "White Pelican", "101": "Western Wood Pewee", "102": "Sayornis", "103": "American Pipit", "104": "Whip poor Will", "105": "Horned Puffin", "106": "Common Raven", "107": "White necked Raven", "108": "American Redstart", "109": "Geococcyx", "110": "Loggerhead Shrike", "111": "Great Grey Shrike", "112": "Baird Sparrow", "113": "Black throated Sparrow", "114": "Brewer Sparrow", "115": "Chipping Sparrow", "116": "Clay colored Sparrow", "117": "House Sparrow", "118": "Field Sparrow", "119": "Fox Sparrow", "120": "Grasshopper Sparrow", "121": "Harris Sparrow", "122": "Henslow Sparrow", "123": "Le Conte Sparrow", "124": "Lincoln Sparrow", "125": "Nelson Sharp tailed Sparrow", "126": "Savannah Sparrow", "127": "Seaside Sparrow", "128": "Song Sparrow", "129": "Tree Sparrow", "130": "Vesper Sparrow", "131": "White crowned Sparrow", "132": "White throated Sparrow", "133": "Cape Glossy Starling", "134": "Bank Swallow", "135": "Barn Swallow", "136": "Cliff Swallow", "137": "Tree Swallow", "138": "Scarlet Tanager", "139": "Summer Tanager", "140": "Artic Tern", "141": "Black Tern", "142": "Caspian Tern", "143": "Common Tern", "144": "Elegant Tern", "145": "Forsters Tern", "146": "Least Tern", "147": "Green tailed Towhee", "148": "Brown Thrasher", "149": "Sage Thrasher", "150": "Black capped Vireo", "151": "Blue headed Vireo", "152": "Philadelphia Vireo", "153": "Red eyed Vireo", "154": "Warbling Vireo", "155": "White eyed Vireo", "156": "Yellow throated Vireo", "157": "Bay breasted Warbler", "158": "Black and white Warbler", "159": "Black throated Blue Warbler", "160": "Blue winged Warbler", "161": "Canada Warbler", "162": "Cape May Warbler", "163": "Cerulean Warbler", "164": "Chestnut sided Warbler", "165": "Golden winged Warbler", "166": "Hooded Warbler", "167": "Kentucky Warbler", "168": "Magnolia Warbler", "169": "Mourning Warbler", "170": "Myrtle Warbler", "171": "Nashville Warbler", "172": "Orange crowned Warbler", "173": "Palm Warbler", "174": "Pine Warbler", "175": "Prairie Warbler", "176": "Prothonotary Warbler", "177": "Swainson Warbler", "178": "Tennessee Warbler", "179": "Wilson Warbler", "180": "Worm eating Warbler", "181": "Yellow Warbler", "182": "Northern Waterthrush", "183": "Louisiana Waterthrush", "184": "Bohemian Waxwing", "185": "Cedar Waxwing", "186": "American Three toed Woodpecker", "187": "Pileated Woodpecker", "188": "Red bellied Woodpecker", "189": "Red cockaded Woodpecker", "190": "Red headed Woodpecker", "191": "Downy Woodpecker", "192": "Bewick Wren", "193": "Cactus Wren", "194": "Carolina Wren", "195": "House Wren", "196": "Marsh Wren", "197": "Rock Wren", "198": "Winter Wren", "199": "Common Yellowthroat"}}}}, {"name": "file_name", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 576586188.934, "num_examples": 5794}], "download_size": 564530335, "dataset_size": 576586188.934}} | 2023-03-22T02:08:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "CUB_test"
More Information needed | [
"# Dataset Card for \"CUB_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"CUB_test\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"CUB_test\"\n\nMore Information needed"
]
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|
d117cdbeba25212c1fa0c6964ebf2fc2abf3736a |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/code-to-code-trans in Semeru
# CodeXGLUE -- Code2Code Translation
## Task Definition
Code translation aims to migrate legacy software from one programming language in a platform toanother.
In CodeXGLUE, given a piece of Java (C#) code, the task is to translate the code into C# (Java) version.
Models are evaluated by BLEU scores, accuracy (exactly match), and [CodeBLEU](https://github.com/microsoft/CodeXGLUE/blob/main/code-to-code-trans/CodeBLEU.MD) scores.
## Dataset
The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).
We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.
### Data Format
The dataset is in the "data" folder. Each line of the files is a function, and the suffix of the file indicates the programming language.
### Data Statistics
Data statistics of the dataset are shown in the below table:
| | #Examples |
| ------- | :-------: |
| Train | 10,300 |
| Valid | 500 |
| Test | 1,000 | | semeru/code-code-translation-java-csharp | [
"license:mit",
"region:us"
]
| 2023-03-22T02:10:54+00:00 | {"license": "mit", "Programminglanguage": "Java/C#", "version": "N/A", "Date": "Most likely 2020", "Contaminated": "Very Likely", "Size": "Standard Tokenizer"} | 2023-03-27T17:22:28+00:00 | []
| []
| TAGS
#license-mit #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/code-to-code-trans in Semeru
CodeXGLUE -- Code2Code Translation
==================================
Task Definition
---------------
Code translation aims to migrate legacy software from one programming language in a platform toanother.
In CodeXGLUE, given a piece of Java (C#) code, the task is to translate the code into C# (Java) version.
Models are evaluated by BLEU scores, accuracy (exactly match), and CodeBLEU scores.
Dataset
-------
The dataset is collected from several public repos, including Lucene(URL POI(URL JGit(URL and Antlr(URL
We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.
### Data Format
The dataset is in the "data" folder. Each line of the files is a function, and the suffix of the file indicates the programming language.
### Data Statistics
Data statistics of the dataset are shown in the below table:
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-to-code-trans in Semeru\n\n\nCodeXGLUE -- Code2Code Translation\n==================================\n\n\nTask Definition\n---------------\n\n\nCode translation aims to migrate legacy software from one programming language in a platform toanother.\nIn CodeXGLUE, given a piece of Java (C#) code, the task is to translate the code into C# (Java) version.\nModels are evaluated by BLEU scores, accuracy (exactly match), and CodeBLEU scores.\n\n\nDataset\n-------\n\n\nThe dataset is collected from several public repos, including Lucene(URL POI(URL JGit(URL and Antlr(URL\n\n\nWe collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.",
"### Data Format\n\n\nThe dataset is in the \"data\" folder. Each line of the files is a function, and the suffix of the file indicates the programming language.",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:"
]
| [
"TAGS\n#license-mit #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-to-code-trans in Semeru\n\n\nCodeXGLUE -- Code2Code Translation\n==================================\n\n\nTask Definition\n---------------\n\n\nCode translation aims to migrate legacy software from one programming language in a platform toanother.\nIn CodeXGLUE, given a piece of Java (C#) code, the task is to translate the code into C# (Java) version.\nModels are evaluated by BLEU scores, accuracy (exactly match), and CodeBLEU scores.\n\n\nDataset\n-------\n\n\nThe dataset is collected from several public repos, including Lucene(URL POI(URL JGit(URL and Antlr(URL\n\n\nWe collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.",
"### Data Format\n\n\nThe dataset is in the \"data\" folder. Each line of the files is a function, and the suffix of the file indicates the programming language.",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:"
]
| [
11,
264,
38,
19
]
| [
"passage: TAGS\n#license-mit #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/code-to-code-trans in Semeru\n\n\nCodeXGLUE -- Code2Code Translation\n==================================\n\n\nTask Definition\n---------------\n\n\nCode translation aims to migrate legacy software from one programming language in a platform toanother.\nIn CodeXGLUE, given a piece of Java (C#) code, the task is to translate the code into C# (Java) version.\nModels are evaluated by BLEU scores, accuracy (exactly match), and CodeBLEU scores.\n\n\nDataset\n-------\n\n\nThe dataset is collected from several public repos, including Lucene(URL POI(URL JGit(URL and Antlr(URL\n\n\nWe collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.### Data Format\n\n\nThe dataset is in the \"data\" folder. Each line of the files is a function, and the suffix of the file indicates the programming language.### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:"
]
| [
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|
73b2c0e8a9e7ac2fe24eaf4e78a4e67378f39efa |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/CodeCompletion-token/dataset/javaCorpus in Semeru
# CodeXGLUE -- Code Completion (token level)
**Update 2021.07.30:** We update the code completion dataset with literals normalized to avoid sensitive information.
Here is the introduction and pipeline for token level code completion task.
## Task Definition
Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.
## Dataset
The dataset is in java.
### Dependency
- javalang == 0.13.0
### Github Java Corpus
We use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper [Mining Source Code Repositories at Massive Scale using Language Modeling](https://homepages.inf.ed.ac.uk/csutton/publications/msr2013.pdf). We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper [Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code](http://homepages.inf.ed.ac.uk/s1467463/documents/icse20-main-1325.pdf).
### Data Format
Code corpus are saved in txt format files. one line is a tokenized code snippets:
```
<s> from __future__ import unicode_literals <EOL> from django . db import models , migrations <EOL> class Migration ( migrations . Migration ) : <EOL> dependencies = [ <EOL> ] <EOL> operations = [ <EOL> migrations . CreateModel ( <EOL> name = '<STR_LIT>' , <EOL> fields = [ <EOL> ( '<STR_LIT:id>' , models . AutoField ( verbose_name = '<STR_LIT>' , serialize = False , auto_created = True , primary_key = True ) ) , <EOL> ( '<STR_LIT:name>' , models . CharField ( help_text = b'<STR_LIT>' , max_length = <NUM_LIT> ) ) , <EOL> ( '<STR_LIT:image>' , models . ImageField ( help_text = b'<STR_LIT>' , null = True , upload_to = b'<STR_LIT>' , blank = True ) ) , <EOL> ] , <EOL> options = { <EOL> '<STR_LIT>' : ( '<STR_LIT:name>' , ) , <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> } , <EOL> bases = ( models . Model , ) , <EOL> ) , <EOL> ] </s>
```
### Data Statistics
Data statistics of Github Java Corpus dataset are shown in the below table:
| Data Split | #Files | #Tokens |
| ----------- | :--------: | :---------: |
| Train | 12,934 | 15.7M |
| Dev | 7,176 | 3.8M |
| Test | 8,268 | 5.3M |
| semeru/code-code-CodeCompletion-TokenLevel-Java | [
"license:mit",
"region:us"
]
| 2023-03-22T03:11:04+00:00 | {"license": "mit", "Programminglanguage": "Java", "version": "N/A", "Date": "From paper: https://homepages.inf.ed.ac.uk/csutton/publications/msr2013.pdf (2013 - paper release date)", "Contaminated": "Very Likely", "Size": "Standard Tokenizer (TreeSitter)"} | 2023-03-24T14:22:48+00:00 | []
| []
| TAGS
#license-mit #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/CodeCompletion-token/dataset/javaCorpus in Semeru
CodeXGLUE -- Code Completion (token level)
==========================================
Update 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.
Here is the introduction and pipeline for token level code completion task.
Task Definition
---------------
Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.
Dataset
-------
The dataset is in java.
### Dependency
* javalang == 0.13.0
### Github Java Corpus
We use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper Mining Source Code Repositories at Massive Scale using Language Modeling. We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.
### Data Format
Code corpus are saved in txt format files. one line is a tokenized code snippets:
### Data Statistics
Data statistics of Github Java Corpus dataset are shown in the below table:
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/CodeCompletion-token/dataset/javaCorpus in Semeru\n\n\nCodeXGLUE -- Code Completion (token level)\n==========================================\n\n\nUpdate 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.\n\n\nHere is the introduction and pipeline for token level code completion task.\n\n\nTask Definition\n---------------\n\n\nPredict next code token given context of previous tokens. Models are evaluated by token level accuracy.\n\n\nCode completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.\n\n\nDataset\n-------\n\n\nThe dataset is in java.",
"### Dependency\n\n\n* javalang == 0.13.0",
"### Github Java Corpus\n\n\nWe use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper Mining Source Code Repositories at Massive Scale using Language Modeling. We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.",
"### Data Format\n\n\nCode corpus are saved in txt format files. one line is a tokenized code snippets:",
"### Data Statistics\n\n\nData statistics of Github Java Corpus dataset are shown in the below table:"
]
| [
"TAGS\n#license-mit #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/CodeCompletion-token/dataset/javaCorpus in Semeru\n\n\nCodeXGLUE -- Code Completion (token level)\n==========================================\n\n\nUpdate 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.\n\n\nHere is the introduction and pipeline for token level code completion task.\n\n\nTask Definition\n---------------\n\n\nPredict next code token given context of previous tokens. Models are evaluated by token level accuracy.\n\n\nCode completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.\n\n\nDataset\n-------\n\n\nThe dataset is in java.",
"### Dependency\n\n\n* javalang == 0.13.0",
"### Github Java Corpus\n\n\nWe use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper Mining Source Code Repositories at Massive Scale using Language Modeling. We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.",
"### Data Format\n\n\nCode corpus are saved in txt format files. one line is a tokenized code snippets:",
"### Data Statistics\n\n\nData statistics of Github Java Corpus dataset are shown in the below table:"
]
| [
11,
289,
13,
87,
28,
23
]
| [
"passage: TAGS\n#license-mit #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/CodeCompletion-token/dataset/javaCorpus in Semeru\n\n\nCodeXGLUE -- Code Completion (token level)\n==========================================\n\n\nUpdate 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.\n\n\nHere is the introduction and pipeline for token level code completion task.\n\n\nTask Definition\n---------------\n\n\nPredict next code token given context of previous tokens. Models are evaluated by token level accuracy.\n\n\nCode completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.\n\n\nDataset\n-------\n\n\nThe dataset is in java.### Dependency\n\n\n* javalang == 0.13.0### Github Java Corpus\n\n\nWe use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper Mining Source Code Repositories at Massive Scale using Language Modeling. We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.### Data Format\n\n\nCode corpus are saved in txt format files. one line is a tokenized code snippets:### Data Statistics\n\n\nData statistics of Github Java Corpus dataset are shown in the below table:"
]
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|
90902c2fe01b7c5672d9e104107be57ce093ff67 |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/CodeCompletion-token/dataset/py150 in Semeru
# CodeXGLUE -- Code Completion (token level)
**Update 2021.07.30:** We update the code completion dataset with literals normalized to avoid sensitive information.
Here is the introduction and pipeline for token level code completion task.
## Task Definition
Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.
## Dataset
The dataset is in python.
### Dependency
- python 3.7
### Github Java Corpus
We use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper [Mining Source Code Repositories at Massive Scale using Language Modeling](https://homepages.inf.ed.ac.uk/csutton/publications/msr2013.pdf). We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper [Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code](http://homepages.inf.ed.ac.uk/s1467463/documents/icse20-main-1325.pdf).
### Data Format
Code corpus are saved in txt format files. one line is a tokenized code snippets:
```
<s> from __future__ import unicode_literals <EOL> from django . db import models , migrations <EOL> class Migration ( migrations . Migration ) : <EOL> dependencies = [ <EOL> ] <EOL> operations = [ <EOL> migrations . CreateModel ( <EOL> name = '<STR_LIT>' , <EOL> fields = [ <EOL> ( '<STR_LIT:id>' , models . AutoField ( verbose_name = '<STR_LIT>' , serialize = False , auto_created = True , primary_key = True ) ) , <EOL> ( '<STR_LIT:name>' , models . CharField ( help_text = b'<STR_LIT>' , max_length = <NUM_LIT> ) ) , <EOL> ( '<STR_LIT:image>' , models . ImageField ( help_text = b'<STR_LIT>' , null = True , upload_to = b'<STR_LIT>' , blank = True ) ) , <EOL> ] , <EOL> options = { <EOL> '<STR_LIT>' : ( '<STR_LIT:name>' , ) , <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> } , <EOL> bases = ( models . Model , ) , <EOL> ) , <EOL> ] </s>
```
### Data Statistics
Data statistics of py150 dataset are shown in the below table, note that there doesn't exist dev set in the origin py150 dataset, we select 5,000 files in the original train set as dev set.
| Data Split | #Files | #Tokens |
| ----------- | :---------: | :---------: |
| Train | 95,000 | 72.1M |
| Dev | 5,000 | 4.4M |
| Test | 50,000 | 37.3M |
| semeru/code-code-CodeCompletion-TokenLevel-Python | [
"license:mit",
"region:us"
]
| 2023-03-22T03:21:32+00:00 | {"license": "mit", "Programminglanguage": "python", "version": "python3", "Date": "From paper [Probabilistic for Code with Decision trees](https://files.sri.inf.ethz.ch/website/papers/oopsla16-dt.pdf)(2016- paper release date)", "Contaminated": "Very Likely", "Size": "Standard Tokenizer (TreeSitter)"} | 2023-03-24T14:10:30+00:00 | []
| []
| TAGS
#license-mit #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/CodeCompletion-token/dataset/py150 in Semeru
CodeXGLUE -- Code Completion (token level)
==========================================
Update 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.
Here is the introduction and pipeline for token level code completion task.
Task Definition
---------------
Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.
Dataset
-------
The dataset is in python.
### Dependency
* python 3.7
### Github Java Corpus
We use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper Mining Source Code Repositories at Massive Scale using Language Modeling. We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.
### Data Format
Code corpus are saved in txt format files. one line is a tokenized code snippets:
### Data Statistics
Data statistics of py150 dataset are shown in the below table, note that there doesn't exist dev set in the origin py150 dataset, we select 5,000 files in the original train set as dev set.
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/CodeCompletion-token/dataset/py150 in Semeru\n\n\nCodeXGLUE -- Code Completion (token level)\n==========================================\n\n\nUpdate 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.\n\n\nHere is the introduction and pipeline for token level code completion task.\n\n\nTask Definition\n---------------\n\n\nPredict next code token given context of previous tokens. Models are evaluated by token level accuracy.\n\n\nCode completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.\n\n\nDataset\n-------\n\n\nThe dataset is in python.",
"### Dependency\n\n\n* python 3.7",
"### Github Java Corpus\n\n\nWe use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper Mining Source Code Repositories at Massive Scale using Language Modeling. We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.",
"### Data Format\n\n\nCode corpus are saved in txt format files. one line is a tokenized code snippets:",
"### Data Statistics\n\n\nData statistics of py150 dataset are shown in the below table, note that there doesn't exist dev set in the origin py150 dataset, we select 5,000 files in the original train set as dev set."
]
| [
"TAGS\n#license-mit #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/CodeCompletion-token/dataset/py150 in Semeru\n\n\nCodeXGLUE -- Code Completion (token level)\n==========================================\n\n\nUpdate 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.\n\n\nHere is the introduction and pipeline for token level code completion task.\n\n\nTask Definition\n---------------\n\n\nPredict next code token given context of previous tokens. Models are evaluated by token level accuracy.\n\n\nCode completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.\n\n\nDataset\n-------\n\n\nThe dataset is in python.",
"### Dependency\n\n\n* python 3.7",
"### Github Java Corpus\n\n\nWe use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper Mining Source Code Repositories at Massive Scale using Language Modeling. We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.",
"### Data Format\n\n\nCode corpus are saved in txt format files. one line is a tokenized code snippets:",
"### Data Statistics\n\n\nData statistics of py150 dataset are shown in the below table, note that there doesn't exist dev set in the origin py150 dataset, we select 5,000 files in the original train set as dev set."
]
| [
11,
288,
10,
87,
28,
51
]
| [
"passage: TAGS\n#license-mit #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/CodeCompletion-token/dataset/py150 in Semeru\n\n\nCodeXGLUE -- Code Completion (token level)\n==========================================\n\n\nUpdate 2021.07.30: We update the code completion dataset with literals normalized to avoid sensitive information.\n\n\nHere is the introduction and pipeline for token level code completion task.\n\n\nTask Definition\n---------------\n\n\nPredict next code token given context of previous tokens. Models are evaluated by token level accuracy.\n\n\nCode completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.\n\n\nDataset\n-------\n\n\nThe dataset is in python.### Dependency\n\n\n* python 3.7### Github Java Corpus\n\n\nWe use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper Mining Source Code Repositories at Massive Scale using Language Modeling. We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.### Data Format\n\n\nCode corpus are saved in txt format files. one line is a tokenized code snippets:### Data Statistics\n\n\nData statistics of py150 dataset are shown in the below table, note that there doesn't exist dev set in the origin py150 dataset, we select 5,000 files in the original train set as dev set."
]
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|
c8cbfeecf4090a18678384c559102a68bfa00829 |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru
# CodeXGLUE -- Defect Detection
## Task Definition
Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
### Dataset
The dataset we use comes from the paper [*Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). We combine all projects and split 80%/10%/10% for training/dev/test.
### Data Format
Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **func:** the source code
- **target:** 0 or 1 (vulnerability or not)
- **idx:** the index of example
### Data Statistics
Data statistics of the dataset are shown in the below table:
| | #Examples |
| ----- | :-------: |
| Train | 21,854 |
| Dev | 2,732 |
| Test | 2,732 |
## Reference
<pre><code>@inproceedings{zhou2019devign,
title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
booktitle={Advances in Neural Information Processing Systems},
pages={10197--10207},
year={2019}
}</code></pre>
| semeru/code-code-DefectDetection | [
"license:mit",
"region:us"
]
| 2023-03-22T03:30:09+00:00 | {"license": "mit", "Programminglanguage": "C", "version": "N/A", "Date": "Devign(Jun 2019 - paper release date)", "Contaminated": "Very Likely", "Size": "Standard Tokenizer"} | 2023-03-27T20:16:02+00:00 | []
| []
| TAGS
#license-mit #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/Defect-detection in Semeru
CodeXGLUE -- Defect Detection
=============================
Task Definition
---------------
Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
### Dataset
The dataset we use comes from the paper *Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.
### Data Format
Three pre-processed .jsonl files, i.e. URL, URL, URL are present
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* func: the source code
* target: 0 or 1 (vulnerability or not)
* idx: the index of example
### Data Statistics
Data statistics of the dataset are shown in the below table:
Reference
---------
```
@inproceedings{zhou2019devign,
title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
booktitle={Advances in Neural Information Processing Systems},
pages={10197--10207},
year={2019}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Defect-detection in Semeru\n\n\nCodeXGLUE -- Defect Detection\n=============================\n\n\nTask Definition\n---------------\n\n\nGiven a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.",
"### Dataset\n\n\nThe dataset we use comes from the paper *Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.",
"### Data Format\n\n\nThree pre-processed .jsonl files, i.e. URL, URL, URL are present\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* func: the source code\n* target: 0 or 1 (vulnerability or not)\n* idx: the index of example",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:\n\n\n\nReference\n---------\n\n\n\n```\n@inproceedings{zhou2019devign,\n title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},\n author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},\n booktitle={Advances in Neural Information Processing Systems},\n pages={10197--10207},\n year={2019}\n}\n```"
]
| [
"TAGS\n#license-mit #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Defect-detection in Semeru\n\n\nCodeXGLUE -- Defect Detection\n=============================\n\n\nTask Definition\n---------------\n\n\nGiven a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.",
"### Dataset\n\n\nThe dataset we use comes from the paper *Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.",
"### Data Format\n\n\nThree pre-processed .jsonl files, i.e. URL, URL, URL are present\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* func: the source code\n* target: 0 or 1 (vulnerability or not)\n* idx: the index of example",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:\n\n\n\nReference\n---------\n\n\n\n```\n@inproceedings{zhou2019devign,\n title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},\n author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},\n booktitle={Advances in Neural Information Processing Systems},\n pages={10197--10207},\n year={2019}\n}\n```"
]
| [
11,
169,
60,
81,
134
]
| [
"passage: TAGS\n#license-mit #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Defect-detection in Semeru\n\n\nCodeXGLUE -- Defect Detection\n=============================\n\n\nTask Definition\n---------------\n\n\nGiven a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.### Dataset\n\n\nThe dataset we use comes from the paper *Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.### Data Format\n\n\nThree pre-processed .jsonl files, i.e. URL, URL, URL are present\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* func: the source code\n* target: 0 or 1 (vulnerability or not)\n* idx: the index of example### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:\n\n\n\nReference\n---------\n\n\n\n```\n@inproceedings{zhou2019devign,\n title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},\n author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},\n booktitle={Advances in Neural Information Processing Systems},\n pages={10197--10207},\n year={2019}\n}\n```"
]
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|
a6c52e2ba656380eb2ddabeddc55c8b3c853f624 | # Dataset Card for "data_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | linhqyy/data_test | [
"region:us"
]
| 2023-03-22T03:34:07+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9016799768.976877, "num_examples": 64418}, {"name": "test", "num_bytes": 1001925014.6631207, "num_examples": 7158}], "download_size": 10000087591, "dataset_size": 10018724783.639997}} | 2023-03-22T11:18:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "data_test"
More Information needed | [
"# Dataset Card for \"data_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"data_test\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"data_test\"\n\nMore Information needed"
]
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|
cb7deb606db022364ebc2639303c0560f49b4714 |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Method-Generation/dataset/codexglue_method_generation in Semeru
# CodeXGLUE -- Method Generation
Here is the introduction and pipeline for method generation task.
## Task Definition
Method generation is the prediction of a method body implementation conditioned on a signature, a docstring, and any more context.
## Dataset
We use CodeSearchNet Python dataset. The CodeSearchNet repositories are re-downloaded to extract all the methods, including their signatures, docstrings and bodies. We remove the methods that don't have docstrings and whose name contains 'test'. We preserve the context around this method for auxiliary information since it is really a difficult task to generator the method body only based on its signature/docstring. We also apply literal normalization for better user experience.
### Data Format
The data format of each line in `train/dev/test.jsonl` is:
```json
{
"signature": "def do_transform(self, v=<NUM_LIT:1>):",
"body": "if not self.transform:<EOL><INDENT>return<EOL><DEDENT>try:<EOL><INDENT>self.latest_value = utils.Transform ...",
"docstring": "Apply the transformation (if it exists) to the latest_value",
"id": "f19:c4:m1"
}
```
The `id` indicts where you can find this method in the raw data. In this instance, it means the 2nd method in the 2nd class in the 19th file. We apply literal normalization to function signature and body, replace `\n` with `<EOL>` and keep track in INDENT and DEDENT.
### Data Statistics
Data statistics are shown in the below table.
| Data Split | #Instances |
| ----------- | :---------: |
| Train | 893,538 |
| Dev | 20,000 |
| Test | 20,000 |
## Reference
<pre><code>@article{clement2021long,
title={Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy},
author={Clement, Colin B and Lu, Shuai and Liu, Xiaoyu and Tufano, Michele and Drain, Dawn and Duan, Nan and Sundaresan, Neel and Svyatkovskiy, Alexey},
journal={arXiv preprint arXiv:2109.08780},
year={2021}
}</code></pre>
| semeru/code-code-MethodGeneration | [
"license:mit",
"region:us"
]
| 2023-03-22T03:34:23+00:00 | {"license": "mit", "Programminglanguage": "python", "version": "N/A", "Date": "Codesearchnet(Jun 2020 - paper release date)", "Contaminated": "Very Likely", "Size": "Standard Tokenizer (TreeSitter)"} | 2023-03-27T17:16:31+00:00 | []
| []
| TAGS
#license-mit #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-code/Method-Generation/dataset/codexglue\_method\_generation in Semeru
CodeXGLUE -- Method Generation
==============================
Here is the introduction and pipeline for method generation task.
Task Definition
---------------
Method generation is the prediction of a method body implementation conditioned on a signature, a docstring, and any more context.
Dataset
-------
We use CodeSearchNet Python dataset. The CodeSearchNet repositories are re-downloaded to extract all the methods, including their signatures, docstrings and bodies. We remove the methods that don't have docstrings and whose name contains 'test'. We preserve the context around this method for auxiliary information since it is really a difficult task to generator the method body only based on its signature/docstring. We also apply literal normalization for better user experience.
### Data Format
The data format of each line in 'train/dev/URL' is:
The 'id' indicts where you can find this method in the raw data. In this instance, it means the 2nd method in the 2nd class in the 19th file. We apply literal normalization to function signature and body, replace '\n' with '' and keep track in INDENT and DEDENT.
### Data Statistics
Data statistics are shown in the below table.
Reference
---------
```
@article{clement2021long,
title={Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy},
author={Clement, Colin B and Lu, Shuai and Liu, Xiaoyu and Tufano, Michele and Drain, Dawn and Duan, Nan and Sundaresan, Neel and Svyatkovskiy, Alexey},
journal={arXiv preprint arXiv:2109.08780},
year={2021}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Method-Generation/dataset/codexglue\\_method\\_generation in Semeru\n\n\nCodeXGLUE -- Method Generation\n==============================\n\n\nHere is the introduction and pipeline for method generation task.\n\n\nTask Definition\n---------------\n\n\nMethod generation is the prediction of a method body implementation conditioned on a signature, a docstring, and any more context.\n\n\nDataset\n-------\n\n\nWe use CodeSearchNet Python dataset. The CodeSearchNet repositories are re-downloaded to extract all the methods, including their signatures, docstrings and bodies. We remove the methods that don't have docstrings and whose name contains 'test'. We preserve the context around this method for auxiliary information since it is really a difficult task to generator the method body only based on its signature/docstring. We also apply literal normalization for better user experience.",
"### Data Format\n\n\nThe data format of each line in 'train/dev/URL' is:\n\n\nThe 'id' indicts where you can find this method in the raw data. In this instance, it means the 2nd method in the 2nd class in the 19th file. We apply literal normalization to function signature and body, replace '\\n' with '' and keep track in INDENT and DEDENT.",
"### Data Statistics\n\n\nData statistics are shown in the below table.\n\n\n\nReference\n---------\n\n\n\n```\n@article{clement2021long,\n title={Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy},\n author={Clement, Colin B and Lu, Shuai and Liu, Xiaoyu and Tufano, Michele and Drain, Dawn and Duan, Nan and Sundaresan, Neel and Svyatkovskiy, Alexey},\n journal={arXiv preprint arXiv:2109.08780},\n year={2021}\n}\n```"
]
| [
"TAGS\n#license-mit #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Method-Generation/dataset/codexglue\\_method\\_generation in Semeru\n\n\nCodeXGLUE -- Method Generation\n==============================\n\n\nHere is the introduction and pipeline for method generation task.\n\n\nTask Definition\n---------------\n\n\nMethod generation is the prediction of a method body implementation conditioned on a signature, a docstring, and any more context.\n\n\nDataset\n-------\n\n\nWe use CodeSearchNet Python dataset. The CodeSearchNet repositories are re-downloaded to extract all the methods, including their signatures, docstrings and bodies. We remove the methods that don't have docstrings and whose name contains 'test'. We preserve the context around this method for auxiliary information since it is really a difficult task to generator the method body only based on its signature/docstring. We also apply literal normalization for better user experience.",
"### Data Format\n\n\nThe data format of each line in 'train/dev/URL' is:\n\n\nThe 'id' indicts where you can find this method in the raw data. In this instance, it means the 2nd method in the 2nd class in the 19th file. We apply literal normalization to function signature and body, replace '\\n' with '' and keep track in INDENT and DEDENT.",
"### Data Statistics\n\n\nData statistics are shown in the below table.\n\n\n\nReference\n---------\n\n\n\n```\n@article{clement2021long,\n title={Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy},\n author={Clement, Colin B and Lu, Shuai and Liu, Xiaoyu and Tufano, Michele and Drain, Dawn and Duan, Nan and Sundaresan, Neel and Svyatkovskiy, Alexey},\n journal={arXiv preprint arXiv:2109.08780},\n year={2021}\n}\n```"
]
| [
11,
261,
90,
138
]
| [
"passage: TAGS\n#license-mit #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-code/Method-Generation/dataset/codexglue\\_method\\_generation in Semeru\n\n\nCodeXGLUE -- Method Generation\n==============================\n\n\nHere is the introduction and pipeline for method generation task.\n\n\nTask Definition\n---------------\n\n\nMethod generation is the prediction of a method body implementation conditioned on a signature, a docstring, and any more context.\n\n\nDataset\n-------\n\n\nWe use CodeSearchNet Python dataset. The CodeSearchNet repositories are re-downloaded to extract all the methods, including their signatures, docstrings and bodies. We remove the methods that don't have docstrings and whose name contains 'test'. We preserve the context around this method for auxiliary information since it is really a difficult task to generator the method body only based on its signature/docstring. We also apply literal normalization for better user experience.### Data Format\n\n\nThe data format of each line in 'train/dev/URL' is:\n\n\nThe 'id' indicts where you can find this method in the raw data. In this instance, it means the 2nd method in the 2nd class in the 19th file. We apply literal normalization to function signature and body, replace '\\n' with '' and keep track in INDENT and DEDENT.### Data Statistics\n\n\nData statistics are shown in the below table.\n\n\n\nReference\n---------\n\n\n\n```\n@article{clement2021long,\n title={Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy},\n author={Clement, Colin B and Lu, Shuai and Liu, Xiaoyu and Tufano, Michele and Drain, Dawn and Duan, Nan and Sundaresan, Neel and Svyatkovskiy, Alexey},\n journal={arXiv preprint arXiv:2109.08780},\n year={2021}\n}\n```"
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|
ea9a5067cdb2fba5a1d49b7392f61b5ae820aec8 | # Dataset Card for "wikipedia_stage2_coverage_20230316"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/wikipedia_stage2_coverage_20230316 | [
"region:us"
]
| 2023-03-22T05:06:39+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C_position", "sequence": "int64"}, {"name": "start_point_list", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 58992077079, "num_examples": 1054477}], "download_size": 18229138004, "dataset_size": 58992077079}} | 2023-03-23T18:28:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia_stage2_coverage_20230316"
More Information needed | [
"# Dataset Card for \"wikipedia_stage2_coverage_20230316\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia_stage2_coverage_20230316\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_stage2_coverage_20230316\"\n\nMore Information needed"
]
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|
7878f4929cce3aba9ce8371f83b32e0eeb2b83dc |
# ChatGPT3.5 Noisy Translation Facebook
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-facebook | mesolitica/chatgpt-noisy-translation-facebook | [
"task_categories:translation",
"language:ms",
"region:us"
]
| 2023-03-22T05:10:38+00:00 | {"language": ["ms"], "task_categories": ["translation"]} | 2023-12-17T04:06:33+00:00 | []
| [
"ms"
]
| TAGS
#task_categories-translation #language-Malay (macrolanguage) #region-us
|
# ChatGPT3.5 Noisy Translation Facebook
Notebooks at URL | [
"# ChatGPT3.5 Noisy Translation Facebook\n\nNotebooks at URL"
]
| [
"TAGS\n#task_categories-translation #language-Malay (macrolanguage) #region-us \n",
"# ChatGPT3.5 Noisy Translation Facebook\n\nNotebooks at URL"
]
| [
25,
14
]
| [
"passage: TAGS\n#task_categories-translation #language-Malay (macrolanguage) #region-us \n# ChatGPT3.5 Noisy Translation Facebook\n\nNotebooks at URL"
]
| [
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|
594ef28b50307c40a756df785a72296cf55e4872 |
# ChatGPT3.5 Noisy Translation IIUM Confession
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-iium-confession | mesolitica/chatgpt-noisy-translation-iium-confession | [
"task_categories:translation",
"language:ms",
"region:us"
]
| 2023-03-22T05:11:08+00:00 | {"language": ["ms"], "task_categories": ["translation"]} | 2023-12-17T04:07:11+00:00 | []
| [
"ms"
]
| TAGS
#task_categories-translation #language-Malay (macrolanguage) #region-us
|
# ChatGPT3.5 Noisy Translation IIUM Confession
Notebooks at URL | [
"# ChatGPT3.5 Noisy Translation IIUM Confession\n\nNotebooks at URL"
]
| [
"TAGS\n#task_categories-translation #language-Malay (macrolanguage) #region-us \n",
"# ChatGPT3.5 Noisy Translation IIUM Confession\n\nNotebooks at URL"
]
| [
25,
18
]
| [
"passage: TAGS\n#task_categories-translation #language-Malay (macrolanguage) #region-us \n# ChatGPT3.5 Noisy Translation IIUM Confession\n\nNotebooks at URL"
]
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|
1277505f152d3e19d5b7f0169e28a533d4a6b445 |
# KoddaDuck/Cylonix_ASR_dataset | KoddaDuck/Cylonix_ASR_dataset | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"region:us"
]
| 2023-03-22T06:03:52+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": {"zh-CN": ["100K<n<10M"]}, "source_datasets": ["extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "paperswithcode_id": "common-voice", "pretty_name": "Common Voice Corpus 11.0", "language_bcp47": ["zh-CN"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."} | 2023-03-27T00:53:16+00:00 | []
| []
| TAGS
#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #region-us
|
# KoddaDuck/Cylonix_ASR_dataset | [
"# KoddaDuck/Cylonix_ASR_dataset"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #region-us \n",
"# KoddaDuck/Cylonix_ASR_dataset"
]
| [
78,
16
]
| [
"passage: TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #region-us \n# KoddaDuck/Cylonix_ASR_dataset"
]
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|
001dad396b843fa88b0b1a637e71cdc8206bab09 | # Dataset Card for "thaigov-v2-corpus-22032023"
This corpus made from Thaigov v2 corpus in 22 Mar 2023. [https://github.com/PyThaiNLP/thaigov-v2-corpus/releases/tag/22032023](https://github.com/PyThaiNLP/thaigov-v2-corpus/releases/tag/22032023)
Corups: [https://github.com/PyThaiNLP/thaigov-v2-corpus](https://github.com/PyThaiNLP/thaigov-v2-corpus)
## English
- Data from Thai government website. https://www.thaigov.go.th
- This part of PyThaiNLP Project.
- Compiled by Mr.Wannaphong Phatthiyaphaibun
- License Dataset is public domain.
## Data format
- 1 file, 1 news, which is extracted from 1 url.
```
topic
(Blank line)
content
content
content
content
content
(Blank line)
ที่มา (URL source) : http://www.thaigov.go.th/news/contents/details/NNN
```
## Thai
- เป็นข้อมูลที่รวบรวมข่าวสารจากเว็บไซต์รัฐบาลไทย https://www.thaigov.go.th
- โครงการนี้เป็นส่วนหนึ่งในแผนพัฒนา [PyThaiNLP](https://github.com/PyThaiNLP/)
- รวบรวมโดย นาย วรรณพงษ์ ภัททิยไพบูลย์
- ข้อมูลที่รวบรวมในคลังข้อความนี้เป็นสาธารณสมบัติ (public domain) ตามพ.ร.บ.ลิขสิทธิ์ พ.ศ. 2537 มาตรา 7 (สิ่งต่อไปนี้ไม่ถือว่าเป็นงานอันมีลิขสิทธิ์ตามพระราชบัญญัตินี้ (1) ข่าวประจำวัน และข้อเท็จจริงต่างๆ ที่มีลักษณะเป็นเพียงข่าวสารอันมิใช่งานในแผนกวรรณคดี แผนกวิทยาศาสตร์ หรือแผนกศิลปะ [...] (3) ระเบียบ ข้อบังคับ ประกาศ คำสั่ง คำชี้แจง และหนังสือตอบโต้ของกระทรวง ทบวง กรม หรือหน่วยงานอื่นใดของรัฐหรือของท้องถิ่น [...])
**สามารถติดตามประวัติการแก้ไขคลังข้อความนี้ได้ผ่านระบบ Git**
### จำนวนข่าว
- วันเริ่มต้นโครงการ 17 ก.ย. 2563
### รูปแบบข้อมูล
- 1 ไฟล์ 1 ข่าว ซึ่งดึงมาจาก 1 url
```
หัวเรื่อง
(บรรทัดว่าง)
เนื้อความ
เนื้อความ
เนื้อความ
เนื้อความ
เนื้อความ
(บรรทัดว่าง)
ที่มา : http://www.thaigov.go.th/news/contents/details/NNN
```
### รายละเอียดชื่อไฟล์
- ชื่อหมวดหมู่_จำนวนที่ของข่าว.txt
### Script
- run.py สำหรับเก็บข้อมูลจากหน้าเว็บ โดยจะดึงหน้าเว็บจาก url ```http://www.thaigov.go.th/news/contents/details/NNN``` โดยที่ NNN คือเลขจำนวนเต็ม
- เปลี่ยนค่าตัวแปร i ในไฟล์เป็นเลขที่ต้องการเริ่มเก็บ
- clean.py สำหรับทำความสะอาดข้อมูลเบื้องต้น โดยจะลบช่องว่างหน้าและท้ายบรรทัด ลบบรรทัดว่าง
- ```clean.py ชื่อไฟล์```
- ```clean.py ชื่อไฟล์1 ชื่อไฟล์2```
- ```clean.py *.txt```
We build Thai NLP.
PyThaiNLP | pythainlp/thaigov-v2-corpus-22032023 | [
"size_categories:10K<n<100K",
"language:th",
"license:cc0-1.0",
"region:us"
]
| 2023-03-22T07:57:03+00:00 | {"language": ["th"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 252319219, "num_examples": 30380}], "download_size": 85313027, "dataset_size": 252319219}} | 2023-03-22T08:44:49+00:00 | []
| [
"th"
]
| TAGS
#size_categories-10K<n<100K #language-Thai #license-cc0-1.0 #region-us
| # Dataset Card for "thaigov-v2-corpus-22032023"
This corpus made from Thaigov v2 corpus in 22 Mar 2023. URL
Corups: URL
## English
- Data from Thai government website. URL
- This part of PyThaiNLP Project.
- Compiled by Mr.Wannaphong Phatthiyaphaibun
- License Dataset is public domain.
## Data format
- 1 file, 1 news, which is extracted from 1 url.
## Thai
- เป็นข้อมูลที่รวบรวมข่าวสารจากเว็บไซต์รัฐบาลไทย URL
- โครงการนี้เป็นส่วนหนึ่งในแผนพัฒนา PyThaiNLP
- รวบรวมโดย นาย วรรณพงษ์ ภัททิยไพบูลย์
- ข้อมูลที่รวบรวมในคลังข้อความนี้เป็นสาธารณสมบัติ (public domain) ตามพ.ร.บ.ลิขสิทธิ์ พ.ศ. 2537 มาตรา 7 (สิ่งต่อไปนี้ไม่ถือว่าเป็นงานอันมีลิขสิทธิ์ตามพระราชบัญญัตินี้ (1) ข่าวประจำวัน และข้อเท็จจริงต่างๆ ที่มีลักษณะเป็นเพียงข่าวสารอันมิใช่งานในแผนกวรรณคดี แผนกวิทยาศาสตร์ หรือแผนกศิลปะ [...] (3) ระเบียบ ข้อบังคับ ประกาศ คำสั่ง คำชี้แจง และหนังสือตอบโต้ของกระทรวง ทบวง กรม หรือหน่วยงานอื่นใดของรัฐหรือของท้องถิ่น [...])
สามารถติดตามประวัติการแก้ไขคลังข้อความนี้ได้ผ่านระบบ Git
### จำนวนข่าว
- วันเริ่มต้นโครงการ 17 ก.ย. 2563
### รูปแบบข้อมูล
- 1 ไฟล์ 1 ข่าว ซึ่งดึงมาจาก 1 url
### รายละเอียดชื่อไฟล์
- ชื่อหมวดหมู่_จำนวนที่ของข่าว.txt
### Script
- URL สำหรับเก็บข้อมูลจากหน้าเว็บ โดยจะดึงหน้าเว็บจาก url โดยที่ NNN คือเลขจำนวนเต็ม
- เปลี่ยนค่าตัวแปร i ในไฟล์เป็นเลขที่ต้องการเริ่มเก็บ
- URL สำหรับทำความสะอาดข้อมูลเบื้องต้น โดยจะลบช่องว่างหน้าและท้ายบรรทัด ลบบรรทัดว่าง
-
-
-
We build Thai NLP.
PyThaiNLP | [
"# Dataset Card for \"thaigov-v2-corpus-22032023\"\n\nThis corpus made from Thaigov v2 corpus in 22 Mar 2023. URL\n\nCorups: URL",
"## English\n- Data from Thai government website. URL\n- This part of PyThaiNLP Project.\n- Compiled by Mr.Wannaphong Phatthiyaphaibun\n- License Dataset is public domain.",
"## Data format\n\n- 1 file, 1 news, which is extracted from 1 url.",
"## Thai\n- เป็นข้อมูลที่รวบรวมข่าวสารจากเว็บไซต์รัฐบาลไทย URL\n- โครงการนี้เป็นส่วนหนึ่งในแผนพัฒนา PyThaiNLP\n- รวบรวมโดย นาย วรรณพงษ์ ภัททิยไพบูลย์\n- ข้อมูลที่รวบรวมในคลังข้อความนี้เป็นสาธารณสมบัติ (public domain) ตามพ.ร.บ.ลิขสิทธิ์ พ.ศ. 2537 มาตรา 7 (สิ่งต่อไปนี้ไม่ถือว่าเป็นงานอันมีลิขสิทธิ์ตามพระราชบัญญัตินี้ (1) ข่าวประจำวัน และข้อเท็จจริงต่างๆ ที่มีลักษณะเป็นเพียงข่าวสารอันมิใช่งานในแผนกวรรณคดี แผนกวิทยาศาสตร์ หรือแผนกศิลปะ [...] (3) ระเบียบ ข้อบังคับ ประกาศ คำสั่ง คำชี้แจง และหนังสือตอบโต้ของกระทรวง ทบวง กรม หรือหน่วยงานอื่นใดของรัฐหรือของท้องถิ่น [...])\n\nสามารถติดตามประวัติการแก้ไขคลังข้อความนี้ได้ผ่านระบบ Git",
"### จำนวนข่าว\n\n- วันเริ่มต้นโครงการ 17 ก.ย. 2563",
"### รูปแบบข้อมูล\n\n- 1 ไฟล์ 1 ข่าว ซึ่งดึงมาจาก 1 url",
"### รายละเอียดชื่อไฟล์\n\n- ชื่อหมวดหมู่_จำนวนที่ของข่าว.txt",
"### Script\n\n- URL สำหรับเก็บข้อมูลจากหน้าเว็บ โดยจะดึงหน้าเว็บจาก url โดยที่ NNN คือเลขจำนวนเต็ม\n - เปลี่ยนค่าตัวแปร i ในไฟล์เป็นเลขที่ต้องการเริ่มเก็บ\n- URL สำหรับทำความสะอาดข้อมูลเบื้องต้น โดยจะลบช่องว่างหน้าและท้ายบรรทัด ลบบรรทัดว่าง\n - \n - \n - \n\n\n\nWe build Thai NLP.\n\nPyThaiNLP"
]
| [
"TAGS\n#size_categories-10K<n<100K #language-Thai #license-cc0-1.0 #region-us \n",
"# Dataset Card for \"thaigov-v2-corpus-22032023\"\n\nThis corpus made from Thaigov v2 corpus in 22 Mar 2023. URL\n\nCorups: URL",
"## English\n- Data from Thai government website. URL\n- This part of PyThaiNLP Project.\n- Compiled by Mr.Wannaphong Phatthiyaphaibun\n- License Dataset is public domain.",
"## Data format\n\n- 1 file, 1 news, which is extracted from 1 url.",
"## Thai\n- เป็นข้อมูลที่รวบรวมข่าวสารจากเว็บไซต์รัฐบาลไทย URL\n- โครงการนี้เป็นส่วนหนึ่งในแผนพัฒนา PyThaiNLP\n- รวบรวมโดย นาย วรรณพงษ์ ภัททิยไพบูลย์\n- ข้อมูลที่รวบรวมในคลังข้อความนี้เป็นสาธารณสมบัติ (public domain) ตามพ.ร.บ.ลิขสิทธิ์ พ.ศ. 2537 มาตรา 7 (สิ่งต่อไปนี้ไม่ถือว่าเป็นงานอันมีลิขสิทธิ์ตามพระราชบัญญัตินี้ (1) ข่าวประจำวัน และข้อเท็จจริงต่างๆ ที่มีลักษณะเป็นเพียงข่าวสารอันมิใช่งานในแผนกวรรณคดี แผนกวิทยาศาสตร์ หรือแผนกศิลปะ [...] (3) ระเบียบ ข้อบังคับ ประกาศ คำสั่ง คำชี้แจง และหนังสือตอบโต้ของกระทรวง ทบวง กรม หรือหน่วยงานอื่นใดของรัฐหรือของท้องถิ่น [...])\n\nสามารถติดตามประวัติการแก้ไขคลังข้อความนี้ได้ผ่านระบบ Git",
"### จำนวนข่าว\n\n- วันเริ่มต้นโครงการ 17 ก.ย. 2563",
"### รูปแบบข้อมูล\n\n- 1 ไฟล์ 1 ข่าว ซึ่งดึงมาจาก 1 url",
"### รายละเอียดชื่อไฟล์\n\n- ชื่อหมวดหมู่_จำนวนที่ของข่าว.txt",
"### Script\n\n- URL สำหรับเก็บข้อมูลจากหน้าเว็บ โดยจะดึงหน้าเว็บจาก url โดยที่ NNN คือเลขจำนวนเต็ม\n - เปลี่ยนค่าตัวแปร i ในไฟล์เป็นเลขที่ต้องการเริ่มเก็บ\n- URL สำหรับทำความสะอาดข้อมูลเบื้องต้น โดยจะลบช่องว่างหน้าและท้ายบรรทัด ลบบรรทัดว่าง\n - \n - \n - \n\n\n\nWe build Thai NLP.\n\nPyThaiNLP"
]
| [
31,
37,
46,
19,
163,
15,
17,
15,
78
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #language-Thai #license-cc0-1.0 #region-us \n# Dataset Card for \"thaigov-v2-corpus-22032023\"\n\nThis corpus made from Thaigov v2 corpus in 22 Mar 2023. URL\n\nCorups: URL## English\n- Data from Thai government website. URL\n- This part of PyThaiNLP Project.\n- Compiled by Mr.Wannaphong Phatthiyaphaibun\n- License Dataset is public domain.## Data format\n\n- 1 file, 1 news, which is extracted from 1 url.## Thai\n- เป็นข้อมูลที่รวบรวมข่าวสารจากเว็บไซต์รัฐบาลไทย URL\n- โครงการนี้เป็นส่วนหนึ่งในแผนพัฒนา PyThaiNLP\n- รวบรวมโดย นาย วรรณพงษ์ ภัททิยไพบูลย์\n- ข้อมูลที่รวบรวมในคลังข้อความนี้เป็นสาธารณสมบัติ (public domain) ตามพ.ร.บ.ลิขสิทธิ์ พ.ศ. 2537 มาตรา 7 (สิ่งต่อไปนี้ไม่ถือว่าเป็นงานอันมีลิขสิทธิ์ตามพระราชบัญญัตินี้ (1) ข่าวประจำวัน และข้อเท็จจริงต่างๆ ที่มีลักษณะเป็นเพียงข่าวสารอันมิใช่งานในแผนกวรรณคดี แผนกวิทยาศาสตร์ หรือแผนกศิลปะ [...] (3) ระเบียบ ข้อบังคับ ประกาศ คำสั่ง คำชี้แจง และหนังสือตอบโต้ของกระทรวง ทบวง กรม หรือหน่วยงานอื่นใดของรัฐหรือของท้องถิ่น [...])\n\nสามารถติดตามประวัติการแก้ไขคลังข้อความนี้ได้ผ่านระบบ Git### จำนวนข่าว\n\n- วันเริ่มต้นโครงการ 17 ก.ย. 2563### รูปแบบข้อมูล\n\n- 1 ไฟล์ 1 ข่าว ซึ่งดึงมาจาก 1 url### รายละเอียดชื่อไฟล์\n\n- ชื่อหมวดหมู่_จำนวนที่ของข่าว.txt### Script\n\n- URL สำหรับเก็บข้อมูลจากหน้าเว็บ โดยจะดึงหน้าเว็บจาก url โดยที่ NNN คือเลขจำนวนเต็ม\n - เปลี่ยนค่าตัวแปร i ในไฟล์เป็นเลขที่ต้องการเริ่มเก็บ\n- URL สำหรับทำความสะอาดข้อมูลเบื้องต้น โดยจะลบช่องว่างหน้าและท้ายบรรทัด ลบบรรทัดว่าง\n - \n - \n - \n\n\n\nWe build Thai NLP.\n\nPyThaiNLP"
]
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|
29cd2c829dd10454e42c0f1fcfc9ef18160891c7 |
Dataset generated using handwritten fonts
=========================================
Number of images: 300000
Sources:
* [Handwriting generation code](https://github.com/NastyBoget/HandwritingGeneration)
The code was executed with `cyrillic` option (more augmentations) | nastyboget/synthetic_cyrillic | [
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-22T08:18:18+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"]} | 2023-03-23T18:45:21+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us
|
Dataset generated using handwritten fonts
=========================================
Number of images: 300000
Sources:
* Handwriting generation code
The code was executed with 'cyrillic' option (more augmentations) | []
| [
"TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
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|
1c24f619ae44d0a4c87fde72efa741dc25f7530e |
### Dataset Summary
This dataset is based on [CloverSearch/cc-news-mutlilingual](https://huggingface.co/datasets/CloverSearch/cc-news-mutlilingual).
We add a script to support access multilingual CC-News dataset with HuggingFace datasets API instead of directly downloading raw data files.
### Data Fields
- `title`: a `string` feature.
- `maintext`: a `string` feature.
- `url`: a `string` feature.
- `date_publish`: a `string` feature.
### How to use this dataset
You can load any subset of CC-News per language:
```python
from datasets import load_dataset
dataset = load_dataset("intfloat/multilingual_cc_news", languages=["af"])
```
## Supported Languages
```
af
als
am
an
ar
arz
as
ast
av
az
azb
ba
bar
bcl
be
bg
bh
bn
bo
bpy
br
bs
bxr
ca
cbk
ce
ceb
ckb
co
cs
cv
cy
da
de
diq
dsb
dty
dv
el
eml
en
eo
es
et
eu
fa
fi
fr
fy
ga
gd
gl
gn
gom
gu
gv
he
hi
hif
hr
hsb
ht
hu
hy
ia
id
ie
ilo
io
is
it
ja
jbo
jv
ka
kk
km
kn
ko
krc
ku
kv
kw
ky
la
lb
lez
li
lmo
lo
lt
lv
mai
mg
mhr
min
mk
ml
mn
mr
mrj
ms
mt
mwl
my
myv
mzn
nah
nap
nds
ne
new
nl
nn
no
oc
or
os
pa
pam
pfl
pl
pms
pnb
ps
pt
qu
rm
ro
ru
sa
sah
sc
scn
sco
sd
sh
si
sk
sl
so
sq
sr
su
sv
sw
ta
te
tg
th
tk
tl
tr
tt
tyv
ug
uk
ur
uz
vec
vep
vi
vls
vo
wa
war
wuu
xal
xmf
yi
yo
yue
zh
```
| intfloat/multilingual_cc_news | [
"size_categories:100M<n<1B",
"language:en",
"language:zh",
"language:fr",
"language:de",
"language:af",
"language:ar",
"region:us"
]
| 2023-03-22T08:25:34+00:00 | {"language": ["en", "zh", "fr", "de", "af", "ar"], "size_categories": ["100M<n<1B"]} | 2023-04-23T07:19:06+00:00 | []
| [
"en",
"zh",
"fr",
"de",
"af",
"ar"
]
| TAGS
#size_categories-100M<n<1B #language-English #language-Chinese #language-French #language-German #language-Afrikaans #language-Arabic #region-us
|
### Dataset Summary
This dataset is based on CloverSearch/cc-news-mutlilingual.
We add a script to support access multilingual CC-News dataset with HuggingFace datasets API instead of directly downloading raw data files.
### Data Fields
- 'title': a 'string' feature.
- 'maintext': a 'string' feature.
- 'url': a 'string' feature.
- 'date_publish': a 'string' feature.
### How to use this dataset
You can load any subset of CC-News per language:
## Supported Languages
| [
"### Dataset Summary\n\nThis dataset is based on CloverSearch/cc-news-mutlilingual.\n\nWe add a script to support access multilingual CC-News dataset with HuggingFace datasets API instead of directly downloading raw data files.",
"### Data Fields\n\n- 'title': a 'string' feature.\n- 'maintext': a 'string' feature.\n- 'url': a 'string' feature.\n- 'date_publish': a 'string' feature.",
"### How to use this dataset\n\nYou can load any subset of CC-News per language:",
"## Supported Languages"
]
| [
"TAGS\n#size_categories-100M<n<1B #language-English #language-Chinese #language-French #language-German #language-Afrikaans #language-Arabic #region-us \n",
"### Dataset Summary\n\nThis dataset is based on CloverSearch/cc-news-mutlilingual.\n\nWe add a script to support access multilingual CC-News dataset with HuggingFace datasets API instead of directly downloading raw data files.",
"### Data Fields\n\n- 'title': a 'string' feature.\n- 'maintext': a 'string' feature.\n- 'url': a 'string' feature.\n- 'date_publish': a 'string' feature.",
"### How to use this dataset\n\nYou can load any subset of CC-News per language:",
"## Supported Languages"
]
| [
47,
58,
52,
21,
5
]
| [
"passage: TAGS\n#size_categories-100M<n<1B #language-English #language-Chinese #language-French #language-German #language-Afrikaans #language-Arabic #region-us \n### Dataset Summary\n\nThis dataset is based on CloverSearch/cc-news-mutlilingual.\n\nWe add a script to support access multilingual CC-News dataset with HuggingFace datasets API instead of directly downloading raw data files.### Data Fields\n\n- 'title': a 'string' feature.\n- 'maintext': a 'string' feature.\n- 'url': a 'string' feature.\n- 'date_publish': a 'string' feature.### How to use this dataset\n\nYou can load any subset of CC-News per language:## Supported Languages"
]
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|
6db8d09e3edeaf2f99dd075a31c3788461adeb59 | # Dataset Card for "thaigov-v2-corpus-22032023-oa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pythainlp/thaigov-v2-corpus-22032023-oa | [
"region:us"
]
| 2023-03-22T08:33:37+00:00 | {"dataset_info": {"features": [{"name": "TEXT", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}, {"name": "url", "struct": [{"name": "url", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 241455880, "num_examples": 30380}], "download_size": 81088077, "dataset_size": 241455880}} | 2023-03-22T08:35:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "thaigov-v2-corpus-22032023-oa"
More Information needed | [
"# Dataset Card for \"thaigov-v2-corpus-22032023-oa\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"thaigov-v2-corpus-22032023-oa\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"thaigov-v2-corpus-22032023-oa\"\n\nMore Information needed"
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|
fa09fc9938f43a55c522def5a0ec83ffaa10052f |
# 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
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | albertvillanova/test | [
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:pl",
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| 2023-03-22T08:42:24+00:00 | {"annotations_creators": ["expert-generated"], "language": ["pl"], "license": ["mit"], "multilinguality": ["monolingual"], "dataset_info": [{"config_name": "config", "features": [{"name": "audio_id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}]}]} | 2023-03-21T13:27:40+00:00 | []
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- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @github-username for adding this dataset. | [
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|
4a01b0a31b572eada953700280cd53250187de59 |
[행정 문서 대상 기계독해 데이터](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=569) | wisenut-nlp-team/aihub_mrc_admin | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10M<n<100M",
"source_datasets:original",
"license:cc-by-4.0",
"mrc",
"region:us"
]
| 2023-03-22T09:04:56+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": [], "license": ["cc-by-4.0"], "multilinguality": [], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa", "closed-domain-qa"], "pretty_name": "wisenut-nlp-team/aihub_mrc_admin", "dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "doc_title", "dtype": "string"}, {"name": "doc_source", "dtype": "string"}, {"name": "doc_published", "dtype": "int64"}, {"name": "doc_class", "struct": [{"name": "class", "dtype": "string"}, {"name": "code", "dtype": "string"}]}, {"name": "created", "dtype": "string"}, {"name": "qa_type", "dtype": "int64"}, {"name": "question_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "is_impossible", "dtype": "bool"}, {"name": "answers", "struct": [{"name": "answer_start", "dtype": "int64"}, {"name": "clue_start", "dtype": "int64"}, {"name": "clue_text", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 646034916, "num_examples": 329464}, {"name": "validation", "num_bytes": 80855200, "num_examples": 41182}], "download_size": 243811004, "dataset_size": 726890116}, "tags": ["mrc"]} | 2023-05-23T23:08:00+00:00 | []
| []
| TAGS
#task_categories-question-answering #task_ids-extractive-qa #task_ids-closed-domain-qa #annotations_creators-crowdsourced #language_creators-found #size_categories-10M<n<100M #source_datasets-original #license-cc-by-4.0 #mrc #region-us
|
행정 문서 대상 기계독해 데이터 | []
| [
"TAGS\n#task_categories-question-answering #task_ids-extractive-qa #task_ids-closed-domain-qa #annotations_creators-crowdsourced #language_creators-found #size_categories-10M<n<100M #source_datasets-original #license-cc-by-4.0 #mrc #region-us \n"
]
| [
95
]
| [
"passage: TAGS\n#task_categories-question-answering #task_ids-extractive-qa #task_ids-closed-domain-qa #annotations_creators-crowdsourced #language_creators-found #size_categories-10M<n<100M #source_datasets-original #license-cc-by-4.0 #mrc #region-us \n"
]
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|
1eca1fa57e387473361ebe828698fd93ebc06b18 | # Dataset Card for "instructions-id-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-id-small | [
"region:us"
]
| 2023-03-22T09:28:19+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48844.8, "num_examples": 90}, {"name": "test", "num_bytes": 2713.6, "num_examples": 5}, {"name": "validation", "num_bytes": 2713.6, "num_examples": 5}], "download_size": 36845, "dataset_size": 54272.0}} | 2023-03-22T09:28:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-id-small"
More Information needed | [
"# Dataset Card for \"instructions-id-small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-id-small\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-id-small\"\n\nMore Information needed"
]
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|
a7ff39d2bccb3d0b41f9e15d2e670c7db5790147 | # Dataset Card for "instructions-id"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-id | [
"region:us"
]
| 2023-03-22T09:30:59+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35749284.66851785, "num_examples": 85242}, {"name": "test", "num_bytes": 1986211.1657410732, "num_examples": 4736}, {"name": "validation", "num_bytes": 1986211.1657410732, "num_examples": 4736}], "download_size": 21158281, "dataset_size": 39721706.99999999}} | 2023-03-22T12:47:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-id"
More Information needed | [
"# Dataset Card for \"instructions-id\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-id\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-id\"\n\nMore Information needed"
]
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|
d74da450df1b05fac0440394ac17189c4c5519e0 |
Dataset collected as part of work done for Alvarado Lab Queens College, CUNY (https://www.alvaradolab.com/)
Github: https://www.github.com/QCAlvaradoLab/Color-Study | hans0812/fish-composite-segmentation | [
"license:afl-3.0",
"region:us"
]
| 2023-03-22T09:31:33+00:00 | {"license": "afl-3.0"} | 2023-03-25T11:12:23+00:00 | []
| []
| TAGS
#license-afl-3.0 #region-us
|
Dataset collected as part of work done for Alvarado Lab Queens College, CUNY (URL
Github: URL | []
| [
"TAGS\n#license-afl-3.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-afl-3.0 #region-us \n"
]
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|
9ca3a2b38dd1dd14739a5144b225df0344f1460d | ---
# Bigcode PII Training Dataset
## Dataset Description
This is the dataset used for the training of [bigcode-pii-model](https://huggingface.co/bigcode/bigcode-pii-model) (after training on pseudo-labeled data).
It is a concatenation of an early version of [bigcode-pii-dataset](https://huggingface.co/datasets/bigcode/bigcode-pii-dataset) which had less samples, and [pii-for-code](https://huggingface.co/datasets/bigcode/pii-for-code-v2)
(a dataset with 400 files we annotated in a previous iteration: MORE INFO TO BE ADDED).
Files with `AMBIGUOUS` and `ID` were excluded. Each PII subtype was remaped to it supertype.
## Statistics
The dataset consists of **11878** files in 31 programming languages. More statistics and information about the original annotated dataset can be found at the dataset card of: [bigcode-pii-dataset](https://huggingface.co/datasets/bigcode/bigcode-pii-dataset).
We provide the training and test splits we used for the training and evaluation of the [bigcode-pii-model](https://huggingface.co/bigcode/bigcode-pii-model).
Below is the distribution of PII entoties in each split.
| Entity type | Train | Validation |
|--------------|-------|------------|
| EMAIL | 4721 | 1742 |
| NAME | 3847 | 1298 |
| IP_ADDRESS | 1941 | 521 |
| USERNAME | 1320 | 346 |
| PASSWORD | 390 | 148 |
| KEY | 171 | 118 |
# How to use
```python
from datasets import load_dataset
ds = load_dataset("bigcode/bigcode-pii-dataset-training")
```
```
DatasetDict({
train: Dataset({
features: ['id', 'content', 'language', 'pii', 'assignment_id'],
num_rows: 7878
})
validation: Dataset({
features: ['id', 'content', 'language', 'pii', 'assignment_id'],
num_rows: 4000
})
})
```
# Considerations for Using the Data
When using this dataset, please be mindful of the data governance risks that come with handling personally identifiable information (PII). Despite sourcing the data from open, permissive GitHub repositories and having it annotated by fairly paid crowd-workers, it does contain sensitive details such as names, usernames, keys, emails, passwords, and IP addresses. To ensure responsible use for research within the open-source community, access to the dataset will be provided through a gated mechanism.
We expect researchers and developers working with the dataset to adhere to the highest ethical standards and employ robust data protection measures.
To assist users in effectively detecting and masking PII, we've also released a PII model trained on this dataset.
Our goal in providing access to both the dataset and the PII model is to foster the development of privacy-preserving AI technologies while minimizing potential risks related to handling PII. | bigcode/bigcode-pii-dataset-training | [
"region:us"
]
| 2023-03-22T10:30:49+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "pii", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "assignment_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17215712, "num_examples": 7878}, {"name": "validation", "num_bytes": 7302111, "num_examples": 4000}], "download_size": 10754489, "dataset_size": 24517823}, "extra_gated_prompt": "## Terms of Use for the dataset\n\nThis is an annotated dataset for Personal Identifiable Information (PII) in code. We ask that you read and agree to the following Terms of Use before using the dataset and fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSfiWKyBB8-PxOCLo-KMsLlYNyQNJEzxJw0gcUAUHT3UY848qA/viewform):\n1. You agree that you will not use the PII dataset for any purpose other than training or evaluating models for PII removal from datasets.\n2. You agree that you will not share the PII dataset or any modified versions for whatever purpose.\n3. Unless required by applicable law or agreed to in writing, the dataset is provided on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using the dataset, and assume any risks associated with your exercise of permissions under these Terms of Use.\n4. 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 DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}} | 2023-05-11T11:46:10+00:00 | []
| []
| TAGS
#region-us
|
---
Bigcode PII Training Dataset
============================
Dataset Description
-------------------
This is the dataset used for the training of bigcode-pii-model (after training on pseudo-labeled data).
It is a concatenation of an early version of bigcode-pii-dataset which had less samples, and pii-for-code
(a dataset with 400 files we annotated in a previous iteration: MORE INFO TO BE ADDED).
Files with 'AMBIGUOUS' and 'ID' were excluded. Each PII subtype was remaped to it supertype.
Statistics
----------
The dataset consists of 11878 files in 31 programming languages. More statistics and information about the original annotated dataset can be found at the dataset card of: bigcode-pii-dataset.
We provide the training and test splits we used for the training and evaluation of the bigcode-pii-model.
Below is the distribution of PII entoties in each split.
Entity type: EMAIL, Train: 4721, Validation: 1742
Entity type: NAME, Train: 3847, Validation: 1298
Entity type: IP\_ADDRESS, Train: 1941, Validation: 521
Entity type: USERNAME, Train: 1320, Validation: 346
Entity type: PASSWORD, Train: 390, Validation: 148
Entity type: KEY, Train: 171, Validation: 118
How to use
==========
Considerations for Using the Data
=================================
When using this dataset, please be mindful of the data governance risks that come with handling personally identifiable information (PII). Despite sourcing the data from open, permissive GitHub repositories and having it annotated by fairly paid crowd-workers, it does contain sensitive details such as names, usernames, keys, emails, passwords, and IP addresses. To ensure responsible use for research within the open-source community, access to the dataset will be provided through a gated mechanism.
We expect researchers and developers working with the dataset to adhere to the highest ethical standards and employ robust data protection measures.
To assist users in effectively detecting and masking PII, we've also released a PII model trained on this dataset.
Our goal in providing access to both the dataset and the PII model is to foster the development of privacy-preserving AI technologies while minimizing potential risks related to handling PII.
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
b317a0dd49ee1830df790e3bf84d8e3fb9403e82 | # Dataset Card for "basemath"
The objective of minimath is to train the mathematical capability of language model in a diverse setting.
The dataset is composed of sampling from the below dataset:
https://huggingface.co/datasets/math_dataset
https://huggingface.co/datasets/math_qa
https://huggingface.co/datasets/competition_math
https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_math_jsonl
https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kenhktsui/basemath | [
"region:us"
]
| 2023-03-22T10:47:42+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "annotated_formula", "dtype": "string"}, {"name": "linear_formula", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57454541, "num_examples": 100000}], "download_size": 28978379, "dataset_size": 57454541}} | 2023-03-22T10:55:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "basemath"
The objective of minimath is to train the mathematical capability of language model in a diverse setting.
The dataset is composed of sampling from the below dataset:
URL
URL
URL
URL
URL
More Information needed | [
"# Dataset Card for \"basemath\"\n\nThe objective of minimath is to train the mathematical capability of language model in a diverse setting.\nThe dataset is composed of sampling from the below dataset:\nURL \nURL \nURL \nURL \nURL \n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"basemath\"\n\nThe objective of minimath is to train the mathematical capability of language model in a diverse setting.\nThe dataset is composed of sampling from the below dataset:\nURL \nURL \nURL \nURL \nURL \n\nMore Information needed"
]
| [
6,
55
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"basemath\"\n\nThe objective of minimath is to train the mathematical capability of language model in a diverse setting.\nThe dataset is composed of sampling from the below dataset:\nURL \nURL \nURL \nURL \nURL \n\nMore Information needed"
]
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|
f17105a540fde74ff651b6a0690a55ce1cbfcb31 |
Russian translations of Murakami novels, to fine-tune a generative language model. Originally downloaded from the FB2 archive http://flibusta.is/a/8570. | vldsavelyev/murakami | [
"region:us"
]
| 2023-03-22T10:55:51+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18352678, "num_examples": 699}, {"name": "test", "num_bytes": 48421, "num_examples": 1}], "download_size": 7244777, "dataset_size": 18401099}} | 2023-03-25T12:29:55+00:00 | []
| []
| TAGS
#region-us
|
Russian translations of Murakami novels, to fine-tune a generative language model. Originally downloaded from the FB2 archive URL | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
9a5fcecac061f17d9ed9e89d3cc7408537260833 | # Dataset Card for "instructions-all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-all | [
"region:us"
]
| 2023-03-22T12:03:36+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 11937376.0, "num_examples": 23011}, {"name": "test", "num_bytes": 11885818.0, "num_examples": 23027}, {"name": "train", "num_bytes": 392052453.0, "num_examples": 751788}], "download_size": 264089102, "dataset_size": 415875647.0}} | 2023-03-22T16:00:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-all"
More Information needed | [
"# Dataset Card for \"instructions-all\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-all\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-all\"\n\nMore Information needed"
]
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|
f7038d331f00bf59950361d415e2c12e1216f613 | # Dataset Card for "disc_cla_primera-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_primera-2 | [
"region:us"
]
| 2023-03-22T13:20:29+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "comision", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 32328969, "num_examples": 11713}], "download_size": 16700030, "dataset_size": 32328969}} | 2023-03-22T13:20:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_primera-2"
More Information needed | [
"# Dataset Card for \"disc_cla_primera-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_primera-2\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_primera-2\"\n\nMore Information needed"
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|
5755c44709eb0008f6e85461dd3bb2a1641c4739 | # Dataset Card for "disc_cla_segunda-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_segunda-2 | [
"region:us"
]
| 2023-03-22T13:20:54+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "comision", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 16832400, "num_examples": 7327}], "download_size": 8416037, "dataset_size": 16832400}} | 2023-03-22T13:20:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_segunda-2"
More Information needed | [
"# Dataset Card for \"disc_cla_segunda-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_segunda-2\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_segunda-2\"\n\nMore Information needed"
]
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|
9d0aa7abc72e9a1d997ca3bc0158fbe9c44cb2ed | # Dataset Card for "disc_cla_tercera-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_tercera-2 | [
"region:us"
]
| 2023-03-22T13:21:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "comision", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 8845905, "num_examples": 4913}], "download_size": 4405442, "dataset_size": 8845905}} | 2023-03-22T13:21:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_tercera-2"
More Information needed | [
"# Dataset Card for \"disc_cla_tercera-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_tercera-2\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_tercera-2\"\n\nMore Information needed"
]
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|
21577af3e9b8cb7cdc906e1ee872749de9b9d08c | # Dataset Card for "disc_cla_-cuarta-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_cuarta-2 | [
"region:us"
]
| 2023-03-22T13:29:14+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "comision", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 8052385, "num_examples": 3349}], "download_size": 4065041, "dataset_size": 8052385}} | 2023-03-22T13:29:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_-cuarta-2"
More Information needed | [
"# Dataset Card for \"disc_cla_-cuarta-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_-cuarta-2\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_-cuarta-2\"\n\nMore Information needed"
]
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|
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