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Jiabin99/graph-matching | 2023-10-30T12:23:09.000Z | [
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] |
jxu124/refcoco-benchmark | 2023-10-30T13:15:05.000Z | [
"region:us"
] | jxu124 | null | null | 0 | 0 | 2023-10-30T13:04:52 | ---
configs:
- config_name: default
data_files:
- split: refcoco_unc_val
path: data/refcoco_unc_val-*
- split: refcoco_unc_testA
path: data/refcoco_unc_testA-*
- split: refcoco_unc_testB
path: data/refcoco_unc_testB-*
- split: refcoco_google_val
path: data/refcoco_google_val-*
- split: refcoco_google_test
path: data/refcoco_google_test-*
- split: refcocog_umd_val
path: data/refcocog_umd_val-*
- split: refcocog_umd_test
path: data/refcocog_umd_test-*
- split: refcocog_google_val
path: data/refcocog_google_val-*
- split: refcoco_plus_unc_val
path: data/refcoco_plus_unc_val-*
- split: refcoco_plus_unc_testA
path: data/refcoco_plus_unc_testA-*
- split: refcoco_plus_unc_testB
path: data/refcoco_plus_unc_testB-*
dataset_info:
features:
- name: ref_list
list:
- name: ann_info
struct:
- name: area
dtype: float64
- name: bbox
sequence: float64
- name: category_id
dtype: int64
- name: id
dtype: int64
- name: image_id
dtype: int64
- name: iscrowd
dtype: int64
- name: segmentation
sequence:
sequence: float64
- name: ref_info
struct:
- name: ann_id
dtype: int64
- name: category_id
dtype: int64
- name: file_name
dtype: string
- name: image_id
dtype: int64
- name: ref_id
dtype: int64
- name: sent_ids
sequence: int64
- name: sentences
list:
- name: raw
dtype: string
- name: sent
dtype: string
- name: sent_id
dtype: int64
- name: tokens
sequence: string
- name: split
dtype: string
- name: image_info
struct:
- name: coco_url
dtype: string
- name: date_captured
dtype: string
- name: file_name
dtype: string
- name: flickr_url
dtype: string
- name: height
dtype: int64
- name: id
dtype: int64
- name: license
dtype: int64
- name: width
dtype: int64
- name: image
dtype: image
splits:
- name: refcoco_unc_val
num_bytes: 264438667.5
num_examples: 1500
- name: refcoco_unc_testA
num_bytes: 129028843.0
num_examples: 750
- name: refcoco_unc_testB
num_bytes: 133102482.0
num_examples: 750
- name: refcoco_google_val
num_bytes: 814855470.214
num_examples: 4559
- name: refcoco_google_test
num_bytes: 800980159.978
num_examples: 4527
- name: refcocog_umd_val
num_bytes: 220021282.2
num_examples: 1300
- name: refcocog_umd_test
num_bytes: 442746080.0
num_examples: 2600
- name: refcocog_google_val
num_bytes: 800691386.6
num_examples: 4650
- name: refcoco_plus_unc_val
num_bytes: 264451297.5
num_examples: 1500
- name: refcoco_plus_unc_testA
num_bytes: 129035632.0
num_examples: 750
- name: refcoco_plus_unc_testB
num_bytes: 133095545.0
num_examples: 750
download_size: 4072689321
dataset_size: 4132446845.9919996
---
# Dataset Card for "refcoco-benchmark"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 3,282 | [
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gabrielmbmb/test-dataset | 2023-10-30T13:08:07.000Z | [
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | gabrielmbmb | null | null | 0 | 0 | 2023-10-30T13:08:05 | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for test-dataset
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("gabrielmbmb/test-dataset")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("gabrielmbmb/test-dataset")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text-field | Text-field | text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| text-question | Text-question | text | True | N/A | N/A |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
**✨ NEW** The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"text-field": "blabla"
},
"metadata": {},
"responses": [],
"suggestions": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"metadata": "{}",
"text-field": "blabla",
"text-question": [],
"text-question-suggestion": null,
"text-question-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **text-field** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **text-question** is of type `text`.
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **text-question-suggestion** is of type `text`.
Additionally, we also have two more fields that are optional and are the following:
* **✨ NEW** **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,927 | [
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] |
jxu124/refclef-benchmark | 2023-10-30T13:28:06.000Z | [
"region:us"
] | jxu124 | null | null | 0 | 0 | 2023-10-30T13:24:55 | ---
configs:
- config_name: default
data_files:
- split: refclef_unc_val
path: data/refclef_unc_val-*
- split: refclef_unc_testA
path: data/refclef_unc_testA-*
- split: refclef_unc_testB
path: data/refclef_unc_testB-*
- split: refclef_unc_testC
path: data/refclef_unc_testC-*
- split: refclef_berkeley_val
path: data/refclef_berkeley_val-*
- split: refclef_berkeley_test
path: data/refclef_berkeley_test-*
dataset_info:
features:
- name: ref_list
list:
- name: ann_info
struct:
- name: area
dtype: int64
- name: bbox
sequence: float64
- name: category_id
dtype: int64
- name: id
dtype: string
- name: image_id
dtype: int64
- name: mask_name
dtype: string
- name: segmentation
list:
- name: counts
dtype: string
- name: size
sequence: int64
- name: ref_info
struct:
- name: ann_id
dtype: string
- name: category_id
dtype: int64
- name: image_id
dtype: int64
- name: ref_id
dtype: int64
- name: sent_ids
sequence: int64
- name: sentences
list:
- name: raw
dtype: string
- name: sent
dtype: string
- name: sent_id
dtype: int64
- name: tokens
sequence: string
- name: split
dtype: string
- name: image_info
struct:
- name: file_name
dtype: string
- name: height
dtype: int64
- name: id
dtype: int64
- name: width
dtype: int64
- name: image
dtype: image
splits:
- name: refclef_unc_val
num_bytes: 176315268.0
num_examples: 2000
- name: refclef_unc_testA
num_bytes: 38748729.0
num_examples: 485
- name: refclef_unc_testB
num_bytes: 41495038.0
num_examples: 490
- name: refclef_unc_testC
num_bytes: 37159288.0
num_examples: 465
- name: refclef_berkeley_val
num_bytes: 90320401.0
num_examples: 1000
- name: refclef_berkeley_test
num_bytes: 889898825.642
num_examples: 9999
download_size: 1256485050
dataset_size: 1273937549.642
---
# Dataset Card for "refclef-benchmark"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 2,383 | [
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ysmail31/kheops_code_civil_dataset_v1.0 | 2023-10-30T13:29:54.000Z | [
"region:us"
] | ysmail31 | null | null | 0 | 0 | 2023-10-30T13:27:00 | Entry not found | 15 | [
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Neubegg/Files | 2023-10-30T14:47:31.000Z | [
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MichaelVeser/opensecurity-axolotl | 2023-10-30T14:19:23.000Z | [
"region:us"
] | MichaelVeser | null | null | 0 | 0 | 2023-10-30T14:19:10 | Entry not found | 15 | [
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varuntrivedi/trial_dataset | 2023-10-30T14:28:01.000Z | [
"region:us"
] | varuntrivedi | null | null | 0 | 0 | 2023-10-30T14:26:26 | [
{
"id": "ner_gpt_0",
"conversations": [
[
{ "from": "human",
"value": "Text: The impact of climate change on marine ecosystems and biodiversity is a pressing concern. Rising sea temperatures, ocean acidification, and changing currents have significant effects on marine life. Researchers are studying the adaptation mechanisms of various species to these environmental changes." },
{ "from": "gpt", "value": "I've read this text." },
{ "from": "human", "value": "What describes the main focus of the text?" },
{ "from": "gpt", "value": "[\"impact of climate change on marine ecosystems and biodiversity\"]" },
{ "from": "human", "value": "What describes environmental factors in the text?" },
{ "from": "gpt", "value": "[\"rising sea temperatures\", \"ocean acidification\", \"changing currents\"]" },
{ "from": "human", "value": "What describes the research subject in the text?" },
{ "from": "gpt", "value": "[\"adaptation mechanisms of various species\"]" },
{ "from": "human", "value": "What describes the time frame in the text?" },
{ "from": "gpt", "value": "[]" }
]
]
}
] | 1,198 | [
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Buggy23/colegio | 2023-10-30T14:40:27.000Z | [
"region:us"
] | Buggy23 | null | null | 0 | 0 | 2023-10-30T14:37:44 | Entry not found | 15 | [
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Almost-AGI-Diffusion/kand2 | 2023-10-30T14:49:24.000Z | [
"region:us"
] | Almost-AGI-Diffusion | null | null | 0 | 0 | 2023-10-30T14:42:57 | ---
dataset_info:
features:
- name: Prompt
dtype: string
- name: Category
dtype: string
- name: Challenge
dtype: string
- name: Note
dtype: string
- name: images
dtype: image
- name: model_name
dtype: string
- name: seed
dtype: int64
- name: upvotes
dtype: int64
splits:
- name: train
num_bytes: 21708501.0
num_examples: 219
download_size: 21693707
dataset_size: 21708501.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Kandinksy 2.2
All images included in this dataset were voted as "Not solved" by the community in https://huggingface.co/spaces/OpenGenAI/open-parti-prompts. This means that according to the community the model did not generate an image that corresponds sufficiently enough to the prompt.
The following script was used to generate the images:
```py
import PIL
import torch
from datasets import Dataset, Features
from datasets import Image as ImageFeature
from datasets import Value, load_dataset
from diffusers import DiffusionPipeline
def main():
print("Loading dataset...")
parti_prompts = load_dataset("nateraw/parti-prompts", split="train")
print("Loading pipeline...")
pipe_prior = DiffusionPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
)
pipe_prior.to("cuda")
pipe_prior.set_progress_bar_config(disable=True)
t2i_pipe = DiffusionPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
)
t2i_pipe.to("cuda")
t2i_pipe.set_progress_bar_config(disable=True)
seed = 0
generator = torch.Generator("cuda").manual_seed(seed)
ckpt_id = (
"kandinsky-community/" + "kandinsky-2-2-prior" + "_" + "kandinsky-2-2-decoder"
)
print("Running inference...")
main_dict = {}
for i in range(len(parti_prompts)):
sample = parti_prompts[i]
prompt = sample["Prompt"]
image_embeds, negative_image_embeds = pipe_prior(
prompt,
generator=generator,
num_inference_steps=100,
guidance_scale=7.5,
).to_tuple()
image = t2i_pipe(
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
generator=generator,
num_inference_steps=100,
guidance_scale=7.5,
).images[0]
image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS)
img_path = f"kandinsky_22_{i}.png"
image.save(img_path)
main_dict.update(
{
prompt: {
"img_path": img_path,
"Category": sample["Category"],
"Challenge": sample["Challenge"],
"Note": sample["Note"],
"model_name": ckpt_id,
"seed": seed,
}
}
)
def generation_fn():
for prompt in main_dict:
prompt_entry = main_dict[prompt]
yield {
"Prompt": prompt,
"Category": prompt_entry["Category"],
"Challenge": prompt_entry["Challenge"],
"Note": prompt_entry["Note"],
"images": {"path": prompt_entry["img_path"]},
"model_name": prompt_entry["model_name"],
"seed": prompt_entry["seed"],
}
print("Preparing HF dataset...")
ds = Dataset.from_generator(
generation_fn,
features=Features(
Prompt=Value("string"),
Category=Value("string"),
Challenge=Value("string"),
Note=Value("string"),
images=ImageFeature(),
model_name=Value("string"),
seed=Value("int64"),
),
)
ds_id = "diffusers-parti-prompts/kandinsky-2-2"
ds.push_to_hub(ds_id)
if __name__ == "__main__":
main()
``` | 3,977 | [
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] |
Almost-AGI-Diffusion/sdxl | 2023-10-30T14:46:58.000Z | [
"region:us"
] | Almost-AGI-Diffusion | null | null | 0 | 0 | 2023-10-30T14:43:04 | ---
dataset_info:
features:
- name: Prompt
dtype: string
- name: Category
dtype: string
- name: Challenge
dtype: string
- name: Note
dtype: string
- name: images
dtype: image
- name: model_name
dtype: string
- name: seed
dtype: int64
- name: upvotes
dtype: int64
splits:
- name: train
num_bytes: 25650684.0
num_examples: 219
download_size: 25640015
dataset_size: 25650684.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# SDXL
All images included in this dataset were voted as "Not solved" by the community in https://huggingface.co/spaces/OpenGenAI/open-parti-prompts.
This means that according to the community the model did not generate an image that corresponds sufficiently enough to the prompt.
The following script was used to generate the images:
```py
import torch
from datasets import Dataset, Features
from datasets import Image as ImageFeature
from datasets import Value, load_dataset
from diffusers import DDIMScheduler, DiffusionPipeline
import PIL
def main():
print("Loading dataset...")
parti_prompts = load_dataset("nateraw/parti-prompts", split="train")
print("Loading pipeline...")
ckpt_id = "stabilityai/stable-diffusion-xl-base-1.0"
refiner_ckpt_id = "stabilityai/stable-diffusion-xl-refiner-1.0"
pipe = DiffusionPipeline.from_pretrained(
ckpt_id, torch_dtype=torch.float16, use_auth_token=True
).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=True)
refiner = DiffusionPipeline.from_pretrained(
refiner_ckpt_id,
torch_dtype=torch.float16,
use_auth_token=True
).to("cuda")
refiner.scheduler = DDIMScheduler.from_config(refiner.scheduler.config)
refiner.set_progress_bar_config(disable=True)
seed = 0
generator = torch.Generator("cuda").manual_seed(seed)
print("Running inference...")
main_dict = {}
for i in range(len(parti_prompts)):
sample = parti_prompts[i]
prompt = sample["Prompt"]
latent = pipe(
prompt,
generator=generator,
num_inference_steps=100,
guidance_scale=7.5,
output_type="latent",
).images[0]
image_refined = refiner(
prompt=prompt,
image=latent[None, :],
generator=generator,
num_inference_steps=100,
guidance_scale=7.5,
).images[0]
image = image_refined.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS)
img_path = f"sd_xl_{i}.png"
image.save(img_path)
main_dict.update(
{
prompt: {
"img_path": img_path,
"Category": sample["Category"],
"Challenge": sample["Challenge"],
"Note": sample["Note"],
"model_name": ckpt_id,
"seed": seed,
}
}
)
def generation_fn():
for prompt in main_dict:
prompt_entry = main_dict[prompt]
yield {
"Prompt": prompt,
"Category": prompt_entry["Category"],
"Challenge": prompt_entry["Challenge"],
"Note": prompt_entry["Note"],
"images": {"path": prompt_entry["img_path"]},
"model_name": prompt_entry["model_name"],
"seed": prompt_entry["seed"],
}
print("Preparing HF dataset...")
ds = Dataset.from_generator(
generation_fn,
features=Features(
Prompt=Value("string"),
Category=Value("string"),
Challenge=Value("string"),
Note=Value("string"),
images=ImageFeature(),
model_name=Value("string"),
seed=Value("int64"),
),
)
ds_id = "diffusers-parti-prompts/sdxl-1.0-refiner"
ds.push_to_hub(ds_id)
if __name__ == "__main__":
main()
``` | 4,072 | [
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] |
Almost-AGI-Diffusion/wuerst | 2023-10-30T14:50:04.000Z | [
"region:us"
] | Almost-AGI-Diffusion | null | null | 0 | 0 | 2023-10-30T14:43:10 | ---
dataset_info:
features:
- name: Prompt
dtype: string
- name: Category
dtype: string
- name: Challenge
dtype: string
- name: Note
dtype: string
- name: images
dtype: image
- name: model_name
dtype: string
- name: seed
dtype: int64
- name: upvotes
dtype: int64
splits:
- name: train
num_bytes: 19633368.0
num_examples: 219
download_size: 19625614
dataset_size: 19633368.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Wuerstchen
All images included in this dataset were voted as "Not solved" by the community in https://huggingface.co/spaces/OpenGenAI/open-parti-prompts. This means that according to the community the model did not generate an image that corresponds sufficiently enough to the prompt.
The following script was used to generate the images:
```py
import torch
from datasets import Dataset, Features
from datasets import Image as ImageFeature
from datasets import Value, load_dataset
from diffusers import AutoPipelineForText2Image
import PIL
def main():
print("Loading dataset...")
parti_prompts = load_dataset("nateraw/parti-prompts", split="train")
print("Loading pipeline...")
seed = 0
device = "cuda"
generator = torch.Generator(device).manual_seed(seed)
dtype = torch.float16
ckpt_id = "warp-diffusion/wuerstchen"
pipeline = AutoPipelineForText2Image.from_pretrained(
ckpt_id, torch_dtype=dtype
).to(device)
pipeline.prior_prior = torch.compile(pipeline.prior_prior, mode="reduce-overhead", fullgraph=True)
pipeline.decoder = torch.compile(pipeline.decoder, mode="reduce-overhead", fullgraph=True)
print("Running inference...")
main_dict = {}
for i in range(len(parti_prompts)):
sample = parti_prompts[i]
prompt = sample["Prompt"]
image = pipeline(
prompt=prompt,
height=1024,
width=1024,
prior_guidance_scale=4.0,
decoder_guidance_scale=0.0,
generator=generator,
).images[0]
image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS)
img_path = f"wuerstchen_{i}.png"
image.save(img_path)
main_dict.update(
{
prompt: {
"img_path": img_path,
"Category": sample["Category"],
"Challenge": sample["Challenge"],
"Note": sample["Note"],
"model_name": ckpt_id,
"seed": seed,
}
}
)
def generation_fn():
for prompt in main_dict:
prompt_entry = main_dict[prompt]
yield {
"Prompt": prompt,
"Category": prompt_entry["Category"],
"Challenge": prompt_entry["Challenge"],
"Note": prompt_entry["Note"],
"images": {"path": prompt_entry["img_path"]},
"model_name": prompt_entry["model_name"],
"seed": prompt_entry["seed"],
}
print("Preparing HF dataset...")
ds = Dataset.from_generator(
generation_fn,
features=Features(
Prompt=Value("string"),
Category=Value("string"),
Challenge=Value("string"),
Note=Value("string"),
images=ImageFeature(),
model_name=Value("string"),
seed=Value("int64"),
),
)
ds_id = "diffusers-parti-prompts/wuerstchen"
ds.push_to_hub(ds_id)
if __name__ == "__main__":
main()
``` | 3,630 | [
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] |
Almost-AGI-Diffusion/karlo | 2023-10-30T14:48:09.000Z | [
"region:us"
] | Almost-AGI-Diffusion | null | null | 0 | 0 | 2023-10-30T14:43:16 | ---
dataset_info:
features:
- name: Prompt
dtype: string
- name: Category
dtype: string
- name: Challenge
dtype: string
- name: Note
dtype: string
- name: images
dtype: image
- name: model_name
dtype: string
- name: seed
dtype: int64
- name: upvotes
dtype: int64
splits:
- name: train
num_bytes: 20834626.0
num_examples: 219
download_size: 20825015
dataset_size: 20834626.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Karlo
All images included in this dataset were voted as "Not solved" by the community in https://huggingface.co/spaces/OpenGenAI/open-parti-prompts.
This means that according to the community the model did not generate an image that corresponds sufficiently enough to the prompt.
The following script was used to generate the images:
```py
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16)
pipe.to("cuda")
prompt = "" # a parti prompt
generator = torch.Generator("cuda").manual_seed(0)
image = pipe(prompt, prior_num_inference_steps=50, decoder_num_inference_steps=100, generator=generator).images[0]
``` | 1,242 | [
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matheushmart/cantores | 2023-10-30T16:57:19.000Z | [
"region:us"
] | matheushmart | null | null | 0 | 0 | 2023-10-30T14:57:50 | Entry not found | 15 | [
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bordman/mihTest | 2023-11-02T09:57:06.000Z | [
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:de",
"language:en",
"language:nl",
"language:sl",
"license:apache-2.0",
"region:us"
] | bordman | null | null | 0 | 0 | 2023-10-30T15:00:28 | ---
license: apache-2.0
task_categories:
- text2text-generation
language:
- de
- en
- nl
- sl
pretty_name: invoice_test
size_categories:
- 10K<n<100K
---
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[More Information Needed] | 4,514 | [
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] |
Abdou/dz-sentiment-yt-comments | 2023-10-30T15:28:33.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ar",
"license:mit",
"region:us"
] | Abdou | null | null | 0 | 0 | 2023-10-30T15:07:21 | ---
license: mit
task_categories:
- text-classification
language:
- ar
size_categories:
- 10K<n<100K
---
# A Sentiment Analysis Dataset for the Algerian Dialect of Arabic
This dataset consists of 50,016 samples of comments extracted from Algerian YouTube channels. It is manually annotated with 3 classes (the `label` column) and is not balanced. Here are the number of rows of each class:
- 0 (Negative): **17,033 (34.06%)**
- 1 (Neutral): **11,136 (22.26%)**
- 2 (Positive): **21,847 (43.68%)** | 496 | [
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skalla/ff | 2023-10-30T15:32:47.000Z | [
"region:us"
] | skalla | null | null | 0 | 0 | 2023-10-30T15:32:47 | Entry not found | 15 | [
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] |
snyamson/covid-tweet-sentiment-analyzer-distilbert-data | 2023-10-30T15:42:25.000Z | [
"region:us"
] | snyamson | null | null | 0 | 0 | 2023-10-30T15:42:22 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 10366704
num_examples: 7999
- name: val
num_bytes: 2592000
num_examples: 2000
download_size: 514530
dataset_size: 12958704
---
# Dataset Card for "covid-tweet-sentiment-analyzer-distilbert-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 656 | [
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BubbleReturns2009/smash-bros-ai-models | 2023-10-30T16:01:02.000Z | [
"region:us"
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BubbleReturns2009/earthbound-ai-models | 2023-10-30T16:08:35.000Z | [
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newpcopera/publicimgs | 2023-11-02T21:26:57.000Z | [
"region:us"
] | newpcopera | null | null | 0 | 0 | 2023-10-30T16:19:49 | Entry not found | 15 | [
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kheopsai/mise_dem | 2023-10-30T16:59:52.000Z | [
"region:us"
] | kheopsai | null | null | 0 | 0 | 2023-10-30T16:59:09 | Entry not found | 15 | [
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] |
flytech/python-codes-25k | 2023-10-30T18:34:12.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:token-classification",
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"code",
"python",
"flytech",
"cleaned",
"instructional",
"dataset 25k",
"text2code",
"code2text",
"behavioral",
"codegeneration",
"region:us"
] | flytech | null | null | 0 | 0 | 2023-10-30T17:03:27 | ---
task_categories:
- text-classification
- question-answering
- token-classification
- summarization
- text2text-generation
- text-generation
tags:
- code
- python
- flytech
- cleaned
- instructional
- dataset 25k
- text2code
- code2text
- behavioral
- codegeneration
size_categories:
- 1M<n<10M
---
### <span style="color:#307090">License</span>
<span style="color:darkorange">MIT</span>
<hr style="height:1px;border:none;color:#333;background-color:#444;" />
## <span style="color:darkcyan">This is a Cleaned Python Dataset Covering 25,000 Instructional Tasks</span>
### <span style="color:#307090">Overview</span>
The dataset has 4 key features (fields): <b><span style="color:#205070">instruction</span></b>, <b><span style="color:#205070">input</span></b>, <b><span style="color:#205070">output</span></b>, and <b><span style="color:#205070">text</span></b>.
<span style="color:darkcyan">It's a rich source for Python codes, tasks, and extends into behavioral aspects.</span>
<hr style="height:1px;border:none;color:#333;background-color:#444;" />
### <span style="color:#307090">Dataset Statistics</span>
- **Total Entries**: <span style="color:darkmagenta">24,813</span>
- **Unique Instructions**: <span style="color:darkmagenta">24,580</span>
- **Unique Inputs**: <span style="color:darkmagenta">3,666</span>
- **Unique Outputs**: <span style="color:darkmagenta">24,581</span>
- **Unique Texts**: <span style="color:darkmagenta">24,813</span>
- **Average Tokens per example**: <span style="color:darkmagenta">508</span>
### <span style="color:#307090">Features</span>
- `instruction`: The instructional task to be performed / User input.
- `input`: Very short, introductive part of AI response or empty.
- `output`: Python code that accomplishes the task.
- `text`: All fields combined together.
<hr style="height:1px;border:none;color:#333;background-color:#444;" />
### <span style="color:#307090">Usage</span>
<span style="color:darkcyan">This dataset can be useful for:</span>
- <span style="color:#607090">Code generation tasks</span>
- <span style="color:#607090">Natural Language Understanding models specialized in coding languages</span>
- <span style="color:#607090">Behavioral analysis based on the given tasks and codes</span>
- <span style="color:#607090">Educational purposes to understand coding styles and task variations</span>
<span style="color:darkcyan">To load the dataset, one can use the following snippet:</span>
```python
from datasets import load_dataset
dataset = load_dataset('flytech/python-codes-25k', split='train')
# One can map the dataset in any way, for the sake of example:
dataset = dataset.map(lambda example: {'text': example['instruction'] + ' ' + example['input'] + ' ' + example['output']})['text']
```
### <span style="color:#307090">Access & Contributions</span>
<span style="color:cyan">Feel free to use this dataset as per the MIT license. Contributions to enhance or expand the dataset are welcome.</span> | 2,983 | [
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patrikwm/htrflow | 2023-10-30T17:04:26.000Z | [
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asoria/bluey | 2023-10-31T12:56:27.000Z | [
"region:us"
] | asoria | null | null | 0 | 0 | 2023-10-30T17:07:57 | Entry not found | 15 | [
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jxu124/refering_expression | 2023-10-31T09:15:19.000Z | [
"region:us"
] | jxu124 | null | null | 0 | 0 | 2023-10-30T17:14:51 | Entry not found | 15 | [
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fzliu/sift1m | 2023-10-30T17:49:42.000Z | [
"region:us"
] | fzliu | null | null | 0 | 0 | 2023-10-30T17:44:48 | Entry not found | 15 | [
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Shirogato/Mkl | 2023-10-31T04:52:03.000Z | [
"region:us"
] | Shirogato | null | null | 0 | 0 | 2023-10-30T18:24:55 | Entry not found | 15 | [
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alvations/units | 2023-10-30T18:50:33.000Z | [
"license:cc0-1.0",
"region:us"
] | alvations | null | null | 0 | 0 | 2023-10-30T18:46:02 | ---
license: cc0-1.0
---
This is a human translated from English list of units of measurements in multiple languages:
- Arabic
- Bengali
- Chinese (CN)
- Chinese (HK)
- Chinese (TW)
- Czech
- Dutch
- English
- French (CA)
- French (FR)
- German
- Hebrew
- Hindi
- Italian
- Japanese
- Korean
- Marathi
- Nepali
- Polish
- Portuguese (BR)
- Portuguese (PT)
- Russian
- Spanish (Latin America)
- Spanish (Mexico)
- Spanish (Spain)
- Swedish
- Turkish | 450 | [
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fort-cyber/CFV-Dataset | 2023-10-31T07:29:55.000Z | [
"size_categories:1B<n<10B",
"language:en",
"license:apache-2.0",
"region:us"
] | fort-cyber | null | null | 0 | 0 | 2023-10-30T19:43:00 | ---
language:
- en
pretty_name: CFV Dataset
size_categories:
- 1B<n<10B
license: apache-2.0
---
# CFV Dataset: Fine-Grained Predictions of Car Orientation from Images
### Description
- **Repository:** TBA
- **Paper:** TBA
### Dataset Summary
We present the CFV Dataset for estimating the car's orientation from images. Our dataset was obtained by recording cars while walking around them and annotating the frames with the pitch angle value in a semi-automatic manner. All images have the license plates anonymized.
### Data Instance
One instance contains the following keys:
- `image`: The RGB image displaying the car
- `identity`: Unique integer for each recorded car
- `angle`: An integer ranging between 0-360, indicating the pitch angle value of the car in the image (in degrees)
- `x1`, `y1`, `x2`, `y2`: the bounding box of the car
### Citation Information
If you utilize this dataset for any project or research, please cite our paper:
TBA | 953 | [
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] |
hippocrates/Dolly_train | 2023-10-30T20:00:39.000Z | [
"region:us"
] | hippocrates | null | null | 0 | 0 | 2023-10-30T20:00:37 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 25006952
num_examples: 15011
download_size: 12127483
dataset_size: 25006952
---
# Dataset Card for "Dolly_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 583 | [
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] |
hippocrates/Alpaca_train | 2023-10-30T20:08:27.000Z | [
"region:us"
] | hippocrates | null | null | 0 | 0 | 2023-10-30T20:08:25 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 44978419
num_examples: 52002
download_size: 16852893
dataset_size: 44978419
---
# Dataset Card for "Alpaca_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 584 | [
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] |
nespc/cnn_dailymail_prompts | 2023-10-30T20:11:05.000Z | [
"region:us"
] | nespc | null | null | 0 | 0 | 2023-10-30T20:10:23 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1354728397
num_examples: 287113
- name: test
num_bytes: 53648492
num_examples: 11490
download_size: 781011544
dataset_size: 1408376889
---
# Dataset Card for "cnn_dailymail_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 560 | [
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awcccc/hydr | 2023-10-30T20:13:48.000Z | [
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] | awcccc | null | null | 0 | 0 | 2023-10-30T20:13:48 | Entry not found | 15 | [
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KIKO0/X | 2023-10-30T20:20:05.000Z | [
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cjon9/repo_name | 2023-10-30T20:26:12.000Z | [
"region:us"
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tjb001/convokit-winning-arguments | 2023-10-30T20:28:13.000Z | [
"region:us"
] | tjb001 | null | null | 0 | 0 | 2023-10-30T20:28:13 | Entry not found | 15 | [
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Farsight-AI/test | 2023-11-02T21:48:14.000Z | [
"region:us"
] | Farsight-AI | null | null | 0 | 0 | 2023-10-30T20:47:04 | ---
dataset_info:
features:
- name: cik
dtype: int64
- name: context
dtype: string
- name: filingDate
dtype: timestamp[s]
- name: docID
dtype: string
- name: generatedQuestion
dtype: string
- name: trueAnswer
dtype: string
- name: falseAnswer
dtype: string
- name: model
dtype: string
splits:
- name: train
num_bytes: 15387
num_examples: 10
download_size: 23750
dataset_size: 15387
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 696 | [
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] |
mpingale/guanaco-llama2-1k | 2023-10-30T20:54:56.000Z | [
"region:us"
] | mpingale | null | null | 0 | 0 | 2023-10-30T20:54:55 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 444 | [
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EleutherAI/pile-standard-pythia-preshuffled | 2023-10-31T13:17:30.000Z | [
"region:us"
] | EleutherAI | null | null | 0 | 0 | 2023-10-30T21:30:17 | Entry not found | 15 | [
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antoniivanov/vdk-test | 2023-10-30T21:53:17.000Z | [
"region:us"
] | antoniivanov | null | null | 0 | 0 | 2023-10-30T21:53:17 | Entry not found | 15 | [
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antoniivanov/test | 2023-10-30T23:04:32.000Z | [
"region:us"
] | antoniivanov | null | null | 0 | 0 | 2023-10-30T21:59:25 | Entry not found | 15 | [
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cardiffnlp/tweet_generation | 2023-10-31T15:48:08.000Z | [
"region:us"
] | cardiffnlp | null | null | 0 | 0 | 2023-10-30T22:02:36 | Entry not found | 15 | [
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bvallegc/videos | 2023-10-30T22:30:16.000Z | [
"region:us"
] | bvallegc | null | null | 0 | 0 | 2023-10-30T22:27:08 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: video_data
dtype: binary
- name: duration_seconds
dtype: float64
- name: video_path
dtype: string
splits:
- name: train
num_bytes: 3786824395
num_examples: 4688
download_size: 3778922511
dataset_size: 3786824395
---
# Dataset Card for "videos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 534 | [
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] |
ContextualAI/nq_open_bge_neighbors | 2023-10-30T23:35:06.000Z | [
"region:us"
] | ContextualAI | null | null | 0 | 0 | 2023-10-30T23:25:21 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
sequence: string
- name: neighbor
dtype: string
splits:
- name: validation
num_bytes: 1883578
num_examples: 3610
download_size: 1346496
dataset_size: 1883578
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "nq_open_bge_neighbors"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 543 | [
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] |
ContextualAI/boolq_bge_neighbors | 2023-10-30T23:35:52.000Z | [
"region:us"
] | ContextualAI | null | null | 0 | 0 | 2023-10-30T23:26:20 | ---
dataset_info:
features:
- name: question
dtype: string
- name: passage
dtype: string
- name: idx
dtype: int32
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: neighbor
dtype: string
splits:
- name: validation
num_bytes: 3632916
num_examples: 3270
download_size: 2372841
dataset_size: 3632916
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "boolq_bge_neighbors"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 676 | [
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inrainbws/SHHQ-example-dataset | 2023-10-30T23:29:54.000Z | [
"region:us"
] | inrainbws | null | null | 0 | 0 | 2023-10-30T23:28:12 | Entry not found | 15 | [
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LuiMito/tx | 2023-10-30T23:42:20.000Z | [
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mcmanaman/test-dataset | 2023-10-30T23:43:49.000Z | [
"region:us"
] | mcmanaman | null | null | 0 | 0 | 2023-10-30T23:43:49 | Entry not found | 15 | [
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] |
ContextualAI/boolq_bge_neighbors_nprobe100 | 2023-10-30T23:50:41.000Z | [
"region:us"
] | ContextualAI | null | null | 0 | 0 | 2023-10-30T23:45:01 | ---
dataset_info:
features:
- name: question
dtype: string
- name: passage
dtype: string
- name: idx
dtype: int32
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: neighbor
dtype: string
splits:
- name: validation
num_bytes: 3632916
num_examples: 3270
download_size: 2372841
dataset_size: 3632916
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "boolq_bge_neighbors_nprobe100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 686 | [
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swcho3/long_eval_summ | 2023-10-30T23:53:27.000Z | [
"region:us"
] | swcho3 | null | null | 0 | 0 | 2023-10-30T23:53:27 | Entry not found | 15 | [
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ajanco/anc | 2023-10-31T00:55:50.000Z | [
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] | ajanco | null | null | 0 | 0 | 2023-10-31T00:47:57 | Entry not found | 15 | [
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LuiMito/gelorum | 2023-10-31T01:21:47.000Z | [
"region:us"
] | LuiMito | null | null | 0 | 0 | 2023-10-31T01:20:52 | Entry not found | 15 | [
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LuiMito/ge | 2023-10-31T01:40:47.000Z | [
"region:us"
] | LuiMito | null | null | 0 | 0 | 2023-10-31T01:40:05 | Entry not found | 15 | [
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zxcvbnm2/ShiggyImages | 2023-10-31T04:34:31.000Z | [
"license:mit",
"region:us"
] | zxcvbnm2 | null | null | 0 | 0 | 2023-10-31T01:55:04 | ---
license: mit
---
# Dataset containing images of Shiggy | 59 | [
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7eu7d7/vlm_data | 2023-11-03T01:09:34.000Z | [
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] |
Santp98/query_generated-title-secop2 | 2023-10-31T03:01:48.000Z | [
"region:us"
] | Santp98 | null | null | 0 | 0 | 2023-10-31T03:01:42 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: description
dtype: string
- name: generated_queries
dtype: string
- name: providers
dtype: int64
- name: publication_date
dtype: timestamp[ns]
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 265439941
num_examples: 939819
- name: test
num_bytes: 27534893
num_examples: 97595
download_size: 63049233
dataset_size: 292974834
---
# Dataset Card for "query_generated-title-secop2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 751 | [
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thomascuddihy/hrw_test_multiclass_flagged_data | 2023-10-31T03:39:36.000Z | [
"region:us"
] | thomascuddihy | null | null | 0 | 0 | 2023-10-31T03:06:32 | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
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[More Information Needed] | 1,416 | [
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] |
qgyd2021/sentence_pair | 2023-10-31T09:51:49.000Z | [
"task_categories:sentence-similarity",
"size_categories:100M<n<1B",
"language:zh",
"language:en",
"license:apache-2.0",
"region:us"
] | qgyd2021 | null | @dataset{sentence_pair,
author = {Xing Tian},
title = {sentence_pair},
month = sep,
year = 2023,
publisher = {Xing Tian},
version = {1.0},
} | 0 | 0 | 2023-10-31T03:26:45 | ---
license: apache-2.0
task_categories:
- sentence-similarity
language:
- zh
- en
size_categories:
- 100M<n<1B
---
## 句子对数据集
数据集从网上收集整理如下:
| 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| ChineseSTS | 汉语 | [ChineseSTS](https://github.com/IAdmireu/ChineseSTS) | 24.7K | STS 中文文本语义相似度（使用时注意打乱数据集） | [ChineseSTS](https://huggingface.co/datasets/tiansz/ChineseSTS) |
| ccks2018_task3 | 汉语 | [CCKS2018_3](https://www.biendata.xyz/competition/CCKS2018_3/data/) | 100K | CCKS 2018 微众银行智能客服问句匹配大赛 | |
| DIAC2019 | 汉语 | [DIAC2019](https://www.biendata.xyz/competition/2019diac/data/) | 6K | 以问题组的形式提供，每组问句又分为等价部分和不等价部分，等价问句之间互相组合可以生成正样本，等价问句和不等价问句之间互相组合可以生成负样本。我们提供6000组问句的训练集。 | |
| LCQMC | 汉语 | [LCQMC](https://www.luge.ai/#/luge/dataDetail?id=14); [C18-1166.pdf](https://aclanthology.org/C18-1166.pdf) | TRAIN: 238766, VALID: 8802, TEST: 12500 | 百度知道领域的中文问题匹配数据集，目的是为了解决在中文领域大规模问题匹配数据集的缺失。该数据集从百度知道不同领域的用户问题中抽取构建数据。| [lcqmc_data](https://github.com/xiaohai-AI/lcqmc_data) |
| AFQMC | 汉语 | [AFQMC](https://tianchi.aliyun.com/dataset/106411) | TRAIN: 34334, VALID: 4316, TEST: 3861 | 蚂蚁金融语义相似度数据集，用于问题相似度计算。即：给定客服里用户描述的两句话，用算法来判断是否表示了相同的语义。 | |
| BUSTM | 汉语 | [BUSTM](https://tianchi.aliyun.com/competition/entrance/531851/information); [BUSTM](https://github.com/xiaobu-coai/BUSTM) | 总样本数为：177173，其中，匹配样本个数为：54805，不匹配样本个数为：122368 | 小布助手对话短文本语义匹配比赛数据集 | [BUSTM](https://github.com/CLUEbenchmark/FewCLUE/tree/main/datasets/bustm) |
| CHIP2019 | 汉语 | [CHIP2019](https://www.biendata.xyz/competition/chip2019/) | 2万 | 平安医疗科技疾病问答迁移学习比赛数据集 | |
<details>
<summary>参考的数据来源,展开查看</summary>
<pre><code>
https://github.com/liucongg/NLPDataSet
https://huggingface.co/datasets/tiansz/ChineseSTS
https://zhuanlan.zhihu.com/p/454173790
</code></pre>
</details>
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TurcoLoko/Insania | 2023-10-31T03:30:55.000Z | [
"region:us"
] | TurcoLoko | null | null | 0 | 0 | 2023-10-31T03:30:18 | Entry not found | 15 | [
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] |
tyzhu/find_first_sent_train_30_eval_10_sentbefore | 2023-10-31T14:57:57.000Z | [
"region:us"
] | tyzhu | null | null | 0 | 0 | 2023-10-31T03:32:24 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 151115
num_examples: 110
- name: validation
num_bytes: 10621
num_examples: 10
download_size: 65086
dataset_size: 161736
---
# Dataset Card for "find_first_sent_train_30_eval_10_sentbefore"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 687 | [
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] |
kpriyanshu256/semeval-task-8-b | 2023-10-31T03:41:15.000Z | [
"region:us"
] | kpriyanshu256 | null | null | 0 | 0 | 2023-10-31T03:41:08 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: text
dtype: string
- name: model
dtype: string
- name: source
dtype: string
- name: label
dtype: int64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 151567991
num_examples: 71027
- name: dev
num_bytes: 4814312
num_examples: 3000
download_size: 84851066
dataset_size: 156382303
---
# Dataset Card for "semeval-task-8-b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 678 | [
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] |
kpriyanshu256/semeval-task-8-c | 2023-10-31T03:41:50.000Z | [
"region:us"
] | kpriyanshu256 | null | null | 0 | 0 | 2023-10-31T03:41:47 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 6125332
num_examples: 3649
- name: dev
num_bytes: 830346
num_examples: 505
download_size: 2838216
dataset_size: 6955678
---
# Dataset Card for "semeval-task-8-c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 602 | [
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whlzy/rubbish | 2023-10-31T04:50:05.000Z | [
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] |
tinhpx2911/wikipedia_20220620_filtered | 2023-10-31T04:46:52.000Z | [
"region:us"
] | tinhpx2911 | null | null | 0 | 0 | 2023-10-31T04:45:16 | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: timestamp
dtype: timestamp[s]
- name: revid
dtype: string
splits:
- name: train
num_bytes: 1202604321
num_examples: 693016
download_size: 575102780
dataset_size: 1202604321
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wikipedia_20220620_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 639 | [
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xDAN2099/xDAN-Voice-Genshin-zh-full | 2023-10-31T05:17:33.000Z | [
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unoooo/alpaca-korean | 2023-10-31T05:36:09.000Z | [
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] |
BangumiBase/kanojookarishimasu | 2023-10-31T08:55:42.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-31T05:36:24 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kanojo, Okarishimasu
This is the image base of bangumi Kanojo, Okarishimasu, we detected 44 characters, 6680 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1417 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 82 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 105 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 58 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 31 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 35 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 32 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 45 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 32 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 15 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 31 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 33 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 36 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 20 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 15 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 18 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 13 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 555 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 71 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 2254 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 20 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 33 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 148 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 31 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 121 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 92 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 88 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 74 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 34 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 14 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 72 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 318 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 16 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 20 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 8 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 264 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 8 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 21 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 11 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 7 | [Download](40/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 41 | 219 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 8 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 146 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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fishaudio/cn-hubert-25hz-vq | 2023-10-31T06:27:47.000Z | [
"region:us"
] | fishaudio | null | null | 0 | 0 | 2023-10-31T06:08:22 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 19256435269
num_examples: 12406672
- name: test
num_bytes: 167208
num_examples: 80
download_size: 3658804204
dataset_size: 19256602477
---
# Dataset Card for "cn-hubert-25hz-vq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 432 | [
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CognizantAI/RNART | 2023-10-31T06:08:31.000Z | [
"region:us"
] | CognizantAI | null | null | 0 | 0 | 2023-10-31T06:08:31 | Entry not found | 15 | [
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ugshanyu/Book-Updated | 2023-10-31T06:26:16.000Z | [
"region:us"
] | ugshanyu | null | null | 0 | 0 | 2023-10-31T06:26:16 | Entry not found | 15 | [
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eduardarbalencar/dataset_test | 2023-10-31T06:27:42.000Z | [
"region:us"
] | eduardarbalencar | null | null | 0 | 0 | 2023-10-31T06:27:42 | Entry not found | 15 | [
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eduardarbalencar/dataset_test1 | 2023-10-31T06:29:24.000Z | [
"region:us"
] | eduardarbalencar | null | null | 0 | 0 | 2023-10-31T06:29:24 | Entry not found | 15 | [
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freetorchflashlight/freetorchflashlight | 2023-10-31T06:38:57.000Z | [
"region:us"
] | freetorchflashlight | null | null | 1 | 0 | 2023-10-31T06:37:41 | In the world of tactical gear, precision, reliability, and durability are the cornerstones upon which the safety and success of professionals and enthusiasts rely. One company that stands as a beacon in this field is **[Torch Tactical](https://snoppymart.com/shop-torch-tactical)**. With a commitment to providing top-notch illumination solutions, they have earned a reputation for excellence that shines brighter than their products. This article delves into the world of **[Torch Tactical](https://snoppymart.com/shop-torch-tactical)**, illuminating its history, products, and why it stands out in the crowded market of tactical gear.
### [**Where To Can Buy Free Torch Flashlight \*\*OFFICIAL WEBSITE\*\***](https://snoppymart.com/shop-torch-tactical)
[](https://snoppymart.com/shop-torch-tactical)
### The Genesis of [Torch Tactical](https://www.facebook.com/people/Torch-Tactical/61552686852436/)
**[Torch Tactical](https://snoppymart.com/shop-torch-tactical)** was born from a simple yet profound idea: to create tactical lighting solutions that empower individuals in high-pressure situations. Founded by a group of dedicated and innovative minds with a deep understanding of the demands of tactical environments, the company set out on a mission to craft tools that would not just meet but exceed the expectations of military, law enforcement, and outdoor enthusiasts alike.
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LuiMito/kadim | 2023-10-31T06:45:39.000Z | [
"region:us"
] | LuiMito | null | null | 0 | 0 | 2023-10-31T06:45:22 | Entry not found | 15 | [
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LuiMito/gelorum2 | 2023-10-31T07:19:37.000Z | [
"region:us"
] | LuiMito | null | null | 0 | 0 | 2023-10-31T07:19:15 | Entry not found | 15 | [
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tasksource/SDOH-NLI | 2023-10-31T07:21:58.000Z | [
"task_categories:text-classification",
"language:en",
"license:cc-by-4.0",
"arxiv:2310.18431",
"region:us"
] | tasksource | null | null | 0 | 0 | 2023-10-31T07:20:13 | ---
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
---
SDOH-NLI is a natural language inference dataset containing ~30k premise-hypothesis pairs with binary entailment labels in the domain of social and behavioral determinants of health.
```
@misc{lelkes2023sdohnli,
title={SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes},
author={Adam D. Lelkes and Eric Loreaux and Tal Schuster and Ming-Jun Chen and Alvin Rajkomar},
year={2023},
eprint={2310.18431},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 604 | [
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] |
Deojoandco/capstone_fromgpt_without_gold_v0 | 2023-10-31T07:30:14.000Z | [
"region:us"
] | Deojoandco | null | null | 0 | 0 | 2023-10-31T07:30:10 | ---
dataset_info:
features:
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: gold_tags
dtype: string
- name: query
dtype: string
- name: gpt_success
dtype: bool
- name: gpt_response
dtype: string
- name: GPT_OUTPUT_FOUND
dtype: bool
- name: gpt_tags
dtype: string
- name: gold_tags_tokens_count
dtype: float64
- name: gpt_tags_tokens_count
dtype: float64
- name: summary_gpt_tags_token_count_match
dtype: bool
- name: gold_gpt_tags_match
dtype: bool
splits:
- name: train
num_bytes: 714337
num_examples: 100
download_size: 111760
dataset_size: 714337
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "capstone_fromgpt_without_gold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 934 | [
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EleutherAI/pile-deduped-pythia-preshuffled | 2023-10-31T13:16:14.000Z | [
"region:us"
] | EleutherAI | null | null | 0 | 0 | 2023-10-31T08:21:23 | Entry not found | 15 | [
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] |
MattiaBonfanti-CS/ML4SE23_G1_EvolInstruct-SCoT-1k | 2023-10-31T08:26:31.000Z | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"code",
"region:us"
] | MattiaBonfanti-CS | null | null | 0 | 0 | 2023-10-31T08:25:03 | ---
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: EvolInstruct enhanced 1k entries dataset with Structured-Chain-of-Thought
size_categories:
- 1K<n<10K
---
# EvolInstruct-SCoT-1k
EvolInstruct enhanced 1k entries dataset with Structured-Chain-of-Thought | 285 | [
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TonyisDead/SatoruGojo | 2023-10-31T09:55:28.000Z | [
"region:us"
] | TonyisDead | null | null | 0 | 0 | 2023-10-31T08:48:52 | Entry not found | 15 | [
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jon345t34/fujsdv | 2023-10-31T08:50:24.000Z | [
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round5534/werfwer | 2023-10-31T08:56:32.000Z | [
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] | round5534 | null | null | 0 | 0 | 2023-10-31T08:54:02 | Entry not found | 15 | [
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] |
mhmtcrkglu/autotrain-data-testtranslation | 2023-10-31T08:55:45.000Z | [
"task_categories:translation",
"language:tr",
"language:ar",
"region:us"
] | mhmtcrkglu | null | null | 0 | 0 | 2023-10-31T08:55:20 | ---
language:
- tr
- ar
task_categories:
- translation
---
# AutoTrain Dataset for project: testtranslation
## Dataset Description
This dataset has been automatically processed by AutoTrain for project testtranslation.
### Languages
The BCP-47 code for the dataset's language is tr2ar.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"source": "TrueMood",
"target": "\u062a\u0631\u0648\u0645\u0648\u062f"
},
{
"source": "cleanwax",
"target": "\u0643\u0644\u064a\u0646\u0648\u0627\u0643\u0633"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"source": "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 | 24 |
| valid | 6 |
| 1,006 | [
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] |
mhmtcrkglu/autotrain-data-test-translation-t5-small | 2023-10-31T09:10:19.000Z | [
"task_categories:translation",
"region:us"
] | mhmtcrkglu | null | null | 0 | 0 | 2023-10-31T08:58:53 | ---
task_categories:
- translation
---
# AutoTrain Dataset for project: test-translation-t5-small
## Dataset Description
This dataset has been automatically processed by AutoTrain for project test-translation-t5-small.
### 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
[
{
"source": "TrueMood",
"target": "\u062a\u0631\u0648\u0645\u0648\u062f"
},
{
"source": "cleanwax",
"target": "\u0643\u0644\u064a\u0646\u0648\u0627\u0643\u0633"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"source": "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 | 24 |
| valid | 6 |
| 1,004 | [
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Falah/architecture_prompts | 2023-10-31T09:04:05.000Z | [
"region:us"
] | Falah | null | null | 0 | 0 | 2023-10-31T09:04:03 | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 313206
num_examples: 1000
download_size: 42117
dataset_size: 313206
---
# Dataset Card for "architecture_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 361 | [
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chirunder/MSCS_40_page | 2023-10-31T09:23:47.000Z | [
"region:us"
] | chirunder | null | null | 0 | 0 | 2023-10-31T09:23:42 | ---
dataset_info:
features:
- name: html
dtype: string
splits:
- name: train
num_bytes: 6973933
num_examples: 40
download_size: 1637020
dataset_size: 6973933
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "MSCS_40_page"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 438 | [
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daytoy-models/CTA-datas | 2023-10-31T09:36:16.000Z | [
"license:unknown",
"region:us"
] | daytoy-models | null | null | 0 | 0 | 2023-10-31T09:33:35 | ---
license: unknown
---
ajajajaja
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davanstrien/SDOH-NLI | 2023-10-31T10:06:04.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"medical",
"arxiv:2310.18431",
"region:us"
] | davanstrien | null | null | 0 | 0 | 2023-10-31T09:51:03 | ---
license: cc-by-4.0
task_categories:
- text-classification
task_ids:
- natural-language-inference
language:
- en
pretty_name: >-
SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical
Notes
size_categories:
- 10K<n<100K
tags:
- medical
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@misc{lelkes2023sdohnli,
title={SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes},
author={Adam D. Lelkes and Eric Loreaux and Tal Schuster and Ming-Jun Chen and Alvin Rajkomar},
year={2023},
eprint={2310.18431},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
**APA:**
[More Information Needed]
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## Dataset Card Contact
[More Information Needed] | 4,943 | [
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] |
Eitanli/meal_type_bu | 2023-11-02T09:42:49.000Z | [
"region:us"
] | Eitanli | null | null | 0 | 0 | 2023-10-31T10:12:21 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int64
- name: recipe
dtype: string
- name: meal_type_title
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 87260080
num_examples: 60001
download_size: 43980711
dataset_size: 87260080
---
# Dataset Card for "meal_type_bu"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 565 | [
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shunk031/Magazine | 2023-10-31T10:32:41.000Z | [
"task_categories:image-to-image",
"task_categories:text-to-image",
"task_categories:unconditional-image-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"graphic design",
"layout",
"content-aware",
"region:us"
] | shunk031 | null | @article{zheng2019content,
title={Content-aware generative modeling of graphic design layouts},
author={Zheng, Xinru and Qiao, Xiaotian and Cao, Ying and Lau, Rynson WH},
journal={ACM Transactions on Graphics (TOG)},
volume={38},
number={4},
pages={1--15},
year={2019},
publisher={ACM New York, NY, USA}
} | 0 | 0 | 2023-10-31T10:22:33 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: Magazine
size_categories: []
source_datasets:
- original
tags:
- graphic design
- layout
- content-aware
task_categories:
- image-to-image
- text-to-image
- unconditional-image-generation
task_ids: []
---
# Dataset Card for Magazine dataset
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://xtqiao.com/projects/content_aware_layout/
- **Repository:** https://github.com/shunk031/huggingface-datasets_Magazine
- **Paper (SIGGRAPH2019):** https://dl.acm.org/doi/10.1145/3306346.3322971
### Dataset Summary
A large-scale magazine layout dataset with fine-grained layout annotations and keyword labeling.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
To use Magazine dataset, you need to download the image and layout annotations from the [OneDrive](https://portland-my.sharepoint.com/:f:/g/personal/xqiao6-c_my_cityu_edu_hk/EhmRh5SFoQ9Hjl_aRjCOltkBKFYefiSagR6QLJ7pWvs3Ww?e=y8HO5Q) in the official page.
Then place the downloaded files in the following structure and specify its path.
```shell
/path/to/datasets
├── MagImage.zip
└── MagLayout.zip
```
```python
import datasets as ds
dataset = ds.load_dataset(
path="shunk031/Magazine",
data_dir="/path/to/datasets/", # Specify the path of the downloaded directory.
)
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
```bibtex
@article{zheng2019content,
title={Content-aware generative modeling of graphic design layouts},
author={Zheng, Xinru and Qiao, Xiaotian and Cao, Ying and Lau, Rynson WH},
journal={ACM Transactions on Graphics (TOG)},
volume={38},
number={4},
pages={1--15},
year={2019},
publisher={ACM New York, NY, USA}
}
```
### Contributions
Thanks to [Xinru Zheng and Xiaotian Qiao*](https://xtqiao.com/projects/content_aware_layout/) for creating this dataset.
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KyriaAnnwyn/plu | 2023-10-31T10:45:15.000Z | [
"region:us"
] | KyriaAnnwyn | null | null | 0 | 0 | 2023-10-31T10:30:37 | Entry not found | 15 | [
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Deepakvictor/new_Data | 2023-10-31T10:34:37.000Z | [
"region:us"
] | Deepakvictor | null | null | 0 | 0 | 2023-10-31T10:34:36 | Entry not found | 15 | [
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] |
sayan1101/test-krra | 2023-10-31T10:52:42.000Z | [
"region:us"
] | sayan1101 | null | null | 0 | 0 | 2023-10-31T10:48:22 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 204
num_examples: 1
download_size: 2504
dataset_size: 204
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "test-krra"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 462 | [
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] |
youyu0105/llm-MIDI2 | 2023-10-31T11:11:50.000Z | [
"region:us"
] | youyu0105 | null | null | 1 | 0 | 2023-10-31T11:11:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 52189587
num_examples: 23112
download_size: 12023169
dataset_size: 52189587
---
# Dataset Card for "llm-MIDI2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 441 | [
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Edgelight/insPS | 2023-10-31T11:24:07.000Z | [
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shethjenil/own-data | 2023-10-31T11:50:04.000Z | [
"region:us"
] | shethjenil | null | null | 0 | 0 | 2023-10-31T11:47:45 | Entry not found | 15 | [
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FrankFacundo/vs_code_ext | 2023-10-31T12:40:18.000Z | [
"region:us"
] | FrankFacundo | null | null | 0 | 0 | 2023-10-31T12:19:46 | Entry not found | 15 | [
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LittleTaro/GrammarCorrection | 2023-10-31T12:51:05.000Z | [
"region:us"
] | LittleTaro | null | null | 0 | 0 | 2023-10-31T12:42:34 | Entry not found | 15 | [
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] |
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