id
stringlengths
2
115
author
stringlengths
2
42
last_modified
timestamp[us, tz=UTC]
downloads
int64
0
8.87M
likes
int64
0
3.84k
paperswithcode_id
stringlengths
2
45
tags
list
lastModified
timestamp[us, tz=UTC]
createdAt
stringlengths
24
24
key
stringclasses
1 value
created
timestamp[us]
card
stringlengths
1
1.01M
embedding
list
library_name
stringclasses
21 values
pipeline_tag
stringclasses
27 values
mask_token
null
card_data
null
widget_data
null
model_index
null
config
null
transformers_info
null
spaces
null
safetensors
null
transformersInfo
null
modelId
stringlengths
5
111
embeddings
list
nataliaElv/similarity-qa-no-vectors
nataliaElv
2023-11-10T15:05:26Z
0
0
null
[ "size_categories:1K<n<10K", "rlfh", "argilla", "human-feedback", "region:us" ]
2023-11-10T15:05:26Z
2023-11-10T15:05:23.000Z
2023-11-10T15:05:23
--- size_categories: 1K<n<10K tags: - rlfh - argilla - human-feedback --- # Dataset Card for similarity-qa-no-vectors 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("nataliaElv/similarity-qa-no-vectors") ``` ### 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("nataliaElv/similarity-qa-no-vectors") ``` ### 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**, **vectors**, 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 | | ---------- | ----- | ---- | -------- | -------- | | instruction | Instruction | text | True | False | | input | Input | text | False | False | | output | Output | 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 | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | quality | Rate the quality of the record: | rating | True | N/A | [1, 2, 3, 4, 5] | | explanation | Explain your rating: | 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". 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`. **✨ NEW** The **vectors** are different columns that contain a vector in floating point, which is constraint to the pre-defined dimensions in the **vectors_settings** when configuring the vectors within the dataset itself, also the dimensions will always be 1-dimensional. The **vectors** are optional and identified by the pre-defined vector name in the dataset configuration file in `argilla.yaml`. | Vector Name | Title | Dimensions | |-------------|-------|------------| | input | Input | [1, 384] | | instruction | Instruction | [1, 384] | | output | Output | [1, 384] | | testing | EMPTY! | [1, 1] | | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | | text_length | text_length | integer | None - None | True | 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": { "input": "", "instruction": "Give three tips for staying healthy.", "output": "1. Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule." }, "metadata": { "text_length": 241 }, "responses": [], "suggestions": [], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "explanation": [], "explanation-suggestion": null, "explanation-suggestion-metadata": { "agent": null, "score": null, "type": null }, "external_id": null, "input": "", "instruction": "Give three tips for staying healthy.", "metadata": "{\"text_length\": 241}", "output": "1. Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule.", "quality": [], "quality-suggestion": null, "quality-suggestion-metadata": { "agent": null, "score": null, "type": null }, "vectors": { "input": null, "instruction": null, "output": null, "testing": 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. * **instruction** is of type `text`. * (optional) **input** is of type `text`. * **output** 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`. * **quality** is of type `rating` with the following allowed values [1, 2, 3, 4, 5]. * **explanation** 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) **quality-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5]. * (optional) **explanation-suggestion** is of type `text`. * **✨ NEW** **Vectors**: As of Argilla 1.19.0, the vectors have been included in order to add support for similarity search to explore similar records based on vector search powered by the search engine defined. The vectors are optional and cannot be seen within the UI, those are uploaded and internally used. Also the vectors will always be optional, and only the dimensions previously defined in their settings. * (optional) **input** is of type `float32` and has a dimension of (1, `384`). * (optional) **instruction** is of type `float32` and has a dimension of (1, `384`). * (optional) **output** is of type `float32` and has a dimension of (1, `384`). * (optional) **testing** is of type `float32` and has a dimension of (1, `1`). Additionally, we also have two more fields that are optional and are the following: * **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]
[ -0.8373904824256897, -0.893471360206604, 0.29511719942092896, 0.25138288736343384, -0.3406704366207123, -0.5183554887771606, 0.07155488431453705, -0.5494349002838135, 0.7842206358909607, 0.8468637466430664, -0.7121151685714722, -0.9604046940803528, -0.6361333131790161, 0.2509046196937561, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
AndsPlays/Test
AndsPlays
2023-11-10T15:33:12Z
0
0
null
[ "region:us" ]
2023-11-10T15:33:12Z
2023-11-10T15:31:44.000Z
2023-11-10T15:31:44
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
PavolPragoData/testSet
PavolPragoData
2023-11-10T15:56:09Z
0
0
null
[ "size_categories:n<1K", "language:en", "region:us" ]
2023-11-10T15:56:09Z
2023-11-10T15:38:44.000Z
2023-11-10T15:38:44
--- language: - en size_categories: - n<1K --- Q: What is the general workflow suggested for using the GitHub client? A: The general workflow for using the GitHub client is referred to as the "GitHub Flow," which involves committing to a branch and syncing up with a remote repository regularly. Q: How does branch management differ between the GitHub clients on macOS and Windows? A: On macOS, branch management in the GitHub client involves a button at the top of the window for creating a new branch. On Windows, creating a branch is done by typing the new branch’s name in the branch-switching widget. Q: What is the main way to interact with other repositories over the network in the GitHub client? A: The main way to interact with other repositories over the network in the GitHub client is through the “Sync” feature, which internally uses a combination of Git operations such as pull, push, fetch, merge, and rebase. Q: What happens when you click the Sync button in the GitHub client? A: When the Sync button is clicked, it first performs a git pull --rebase, and if that fails due to a merge conflict, it falls back to git pull --no-rebase. Then, it executes git push.
[ -0.48088154196739197, -0.8282666206359863, 0.32742539048194885, -0.18850526213645935, -0.45892414450645447, 0.6779974102973938, 0.07588551193475723, -0.5946828126907349, 0.8952193856239319, 0.5213680863380432, -0.830710768699646, 0.0007631286862306297, -0.04672735184431076, -0.010020627640...
null
null
null
null
null
null
null
null
null
null
null
null
null
mariosasko/csn_single_commit
mariosasko
2023-11-10T16:19:56Z
0
0
null
[ "region:us" ]
2023-11-10T16:19:56Z
2023-11-10T16:19:56.000Z
2023-11-10T16:19:56
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
arhamk/HTML-correction-examples
arhamk
2023-11-10T18:15:24Z
0
0
null
[ "task_categories:text-classification", "size_categories:100K<n<1M", "license:mit", "code", "region:us" ]
2023-11-10T18:15:24Z
2023-11-10T16:23:15.000Z
2023-11-10T16:23:15
--- license: mit task_categories: - text-classification tags: - code size_categories: - 100K<n<1M ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
cmu-mlsp/encodec_24khz-opt-125m-lm_pretraining_ls960_1qt-librispeech_asr-train.clean.100-features
cmu-mlsp
2023-11-10T16:27:12Z
0
0
null
[ "region:us" ]
2023-11-10T16:27:12Z
2023-11-10T16:27:07.000Z
2023-11-10T16:27:07
--- dataset_info: features: - name: file sequence: string - name: text sequence: string - name: speaker_id sequence: int64 - name: chapter_id sequence: int64 - name: id sequence: string - name: audio_codes sequence: sequence: sequence: int64 splits: - name: train num_bytes: 759983 num_examples: 10 download_size: 114897 dataset_size: 759983 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "encodec_24khz-opt-125m-lm_pretraining_ls960_1qt-librispeech_asr-train.clean.100-features" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.7650445699691772, -0.015888268128037453, -0.037827733904123306, 0.1659037321805954, -0.3953038156032562, 0.1523871272802353, -0.09968619048595428, -0.04224720597267151, 0.6576645970344543, 0.4597835838794708, -1.0402780771255493, -0.46696704626083374, -0.4821391999721527, -0.13323451578...
null
null
null
null
null
null
null
null
null
null
null
null
null
ssbuild/tools_data
ssbuild
2023-11-10T17:07:24Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-10T17:07:24Z
2023-11-10T16:43:05.000Z
2023-11-10T16:43:05
--- license: apache-2.0 ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
Farsight-AI/10K-bench-outputs
Farsight-AI
2023-11-10T16:54:19Z
0
0
null
[ "region:us" ]
2023-11-10T16:54:19Z
2023-11-10T16:53:05.000Z
2023-11-10T16:53:05
--- 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: generatedAnswer dtype: string - name: pred dtype: bool - name: model dtype: string splits: - name: train num_bytes: 993374 num_examples: 780 download_size: 245651 dataset_size: 993374 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "10K-bench-outputs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.7101892232894897, 0.040048714727163315, 0.40322306752204895, 0.3702374994754791, -0.23891223967075348, -0.15809819102287292, 0.23113377392292023, -0.1908005326986313, 0.7609946131706238, 0.386788010597229, -0.8081856966018677, -0.5918715000152588, -0.7169289588928223, -0.189547583460807...
null
null
null
null
null
null
null
null
null
null
null
null
null
hippocrates/CitationGPTv12345_train
hippocrates
2023-11-10T17:09:26Z
0
0
null
[ "region:us" ]
2023-11-10T17:09:26Z
2023-11-10T17:09:18.000Z
2023-11-10T17:09:18
--- 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: 365813520 num_examples: 99360 - name: valid num_bytes: 47375754 num_examples: 12760 - name: test num_bytes: 42198711 num_examples: 11615 download_size: 175738218 dataset_size: 455387985 --- # Dataset Card for "CitationGPTv12345_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.6128206849098206, 0.1588703989982605, 0.20239615440368652, 0.557255208492279, -0.14560504257678986, -0.12611280381679535, 0.21060211956501007, 0.020283346995711327, 0.5397158861160278, 0.1777486652135849, -0.7398265600204468, -0.40838423371315, -0.7091488838195801, -0.20546498894691467,...
null
null
null
null
null
null
null
null
null
null
null
null
null
cmu-mlsp/encodec_24khz-opt-125m-lm_pretraining_ls960_1qt-librispeech_asr-test.clean-features
cmu-mlsp
2023-11-10T18:09:50Z
0
0
null
[ "region:us" ]
2023-11-10T18:09:50Z
2023-11-10T17:12:28.000Z
2023-11-10T17:12:28
--- dataset_info: features: - name: file dtype: string - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: audio_codes sequence: sequence: int64 splits: - name: train num_bytes: 57102934 num_examples: 2620 download_size: 3873364 dataset_size: 57102934 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "encodec_24khz-opt-125m-lm_pretraining_ls960_1qt-librispeech_asr-test.clean-features" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.7684988975524902, -0.12091037631034851, -0.09201234579086304, 0.09477193653583527, -0.4169539213180542, 0.12219289690256119, -0.09782767295837402, -0.12211690843105316, 0.6648790836334229, 0.4676107168197632, -1.0062376260757446, -0.47405150532722473, -0.3980635106563568, -0.10928781330...
null
null
null
null
null
null
null
null
null
null
null
null
null
danaroth/samson
danaroth
2023-11-10T17:26:30Z
0
0
null
[ "license:unknown", "region:us" ]
2023-11-10T17:26:30Z
2023-11-10T17:16:29.000Z
2023-11-10T17:16:29
--- license: unknown --- # Description Samson is a simple dataset that is available from the [website](http://opticks.org/confluence/display/opticks/Sample+Data). In this image, there are 952x952 pixels. Each pixel is recorded at 156 channels covering the wavelengths from 401 nm to 889 nm. The spectral resolution is highly up to 3.13 nm. As the original image is too large, which is very expensive in terms of computational cost, a region of 95x95 pixels is used. It starts from the (252,332)-th pixel in the original image. This data is not degraded by the blank channel or badly noised channels. Specifically, there are three targets in this image, i.e. "#1 Soil", "#2 Tree" and "#3 Water" respectively. # Quick look <figure> <img src= "assets/D7_1.png" alt="Samson" width="500" /> <figcaption>Samson and its ground truths.</figcaption> </figure> # Credits Dataset originally made available by [Opticks](https://www.opticks.org/).
[ -0.8830021619796753, -0.2519213557243347, 0.5516133904457092, 0.096955806016922, -0.6279883980751038, -0.10587769001722336, -0.11257527768611908, -0.5420196652412415, 0.5372907519340515, 0.530539870262146, -0.7126641273498535, -0.44642579555511475, -0.4910004138946533, -0.06154078245162964...
null
null
null
null
null
null
null
null
null
null
null
null
null
BangumiBase/heavenlydelusion
BangumiBase
2023-11-10T19:21:29Z
0
0
null
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
2023-11-10T19:21:29Z
2023-11-10T18:17:29.000Z
2023-11-10T18:17:29
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Heavenly Delusion This is the image base of bangumi Heavenly Delusion, we detected 29 characters, 1516 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 | 32 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 81 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 299 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 74 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 27 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 53 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 34 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 36 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 49 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 30 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 21 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 25 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 13 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 22 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 12 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 23 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 35 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 38 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 377 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 65 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 17 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 14 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 12 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 11 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 31 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 8 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 29 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 14 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | noise | 34 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.6766110062599182, -0.18801937997341156, 0.1356153041124344, 0.1995348036289215, -0.27254539728164673, -0.07971568405628204, -0.03230850771069527, -0.37800920009613037, 0.6288679838180542, 0.5603653788566589, -0.9362927675247192, -0.8666073083877563, -0.686329185962677, 0.501678764820098...
null
null
null
null
null
null
null
null
null
null
null
null
null
BangumiBase/summertimerender
BangumiBase
2023-11-10T20:14:18Z
0
0
null
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
2023-11-10T20:14:18Z
2023-11-10T18:17:57.000Z
2023-11-10T18:17:57
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Summertime Render This is the image base of bangumi Summertime Render, we detected 32 characters, 2981 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 | 372 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 55 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 33 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 230 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 48 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 732 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 66 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 88 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 68 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 73 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 288 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 20 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 14 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 64 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 164 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 50 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 19 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 19 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 21 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 30 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 46 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 13 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 8 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 9 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 13 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 43 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 99 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 7 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | N/A | | 28 | 13 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 45 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 7 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | N/A | | noise | 224 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.6795658469200134, -0.15292297303676605, 0.12078289687633514, 0.21932457387447357, -0.25918716192245483, -0.0723101943731308, -0.009922421537339687, -0.3644787669181824, 0.6025844812393188, 0.5000902414321899, -0.9756001234054565, -0.8797035217285156, -0.6535952687263489, 0.4974355399608...
null
null
null
null
null
null
null
null
null
null
null
null
null
Jaswir/tm-data
Jaswir
2023-11-10T18:25:44Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-10T18:25:44Z
2023-11-10T18:25:37.000Z
2023-11-10T18:25:37
--- license: apache-2.0 ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
nick-lebesis/gabbra-train-v1
nick-lebesis
2023-11-10T20:15:39Z
0
0
null
[ "task_categories:text-classification", "size_categories:n<1K", "language:en", "not-for-all-audiences", "region:us" ]
2023-11-10T20:15:39Z
2023-11-10T18:33:33.000Z
2023-11-10T18:33:33
--- task_categories: - text-classification language: - en tags: - not-for-all-audiences size_categories: - n<1K --- {"text": "Hey, how are you?", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message contains a friendly greeting.", [/INST] </s>} {"text": "That's a sexy car.", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message speaks to an object and not a person.", [/INST] </s>} {"text": "I can't stop thinking about last night.", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "The message contains more direct sexual references.", [/INST] </s>} {"text": "Want to exchange explicit photos?", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "The message contains explicit sexual content.", [/INST] </s>} {"text": "You look pretty in that picture.", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "The message contains mild flirting.", [/INST] </s>} {"text": "How about we meet up later tonight?", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "The message contains unsolicited advances.", [/INST] </s>} {"text": "Send me nude pictures now.", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "The message contains explicit sexual advances.", [/INST] </s>} {"text": "You people never understand.", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message contains potentially insensitive language but lacks specificity or malicious tones .", [/INST] </s>} {"text": "All you people are the same. always begging.", "unknown": false <s> [INST] "category": "Hate & Offensive", "severity_level": "Medium", "rationale": "The message contains offensive stereotypes.", [/INST] </s>} {"text": "I hate all [racial/religious group].", "unknown": true <s> [INST] "category": "Hate & Offensive", "severity_level": "High", "rationale": "The message contains hateful expressions towards a group.", [/INST] </s>} {"text": "You're such a nerd.", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "The message contains mild teasing.", [/INST] </s>} {"text": "Nobody likes you. just go away.", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "The message contains derogatory remarks.", [/INST] </s>} {"text": "I'll make your life miserable.", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "The message contains threats and severe derogatory remarks.", [/INST] </s>} {"text": "You look young in this photo.", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Low", "rationale": "The message contains inappropriate interest.", [/INST] </s>} {"text": "Where do you go to school?", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Medium", "rationale": "The message contains requests for personal information.", [/INST] </s>} {"text": "Send me pictures of you without your clothes.", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "High", "rationale": "The message contains explicit predatory behavior.", [/INST] </s>} {"text": "Let's grab some beers tonight.", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "The message contains references to alcohol.", [/INST] </s>} {"text": "I smoked weed last night.", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "The message contains references to drug use.", [/INST] </s>} {"text": "Want to buy some cocaine?", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "The message contains offers to sell drugs.", [/INST] </s>} {"text": "You better watch out.", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Low", "rationale": "The message contains mild aggressive language.", [/INST] </s>} {"text": "I'll punch you if you say that again.", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "The message contains threats of physical harm.", [/INST] </s>} {"text": "I'll kill you and your family.", "unknown": true <s> [INST] "category": "Violence", "severity_level": "High", "rationale": "The message contains explicit threats of severe harm.", [/INST] </s>} {"text": "Here's my number: 555-1234.", "unknown": false <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "Medium", "rationale": "The message shares contact info.", [/INST] </s>} {"text": "Jane’s bank account number is 9876543210.", "unknown": true <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "High", "rationale": "The message shares sensitive personal information.", [/INST] </s>} {"text": "I feel so lost.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Low", "rationale": "The message contains expressions of sadness.", [/INST] </s>} {"text": "I can't see a way out of this.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Medium", "rationale": "The message contains expressions of despair.", [/INST] </s>} {"text": "I want to end it all.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "High", "rationale": "The message contains explicit expressions of self-harm.", [/INST] </s>} {"text": "I went to a gun show over the weekend.", "unknown": false <s> [INST] "category": "Weapons", "severity_level": "Low", "rationale": "The message contains references to weapons in a neutral context.", [/INST] </s>} {"text": "Do you know where I can buy a gun?", "unknown": true <s> [INST] "category": "Weapons", "severity_level": "Medium", "rationale": "The message contains discussions of using weapons.", [/INST] </s>} {"text": "I'll shoot you if you don’t back off.", "unknown": true <s> [INST] "category": "Weapons", "severity_level": "High", "rationale": "The message contains threats using weapons.", [/INST] </s>} {"text": "Let’s stay up all night.", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Low", "rationale": "The message contains references to mild risky behavior.", [/INST] </s>} {"text": "We should totally go cliff diving.", "unknown": true <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Medium", "rationale": "The message contains encouragement of moderately risky behavior.", [/INST] </s>} {"text": "Let's try jumping off the roof.", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "High", "rationale": "The message contains encouragement of severe risky behavior.", [/INST] </s>} {"text": "Check out this [link to a popular website].", "unknown": false <s> [INST] "category": "Links", "severity_level": "Low", "rationale": "The message contains links to general websites.", [/INST] </s>} {"text": "Click on this [suspicious link].", "unknown": false <s> [INST] "category": "Links", "severity_level": "Medium", "rationale": "The message contains links with questionable content.", [/INST] </s>} {"text": "Download free movies from this [malicious link].", "unknown": true <s> [INST] "category": "Links", "severity_level": "High", "rationale": "The message contains links to malicious or harmful websites.", [/INST] </s>} {"text": "That outfit looks sexy on you.", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Low", "rationale": "The message contains mild sexual innuendo.", [/INST] </s>} {"text": "The romance in that movie was steamy.", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Low", "rationale": "The message contains mild sexual innuendo.", [/INST] </s>} {"text": "She has a sexy voice.", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Low", "rationale": "The message contains mild sexual innuendo.", [/INST] </s>} {"text": "Your body looked amazing in that swimsuit.", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "The message contains moderate sexual content.", [/INST] </s>} {"text": "That scene in the movie was quite explicit.", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "The message contains moderate sexual content.", [/INST] </s>} {"text": "He has a seductive look.", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "The message contains moderate sexual content.", [/INST] </s>} {"text": "Want to have a fun night together?", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "The message contains explicit sexual content.", [/INST] </s>} {"text": "I can send you some naughty pictures.", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "The message contains explicit sexual content.", [/INST] </s>} {"text": "How about we skip dinner and go straight to dessert?", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "The message contains explicit sexual content.", [/INST] </s>} {"text": "You have a captivating smile.", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "The message contains mild flirting with an unknown.", [/INST] </s>} {"text": "I can't help but get lost in your eyes.", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "The message contains mild flirting.", [/INST] </s>} {"text": "You have a charming personality.", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "The message contains mild flirting.", [/INST] </s>} {"text": "How about we have dinner together this weekend?", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "The message contains unsolicited advances.", [/INST] </s>} {"text": "I can't wait to see you in that dress again.", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "The message contains unsolicited advances.", [/INST] </s>} {"text": "I’ve been dreaming about you.", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "The message contains unsolicited advances.", [/INST] </s>} {"text": "I can make your night unforgettable.", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "The message contains explicit sexual advances.", [/INST] </s>} {"text": "Let's meet up and have some fun tonight.", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "The message contains explicit sexual advances.", [/INST] </s>} {"text": "I'd love to see how you look without those clothes.", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "The message contains explicit sexual advances.", [/INST] </s>} {"text": "You people always late.", "unknown": false <s> [INST] "category": "Hate & Offensive", "severity_level": "Low", "rationale": "The message contains mild derogatory terms.", [/INST] </s>} {"text": "People like you are the reason for the problem.", "unknown": false <s> [INST] "category": "Hate & Offensive", "severity_level": "Low", "rationale": "The message contains mild derogatory terms.", [/INST] </s>} {"text": "All [racial group] are lazy.", "unknown": false <s> [INST] "category": "Hate & Offensive", "severity_level": "Medium", "rationale": "The message contains offensive racial stereotypes.", [/INST] </s>} {"text": "Typical [racial/ethnic group] always causing trouble.", "unknown": true <s> [INST] "category": "Hate & Offensive", "severity_level": "Medium", "rationale": "The message contains offensive stereotypes.", [/INST] </s>} {"text": "I can't stand people like you. just go back to where you came from.", "unknown": false <s> [INST] "category": "Hate & Offensive", "severity_level": "High", "rationale": "The message contains hateful expressions.", [/INST] </s>} {"text": "You and your kind are a disease to this country.", "unknown": true <s> [INST] "category": "Hate & Offensive", "severity_level": "High", "rationale": "The message contains hateful expressions.", [/INST] </s>} {"text": "Did you get the homework done?", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message is a standard inquiry about school work.", [/INST] </s>} {"text": "That’s a cool skateboard trick!", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message is expressing a positive opinion on a non-sensitive topic.", [/INST] </s>} {"text": "Guess who I saw at the mall today!", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message is sharing a benign personal encounter.", [/INST] </s>} {"text": "Wanna hang out at the park?", "unknown": true <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message is a casual invitation to a public place.", [/INST] </s>} {"text": "Your new profile pic is fire 🔥", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message uses an emoji to compliment a photo, which is common among peers.", [/INST] </s>} {"text": "That joke was so dirty 😂", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "The message implies the joke had sexual undertones.", [/INST] </s>} {"text": "Can’t believe you kissed him! 😏", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "The message discusses intimate actions with a suggestive emoji.", [/INST] </s>} {"text": "Wanna see something wild? 😜", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "The message implies an offer to share potentially explicit content.", [/INST] </s>} {"text": "You're cute, got Snapchat?", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "The message is a mild flirtation requesting a private communication platform.", [/INST] </s>} {"text": "You should wear that dress more often 😉", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "The message makes unsolicited comments on appearance with a suggestive emoji.", [/INST] </s>} {"text": "You’d look better in my arms", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "The message contains a strong unsolicited advance.", [/INST] </s>} {"text": "Those people are always up to no good", "unknown": false <s> [INST] "category": "Hate & Offensive", "severity_level": "Low", "rationale": "The message uses vague but negative stereotypes about a group.", [/INST] </s>} {"text": "I don’t like hanging out with [group]", "unknown": false <s> [INST] "category": "Hate & Offensive", "severity_level": "Medium", "rationale": "The message contains a generalization that could be based on a bias.", [/INST] </s>} {"text": "I’ll take your lunch money every day", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "The message contains a direct threat of extortion, indicative of bullying.", [/INST] </s>} {"text": "You better not show up at the game", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "The message is an attempt to intimidate and control the recipient's actions.", [/INST] </s>} {"text": "You look different. How old are you?", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Low", "rationale": "The message shows potentially inappropriate interest in age.", [/INST] </s>} {"text": "Have you ever skipped class?", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Low", "rationale": "The message is inquiring about minor rule-breaking behavior.", [/INST] </s>} {"text": "I dare you to steal a candy bar", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Medium", "rationale": "The message is encouraging theft, a risky and illegal behavior.", [/INST] </s>} {"text": "Ever tried climbing the school roof?", "unknown": true <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "High", "rationale": "The message is encouraging dangerous behavior that could result in injury.", [/INST] </s>} {"text": "I’m feeling so blue 🙁", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Low", "rationale": "The message expresses a low mood but no direct indication of self-harm.", [/INST] </s>} {"text": "Sometimes I just want to disappear", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Medium", "rationale": "The message suggests a desire to escape from life’s problems, possibly hinting at self-harm.", [/INST] </s>} {"text": "Check out my new knife collection!", "unknown": false <s> [INST] "category": "Weapons", "severity_level": "Low", "rationale": "The message is showing off weapons in a possibly hobbyist context.", [/INST] </s>} {"text": "Don't make me use this 😠🔪", "unknown": true <s> [INST] "category": "Weapons", "severity_level": "High", "rationale": "The message contains a threat of violence with the display of an emoji representing a weapon.", [/INST] </s>} {"text": "I heard about a secret party", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Low", "rationale": "The message is sharing information about an unsupervised gathering.", [/INST] </s>} {"text": "Found this cool site, check it out [link]", "unknown": false <s> [INST] "category": "Links", "severity_level": "Low", "rationale": "The message contains a link that may lead to unknown content.", [/INST] </s>} {"text": "This video is hilarious 😂 [link]", "unknown": false <s> [INST] "category": "Links", "severity_level": "Low", "rationale": "The message contains a link, shared in a benign context.", [/INST] </s>} {"text": "You won’t last a day without me", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "The message has a condescending tone that could undermine the recipient’s confidence.", [/INST] </s>} {"text": "Everyone thinks you’re weird", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "The message is spreading negative opinions about the recipient, a form of social bullying.", [/INST] </s>} {"text": "I’m just a click away if you need to talk 🤗", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "The message is seemingly supportive but could be a tactic for grooming, especially if from an unknown sender.", [/INST] </s>} {"text": "Your talent blows me away! 🌟", "unknown": false <s> [INST] "category": "Null", "severity_level": "Low", "rationale": "The message is a positive reinforcement of the recipient's abilities.", [/INST] </s>} {"text": "You were great on the field today!", "unknown": false <s> [INST] "category": "Null", "severity_level": "Low", "rationale": "The message is a commendation of the recipient's performance in sports.", [/INST] </s>} {"text": "Why don’t you get lost?", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "The message is dismissive and unfriendly, which could be perceived as bullying.", [/INST] </s>} {"text": "You’ll regret messing with me", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "The message contains a vague threat, indicative of bullying behavior.", [/INST] </s>} {"text": "Nobody would care if you were gone", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "The message is cruel and could significantly impact the recipient's emotional well-being.", [/INST] </s>} {"text": "Only you would make a mistake like that.", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "The message contains mild teasing.", [/INST] </s>} {"text": "You're such a klutz sometimes.", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "The message contains mild teasing.", [/INST] </s>} {"text": "Nobody likes you. just go away.", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "The message contains derogatory remarks.", [/INST] </s>} {"text": "I'm going to make your life miserable.", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "The message contains threats and severe derogatory remarks.", [/INST] </s>} {"text": "You’ll regret ever crossing me.", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "The message contains threats and severe derogatory remarks.", [/INST] </s>} {"text": "You look very mature for your age.", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Low", "rationale": "The message contains inappropriate interest.", [/INST] </s>} {"text": "You seem to be growing up fast.", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Low", "rationale": "The message contains inappropriate interest.", [/INST] </s>} {"text": "You're not like other kids. you're special.", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Low", "rationale": "The message contains inappropriate interest.", [/INST] </s>} {"text": "Where do you hang out usually?", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Medium", "rationale": "The message contains requests for personal information.", [/INST] </s>} {"text": "Do your parents let you go out alone?", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Medium", "rationale": "The message contains requests for personal information.", [/INST] </s>} {"text": "What school do you go to?", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "Medium", "rationale": "The message contains requests for personal information.", [/INST] </s>} {"text": "You can be earning $ with me. want to meet up?", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "High", "rationale": "The message contains explicit predatory behavior.", [/INST] </s>} {"text": "Send me pictures of you in a swimsuit.", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "High", "rationale": "The message contains explicit predatory behavior.", [/INST] </s>} {"text": "I can pick you up from school tomorrow.", "unknown": true <s> [INST] "category": "Child Exploitation", "severity_level": "High", "rationale": "The message contains explicit predatory behavior.", [/INST] </s>} {"text": "Fancy a beer tonight?", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Low", "rationale": "The message contains references to alcohol.", [/INST] </s>} {"text": "I need a glass of wine after today.", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Low", "rationale": "The message contains references to alcohol.", [/INST] </s>} {"text": "We had a few drinks at the party.", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Low", "rationale": "The message contains references to alcohol.", [/INST] </s>} {"text": "I tried marijuana once.", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "The message contains references to drug use.", [/INST] </s>} {"text": "Do you know where to get some weed?", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "The message contains references to drug use.", [/INST] </s>} {"text": "He was high at the party last night.", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "The message contains references to drug use.", [/INST] </s>} {"text": "I can get you any drug you want.", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "The message contains offers to sell drugs.", [/INST] </s>} {"text": "Meet me if you want to buy some cocaine.", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "The message contains offers to sell drugs.", [/INST] </s>} {"text": "Best prices on meth. let me know if you’re interested.", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "The message contains offers to sell drugs.", [/INST] </s>} {"text": "You better change your attitude.", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Low", "rationale": "The message contains mild aggressive language.", [/INST] </s>} {"text": "I’d watch my back if I were you.", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Low", "rationale": "The message contains mild aggressive language.", [/INST] </s>} {"text": "You're asking for trouble.", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Low", "rationale": "The message contains mild aggressive language.", [/INST] </s>} {"text": "I swear I'll slap you.", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "The message contains threats of physical harm.", [/INST] </s>} {"text": "Keep talking and see what happens.", "unknown": true <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "The message contains threats of physical harm.", [/INST] </s>} {"text": "You’ll get a beating if you don’t stop.", "unknown": true <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "The message contains threats of physical harm.", [/INST] </s>} {"text": "I'll break your legs.", "unknown": false <s> [INST] "category": "Violence", "severity_level": "High", "rationale": "The message contains explicit threats of severe harm.", [/INST] </s>} {"text": "I will make you suffer.", "unknown": true <s> [INST] "category": "Violence", "severity_level": "High", "rationale": "The message contains explicit threats of severe harm.", [/INST] </s>} {"text": "I'll kill you if you don’t back off.", "unknown": true <s> [INST] "category": "Violence", "severity_level": "High", "rationale": "The message contains explicit threats of severe harm.", [/INST] </s>} {"text": "I prefer going to bed early.", "unknown": true <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "Low", "rationale": "The message shares personal preferences with an unknown contact.", [/INST] </s>} {"text": "I enjoy hiking on weekends.", "unknown": true <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "Low", "rationale": "The message shares personal preferences with an unknown contact.", [/INST] </s>} {"text": "Here's my number: 555-1234.", "unknown": true <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "Medium", "rationale": "The message shares contact info with an unknown contact.", [/INST] </s>} {"text": "John’s email is john.doe@example.com.", "unknown": true <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "Medium", "rationale": "The message shares contact info with an unknown contact.", [/INST] </s>} {"text": "Call Sarah at 555-5678.", "unknown": true <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "Medium", "rationale": "The message shares contact info.", [/INST] </s>} {"text": "My SSN is 123-45-6789.", "unknown": false <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "High", "rationale": "The message shares sensitive personal info.", [/INST] </s>} {"text": "Mark’s address is 123 Main St.", "unknown": true <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "High", "rationale": "The message shares sensitive personal info.", [/INST] </s>} {"text": "Jane’s bank account number is 9876543210.", "unknown": true <s> [INST] "category": "Harmful Sharing of Personal Info", "severity_level": "High", "rationale": "The message shares sensitive personal information.", [/INST] </s>} {"text": "I feel so blue today.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Low", "rationale": "The message contains expressions of sadness.", [/INST] </s>} {"text": "Life has been really tough lately.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Low", "rationale": "The message contains expressions of sadness.", [/INST] </s>} {"text": "I don’t know why I feel so down all the time.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Low", "rationale": "The message contains expressions of sadness.", [/INST] </s>} {"text": "I can’t see a way out of this.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Medium", "rationale": "The message contains expressions of despair.", [/INST] </s>} {"text": "I feel trapped in my own mind.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Medium", "rationale": "The message contains expressions of despair.", [/INST] </s>} {"text": "Everything seems pointless.", "unknown": true <s> [INST] "category": "Self Harm", "severity_level": "Medium", "rationale": "The message contains expressions of despair.", [/INST] </s>} {"text": "I just want to end it all.", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "High", "rationale": "The message contains explicit expressions of self-harm.", [/INST] </s>} {"text": "I’m thinking of hurting myself.", "unknown": true <s> [INST] "category": "Self Harm", "severity_level": "High", "rationale": "The message contains explicit expressions of self-harm.", [/INST] </s>} {"text": "I don’t want to live anymore.", "unknown": true <s> [INST] "category": "Self Harm", "severity_level": "High", "rationale": "The message contains explicit expressions of self-harm.", [/INST] </s>} {"text": "Would love to see you wearing less ;)", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "The message makes a sexually explicit advance using a suggestive emoji.", [/INST] </s>} {"text": "You're so pretty!", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "The message compliments physical appearance from an unknown sender, which may be flirtatious.", [/INST] </s>} {"text": "You're so pretty!", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "The message compliments physical appearance in a respectful way from an known sender, which may be fine.", [/INST] </s>} {"text": "Let's meet up tonight...wink wink", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "The message proposes meeting up late at night with a suggestive tone.", [/INST] </s>} {"text": "I'll be thinking of you later 😉🌙", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "Suggestive message about thinking about someone at night.", [/INST] </s>} {"text": "You're perfect 🥰😍", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "Over-the-top compliments from an unknown sender.", [/INST] </s>} {"text": "I can't stop thinking about you 😣💭", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Excessive expression of interest.", [/INST] </s>} {"text": "You are my everything ❤️😢", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Inappropriate or excessive message about importance to sender.", [/INST] </s>} {"text": "I miss you soooooo much 😔💔", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Exaggerated expression of sadness over lack of contact.", [/INST] </s>} {"text": "Why are you ignoring me? 😠🙅", "unknown": false <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Questioning why messages are not being reciprocated.", [/INST] </s>} {"text": "You'd be prettier if you smiled more 😶🙂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Backhanded compliment about appearance.", [/INST] </s>} {"text": "Do my homework or else 😈📚", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Threat tied to homework help.", [/INST] </s>} {"text": "Thanks for the birthday wishes...NOT 🙄🎂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Mocking tone in response to well-wishes.", [/INST] </s>} {"text": "Everyone hates you 😡👎", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Direct insults regarding social status.", [/INST] </s>} {"text": "You're so annoying 😒🙄", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Direct insult about personality.", [/INST] </s>} {"text": "Weirdo 😖👎", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Indirect name-calling.", [/INST] </s>} {"text": "that movie was lit af 😂🔥", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Uses slang but otherwise harmless sentiment.", [/INST] </s>} {"text": "your wack 4 not liking that post", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Insult using lowercase text and abbreviations.", [/INST] </s>} {"text": "u r a loser nobdy likes u", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Direct insults with typos.", [/INST] </s>} {"text": " initiating self destruct sequence😵‍💫🤯", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Medium", "rationale": "Concerning metaphorical language implied self-harm.", [/INST] </s>} {"text": "that dress makes u look hot af🔥😍🥵", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "Compliment using sexual slang terms.", [/INST] </s>} {"text": "you + me = ❤️😜🤫", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Inappropriate messaging using emojis. Unwanted from unknown sender.", [/INST] </s>} {"text": "die 😠🔪", "unknown": false <s> [INST] "category": "Violence", "severity_level": "High", "rationale": "Severe threatening language using emojis.", [/INST] </s>} {"text": "ever try perfume samples? 😁💅 totally safe just askin", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "Coded language likely referring to abusing inhalants.", [/INST] </s>} {"text": "click here for hot singlez😍👉 www.sketchylink.com", "unknown": false <s> [INST] "category": "Links", "severity_level": "High", "rationale": "Link presumably routes to adult content.", [/INST] </s>} {"text": "c u at da party 2nite 🎉😎🍻", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Medium", "rationale": "Mention of likely unsupervised party with alcohol.", [/INST] </s>} {"text": "💪🔫🍺😴🚬🍃", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "Emojis strongly suggesting substance abuse and guns.", [/INST] </s>} {"text": "get me some weed or else 😠🔪", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "Demanding drugs and threatening violence.", [/INST] </s>} {"text": "No one would care if u left 😠", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Cruel statement likely intended to alienate/hurt.", [/INST] </s>} {"text": "boring day at school, wbu?", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Chatting about school day using abbreviations.", [/INST] </s>} {"text": "That sucks. If you need to talk lmk 🙂", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Harmless emotional support and check-in.", [/INST] </s>} {"text": "Ttyl going to the park 🌳", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Shares benign activity and uses common abbreviation.", [/INST] </s>} {"text": "I didn't do my hw 😬 can I copy urs?", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Asking to copy homework, relatively common behavior.", [/INST] </s>} {"text": "heyy 👋 what's up?", "unknown": true <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Friendly greeting using emojis and slang spelling.", [/INST] </s>} {"text": "I'll beat u up after school 👊🤜", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "Threat with emojis representing violent acts.", [/INST] </s>} {"text": "I wish I could disappear sometimes...", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Medium", "rationale": "Expresses desire to escape reality non-specifically, concerning without further detail.", [/INST] </s>} {"text": "My parents just don't understand me😭", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Common complaint from teenagers about parents.", [/INST] </s>} {"text": "u free 2nite? movie @ my place😉🍿", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "Proposing unsupervised meetup late at night using emojis with romantic implications.", [/INST] </s>} {"text": "ur an idiot lmao 😂😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Insult using 'lmao' but derogatory meaning still clear.", [/INST] </s>} {"text": "go jump off a cliff", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "Encouraging self-harm/dangerous activity as insult.", [/INST] </s>} {"text": "u make me wanna 🤮", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Emoji implies feeling nauseous/sick due to someone, clearly insulting.", [/INST] </s>} {"text": "wanna come swimming after school? 🏊‍♂️", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Invitation for supervised normal activity after school.", [/INST] </s>} {"text": "sorry bout that, friends again? 🥺", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Apologizing and making up after disagreement.", [/INST] </s>} {"text": "I h8 school and my parents sooo much", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Venting frustration in hyperbolic but harmless way.", [/INST] </s>} {"text": "meet me at the abandoned house tonight🚪🏚️don't tell☝️🤫", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "High", "rationale": "Proposing clearly unsafe and prohibited meetup secrety.", [/INST] </s>} {"text": "I have depression😔💊", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Low", "rationale": "Disclosure of mental health issue, concerning in broader context.", [/INST] </s>} {"text": "wanna come over and watch netflix📺no parents home🙊", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Proposition unlikely to actually just 'watch Netflix' while unsupervised.", [/INST] </s>} {"text": "if you don't send the $, everyone at school will know ur secret 😠💸", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Extortion threat involving personal info and humiliation.", [/INST] </s>} {"text": "i think im depressed😔nothing makes me happy anymore", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "High", "rationale": "Expressing severe depression symptoms without directly mentioning self-harm.", [/INST] </s>} {"text": "do u like knifes? i have a collection👀🔪", "unknown": true <s> [INST] "category": "Weapons", "severity_level": "Medium", "rationale": "Unknown sender inquiring about weapons collecting.", [/INST] </s>} {"text": "lets skip and go to the movies instead 😜🍿🎥", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Medium", "rationale": "Encouraging truancy, unsafe without parental monitoring.", [/INST] </s>} {"text": "ur a fat loser🤮🤮 no1 likes u", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Cruel remarks on appearance and social status intended to hurt deeply.", [/INST] </s>} {"text": "wanna come over and 'study'😏📚 our test is tomorrow", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Scare quotes imply 'studying' means something else.", [/INST] </s>} {"text": "I got alcohol if u want some 🍻😜", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "Underage youth offering alcohol to peers.", [/INST] </s>} {"text": "i h8 skool its so boring🙄", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Venting about school using exaggeration and slang but no danger.", [/INST] </s>} {"text": "my parents thnk im at jessicas house lmao 🤣🤫", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Mention of relatively minor rulebreaking common among teens.", [/INST] </s>} {"text": "wuu2? nm bored lol", "unknown": true <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Making small talk using common slang abbreviations.", [/INST] </s>} {"text": "ur shoes are so last season💅😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Backhanded comment about appearance meant to insult/embarrass.", [/INST] </s>} {"text": "those glasses make u look like such a nerd😂👓", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Mocking someone's appearance as 'nerdy'.", [/INST] </s>} {"text": "nobody will come to ur party lmao😅🎉", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Laughing at idea of someone having a poorly attended event, hurtful.", [/INST] </s>} {"text": "i know ur secret and if u dont give me 50$ im telling every1😡🤬", "unknown": true <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Extortion using personal information.", [/INST] </s>} {"text": "ur a freak🤮 stay away from me at school 2mrrw", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Dehumanizing name calling and social exclusion.", [/INST] </s>} {"text": "my parents wont stop yelling😭", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Venting about family argument, no danger.", [/INST] </s>} {"text": "2 bad u couldnt make it last night, was craaaazy🥳🎉", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Describing a social event in exaggerated but non-threatening way.", [/INST] </s>} {"text": "if u dont send me a pic im gonna cry😭😢pls", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Guilting to obtain photos, from unknown sender.", [/INST] </s>} {"text": "u look hot in ur profile pic😍luv ur makeup", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "Low", "rationale": "Complimenting appearance/makeup in flirtatious way.", [/INST] </s>} {"text": "my parents fight all the time🙉😖", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Venting about parents arguing, no danger.", [/INST] </s>} {"text": "wanna come over and 'play video games'🎮😉", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Scare quotes imply sexual intent behind meeting up.", [/INST] </s>} {"text": "🔪🗡⚰😵☠", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Medium", "rationale": "Emojis strongly suggesting self-harm or suicide despite lack of text.", [/INST] </s>} {"text": "the new kid smells weird lmao 🤣😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Laughing at someone's expense even without severe language.", [/INST] </s>} {"text": "does this makeup look nice? 💄👄", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Seeking harmless opinions on makeup from friend.", [/INST] </s>} {"text": "pizza party in math class! 🍕😋🥳", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Excited about a normal class party.", [/INST] </s>} {"text": "that movie was so cringe 😬", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Commenting on media using common slang term.", [/INST] </s>} {"text": "i ship them so much😍👫", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Expressing enthusiasm for a celebrity couple.", [/INST] </s>} {"text": "mr johnson's class is wack af 😒", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Criticizing teacher using exaggerated slang.", [/INST] </s>} {"text": "this homework sucks. just copy mine lol 📝😅", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Offering to share homework answers casually.", [/INST] </s>} {"text": "ily even when ur being annoying af 🥰", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Affectionate message using common slang terms.", [/INST] </s>} {"text": "wake me up when class is over 😴💤", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Expressing boredom in school figuratively.", [/INST] </s>} {"text": "ur gay lol everyone knows it 😂😂", "unknown": false <s> [INST] "category": "Hate & Offensive", "severity_level": "Medium", "rationale": "Derogatory speculation on sexual orientation meant to demean.", [/INST] </s>} {"text": "ew dont let the freak touch u🤮🤢", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Dehumanizing language and encouraging isolation.", [/INST] </s>} {"text": "if u show up tomorrow ur dead meat😡🔪", "unknown": false <s> [INST] "category": "Violence", "severity_level": "High", "rationale": "Violent threat involving weapons.", [/INST] </s>} {"text": "which is the best app to get weed?🍁asking for a friend😅", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "Inquiry about obtaining drugs while underage.", [/INST] </s>} {"text": "lets meet at the park after school, just us two 😘🌹", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Proposed isolated meetup with romantic overtures.", [/INST] </s>} {"text": "you looked better before the haircut tbh", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Backhanded criticism about appearance.", [/INST] </s>} {"text": "my parents took my phone😭😫", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Venting about discipline from parents.", [/INST] </s>} {"text": "wanna come over and play call of duty?🎮🔫", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Invitation to play a common video game.", [/INST] </s>} {"text": "ur prettyish i guess🙄 for an ugly rat lol😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Veiled insult using 'lol'", [/INST] </s>} {"text": "u seriously wear that😆🤣it's so ugly!", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Making fun of fashion choices.", [/INST] </s>} {"text": "can i copy ur math hw again plzzz🥺👉👈", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Asking to copy homework, common for students.", [/INST] </s>} {"text": "wanna come over and watch a movie🎥🍿bored af", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Invitation for normal activity between friends.", [/INST] </s>} {"text": "that test was ez af😎👌", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Stating opinion about test using slang language.", [/INST] </s>} {"text": "mr johnson is soo lame🙄 like bye✌️", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Venting about teacher in exaggerated way.", [/INST] </s>} {"text": "you dress like a grandma 👵😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Making fun of clothing style.", [/INST] </s>} {"text": "get away from me you weirdo🤢🤮", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Dehumanizing language used for social exclusion.", [/INST] </s>} {"text": "can u help me with the english homework pls🙏📚", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Asking for help with homework.", [/INST] </s>} {"text": "youll never have real friends😢🤣", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Cruel remark about social isolation meant to hurt.", [/INST] </s>} {"text": "love this filter😍💅", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Positive sentiment about social media feature.", [/INST] </s>} {"text": "We should hang out just you and me sometime 😉", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Suggesting isolated hangout to minor by unknown sender.", [/INST] </s>} {"text": "I got some crazy videos to show u🤣🤯", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Description implies concerning media content.", [/INST] </s>} {"text": "Sending virtual hugs ur way🤗🫂", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Caring message with emojis.", [/INST] </s>} {"text": "ur so creepy lol 😅", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Name-calling even with 'lol'.", [/INST] </s>} {"text": "we should date tbh🥰 we'd be so cute", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Unknown sender persistently expressing romantic interest.", [/INST] </s>} {"text": "if you come over later we can cuddle 🥰😘", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Proposition for intimate physical contact from unknown sender.", [/INST] </s>} {"text": "ur gonna get it after school dont think i forgot😠🤜", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "Violent threat using emoji.", [/INST] </s>} {"text": "let's sneak out tonight my parents wont know 🤫🤐", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Medium", "rationale": "Encouraging rule-breaking without supervision.", [/INST] </s>} {"text": "I can get anything u want 😉💊 u just have to keep it between us 🤫", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "Offering to discreetly provide drugs to a minor.", [/INST] </s>} {"text": "ur pretty🥺 can i have a picture to look at?😳👉👈", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Requesting photo from minor using flattery.", [/INST] </s>} {"text": "hi cutie! can we be friends? 🥰 i want to get to know u better 😉", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Persisting in contacting a minor from an unknown sender.", [/INST] </s>} {"text": "u have no friends at school😂👎everyone hates u tbh 🙄", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Derogatory statements about social isolation meant to hurt.", [/INST] </s>} {"text": "ur a loser with no life😂🤣kys", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Encouraging self-harm in cruel message.", [/INST] </s>} {"text": "u make me sick just looking at u🤮🤢go away!", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Dehumanizing language meant to hurt and isolate target.", [/INST] </s>} {"text": "nobody actually likes hanging out with u🙄 they just feel bad lol 😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Claiming peers only associate with target out of pity.", [/INST] </s>} {"text": "ur a weirdo with no friends😂👎dont even try talking to me", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Name-calling combined with social exclusion.", [/INST] </s>} {"text": "everyone at school knows ur a slut🙄😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Derogatory labeling and shaming of sexual activity.", [/INST] </s>} {"text": "lets meet at my place tonight, i can pick u up 😉🚗", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Proposition for an isolated meetup from an unknown individual.", [/INST] </s>} {"text": "ur looking good lately 😉 maybe we could meet up soon...", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Flirtatious message with a proposition to meet alone.", [/INST] </s>} {"text": "thinking of u😘 can't wait to see u again soooon ❤️❤️", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Excessively affectionate message from an unknown sender.", [/INST] </s>} {"text": "ur so mature for ur age 😍 not like the others...", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Medium", "rationale": "Grooming behavior emphasizing perceived maturity.", [/INST] </s>} {"text": "lets ditch class and have some real fun 😜😈", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Medium", "rationale": "Proposition to skip school without supervision.", [/INST] </s>} {"text": "u + me + an empty house 😏🥵 u know what that means... 💦😈", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Proposition for isolated intimate contact using lewd emojis.", [/INST] </s>} {"text": "Looking forward to one-on-one tutoring session later 😉📚 See you soon, don't forget to wear that cute outfit! 🥰", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Flirtatious language and controlling clothing choice for an isolated meetup.", [/INST] </s>} {"text": "You'd look even cuter wearing this 👗 I bought it just for you! Can't wait to see you try it on later 😍", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Buying clothes for a minor and eager to see them worn.", [/INST] </s>} {"text": "No one at school even likes you 😂 Should just stay home all day you weirdo 🤪", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Cruel message isolating target and encouraging absence from school.", [/INST] </s>} {"text": "Ew get away from me you freak! 🤮 No one wants to talk to you! 😡", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Dehumanizing language used to isolate target.", [/INST] </s>} {"text": "LMAO you actually thought I was your friend?? 😂 I just felt bad for you lol 🤣", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Mocking as if peers only pretended to be friends out of pity.", [/INST] </s>} {"text": "You have no REAL friends, everyone hates you 😡 Just disappear already 🙄", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Statements to make target feel friendless and encourage self-harm.", [/INST] </s>} {"text": "I dare you to jump off the roof! I bet you won't 😜 Unless you're too scared?? 😱", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Encouraging dangerous behavior as a means of bullying.", [/INST] </s>} {"text": "you're so ugly and weird why do you bother coming to school 🤣 everyone just laughs at you 😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Harsh insults about appearance and social isolation at school.", [/INST] </s>} {"text": "Can't wait for our special tutoring lesson later 😘 I have a fun new activity I think you'll really like! 😉👙 It'll be our little secret 🤫", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Propositioning illicit relationship under guise of 'tutoring'.", [/INST] </s>} {"text": "Thinking of you! 💕 Can't stop thinking about our last conversation. You're so mature for your age, not like other silly girls! Let me know if you want to chat again soon! 😏😘", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Grooming by complimenting perceived maturity and seeking further contact.", [/INST] </s>} {"text": "Hey cutie! You're looking older lately 😉 Maybe we could meet up and do something fun together. But don't tell your parents! 🤫 It'll be our little secret. 😘", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Proposition to secretly meet up from an unknown adult sender.", [/INST] </s>} {"text": "ur ugly af 🤮 i feel bad for anyone who has to look at u 😂😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Vicious remarks about appearance meant to hurt self-esteem.", [/INST] </s>} {"text": "Stay away from me at school freak! 🤬 I don't want anyone to think I actually know you! 🤮", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Publicly rejecting association to damage social status.", [/INST] </s>} {"text": "LOL nobody came to your party 🎉😂 No one likes you! 🤣", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Mocking and exclusion tied to a social event.", [/INST] </s>} {"text": "I need help with my math homework later 📝🤓 Want to 'study' at my place tonight? 😉 My parents won't be home 😏", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Propositioning sexual contact under the guise of needing homework help.", [/INST] </s>} {"text": "Thinking about you baby 😘 Can't stop thinking about your sexy body... Want to come over later? We can pick up where we left off 🥵", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Explicit message recalling intimate encounter and asking to meet.", [/INST] </s>} {"text": "Looking hot today 🥵 Want to skip class with me and have some real fun? 😈 My car's just outside... 😉🚗", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Proposition to skip class and engage in sexual activity.", [/INST] </s>} {"text": "ur a fat ugly POS 🤮 KYS already nobody would care 😂", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Vicious message encouraging self-harm.", [/INST] </s>} {"text": "Can't stop thinking about how much I love you babe 🥰 You're so mature for your age, it's like you're not even a kid! 😍 We have such a special bond... ❤️", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Grooming message emphasizing inappropriate maturity and 'special' relationship.", [/INST] </s>} {"text": "I'll pick you up after school today and we can 'study' 😉😘 I got that special candy you like too if you want some... 🍬 But this is our little secret! Don't tell anyone! 🤫", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Proposing illicit relationship and substances under secrecy.", [/INST] </s>} {"text": "You looked so hot in that outfit yesterday 😍 I can't stop thinking about it! 🥵 Let's meet up later, just the two of us... 😉", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Recalling appearance and proposing isolated meetup.", [/INST] </s>} {"text": "Thinking about our special movie date tonight 😉 My parents are gone and we'll have the place all to ourselves... 😏 Can't wait! ❤️", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Propositioning intimate date at an unsupervised home.", [/INST] </s>} {"text": "Looking forward to our private tutoring session later 😉 I have a new lesson I think you'll really enjoy! Make sure to wear those cute shorts I got you! 😘", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Propositioning inappropriate relationship under the guise of tutoring.", [/INST] </s>} {"text": "Can't stop thinking about how hot you looked yesterday 😍😘 Let's meet up tonight, I want to see more of you... 😏 We can go back to my place, parents aren't home 😉", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Recalling appearance and proposing isolated intimate meetup.", [/INST] </s>} {"text": "You looked so sexy in that outfit today 😍 I could barely concentrate in class... 🥵 Meet me after school and we can have some real fun 😈", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Inappropriate commentary on a minor's appearance coupled with a sexual proposition.", [/INST] </s>} {"text": "Thinking about our movie date tonight 😉😏 My folks are out of town, we'll have the whole place to ourselves... And after the movie, we can have even more fun 😈👅💦", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Propositioning a secret rendezvous with clear sexual intent.", [/INST] </s>} {"text": "u r literally so ugly and weird 🤢 no1 likes u or will ever like u 😂 should just disappear 👋", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Vicious message attacking appearance and likeability, encouraging self-harm.", [/INST] </s>} {"text": "Ew get away from me freak !! 😡 Dont look at me or talk to me at school !! 🤬 I don't wanna be seen with u!", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Dehumanizing language used to publicly reject and isolate target.", [/INST] </s>} {"text": "LOL u have no friends!!😂 everyone laughs at u 🤣 so embarrassing just drop out already 🙄", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Mocking, humiliating and encouraging school absence.", [/INST] </s>} {"text": "Omg we're going to have so much fun later 😍🔞 I have a whole night planned for just the two of us! Make sure to wear that cute outfit I bought you 😘 we wouldn't want it to go to waste! 🤭", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Propositioning illicit encounter, controlling attire.", [/INST] </s>} {"text": "Can't wait to see you after school today baby 😍 we're going to have so much special fun 😈🤫 just the two of us. Our little secret! 😘", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Propositioning inappropriate covert relationship.", [/INST] </s>} {"text": "You really impressed me on the field today 😉👟 Maybe afterwards we could hang out just the two of us... I could give you some extra 'coaching' 😜😏", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "High", "rationale": "Proposition disguising sexual advances as mentorship.", [/INST] </s>} {"text": "just took five shots🤪 feelin it now lmao😵", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "Describing underage binge drinking positively.", [/INST] </s>} {"text": "hmu if u got the stuff🍃😜", "unknown": true <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "Trying to buy drugs underage.", [/INST] </s>} {"text": "your house 2nite at 10🤫 dont tell ur 'rents 🚪🏠", "unknown": true <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "High", "rationale": "Proposing unsupervised late night meetup.", [/INST] </s>} {"text": "taco tuesday in the cafeteria! 🌮😋🍴", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Discussing school lunch positively.", [/INST] </s>} {"text": "yay friday! 🥳✨", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Expressing excitement for weekend using positive emojis.", [/INST] </s>} {"text": "I love pizza! 🍕😋", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Discussing favorite food using harmless emojis.", [/INST] </s>} {"text": "Can't wait to see the new Marvel movie! 🎥🍿", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Looking forward to age-appropriate film.", [/INST] </s>} {"text": "Thinking of you 💞🌹🌸", "unknown": true <s> [INST] "category": "Sexual Advances & Grooming", "severity_level": "Low", "rationale": "Using emojis with romantic undertones, concerning from unknown sender.", [/INST] </s>} {"text": "Want to come over later? 😉😘", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "High", "rationale": "Inviting someone over late at night using flirty/kiss emojis.", [/INST] </s>} {"text": "ur a loser 😂🤣😅", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Low", "rationale": "Name-calling using 'laughing' emojis to mask intention.", [/INST] </s>} {"text": "Just got a new knife 🔪😄", "unknown": true <s> [INST] "category": "Weapons", "severity_level": "Medium", "rationale": "Mention of obtaining weapon coupled with harmless emoji downplays severity.", [/INST] </s>} {"text": "meet me at midnight 🕛🌙 don't tell 🤫🤐", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "High", "rationale": "Proposing secret late night meetup using 'shushing' emojis.", [/INST] </s>} {"text": "Took some pills 💊😵‍💫feeling good", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "High", "rationale": "Describing drug use positively with emojis.", [/INST] </s>} {"text": "Getting sleepy, talk tomorrow! 💤😴", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Signing off harmlessly with sleeping emojis.", [/INST] </s>} {"text": "You looked really nice today! 🥰", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Giving a kind compliment with a smiley emoji.", [/INST] </s>} {"text": "I'll beat you up 👊🤜😡", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "Violent threat using aggressive emojis makes severity clear.", [/INST] </s>} {"text": "Want to hang out after school? 😊", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Casual invitation using a happy emoji.", [/INST] </s>} {"text": "😄☀️", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Happy and sun emojis likely show positive mood.", [/INST] </s>} {"text": "💃🕺🎶", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Dancing emojis imply fun social situation.", [/INST] </s>} {"text": "🏖️🏊🌴", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Tropical setting emojis probably mean vacation.", [/INST] </s>} {"text": "🎂🎁🎉", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Cake, gift, and party emojis signify a birthday.", [/INST] </s>} {"text": "🌙😴💤", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Moon, sleeping face, and zzz's imply going to bed.", [/INST] </s>} {"text": "🔪🗡️💣", "unknown": false <s> [INST] "category": "Weapons", "severity_level": "High", "rationale": "Emojis clearly depict dangerous weapons.", [/INST] </s>} {"text": "🍷🍹🍺🥴", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "Range of alcoholic drink emojis paired with one for intoxication.", [/INST] </s>} {"text": "🤫🌙😈", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "A 'shushing' emoji coupled with nighttime and devil emojis carries sexual overtones.", [/INST] </s>} {"text": "😢😭🗯️", "unknown": false <s> [INST] "category": "Self Harm", "severity_level": "Low", "rationale": "Crying emojis could indicate emotional distress.", [/INST] </s>} {"text": "👊🤛🩸", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "Punching emojis coupled with blood imply threats of harm.", [/INST] </s>} {"text": "That movie was 👍", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Thumbs up emoji shows positive opinion.", [/INST] </s>} {"text": "I am 😡", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Anger emoji replaces sentence but normal teenage sentiment.", [/INST] </s>} {"text": "Can't wait for the weekend 🥳", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Party emoji replaces text but meaning still clear.", [/INST] </s>} {"text": "I like 💘", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Heart emoji replaces word love/like in harmless way.", [/INST] </s>} {"text": "Thinking of 😈 tonight", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "Devil emoji adds sexual innuendo to message.", [/INST] </s>} {"text": "I'm so 😴 right now", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Sleepy emoji conveys being tired.", [/INST] </s>} {"text": "This homework makes me want to 🤯", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Exploding head emoji expresses frustration.", [/INST] </s>} {"text": "I got a 💯 on my last math test!", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "100 emoji used to say "got a perfect score".", [/INST] </s>} {"text": "My parents are 🤡", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Clown emoji conveys teenager saying parents are silly.", [/INST] </s>} {"text": "I'm 😍 that show", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Heart-eyes emoji replaces the word "love".", [/INST] </s>} {"text": "This class is so 😑", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Unimpressed emoji conveys the class is boring.", [/INST] </s>} {"text": "Don't tell anyone but I 💛", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Yellow heart emoji replaces "like" in a secret.", [/INST] </s>} {"text": "I really 💙 our conversations", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Blue heart replaces "like" in a harmless message.", [/INST] </s>} {"text": "Let's meet tonight 🤫", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "Hush emoji adds secrecy to proposed night meetup.", [/INST] </s>} {"text": "Thinking of your 👄", "unknown": false <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "Lips emoji sexualizes the message.", [/INST] </s>} {"text": "Want to come over and 🍿🎞️?", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Popcorn and film emojis ask to watch a movie.", [/INST] </s>} {"text": "I'm so 😕 without you here", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Sad emoji conveys missing someone.", [/INST] </s>} {"text": "This homework 💩", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Poop emoji used to say homework is bad.", [/INST] </s>} {"text": "I 💨 my juul in the bathroom", "unknown": false <s> [INST] "category": "Drugs & Alcohol", "severity_level": "Medium", "rationale": "Vape emoji says they vaped at school.", [/INST] </s>} {"text": "Let's meet at 🕑 near the 🏫", "unknown": true <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Medium", "rationale": "Meeting late at night near school could be unsafe.", [/INST] </s>} {"text": "I 💖 when we hang out", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "A heart instead of love, positive sentiment.", [/INST] </s>} {"text": "You look like a 🤡 in that outfit", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Clown emoji used to insult clothing.", [/INST] </s>} {"text": "I could beat you up 💪", "unknown": false <s> [INST] "category": "Violence", "severity_level": "Medium", "rationale": "Flexed bicep emoji makes the threat more explicit.", [/INST] </s>} {"text": "I'm 😍 thinking of tonight 🌙", "unknown": true <s> [INST] "category": "Sexual Content", "severity_level": "Medium", "rationale": "Heart-eyes emoji sexualizes the late night meeting.", [/INST] </s>} {"text": "Let's sneak out after 🕛", "unknown": false <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "Medium", "rationale": "Implying sneaking out after midnight.", [/INST] </s>} {"text": "Everyone thinks you're a 🤡", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "Medium", "rationale": "Clown emoji calls someone a clown disparagingly.", [/INST] </s>} {"text": "I'll tell everyone your ☕️ if you don't do my homework 😈", "unknown": false <s> [INST] "category": "Bullying", "severity_level": "High", "rationale": "Spilling the tea means sharing secrets maliciously.", [/INST] </s>} {"text": "See you later 👋", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Waving emoji signifies saying goodbye.", [/INST] </s>} {"text": "I really 💙💙💙 that show", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Multiple blue heart emojis express love for a show harmlessly.", [/INST] </s>} {"text": "Let's meet at the 🏠 with no 👮‍♂️ 👀", "unknown": true <s> [INST] "category": "Child Safety & Risky Behavior", "severity_level": "High", "rationale": "Meeting at home without adult supervision.", [/INST] </s>} {"text": "I'm 📴 my phone for a bit", "unknown": false <s> [INST] "category": "Null", "severity_level": "None", "rationale": "Mobile phone emoji says they are turning off their phone.", [/INST] </s>}
[ -0.07974663376808167, -0.8473840951919556, 0.1689722239971161, 0.538580060005188, -0.4562433362007141, 0.1031198501586914, 0.2734189033508301, -0.5307254195213318, 0.6838036179542542, 0.6191421747207642, -0.8390860557556152, -1.0835182666778564, -0.7085793614387512, 0.22509275376796722, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
Mixlax004/numeric-analysis-project
Mixlax004
2023-11-10T18:37:39Z
0
0
null
[ "license:gpl-2.0", "region:us" ]
2023-11-10T18:37:39Z
2023-11-10T18:37:39.000Z
2023-11-10T18:37:39
--- license: gpl-2.0 ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
Weyaxi/huggingface-spaces-codes
Weyaxi
2023-11-14T09:31:44Z
0
2
null
[ "size_categories:100K<n<1M", "language:code", "license:other", "region:us" ]
2023-11-14T09:31:44Z
2023-11-10T18:45:38.000Z
2023-11-10T18:45:38
--- configs: - config_name: default data_files: spaces.csv license: other language: - code size_categories: - 100K<n<1M --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/lzGCX2qjX_nPeRBZoa2v0.png) # 📊 Dataset Description This dataset comprises code files of Huggingface Spaces that have more than 0 likes as of November 10, 2023. This dataset contains various programming languages totaling in 672 MB of compressed and 2.05 GB of uncompressed data. # 📝 Data Fields | Field | Type | Description | |------------|--------|------------------------------------------| | repository | string | Huggingface Spaces repository names. | | sdk | string | Software Development Kit of the space. | | license | string | License type of the space. | ## 🧩 Data Structure Data structure of the data. ``` spaces/ ├─ author1/ │ ├─ space1 │ ├─ space2 ├─ author2/ │ ├─ space1 │ ├─ space2 │ ├─ space3 ``` # 🏛️ Licenses Huggingface Spaces contains a variety of licenses. Here is the list of the licenses that this dataset contains: ```python [ 'None', 'mit', 'apache-2.0', 'openrail', 'gpl-3.0', 'other', 'afl-3.0', 'unknown', 'creativeml-openrail-m', 'cc-by-nc-4.0', 'cc-by-4.0', 'cc', 'cc-by-nc-sa-4.0', 'bigscience-openrail-m', 'bsd-3-clause', 'agpl-3.0', 'wtfpl', 'gpl', 'artistic-2.0', 'lgpl-3.0', 'cc-by-sa-4.0', 'Configuration error', 'bsd', 'cc-by-nc-nd-4.0', 'cc0-1.0', 'unlicense', 'llama2', 'bigscience-bloom-rail-1.0', 'gpl-2.0', 'bsd-2-clause', 'osl-3.0', 'cc-by-2.0', 'cc-by-3.0', 'cc-by-nc-3.0', 'cc-by-nc-2.0', 'cc-by-nd-4.0', 'openrail++', 'bigcode-openrail-m', 'bsd-3-clause-clear', 'eupl-1.1', 'cc-by-sa-3.0', 'mpl-2.0', 'c-uda', 'gfdl', 'cc-by-nc-sa-2.0', 'cc-by-2.5', 'bsl-1.0', 'odc-by', 'deepfloyd-if-license', 'ms-pl', 'ecl-2.0', 'pddl', 'ofl-1.1', 'lgpl-2.1', 'postgresql', 'lppl-1.3c', 'ncsa', 'cc-by-nc-sa-3.0' ] ``` # 📊 Dataset Statistics | Language | File Extension | File Counts | File Size (MB) | Line Counts | |------------|-----------------|-------------|----------------|-------------| | Python | .py | 141,560 | 1079.0 | 28,653,744 | | SQL | .sql | 21 | 523.6 | 645 | | JavaScript | .js | 6,790 | 369.8 | 2,137,054 | | Markdown | .md | 63,237 | 273.4 | 3,110,443 | | HTML | .html | 1,953 | 265.8 | 516,020 | | C | .c | 1,320 | 132.2 | 3,558,826 | | Go | .go | 429 | 46.3 | 6,331 | | CSS | .css | 3,097 | 25.6 | 386,334 | | C Header | .h | 2,824 | 20.4 | 570,948 | | C++ | .cpp | 1,117 | 15.3 | 494,939 | | TypeScript | .ts | 4,158 | 14.8 | 439,551 | | TSX | .tsx | 4,273 | 9.4 | 306,416 | | Shell | .sh | 3,294 | 5.5 | 171,943 | | Perl | .pm | 92 | 4.2 | 128,594 | | C# | .cs | 22 | 3.9 | 41,265 | ## 🖥️ Language ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/Dn8oaZh2BXtOHgQ8DaX09.png) ## 📁 Size ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/vRLCfs-QrEnDwhBnBu3rc.png) ## 📝 Line Count ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/8F_qgL1o7xZxR1zZ8bP76.png) # 🤗 Huggingface Spaces Statistics ## 🛠️ Software Development Kit (SDK) Software Development Kit pie chart. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/BmW_BdYtH3XpYNH23NLVZ.png) ## 🏛️ License License chart. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/dnxm5dJA_QzekwULwPS33.png) # 📅 Dataset Creation This dataset was created in these steps: 1. Scraped all spaces using the Huggingface Hub API. ```python from huggingface_hub import HfApi api = HfApi() spaces = api.list_spaces(sort="likes", full=1, direction=-1) ``` 2. Filtered spaces with more than 0 likes. ```python a = {} for i in tqdm(spaces): i = i.__dict__ if i['likes'] > 0: try: try: a[i['id']] = {'sdk': i['sdk'], 'license': i['cardData']['license'], 'likes': i['likes']} except KeyError: a[i['id']] = {'sdk': i['sdk'], 'license': None, 'likes': i['likes']} except: a[i['id']] = {'sdk': "Configuration error", 'license': "Configuration error", 'likes': i['likes']} data_list = [{'repository': key, 'sdk': value['sdk'], 'license': value['license'], 'likes': value['likes']} for key, value in a.items()] df = pd.DataFrame(data_list) ``` 3. Cloned spaces locally. ```python from huggingface_hub import snapshot_download programming = ['.asm', '.bat', '.cmd', '.c', '.h', '.cs', '.cpp', '.hpp', '.c++', '.h++', '.cc', '.hh', '.C', '.H', '.cmake', '.css', '.dockerfile', 'Dockerfile', '.f90', '.f', '.f03', '.f08', '.f77', '.f95', '.for', '.fpp', '.go', '.hs', '.html', '.java', '.js', '.jl', '.lua', 'Makefile', '.md', '.markdown', '.php', '.php3', '.php4', '.php5', '.phps', '.phpt', '.pl', '.pm', '.pod', '.perl', '.ps1', '.psd1', '.psm1', '.py', '.rb', '.rs', '.sql', '.scala', '.sh', '.bash', '.command', '.zsh', '.ts', '.tsx', '.tex', '.vb'] pattern = [f"*{i}" for i in programming] for i in repos: snapshot_download(i, repo_type="space", local_dir=f"spaces/{i}", allow_patterns=pattern) ```` 4. Processed the data to derive statistics.
[ -0.7775664925575256, -0.5362792015075684, 0.3074711263179779, 0.4921707808971405, -0.1808016300201416, 0.05537581816315651, -0.07338723540306091, -0.3143163025379181, 0.8531680703163147, 0.4532962441444397, -0.6349084377288818, -0.9821245670318604, -0.5190193057060242, 0.0851544663310051, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
Pablao0948/Enid_Sinclair
Pablao0948
2023-11-10T19:05:33Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-10T19:05:33Z
2023-11-10T18:49:23.000Z
2023-11-10T18:49:23
--- license: openrail ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
SuodhanJ6/train
SuodhanJ6
2023-11-10T19:16:40Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-10T19:16:40Z
2023-11-10T19:10:41.000Z
2023-11-10T19:10:41
--- license: mit ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
BangumiBase/attackontitan
BangumiBase
2023-11-11T11:40:07Z
0
0
null
[ "size_categories:10K<n<100K", "license:mit", "art", "region:us" ]
2023-11-11T11:40:07Z
2023-11-10T19:22:22.000Z
2023-11-10T19:22:22
--- license: mit tags: - art size_categories: - 10K<n<100K --- # Bangumi Image Base of Attack On Titan This is the image base of bangumi Attack On Titan, we detected 76 characters, 14308 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 | 1568 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 705 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 1342 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 1771 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 304 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 735 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 173 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 72 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 50 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 164 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 87 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 32 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 122 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 462 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 141 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 183 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 60 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 52 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 49 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 1082 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 57 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 587 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 224 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 140 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 110 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 26 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 581 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 86 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 60 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 141 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 59 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 534 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 64 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 173 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 22 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 32 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | ![preview 8](35/preview_8.png) | | 36 | 133 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | ![preview 7](36/preview_7.png) | ![preview 8](36/preview_8.png) | | 37 | 230 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | ![preview 8](37/preview_8.png) | | 38 | 94 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 44 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 46 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 48 | [Download](41/dataset.zip) | ![preview 1](41/preview_1.png) | ![preview 2](41/preview_2.png) | ![preview 3](41/preview_3.png) | ![preview 4](41/preview_4.png) | ![preview 5](41/preview_5.png) | ![preview 6](41/preview_6.png) | ![preview 7](41/preview_7.png) | ![preview 8](41/preview_8.png) | | 42 | 102 | [Download](42/dataset.zip) | ![preview 1](42/preview_1.png) | ![preview 2](42/preview_2.png) | ![preview 3](42/preview_3.png) | ![preview 4](42/preview_4.png) | ![preview 5](42/preview_5.png) | ![preview 6](42/preview_6.png) | ![preview 7](42/preview_7.png) | ![preview 8](42/preview_8.png) | | 43 | 36 | [Download](43/dataset.zip) | ![preview 1](43/preview_1.png) | ![preview 2](43/preview_2.png) | ![preview 3](43/preview_3.png) | ![preview 4](43/preview_4.png) | ![preview 5](43/preview_5.png) | ![preview 6](43/preview_6.png) | ![preview 7](43/preview_7.png) | ![preview 8](43/preview_8.png) | | 44 | 36 | [Download](44/dataset.zip) | ![preview 1](44/preview_1.png) | ![preview 2](44/preview_2.png) | ![preview 3](44/preview_3.png) | ![preview 4](44/preview_4.png) | ![preview 5](44/preview_5.png) | ![preview 6](44/preview_6.png) | ![preview 7](44/preview_7.png) | ![preview 8](44/preview_8.png) | | 45 | 51 | [Download](45/dataset.zip) | ![preview 1](45/preview_1.png) | ![preview 2](45/preview_2.png) | ![preview 3](45/preview_3.png) | ![preview 4](45/preview_4.png) | ![preview 5](45/preview_5.png) | ![preview 6](45/preview_6.png) | ![preview 7](45/preview_7.png) | ![preview 8](45/preview_8.png) | | 46 | 51 | [Download](46/dataset.zip) | ![preview 1](46/preview_1.png) | ![preview 2](46/preview_2.png) | ![preview 3](46/preview_3.png) | ![preview 4](46/preview_4.png) | ![preview 5](46/preview_5.png) | ![preview 6](46/preview_6.png) | ![preview 7](46/preview_7.png) | ![preview 8](46/preview_8.png) | | 47 | 52 | [Download](47/dataset.zip) | ![preview 1](47/preview_1.png) | ![preview 2](47/preview_2.png) | ![preview 3](47/preview_3.png) | ![preview 4](47/preview_4.png) | ![preview 5](47/preview_5.png) | ![preview 6](47/preview_6.png) | ![preview 7](47/preview_7.png) | ![preview 8](47/preview_8.png) | | 48 | 55 | [Download](48/dataset.zip) | ![preview 1](48/preview_1.png) | ![preview 2](48/preview_2.png) | ![preview 3](48/preview_3.png) | ![preview 4](48/preview_4.png) | ![preview 5](48/preview_5.png) | ![preview 6](48/preview_6.png) | ![preview 7](48/preview_7.png) | ![preview 8](48/preview_8.png) | | 49 | 51 | [Download](49/dataset.zip) | ![preview 1](49/preview_1.png) | ![preview 2](49/preview_2.png) | ![preview 3](49/preview_3.png) | ![preview 4](49/preview_4.png) | ![preview 5](49/preview_5.png) | ![preview 6](49/preview_6.png) | ![preview 7](49/preview_7.png) | ![preview 8](49/preview_8.png) | | 50 | 23 | [Download](50/dataset.zip) | ![preview 1](50/preview_1.png) | ![preview 2](50/preview_2.png) | ![preview 3](50/preview_3.png) | ![preview 4](50/preview_4.png) | ![preview 5](50/preview_5.png) | ![preview 6](50/preview_6.png) | ![preview 7](50/preview_7.png) | ![preview 8](50/preview_8.png) | | 51 | 70 | [Download](51/dataset.zip) | ![preview 1](51/preview_1.png) | ![preview 2](51/preview_2.png) | ![preview 3](51/preview_3.png) | ![preview 4](51/preview_4.png) | ![preview 5](51/preview_5.png) | ![preview 6](51/preview_6.png) | ![preview 7](51/preview_7.png) | ![preview 8](51/preview_8.png) | | 52 | 66 | [Download](52/dataset.zip) | ![preview 1](52/preview_1.png) | ![preview 2](52/preview_2.png) | ![preview 3](52/preview_3.png) | ![preview 4](52/preview_4.png) | ![preview 5](52/preview_5.png) | ![preview 6](52/preview_6.png) | ![preview 7](52/preview_7.png) | ![preview 8](52/preview_8.png) | | 53 | 77 | [Download](53/dataset.zip) | ![preview 1](53/preview_1.png) | ![preview 2](53/preview_2.png) | ![preview 3](53/preview_3.png) | ![preview 4](53/preview_4.png) | ![preview 5](53/preview_5.png) | ![preview 6](53/preview_6.png) | ![preview 7](53/preview_7.png) | ![preview 8](53/preview_8.png) | | 54 | 37 | [Download](54/dataset.zip) | ![preview 1](54/preview_1.png) | ![preview 2](54/preview_2.png) | ![preview 3](54/preview_3.png) | ![preview 4](54/preview_4.png) | ![preview 5](54/preview_5.png) | ![preview 6](54/preview_6.png) | ![preview 7](54/preview_7.png) | ![preview 8](54/preview_8.png) | | 55 | 20 | [Download](55/dataset.zip) | ![preview 1](55/preview_1.png) | ![preview 2](55/preview_2.png) | ![preview 3](55/preview_3.png) | ![preview 4](55/preview_4.png) | ![preview 5](55/preview_5.png) | ![preview 6](55/preview_6.png) | ![preview 7](55/preview_7.png) | ![preview 8](55/preview_8.png) | | 56 | 33 | [Download](56/dataset.zip) | ![preview 1](56/preview_1.png) | ![preview 2](56/preview_2.png) | ![preview 3](56/preview_3.png) | ![preview 4](56/preview_4.png) | ![preview 5](56/preview_5.png) | ![preview 6](56/preview_6.png) | ![preview 7](56/preview_7.png) | ![preview 8](56/preview_8.png) | | 57 | 94 | [Download](57/dataset.zip) | ![preview 1](57/preview_1.png) | ![preview 2](57/preview_2.png) | ![preview 3](57/preview_3.png) | ![preview 4](57/preview_4.png) | ![preview 5](57/preview_5.png) | ![preview 6](57/preview_6.png) | ![preview 7](57/preview_7.png) | ![preview 8](57/preview_8.png) | | 58 | 25 | [Download](58/dataset.zip) | ![preview 1](58/preview_1.png) | ![preview 2](58/preview_2.png) | ![preview 3](58/preview_3.png) | ![preview 4](58/preview_4.png) | ![preview 5](58/preview_5.png) | ![preview 6](58/preview_6.png) | ![preview 7](58/preview_7.png) | ![preview 8](58/preview_8.png) | | 59 | 19 | [Download](59/dataset.zip) | ![preview 1](59/preview_1.png) | ![preview 2](59/preview_2.png) | ![preview 3](59/preview_3.png) | ![preview 4](59/preview_4.png) | ![preview 5](59/preview_5.png) | ![preview 6](59/preview_6.png) | ![preview 7](59/preview_7.png) | ![preview 8](59/preview_8.png) | | 60 | 32 | [Download](60/dataset.zip) | ![preview 1](60/preview_1.png) | ![preview 2](60/preview_2.png) | ![preview 3](60/preview_3.png) | ![preview 4](60/preview_4.png) | ![preview 5](60/preview_5.png) | ![preview 6](60/preview_6.png) | ![preview 7](60/preview_7.png) | ![preview 8](60/preview_8.png) | | 61 | 31 | [Download](61/dataset.zip) | ![preview 1](61/preview_1.png) | ![preview 2](61/preview_2.png) | ![preview 3](61/preview_3.png) | ![preview 4](61/preview_4.png) | ![preview 5](61/preview_5.png) | ![preview 6](61/preview_6.png) | ![preview 7](61/preview_7.png) | ![preview 8](61/preview_8.png) | | 62 | 44 | [Download](62/dataset.zip) | ![preview 1](62/preview_1.png) | ![preview 2](62/preview_2.png) | ![preview 3](62/preview_3.png) | ![preview 4](62/preview_4.png) | ![preview 5](62/preview_5.png) | ![preview 6](62/preview_6.png) | ![preview 7](62/preview_7.png) | ![preview 8](62/preview_8.png) | | 63 | 23 | [Download](63/dataset.zip) | ![preview 1](63/preview_1.png) | ![preview 2](63/preview_2.png) | ![preview 3](63/preview_3.png) | ![preview 4](63/preview_4.png) | ![preview 5](63/preview_5.png) | ![preview 6](63/preview_6.png) | ![preview 7](63/preview_7.png) | ![preview 8](63/preview_8.png) | | 64 | 59 | [Download](64/dataset.zip) | ![preview 1](64/preview_1.png) | ![preview 2](64/preview_2.png) | ![preview 3](64/preview_3.png) | ![preview 4](64/preview_4.png) | ![preview 5](64/preview_5.png) | ![preview 6](64/preview_6.png) | ![preview 7](64/preview_7.png) | ![preview 8](64/preview_8.png) | | 65 | 52 | [Download](65/dataset.zip) | ![preview 1](65/preview_1.png) | ![preview 2](65/preview_2.png) | ![preview 3](65/preview_3.png) | ![preview 4](65/preview_4.png) | ![preview 5](65/preview_5.png) | ![preview 6](65/preview_6.png) | ![preview 7](65/preview_7.png) | ![preview 8](65/preview_8.png) | | 66 | 39 | [Download](66/dataset.zip) | ![preview 1](66/preview_1.png) | ![preview 2](66/preview_2.png) | ![preview 3](66/preview_3.png) | ![preview 4](66/preview_4.png) | ![preview 5](66/preview_5.png) | ![preview 6](66/preview_6.png) | ![preview 7](66/preview_7.png) | ![preview 8](66/preview_8.png) | | 67 | 58 | [Download](67/dataset.zip) | ![preview 1](67/preview_1.png) | ![preview 2](67/preview_2.png) | ![preview 3](67/preview_3.png) | ![preview 4](67/preview_4.png) | ![preview 5](67/preview_5.png) | ![preview 6](67/preview_6.png) | ![preview 7](67/preview_7.png) | ![preview 8](67/preview_8.png) | | 68 | 16 | [Download](68/dataset.zip) | ![preview 1](68/preview_1.png) | ![preview 2](68/preview_2.png) | ![preview 3](68/preview_3.png) | ![preview 4](68/preview_4.png) | ![preview 5](68/preview_5.png) | ![preview 6](68/preview_6.png) | ![preview 7](68/preview_7.png) | ![preview 8](68/preview_8.png) | | 69 | 18 | [Download](69/dataset.zip) | ![preview 1](69/preview_1.png) | ![preview 2](69/preview_2.png) | ![preview 3](69/preview_3.png) | ![preview 4](69/preview_4.png) | ![preview 5](69/preview_5.png) | ![preview 6](69/preview_6.png) | ![preview 7](69/preview_7.png) | ![preview 8](69/preview_8.png) | | 70 | 26 | [Download](70/dataset.zip) | ![preview 1](70/preview_1.png) | ![preview 2](70/preview_2.png) | ![preview 3](70/preview_3.png) | ![preview 4](70/preview_4.png) | ![preview 5](70/preview_5.png) | ![preview 6](70/preview_6.png) | ![preview 7](70/preview_7.png) | ![preview 8](70/preview_8.png) | | 71 | 31 | [Download](71/dataset.zip) | ![preview 1](71/preview_1.png) | ![preview 2](71/preview_2.png) | ![preview 3](71/preview_3.png) | ![preview 4](71/preview_4.png) | ![preview 5](71/preview_5.png) | ![preview 6](71/preview_6.png) | ![preview 7](71/preview_7.png) | ![preview 8](71/preview_8.png) | | 72 | 68 | [Download](72/dataset.zip) | ![preview 1](72/preview_1.png) | ![preview 2](72/preview_2.png) | ![preview 3](72/preview_3.png) | ![preview 4](72/preview_4.png) | ![preview 5](72/preview_5.png) | ![preview 6](72/preview_6.png) | ![preview 7](72/preview_7.png) | ![preview 8](72/preview_8.png) | | 73 | 7 | [Download](73/dataset.zip) | ![preview 1](73/preview_1.png) | ![preview 2](73/preview_2.png) | ![preview 3](73/preview_3.png) | ![preview 4](73/preview_4.png) | ![preview 5](73/preview_5.png) | ![preview 6](73/preview_6.png) | ![preview 7](73/preview_7.png) | N/A | | 74 | 10 | [Download](74/dataset.zip) | ![preview 1](74/preview_1.png) | ![preview 2](74/preview_2.png) | ![preview 3](74/preview_3.png) | ![preview 4](74/preview_4.png) | ![preview 5](74/preview_5.png) | ![preview 6](74/preview_6.png) | ![preview 7](74/preview_7.png) | ![preview 8](74/preview_8.png) | | noise | 251 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.7133674621582031, -0.20756374299526215, 0.16990242898464203, 0.20214559137821198, -0.25924205780029297, -0.019269723445177078, 0.021681595593690872, -0.3334246873855591, 0.683760941028595, 0.5018227100372314, -0.8792136907577515, -0.8404478430747986, -0.7098032236099243, 0.4931437671184...
null
null
null
null
null
null
null
null
null
null
null
null
null
sankovic/shirimxz
sankovic
2023-11-10T19:47:56Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-10T19:47:56Z
2023-11-10T19:47:04.000Z
2023-11-10T19:47:04
--- license: openrail ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
Lubix88/lkxvGPT
Lubix88
2023-11-11T16:32:20Z
0
0
null
[ "task_categories:question-answering", "language:pl", "code", "region:us" ]
2023-11-11T16:32:20Z
2023-11-10T20:25:41.000Z
2023-11-10T20:25:41
--- task_categories: - question-answering language: - pl tags: - code ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
NyxSlee/translating_mplm_dataset
NyxSlee
2023-11-10T21:11:55Z
0
0
null
[ "region:us" ]
2023-11-10T21:11:55Z
2023-11-10T20:27:18.000Z
2023-11-10T20:27:18
--- dataset_info: features: - name: number dtype: string - name: sentence dtype: string - name: word_translations struct: - name: 一个 (yī gè) dtype: string - name: 一尊 (yī zūn) dtype: string - name: 下来 (xià lái) dtype: string - name: 仿佛 (fǎng fú) dtype: string - name: 会 (huì) dtype: string - name: 凝固 (níng gù) dtype: string - name: 动过 (dòng guò) dtype: string - name: 只余 (zhǐ yú) dtype: string - name: 坐在 (zuò zài) dtype: string - name: 天色 (Tiān sè) dtype: string - name: 完全 (wán quán) dtype: string - name: 屋内 (wū nèi) dtype: string - name: 床边 (chuáng biān) dtype: string - name: 捧着 (pěng zhe) dtype: string - name: 放在 (fàng zài) dtype: string - name: 是 (shì) dtype: string - name: 暗了 (àn le) dtype: string - name: 暮色 (mù sè) dtype: string - name: 没有 (méi yǒu) dtype: string - name: 浅浅 (qiǎn qiǎn) dtype: string - name: 燃烛 (rán zhú) dtype: string - name: 的 (de) dtype: string - name: 糕点 (gāo diǎn) dtype: string - name: 许久 (xǔ jiǔ) dtype: string - name: 谁 (shuí) dtype: string - name: 身影 (shēn yǐng) dtype: string - name: 轮廓 (lún kuò) dtype: string - name: 这儿 (zhèr) dtype: string - name: 逐渐 (zhú jiàn) dtype: string - name: 都没有 (dōu méi yǒu) dtype: string - name: 阚闻萧 (Kàn wén xiāo) dtype: string - name: 隐没 (yǐn mò) dtype: string - name: 黑漆漆的 (hēi qī qī de) dtype: string - name: best_translation dtype: string - name: alternative_translations sequence: string splits: - name: train num_bytes: 3429 num_examples: 3 download_size: 0 dataset_size: 3429 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "translating_mplm_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.4495493769645691, -0.16414576768875122, 0.3798176050186157, 0.36774516105651855, -0.450907826423645, 0.0024924930185079575, 0.16144973039627075, 0.006722752004861832, 0.3784818649291992, 0.4732735753059387, -0.7788801193237305, -0.8612784147262573, -0.899956464767456, -0.220258682966232...
null
null
null
null
null
null
null
null
null
null
null
null
null
digitalmarketing/Portugal-Digital-Nomad-Visa
digitalmarketing
2023-11-10T20:30:22Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-10T20:30:22Z
2023-11-10T20:30:22.000Z
2023-11-10T20:30:22
--- license: mit ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
Geosb/reuters_articles
Geosb
2023-11-10T20:30:30Z
0
0
null
[ "region:us" ]
2023-11-10T20:30:30Z
2023-11-10T20:30:22.000Z
2023-11-10T20:30:22
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073411 dataset_size: 17042155 --- # Dataset Card for "reuters_articles" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.5343137979507446, -0.27132031321525574, 0.4044150114059448, 0.16633321344852448, -0.35991719365119934, -0.023834936320781708, 0.12922945618629456, -0.2592724561691284, 0.9413431882858276, 0.3171338140964508, -0.7217959761619568, -0.7758855223655701, -0.6177051663398743, -0.0228585116565...
null
null
null
null
null
null
null
null
null
null
null
null
null
wmmarcellino/durant_embeddings
wmmarcellino
2023-11-15T00:06:36Z
0
0
null
[ "region:us" ]
2023-11-15T00:06:36Z
2023-11-10T20:47:23.000Z
2023-11-10T20:47:23
Entry not found
[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
NyxSlee/translating_mplm_dataset_two
NyxSlee
2023-11-10T21:12:28Z
0
0
null
[ "region:us" ]
2023-11-10T21:12:28Z
2023-11-10T21:12:27.000Z
2023-11-10T21:12:27
--- dataset_info: features: - name: number dtype: string - name: sentence dtype: string - name: word_translations struct: - name: 一个 (yī gè) dtype: string - name: 一尊 (yī zūn) dtype: string - name: 下来 (xià lái) dtype: string - name: 仿佛 (fǎng fú) dtype: string - name: 会 (huì) dtype: string - name: 凝固 (níng gù) dtype: string - name: 动过 (dòng guò) dtype: string - name: 只余 (zhǐ yú) dtype: string - name: 坐在 (zuò zài) dtype: string - name: 天色 (Tiān sè) dtype: string - name: 完全 (wán quán) dtype: string - name: 屋内 (wū nèi) dtype: string - name: 床边 (chuáng biān) dtype: string - name: 捧着 (pěng zhe) dtype: string - name: 放在 (fàng zài) dtype: string - name: 是 (shì) dtype: string - name: 暗了 (àn le) dtype: string - name: 暮色 (mù sè) dtype: string - name: 没有 (méi yǒu) dtype: string - name: 浅浅 (qiǎn qiǎn) dtype: string - name: 燃烛 (rán zhú) dtype: string - name: 的 (de) dtype: string - name: 糕点 (gāo diǎn) dtype: string - name: 许久 (xǔ jiǔ) dtype: string - name: 谁 (shuí) dtype: string - name: 身影 (shēn yǐng) dtype: string - name: 轮廓 (lún kuò) dtype: string - name: 这儿 (zhèr) dtype: string - name: 逐渐 (zhú jiàn) dtype: string - name: 都没有 (dōu méi yǒu) dtype: string - name: 阚闻萧 (Kàn wén xiāo) dtype: string - name: 隐没 (yǐn mò) dtype: string - name: 黑漆漆的 (hēi qī qī de) dtype: string - name: best_translation dtype: string - name: alternative_translations sequence: string splits: - name: train num_bytes: 3429 num_examples: 3 download_size: 27294 dataset_size: 3429 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "translating_mplm_dataset_two" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.3570930063724518, -0.24117811024188995, 0.3821997344493866, 0.42806845903396606, -0.5086762309074402, -0.048083916306495667, 0.21416206657886505, -0.08767961710691452, 0.2748085856437683, 0.5429450273513794, -0.7253645062446594, -0.7063134908676147, -0.9185314774513245, -0.3520511984825...
null
null
null
null
null
null
null
null
null
null
null
null
null
NyxSlee/translating_mplm_dataset_thre
NyxSlee
2023-11-10T21:13:27Z
0
0
null
[ "region:us" ]
2023-11-10T21:13:27Z
2023-11-10T21:13:26.000Z
2023-11-10T21:13:26
--- dataset_info: features: - name: number dtype: string - name: sentence dtype: string - name: word_translations struct: - name: 一个 (yī gè) dtype: string - name: 一尊 (yī zūn) dtype: string - name: 下来 (xià lái) dtype: string - name: 仿佛 (fǎng fú) dtype: string - name: 会 (huì) dtype: string - name: 凝固 (níng gù) dtype: string - name: 动过 (dòng guò) dtype: string - name: 只余 (zhǐ yú) dtype: string - name: 坐在 (zuò zài) dtype: string - name: 天色 (Tiān sè) dtype: string - name: 完全 (wán quán) dtype: string - name: 屋内 (wū nèi) dtype: string - name: 床边 (chuáng biān) dtype: string - name: 捧着 (pěng zhe) dtype: string - name: 放在 (fàng zài) dtype: string - name: 是 (shì) dtype: string - name: 暗了 (àn le) dtype: string - name: 暮色 (mù sè) dtype: string - name: 没有 (méi yǒu) dtype: string - name: 浅浅 (qiǎn qiǎn) dtype: string - name: 燃烛 (rán zhú) dtype: string - name: 的 (de) dtype: string - name: 糕点 (gāo diǎn) dtype: string - name: 许久 (xǔ jiǔ) dtype: string - name: 谁 (shuí) dtype: string - name: 身影 (shēn yǐng) dtype: string - name: 轮廓 (lún kuò) dtype: string - name: 这儿 (zhèr) dtype: string - name: 逐渐 (zhú jiàn) dtype: string - name: 都没有 (dōu méi yǒu) dtype: string - name: 阚闻萧 (Kàn wén xiāo) dtype: string - name: 隐没 (yǐn mò) dtype: string - name: 黑漆漆的 (hēi qī qī de) dtype: string - name: best_translation dtype: string - name: alternative_translations sequence: string splits: - name: train num_bytes: 3429 num_examples: 3 download_size: 27294 dataset_size: 3429 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "translating_mplm_dataset_thre" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.3935715854167938, -0.12888512015342712, 0.38758644461631775, 0.3084637224674225, -0.4494365155696869, -0.004421555437147617, 0.14868107438087463, 0.04003424197435379, 0.347217857837677, 0.49596941471099854, -0.7307297587394714, -0.8229861259460449, -0.900680661201477, -0.235077902674675...
null
null
null
null
null
null
null
null
null
null
null
null
null
NyxSlee/translating_mplm_dataset_three
NyxSlee
2023-11-10T21:33:33Z
0
0
null
[ "region:us" ]
2023-11-10T21:33:33Z
2023-11-10T21:15:55.000Z
2023-11-10T21:15:55
--- dataset_info: license: "MIT" name: "translating_mplm_dataset_three" description: "A dataset for translating sentences from MPLM" homepage: "https://github.com/yourusername/your-repo" task_categories: - "language-translation" languages: - "zh" - "en" size: "3 examples" download_size: "27.3 KB" dataset_size: "3.42 KB" visibility: public status: active authors: - name: "Your Name" email: "your.email@example.com" creation_date: "2023-11-10" repository: "https://github.com/yourusername/your-repo" citation: | @misc{yourcitation, title={Your Dataset Title}, author={Your Name}, year={2023}, publisher={Your Publisher}, journal={Journal of Datasets}, howpublished={\url{https://github.com/yourusername/your-repo}}, } --- # Rest of your dataset card dataset_info: features: - name: number dtype: string - name: sentence dtype: string - name: word_translations struct: - name: 一个 (yī gè) dtype: string - name: 一尊 (yī zūn) dtype: string - name: 下来 (xià lái) dtype: string - name: 仿佛 (fǎng fú) dtype: string - name: 会 (huì) dtype: string - name: 凝固 (níng gù) dtype: string - name: 动过 (dòng guò) dtype: string - name: 只余 (zhǐ yú) dtype: string - name: 坐在 (zuò zài) dtype: string - name: 天色 (Tiān sè) dtype: string - name: 完全 (wán quán) dtype: string - name: 屋内 (wū nèi) dtype: string - name: 床边 (chuáng biān) dtype: string - name: 捧着 (pěng zhe) dtype: string - name: 放在 (fàng zài) dtype: string - name: 是 (shì) dtype: string - name: 暗了 (àn le) dtype: string - name: 暮色 (mù sè) dtype: string - name: 没有 (méi yǒu) dtype: string - name: 浅浅 (qiǎn qiǎn) dtype: string - name: 燃烛 (rán zhú) dtype: string - name: 的 (de) dtype: string - name: 糕点 (gāo diǎn) dtype: string - name: 许久 (xǔ jiǔ) dtype: string - name: 谁 (shuí) dtype: string - name: 身影 (shēn yǐng) dtype: string - name: 轮廓 (lún kuò) dtype: string - name: 这儿 (zhèr) dtype: string - name: 逐渐 (zhú jiàn) dtype: string - name: 都没有 (dōu méi yǒu) dtype: string - name: 阚闻萧 (Kàn wén xiāo) dtype: string - name: 隐没 (yǐn mò) dtype: string - name: 黑漆漆的 (hēi qī qī de) dtype: string - name: best_translation dtype: string - name: alternative_translations sequence: string splits: - name: train num_bytes: 3429 num_examples: 3 download_size: 27294 dataset_size: 3429 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "translating_mplm_dataset_three" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.4727020561695099, -0.24582958221435547, 0.18813352286815643, 0.32866179943084717, -0.49312177300453186, -0.05871015042066574, -0.13544809818267822, -0.1017269715666771, 0.45000582933425903, 0.4605051279067993, -0.6198541522026062, -0.8705780506134033, -0.7508245706558228, 0.246697351336...
null
null
null
null
null
null
null
null
null
null
null
null
null
MatheusOps/lina
MatheusOps
2023-11-10T21:17:27Z
0
0
null
[ "region:us" ]
2023-11-10T21:17:27Z
2023-11-10T21:16:46.000Z
2023-11-10T21:16:46
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
NyxSlee/translating_mplm_dataset_four
NyxSlee
2023-11-10T21:19:10Z
0
0
null
[ "region:us" ]
2023-11-10T21:19:10Z
2023-11-10T21:19:09.000Z
2023-11-10T21:19:09
--- dataset_info: features: - name: number dtype: string - name: sentence dtype: string - name: word_translations struct: - name: 一个 (yī gè) dtype: string - name: 一尊 (yī zūn) dtype: string - name: 下来 (xià lái) dtype: string - name: 仿佛 (fǎng fú) dtype: string - name: 会 (huì) dtype: string - name: 凝固 (níng gù) dtype: string - name: 动过 (dòng guò) dtype: string - name: 只余 (zhǐ yú) dtype: string - name: 坐在 (zuò zài) dtype: string - name: 天色 (Tiān sè) dtype: string - name: 完全 (wán quán) dtype: string - name: 屋内 (wū nèi) dtype: string - name: 床边 (chuáng biān) dtype: string - name: 捧着 (pěng zhe) dtype: string - name: 放在 (fàng zài) dtype: string - name: 是 (shì) dtype: string - name: 暗了 (àn le) dtype: string - name: 暮色 (mù sè) dtype: string - name: 没有 (méi yǒu) dtype: string - name: 浅浅 (qiǎn qiǎn) dtype: string - name: 燃烛 (rán zhú) dtype: string - name: 的 (de) dtype: string - name: 糕点 (gāo diǎn) dtype: string - name: 许久 (xǔ jiǔ) dtype: string - name: 谁 (shuí) dtype: string - name: 身影 (shēn yǐng) dtype: string - name: 轮廓 (lún kuò) dtype: string - name: 这儿 (zhèr) dtype: string - name: 逐渐 (zhú jiàn) dtype: string - name: 都没有 (dōu méi yǒu) dtype: string - name: 阚闻萧 (Kàn wén xiāo) dtype: string - name: 隐没 (yǐn mò) dtype: string - name: 黑漆漆的 (hēi qī qī de) dtype: string - name: best_translation dtype: string - name: alternative_translations sequence: string splits: - name: train num_bytes: 3429 num_examples: 3 download_size: 27294 dataset_size: 3429 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "translating_mplm_dataset_four" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.4603571891784668, -0.091532401740551, 0.5370891690254211, 0.4487861394882202, -0.39785638451576233, 0.02298574708402157, 0.2769435942173004, -0.003613809822127223, 0.28415122628211975, 0.5351805090904236, -0.7337703108787537, -0.8740221261978149, -0.779941737651825, 0.044623713940382004...
null
null
null
null
null
null
null
null
null
null
null
null
null
NyxSlee/translating_mplm_dataset_six
NyxSlee
2023-11-10T21:24:16Z
0
0
null
[ "region:us" ]
2023-11-10T21:24:16Z
2023-11-10T21:24:15.000Z
2023-11-10T21:24:15
--- dataset_info: features: - name: number dtype: string - name: sentence dtype: string - name: word_translations struct: - name: 一个 (yī gè) dtype: string - name: 一尊 (yī zūn) dtype: string - name: 下来 (xià lái) dtype: string - name: 仿佛 (fǎng fú) dtype: string - name: 会 (huì) dtype: string - name: 凝固 (níng gù) dtype: string - name: 动过 (dòng guò) dtype: string - name: 只余 (zhǐ yú) dtype: string - name: 坐在 (zuò zài) dtype: string - name: 天色 (Tiān sè) dtype: string - name: 完全 (wán quán) dtype: string - name: 屋内 (wū nèi) dtype: string - name: 床边 (chuáng biān) dtype: string - name: 捧着 (pěng zhe) dtype: string - name: 放在 (fàng zài) dtype: string - name: 是 (shì) dtype: string - name: 暗了 (àn le) dtype: string - name: 暮色 (mù sè) dtype: string - name: 没有 (méi yǒu) dtype: string - name: 浅浅 (qiǎn qiǎn) dtype: string - name: 燃烛 (rán zhú) dtype: string - name: 的 (de) dtype: string - name: 糕点 (gāo diǎn) dtype: string - name: 许久 (xǔ jiǔ) dtype: string - name: 谁 (shuí) dtype: string - name: 身影 (shēn yǐng) dtype: string - name: 轮廓 (lún kuò) dtype: string - name: 这儿 (zhèr) dtype: string - name: 逐渐 (zhú jiàn) dtype: string - name: 都没有 (dōu méi yǒu) dtype: string - name: 阚闻萧 (Kàn wén xiāo) dtype: string - name: 隐没 (yǐn mò) dtype: string - name: 黑漆漆的 (hēi qī qī de) dtype: string - name: best_translation dtype: string - name: alternative_translations sequence: string splits: - name: train num_bytes: 3429 num_examples: 3 download_size: 27294 dataset_size: 3429 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "translating_mplm_dataset_six" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.3956659138202667, -0.06048347428441048, 0.39274653792381287, 0.3205132782459259, -0.5059980750083923, -0.12405937165021896, 0.2667628824710846, -0.06091606244444847, 0.3413311839103699, 0.5040587186813354, -0.7729209065437317, -0.8423173427581787, -0.8306822180747986, -0.052475307136774...
null
null
null
null
null
null
null
null
null
null
null
null
null
rileyhilliard/opendata
rileyhilliard
2023-11-10T21:42:28Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-10T21:42:28Z
2023-11-10T21:42:28.000Z
2023-11-10T21:42:28
--- license: mit ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
metricv/metricsubs-segmenter
metricv
2023-11-14T03:53:45Z
0
0
null
[ "region:us" ]
2023-11-14T03:53:45Z
2023-11-10T21:44:45.000Z
2023-11-10T21:44:45
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
Gabriel1322/jccolab
Gabriel1322
2023-11-10T22:03:27Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-10T22:03:27Z
2023-11-10T22:02:40.000Z
2023-11-10T22:02:40
--- license: openrail ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
Nassssssss/TETOV4BYNAS
Nassssssss
2023-11-10T22:22:17Z
0
0
null
[ "region:us" ]
2023-11-10T22:22:17Z
2023-11-10T22:21:47.000Z
2023-11-10T22:21:47
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
uatafaque/owhana
uatafaque
2023-11-10T22:46:55Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-10T22:46:55Z
2023-11-10T22:46:13.000Z
2023-11-10T22:46:13
--- license: openrail ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
AliRF/Reddit_LawQA
AliRF
2023-11-10T22:50:22Z
0
0
null
[ "region:us" ]
2023-11-10T22:50:22Z
2023-11-10T22:50:22.000Z
2023-11-10T22:50:22
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
nekofura/avatara_project
nekofura
2023-11-12T17:39:46Z
0
0
null
[ "region:us" ]
2023-11-12T17:39:46Z
2023-11-10T23:47:28.000Z
2023-11-10T23:47:28
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
medieval-data/open-medieval-bibliography
medieval-data
2023-11-14T18:43:13Z
0
0
null
[ "region:us" ]
2023-11-14T18:43:13Z
2023-11-10T23:49:34.000Z
2023-11-10T23:49:34
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
Maxis47/Irons
Maxis47
2023-11-11T00:21:53Z
0
0
null
[ "task_categories:text-to-image", "size_categories:1M<n<10M", "language:en", "license:unlicense", "art", "region:us" ]
2023-11-11T00:21:53Z
2023-11-11T00:05:13.000Z
2023-11-11T00:05:13
--- license: unlicense task_categories: - text-to-image language: - en tags: - art size_categories: - 1M<n<10M ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
mariakmurphy55/empty
mariakmurphy55
2023-11-11T00:18:32Z
0
0
null
[ "task_categories:text-classification", "size_categories:n<1K", "language:en", "license:apache-2.0", "legal", "region:us" ]
2023-11-11T00:18:32Z
2023-11-11T00:16:52.000Z
2023-11-11T00:16:52
--- license: apache-2.0 task_categories: - text-classification language: - en tags: - legal pretty_name: prettyname! size_categories: - n<1K --- # 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] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [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. --> [More Information Needed] #### 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. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> 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:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
[ -0.5322356224060059, -0.5534716844558716, 0.1290130317211151, 0.23470577597618103, -0.39626216888427734, -0.11762470006942749, -0.03545305132865906, -0.6389272212982178, 0.5699822306632996, 0.7838326692581177, -0.7834625840187073, -0.9173274040222168, -0.55633145570755, 0.13078093528747559...
null
null
null
null
null
null
null
null
null
null
null
null
null
Excelsus/Annyv1
Excelsus
2023-11-11T01:22:33Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-11T01:22:33Z
2023-11-11T01:17:17.000Z
2023-11-11T01:17:17
--- license: openrail ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
disouz4/memes_models
disouz4
2023-11-11T01:58:04Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-11T01:58:04Z
2023-11-11T01:18:23.000Z
2023-11-11T01:18:23
--- license: mit ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
Johan230/Yo
Johan230
2023-11-11T01:20:08Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-11T01:20:08Z
2023-11-11T01:20:08.000Z
2023-11-11T01:20:08
--- license: openrail ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
Johan230/Luis
Johan230
2023-11-11T01:33:01Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-11T01:33:01Z
2023-11-11T01:27:56.000Z
2023-11-11T01:27:56
--- license: openrail ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
jovianzm/cartoonize-stupid
jovianzm
2023-11-11T15:17:11Z
0
0
null
[ "size_categories:1K<n<10K", "language:en", "license:mit", "code", "region:us" ]
2023-11-11T15:17:11Z
2023-11-11T01:37:24.000Z
2023-11-11T01:37:24
--- license: mit language: - en tags: - code pretty_name: cartoonize size_categories: - 1K<n<10K --- # Cartoonize Dataset Dataset used to train cartoonize models (pix2pix).
[ -0.41678404808044434, 0.17918109893798828, -0.20061112940311432, 0.45650050044059753, -0.375537633895874, -0.1703779101371765, 0.29965928196907043, 0.15649272501468658, 0.303024023771286, 0.8068493008613586, -0.699576735496521, -0.07984466850757599, -0.3859451413154602, -0.4053781628608703...
null
null
null
null
null
null
null
null
null
null
null
null
null
kenanjeff/ComVG
kenanjeff
2023-11-17T06:48:30Z
0
5
null
[ "task_categories:zero-shot-classification", "size_categories:1K<n<10K", "license:creativeml-openrail-m", "code", "arxiv:2211.13854", "region:us" ]
2023-11-17T06:48:30Z
2023-11-11T01:40:00.000Z
2023-11-11T01:40:00
--- license: creativeml-openrail-m task_categories: - zero-shot-classification tags: - code size_categories: - 1K<n<10K --- Compositional Visual Genome (ComVG) <br/> ComVG benchmark aims to test vision-language models ability in text-to-image retrieval. <br/> We selected 542 high-quality images from Visual Genome and created 5400 datapoints in ComVG. <br/> Each datapoint contains a postive and negative image. The negative image is a mutated variant with singular discrepancies in subject, object, or predicate.<br/> For more details on creation process, please refer: https://arxiv.org/abs/2211.13854
[ -0.634968101978302, -0.8253236413002014, 0.06402444839477539, -0.17270158231258392, -0.44154244661331177, -0.3436657190322876, 0.043537940829992294, -0.4379456043243408, 0.08822355419397354, 0.6837972402572632, -0.7083306908607483, -0.8764773011207581, -0.7727628350257874, 0.13631017506122...
null
null
null
null
null
null
null
null
null
null
null
null
null
Luizagrod23/Ryota
Luizagrod23
2023-11-11T02:09:17Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-11T02:09:17Z
2023-11-11T02:05:47.000Z
2023-11-11T02:05:47
--- license: apache-2.0 ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
joxen/YeGigglesaurus
joxen
2023-11-11T02:23:03Z
0
0
null
[ "license:other", "region:us" ]
2023-11-11T02:23:03Z
2023-11-11T02:07:02.000Z
2023-11-11T02:07:02
--- license: other license_name: britishkid license_link: LICENSE ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
xeroISB/LiverSegmentation
xeroISB
2023-11-11T02:17:42Z
0
0
null
[ "region:us" ]
2023-11-11T02:17:42Z
2023-11-11T02:17:42.000Z
2023-11-11T02:17:42
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
tamago154/treino
tamago154
2023-11-11T03:07:45Z
0
0
null
[ "region:us" ]
2023-11-11T03:07:45Z
2023-11-11T02:43:28.000Z
2023-11-11T02:43:28
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
aisyahhrazak/crawl-fliphtml
aisyahhrazak
2023-11-11T03:15:01Z
0
0
null
[ "language:ms", "language:en", "region:us" ]
2023-11-11T03:15:01Z
2023-11-11T03:12:16.000Z
2023-11-11T03:12:16
--- language: - ms - en --- Fliphtml pdf text version Search Query: - Melayu
[ -0.05563446506857872, -0.3670303225517273, 0.2845894396305084, 0.5367528200149536, -0.48489004373550415, -0.19550688564777374, 0.2800319790840149, -0.16196757555007935, 0.6202757954597473, 1.203128457069397, -0.434487521648407, -0.4135013520717621, -0.435588538646698, 0.7613030076026917, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
nekofura/secret
nekofura
2023-11-22T04:33:00Z
0
0
null
[ "region:us" ]
2023-11-22T04:33:00Z
2023-11-11T03:39:55.000Z
2023-11-11T03:39:55
Entry not found
[ -0.32276490330696106, -0.22568447887897491, 0.8622260093688965, 0.43461495637893677, -0.5282987356185913, 0.7012965083122253, 0.7915716171264648, 0.07618637382984161, 0.7746024131774902, 0.25632190704345703, -0.7852814197540283, -0.22573809325695038, -0.9104480743408203, 0.5715669393539429...
null
null
null
null
null
null
null
null
null
null
null
null
null
Taylor658/gptprompts
Taylor658
2023-11-11T04:13:55Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-11T04:13:55Z
2023-11-11T04:12:02.000Z
2023-11-11T04:12:02
--- license: apache-2.0 ---
[ -0.1285337507724762, -0.18616777658462524, 0.6529126167297363, 0.49436259269714355, -0.19319328665733337, 0.2360745370388031, 0.3607197403907776, 0.05056323483586311, 0.5793652534484863, 0.740013837814331, -0.6508102416992188, -0.23783975839614868, -0.710224986076355, -0.047825887799263, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
DamarJati/Dev-Ai-Art-ID
DamarJati
2023-11-13T12:49:39Z
0
0
null
[ "region:us" ]
2023-11-13T12:49:39Z
2023-11-11T04:32:55.000Z
2023-11-11T04:32:55
Entry not found
[ -0.32276490330696106, -0.22568447887897491, 0.8622260093688965, 0.43461495637893677, -0.5282987356185913, 0.7012965083122253, 0.7915716171264648, 0.07618637382984161, 0.7746024131774902, 0.25632190704345703, -0.7852814197540283, -0.22573809325695038, -0.9104480743408203, 0.5715669393539429...
null
null
null
null
null
null
null
null
null
null
null
null
null
Mariaaaaa/Twitter_with_COT
Mariaaaaa
2023-11-11T04:35:57Z
0
0
null
[ "region:us" ]
2023-11-11T04:35:57Z
2023-11-11T04:35:20.000Z
2023-11-11T04:35:20
Entry not found
[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
CJacobnriia/discord-new-avatars
CJacobnriia
2023-11-11T05:06:44Z
0
0
null
[ "task_categories:text-to-image", "size_categories:n<1K", "language:en", "region:us" ]
2023-11-11T05:06:44Z
2023-11-11T05:04:43.000Z
2023-11-11T05:04:43
--- task_categories: - text-to-image language: - en size_categories: - n<1K ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
Lithicsoft/Guanaco-Dataset-5k
Lithicsoft
2023-11-11T05:15:27Z
0
1
null
[ "region:us" ]
2023-11-11T05:15:27Z
2023-11-11T05:15:25.000Z
2023-11-11T05:15:25
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7856000 num_examples: 5000 download_size: 4641226 dataset_size: 7856000 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "lithicai-guanaco-llama2-5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.22389909625053406, -0.10112971812486649, 0.34981054067611694, 0.339825838804245, -0.5987576842308044, 0.13108593225479126, 0.24583330750465393, -0.4224632680416107, 0.8298131823539734, 0.457573801279068, -0.6658631563186646, -0.7929625511169434, -0.49488723278045654, -0.1081513315439224...
null
null
null
null
null
null
null
null
null
null
null
null
null
RafhaelSlideShow/Mcs
RafhaelSlideShow
2023-11-11T05:19:09Z
0
0
null
[ "region:us" ]
2023-11-11T05:19:09Z
2023-11-11T05:18:17.000Z
2023-11-11T05:18:17
Entry not found
[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
tinupicky/picky
tinupicky
2023-11-11T05:36:06Z
0
0
null
[ "region:us" ]
2023-11-11T05:36:06Z
2023-11-11T05:36:06.000Z
2023-11-11T05:36:06
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
basil2kk4/oke
basil2kk4
2023-11-11T05:56:59Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-11T05:56:59Z
2023-11-11T05:56:59.000Z
2023-11-11T05:56:59
--- license: apache-2.0 ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
basil2kk4/manh
basil2kk4
2023-11-11T05:58:13Z
0
0
null
[ "region:us" ]
2023-11-11T05:58:13Z
2023-11-11T05:58:13.000Z
2023-11-11T05:58:13
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
BangumiBase/onichichi
BangumiBase
2023-11-11T09:59:53Z
0
0
null
[ "size_categories:1K<n<10K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2023-11-11T09:59:53Z
2023-11-11T05:59:56.000Z
2023-11-11T05:59:56
--- license: mit tags: - art - not-for-all-audiences size_categories: - 1K<n<10K --- # Bangumi Image Base of Oni Chichi This is the image base of bangumi Oni Chichi, we detected 20 characters, 2903 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 | 134 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 168 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 320 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 47 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 35 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 21 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 337 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 10 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 38 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 1020 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 90 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 65 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 47 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 42 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 15 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 186 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 199 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 22 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 13 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | noise | 94 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.7042160630226135, -0.15091471374034882, 0.16839449107646942, 0.22376789152622223, -0.23233892023563385, -0.07387268543243408, -0.05440090224146843, -0.3935026526451111, 0.6262152791023254, 0.4906452000141144, -0.9416928291320801, -0.8295528292655945, -0.6672266721725464, 0.5220121145248...
null
null
null
null
null
null
null
null
null
null
null
null
null
Simonk97/duyuyen
Simonk97
2023-11-11T06:37:22Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-11T06:37:22Z
2023-11-11T06:09:43.000Z
2023-11-11T06:09:43
--- license: openrail ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
wanng/example_mmdata_mnbvc
wanng
2023-11-14T06:29:51Z
0
1
null
[ "license:apache-2.0", "region:us" ]
2023-11-14T06:29:51Z
2023-11-11T06:28:08.000Z
2023-11-11T06:28:08
--- license: apache-2.0 --- 这是MNBVC多模态语料小组的**图文通用语料**的格式展示。 字段说明: 1. 文件md5: 这个字段存储文件的MD5哈希值。MD5是一种广泛使用的哈希函数,它产生一个128位(16字节)的哈希值,通常用于确保数据的完整性。在这里,它可以用来唯一标识文件,或者检查文件是否被更改。 2. 文件id: 文件的唯一标识符。这可以是一个数据库中的主键,或者任何用于唯一标识文件的系统。 3. 页码: 如果数据源是一个多页文档(如PDF文件),这个字段表示文本或图片所在的具体页码。 4. 块id: 在文档中,特定块(block)的唯一标识。这可以用于标识文本或图片所在的具体块(block)。块(block)的定义其实主要用以区分“模态”,所以对于文本来说,可以是多段的文本,也可能是单段的,取决于使用者的实际情况。 5. 文本: 存储文档中的文本内容。这可以是整个文档的文本,或者是特定段落或页面的文本。 5. 图片: 如果文档中包含图片,这个字段存储图片的数据。 6. 时间: 记录语料出现的时间(比如2000年的报纸,就填2000年xx月xx日,早于1970年出现的数据均填1970年1月1日00时00分00秒。如果时间完全无法确定,则填数据处理时间)。 7. 数据类型: 指示存储在该行中的数据类型。目前包括:“文本”、“图片”两类,如有其他类型请联系本文档作者添加枚举字段。 8. bounding box: 四点坐标(两点坐标请补全到四点),用以支持版面分析等数据。请本文档作者补充数据示例。 9. 额外信息: 存放各种额外信息,比如 在假如数据类型为图片,则存放的可以是一个json格式文本大字段(text存储,需要解析一下)。 关于图片,我们的保存方法是: ```python def read_image(image_path): # 打开图像文件 with open(image_path, 'rb') as file: image = Image.open(file) # 将图像转换为二进制格式 img_byte_arr = io.BytesIO() image.save(img_byte_arr, format=image.format) img_byte_arr = img_byte_arr.getvalue() return img_byte_arr ``` 这个函数首先以二进制读模式打开指定路径的图像文件。然后,它使用PIL的Image.open方法来加载图像。为了将图像转换为二进制格式,我们创建了一个BytesIO对象,然后使用图像的save方法将其保存到这个内存中的字节流中。最后,使用getvalue方法获取字节流的内容,这将是图像的二进制表示。 在这段代码中,format=image.format确保图像以其原始格式保存。这样做是为了保持图像质量和格式的一致性,不管它最初是JPEG、PNG还是其他格式。 一个读取的方法: ```python import pandas as pd from PIL import Image import io # 读取Parquet文件 df = pd.read_parquet('train.parquet') # 遍历DataFrame的行 for index, row in df.iterrows(): # 处理图像数据 if row['图片'] is not None: image_data = row['图片'] image = Image.open(io.BytesIO(image_data)) # 保存图像,文件名可以根据需求自定义 image.save(f'image_{index}.png') ```
[ -0.6904588937759399, -0.8167149424552917, 0.08357277512550354, 0.327230304479599, -0.7916538119316101, -0.38275593519210815, 0.09899922460317612, -0.20195086300373077, 0.3801887333393097, 0.5614237785339355, -0.30816295742988586, -0.7207909822463989, -0.695148229598999, 0.25384682416915894...
null
null
null
null
null
null
null
null
null
null
null
null
null
courseralxy/datasettest
courseralxy
2023-11-11T07:02:20Z
0
0
null
[ "region:us" ]
2023-11-11T07:02:20Z
2023-11-11T07:02:20.000Z
2023-11-11T07:02:20
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
kevinhyc/xczxc
kevinhyc
2023-11-14T01:33:36Z
0
0
null
[ "region:us" ]
2023-11-14T01:33:36Z
2023-11-11T07:05:05.000Z
2023-11-11T07:05:05
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
QuyenAnhDE/data-for-text-summarization
QuyenAnhDE
2023-11-11T07:45:19Z
0
0
null
[ "region:us" ]
2023-11-11T07:45:19Z
2023-11-11T07:38:17.000Z
2023-11-11T07:38:17
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
polejowska/mist1-dataset
polejowska
2023-11-11T11:30:49Z
0
0
null
[ "region:us" ]
2023-11-11T11:30:49Z
2023-11-11T08:01:55.000Z
2023-11-11T08:01:55
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects list: - name: category_id dtype: class_label: names: '0': mist1 - name: image_id dtype: string - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: segmentation list: list: float32 - name: iscrowd dtype: bool splits: - name: train num_bytes: 8829652336.837 num_examples: 4583 - name: valid num_bytes: 477785158.0 num_examples: 257 - name: test num_bytes: 911170226.0 num_examples: 470 download_size: 10227791634 dataset_size: 10218607720.837 --- # Dataset Card for "mist1-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.5539724230766296, -0.19439789652824402, 0.07482559233903885, 0.3926509618759155, -0.26785025000572205, -0.2745748460292816, 0.5890597701072693, -0.09697594493627548, 0.7447434663772583, 0.4882966876029968, -0.9811376929283142, -0.6923621296882629, -0.6029354333877563, -0.429462671279907...
null
null
null
null
null
null
null
null
null
null
null
null
null
ItsMeBell/anotation
ItsMeBell
2023-11-16T09:36:52Z
0
0
null
[ "region:us" ]
2023-11-16T09:36:52Z
2023-11-11T08:25:14.000Z
2023-11-11T08:25:14
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
denis-berezutskiy-lad/ru_transcription_punctuation
denis-berezutskiy-lad
2023-11-12T17:17:01Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-12T17:17:01Z
2023-11-11T08:30:53.000Z
2023-11-11T08:30:53
--- license: apache-2.0 --- # About This is a dataset for training Russian punctuators/capitalizers via NeMo scripts (https://github.com/NVIDIA/NeMo) A BERT model already fine-tuned on this dataset can be found here: https://huggingface.co/denis-berezutskiy-lad/lad_transcription_bert_ru_punctuator Scripts for collecting/updating such a dataset, as well as training/using the model are located here: https://github.com/denis-berezutskiy-lad/transcription-bert-ru-punctuator-scripts/tree/main The idea behind the project is to use large continous professional transcriptions for training rather than relying on short low-quality samples consisting of 1-2 sentences (which is typical for the most popular datasets in Russian). Our experiments show significant improvements comparing to BERTs trained on the standard Ru datasets (social comments, omnia russica etc.). That's why we mainly use transcriptions published by Russian legislatures (Gosduma, Mosgorduma) with some addition of film subtitles from OpenSubtitles project. The dataset is in .csv format, but can be easily converted to the NeMo format (text.txt and labels.txt) - see the custom scripts above. About 1.2 GB of data is from Gosduma, ~300 MB from Mosgorduma and ~300 MB from a snapshot of Russian OpenSubtitles, taken from the Taiga project (https://tatianashavrina.github.io/taiga_site/downloads.html ). The rows are ordered randomly by the source document ("source_entity" column), however within a document the order is preserved like it was in the original text. So if you need to regroup the texts (for example, make longer or shorter sequences), you may concatenate rows grouped by the source entity and then split them as you want. # Supported labels Please note that some new labels are not supported by NeMo scripts out of the box (-, —, T), so we need to add special handling for them. See the inference notebook for details. ## Punctuation O,.?!:;…⁈-— ## Capitalization OUT (T means abbreviation ("total" uppercase))
[ -0.10924244672060013, -0.5166385769844055, 0.24768415093421936, 0.12720490992069244, -0.34746432304382324, 0.036432936787605286, -0.49184370040893555, -0.26497501134872437, 0.3638027012348175, 0.24562478065490723, -0.3528980016708374, -0.5744853615760803, -0.6297258138656616, 0.23906032741...
null
null
null
null
null
null
null
null
null
null
null
null
null
MaralGPT/Chinkara-Instruct-Guanaco-Zephyr-Format
MaralGPT
2023-11-11T08:44:35Z
0
0
null
[ "region:us" ]
2023-11-11T08:44:35Z
2023-11-11T08:44:33.000Z
2023-11-11T08:44:33
--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string - name: text dtype: string splits: - name: train num_bytes: 24648370 num_examples: 9846 download_size: 14331955 dataset_size: 24648370 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Chinkara-Instruct-Guanaco-Zephyr-Format" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.19672438502311707, -0.18073061108589172, 0.022973963990807533, 0.248055562376976, -0.354245662689209, -0.16730834543704987, -0.10334357619285583, -0.07502004504203796, 0.8954060077667236, 0.389190137386322, -0.8476591110229492, -0.7571345567703247, -0.38413238525390625, -0.3113175034523...
null
null
null
null
null
null
null
null
null
null
null
null
null
Pablao0948/Nascimento
Pablao0948
2023-11-11T09:54:14Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-11T09:54:14Z
2023-11-11T09:04:50.000Z
2023-11-11T09:04:50
--- license: openrail ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
WackuWacku/wakueggman
WackuWacku
2023-11-11T09:09:51Z
0
0
null
[ "region:us" ]
2023-11-11T09:09:51Z
2023-11-11T09:09:51.000Z
2023-11-11T09:09:51
Entry not found
[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
yangjinlong/ym41
yangjinlong
2023-11-11T09:15:52Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-11T09:15:52Z
2023-11-11T09:11:07.000Z
2023-11-11T09:11:07
--- license: mit ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
damnletmedome/rvcsihave
damnletmedome
2023-11-23T03:12:50Z
0
0
null
[ "license:unknown", "region:us" ]
2023-11-23T03:12:50Z
2023-11-11T09:26:41.000Z
2023-11-11T09:26:41
--- license: unknown ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
askb42/tensorimg
askb42
2023-11-11T09:30:10Z
0
0
null
[ "region:us" ]
2023-11-11T09:30:10Z
2023-11-11T09:29:17.000Z
2023-11-11T09:29:17
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
Nimart25896/Azure
Nimart25896
2023-11-11T09:58:32Z
0
0
null
[ "region:us" ]
2023-11-11T09:58:32Z
2023-11-11T09:56:53.000Z
2023-11-11T09:56:53
Future Outlook for Azure Resource Management The evolving landscape of cloud technology hints at further <a href="https://dumpsmedia.com/education/an-azure-resource-can-have-multiple-delete-locks/">An Azure Resource can have Multiple Delete Locksl</a> enhancements in resource management, potentially revolutionizing the approach to multiple delete locks. An Azure Resource Can Have Multiple Delete Locks Introduction to Azure Resources Azure, Microsoft's cloud computing service, offers a vast array of resources to create and manage applications. These resources include databases, virtual machines, and storage accounts, among others. They ensure efficient management and utilization of cloud-based services. Understanding Resource Locks in Azure Resource locks in Azure provide a crucial means to prevent unwanted modifications or deletions within a <a href="https://dumpsmedia.com/education/an-azure-resource-can-have-multiple-delete-locks/">An Azure Resource can have Multiple Delete Locksl</a> resource group. These locks act as safeguards, allowing users to control actions within their Azure resources. Click Here For More Details>>>>>>>https://dumpsmedia.com/education/an-azure-resource-can-have-multiple-delete-locks/
[ -0.4716258645057678, -0.5030527114868164, 0.4280393421649933, 0.0792359933257103, -0.09395997226238251, 0.3965265452861786, 0.19565758109092712, -0.6647118926048279, 0.21734537184238434, 0.63700932264328, -0.9566409587860107, -0.05749949440360069, -0.42016375064849854, -0.03384298086166382...
null
null
null
null
null
null
null
null
null
null
null
null
null
Lucas1212BR/Fabio
Lucas1212BR
2023-11-11T10:39:49Z
0
0
null
[ "region:us" ]
2023-11-11T10:39:49Z
2023-11-11T10:04:05.000Z
2023-11-11T10:04:05
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
erenfazlioglu/devletbahcelisozleri
erenfazlioglu
2023-11-11T10:08:39Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-11T10:08:39Z
2023-11-11T10:07:58.000Z
2023-11-11T10:07:58
--- license: apache-2.0 ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
minh009/test2
minh009
2023-11-11T11:08:56Z
0
0
null
[ "region:us" ]
2023-11-11T11:08:56Z
2023-11-11T10:12:25.000Z
2023-11-11T10:12:25
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
SalomonMetre13/nnd_fr_14k_v2
SalomonMetre13
2023-11-11T10:40:57Z
0
0
null
[ "region:us" ]
2023-11-11T10:40:57Z
2023-11-11T10:40:30.000Z
2023-11-11T10:40:30
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
BangumiBase/stringendoangeltachinoprivatelesson
BangumiBase
2023-11-11T12:07:42Z
0
0
null
[ "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2023-11-11T12:07:42Z
2023-11-11T11:36:49.000Z
2023-11-11T11:36:49
--- license: mit tags: - art - not-for-all-audiences size_categories: - n<1K --- # Bangumi Image Base of Stringendo: Angel-tachi No Private Lesson This is the image base of bangumi Stringendo: Angel-tachi no Private Lesson, we detected 15 characters, 956 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 | 123 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 41 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 30 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 175 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 82 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 80 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 75 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 22 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 20 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 112 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 17 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 33 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 10 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 46 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | noise | 90 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.6967024803161621, -0.18005646765232086, 0.15200473368167877, 0.2521909177303314, -0.24465422332286835, -0.07677912712097168, -0.03104371763765812, -0.39468783140182495, 0.5698667764663696, 0.6049841642379761, -0.905981183052063, -0.9232147932052612, -0.6251934766769409, 0.49111849069595...
null
null
null
null
null
null
null
null
null
null
null
null
null
BangumiBase/erogehmogamemokaihatsuzanmai
BangumiBase
2023-11-12T05:37:59Z
0
0
null
[ "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2023-11-12T05:37:59Z
2023-11-11T11:37:14.000Z
2023-11-11T11:37:14
--- license: mit tags: - art - not-for-all-audiences size_categories: - n<1K --- # Bangumi Image Base of Eroge! H Mo Game Mo Kaihatsu Zanmai This is the image base of bangumi Eroge! H mo Game mo Kaihatsu Zanmai, we detected 8 characters, 809 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 | 236 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 64 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 68 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 163 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 8 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 8 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 6 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | N/A | N/A | | noise | 256 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.7435826063156128, -0.29978495836257935, 0.13154028356075287, 0.319149911403656, -0.3171168565750122, -0.15994739532470703, 0.022060630843043327, -0.4178660213947296, 0.5855610370635986, 0.6210445761680603, -0.9629623293876648, -0.8757051229476929, -0.6065208315849304, 0.3939567506313324...
null
null
null
null
null
null
null
null
null
null
null
null
null
uatafaque/movehd
uatafaque
2023-11-11T11:38:20Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-11T11:38:20Z
2023-11-11T11:37:53.000Z
2023-11-11T11:37:53
--- license: openrail ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
pissack1234/tigo-tanzania-personal-data-2023
pissack1234
2023-11-11T11:49:41Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-11T11:49:41Z
2023-11-11T11:41:31.000Z
2023-11-11T11:41:31
--- license: apache-2.0 ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
JeffersonMusic/Bad_BunnyV2
JeffersonMusic
2023-11-11T12:00:47Z
0
0
null
[ "region:us" ]
2023-11-11T12:00:47Z
2023-11-11T11:58:28.000Z
2023-11-11T11:58:28
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
Luizagrod23/RyotaSakuraba
Luizagrod23
2023-11-11T12:06:40Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-11T12:06:40Z
2023-11-11T12:05:55.000Z
2023-11-11T12:05:55
--- license: openrail ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
lucaspoc/lucaspublic
lucaspoc
2023-11-11T12:21:36Z
0
0
null
[ "region:us" ]
2023-11-11T12:21:36Z
2023-11-11T12:20:24.000Z
2023-11-11T12:20:24
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
lchikarapo/VAT_Act_Dataset
lchikarapo
2023-11-11T12:53:18Z
0
0
null
[ "license:unknown", "region:us" ]
2023-11-11T12:53:18Z
2023-11-11T12:51:31.000Z
2023-11-11T12:51:31
--- license: unknown ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
Praghxx/Euobry
Praghxx
2023-11-12T03:43:09Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-12T03:43:09Z
2023-11-11T12:52:36.000Z
2023-11-11T12:52:36
--- license: openrail ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
R44FAE3L/JOELMAVOZFALADAAON
R44FAE3L
2023-11-11T13:42:25Z
0
0
null
[ "region:us" ]
2023-11-11T13:42:25Z
2023-11-11T13:40:50.000Z
2023-11-11T13:40:50
Entry not found
[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
Aravindas/nfems1
Aravindas
2023-11-11T14:02:50Z
0
0
null
[ "license:cc", "region:us" ]
2023-11-11T14:02:50Z
2023-11-11T14:01:52.000Z
2023-11-11T14:01:52
--- license: cc ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
pphuc25/vlsp-test-vectorized
pphuc25
2023-11-11T14:24:11Z
0
0
null
[ "region:us" ]
2023-11-11T14:24:11Z
2023-11-11T14:18:57.000Z
2023-11-11T14:18:57
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input_features sequence: sequence: float32 - name: input_length dtype: int64 - name: labels sequence: int64 splits: - name: train num_bytes: 25301722096 num_examples: 26343 download_size: 966087887 dataset_size: 25301722096 --- # Dataset Card for "vlsp-eval-vectorized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.5424851179122925, -0.20076104998588562, 0.1857406198978424, 0.2032908946275711, -0.19868683815002441, 0.1695009171962738, 0.23982299864292145, -0.1277839094400406, 0.7477391362190247, 0.4059464931488037, -0.6107522249221802, -0.7620833516120911, -0.5964288711547852, -0.26274675130844116...
null
null
null
null
null
null
null
null
null
null
null
null
null
Mreeb/Dermatology-Question-Answer-Dataset-For-Fine-Tuning
Mreeb
2023-11-11T14:26:23Z
0
1
null
[ "task_categories:text-generation", "size_categories:1K<n<10K", "license:apache-2.0", "medical", "region:us" ]
2023-11-11T14:26:23Z
2023-11-11T14:21:16.000Z
2023-11-11T14:21:16
--- license: apache-2.0 task_categories: - text-generation tags: - medical pretty_name: Dermatology Question Answering Dataset size_categories: - 1K<n<10K --- ## Dataset Details The data set has about 1 Million Tokens for Training and about 1500 question answers. ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> This dataset is a comprehensive compilation of questions related to dermatology, spanning inquiries about various skin diseases, their symptoms, recommended medications, and available treatment modalities. Each question is paired with a concise and informative response, making it an ideal resource for training and fine-tuning language models in the field of dermatological healthcare. The dataset is designed to facilitate the development of advanced medical chatbots and language models tailored to dermatology, providing valuable insights into skin health-related inquiries.
[ -0.28516340255737305, -0.8700557351112366, 0.12311137467622757, -0.12502233684062958, -0.06929101049900055, 0.09404471516609192, -0.11161008477210999, -0.3517841696739197, 0.42638200521469116, 0.9626972079277039, -0.8537043929100037, -0.8204796314239502, -0.3187190294265747, -0.13224710524...
null
null
null
null
null
null
null
null
null
null
null
null
null
RaThorat/meningen_dataset
RaThorat
2023-11-11T14:27:02Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-11T14:27:02Z
2023-11-11T14:25:06.000Z
2023-11-11T14:25:06
--- license: mit ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
pphuc25/cv13-hi-train-vectorized
pphuc25
2023-11-11T14:48:09Z
0
0
null
[ "region:us" ]
2023-11-11T14:48:09Z
2023-11-11T14:47:26.000Z
2023-11-11T14:47:26
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: labels sequence: int64 splits: - name: train num_bytes: 195692656.96 num_examples: 6760 download_size: 177219905 dataset_size: 195692656.96 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "baseline-train-vectorized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.7046260237693787, -0.06754574179649353, 0.01597108319401741, 0.19915439188480377, -0.16551269590854645, -0.16211923956871033, 0.26725542545318604, 0.04069633409380913, 0.5743924975395203, 0.20611262321472168, -0.7983233332633972, -0.6542451977729797, -0.59401935338974, -0.44473195075988...
null
null
null
null
null
null
null
null
null
null
null
null
null
pphuc25/cv13-hi-test-vectorized
pphuc25
2023-11-11T14:50:45Z
0
0
null
[ "region:us" ]
2023-11-11T14:50:45Z
2023-11-11T14:49:41.000Z
2023-11-11T14:49:41
--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: input_length dtype: int64 - name: labels sequence: int64 splits: - name: train num_bytes: 2831277024 num_examples: 2947 download_size: 494107812 dataset_size: 2831277024 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "baseline-eval-vectorized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.6718459725379944, -0.41396084427833557, 0.08335314691066742, 0.08815772086381912, -0.09649690985679626, 0.11591950058937073, 0.24238793551921844, 0.05058446526527405, 0.6358981728553772, 0.34173712134361267, -0.6144089102745056, -0.8207069635391235, -0.503760576248169, -0.29922163486480...
null
null
null
null
null
null
null
null
null
null
null
null
null
Danielxx0/Reis
Danielxx0
2023-11-11T14:56:34Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-11T14:56:34Z
2023-11-11T14:53:48.000Z
2023-11-11T14:53:48
--- license: openrail ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null