Upload batch 367 (20 files, last=huggingface_dataset/Dataset_Card/midas_kdd.md)

huggingface_dataset/Dataset_Card/Champion_vpc2020_clear_anon_speech.md

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+Repo to share original and anonymized speech of vpc2020

huggingface_dataset/Dataset_Card/Datatang_Multi-race_7_Expressions_Recognition_Data.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for Datatang/Multi-race_7_Expressions_Recognition_Data
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://bit.ly/3HS20oG
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+25,998 People Multi-race 7 Expressions Recognition Data. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. For each person, 7 images were collected. The data diversity includes different facial postures, different expressions, different light conditions and different scenes. The data can be used for tasks such as face expression recognition.
+                                                                                                                                  
+For more details, please refer to the link: https://bit.ly/3HS20oG
+
+### Supported Tasks and Leaderboards
+
+face-detection, computer-vision: The dataset can be used to train a model for face detection.
+### Languages
+
+English
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing  
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions

huggingface_dataset/Dataset_Card/GEM-submissions_lewtun__this-is-a-test-name__1655666361.md

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+---
+benchmark: gem
+type: prediction
+submission_name: This is a test name
+tags:
+- evaluation
+- benchmark
+---
+# GEM Submission
+
+Submission name: This is a test name
+

huggingface_dataset/Dataset_Card/GEM-submissions_lewtun__this-is-a-test__1646052811.md

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+---
+benchmark: gem
+type: prediction
+submission_name: This is a test
+tags:
+- evaluation
+- benchmark
+---
+# GEM Submission
+
+Submission name: This is a test
+

huggingface_dataset/Dataset_Card/MorVentura_TRBLLmaker.md

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+---
+TODO: Add YAML tags here. 
+---
+name: **TRBLLmaker** 
+
+annotations_creators: found
+
+language_creators: found
+
+languages: en-US
+
+licenses: Genius-Ventura-Toker
+
+multilinguality: monolingual
+
+source_datasets: original
+
+task_categories: sequence-modeling
+
+task_ids: sequence-modeling-seq2seq_generate
+
+
+# Dataset Card for TRBLLmaker Dataset
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+    - [Split info](#Split-info)
+- [Dataset Creation](#dataset-creation)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** https://github.com/venturamor/TRBLLmaker-NLP
+- **Paper:** in git
+
+### Dataset Summary
+TRBLLmaker - To Read Between Lyrics Lines.
+Dataset used in order to train a model to get as an input - several lines of song's lyrics and generate optional interpretation / meaning of them or use the songs' metdata for various tasks such as classification.
+
+This dataset is based on 'Genius' website's data, which contains global collection of songs lyrics and provides annotations and interpretations to songs lyrics and additional music knowledge.
+We used 'Genius' API, created private client and extracted the relevant raw data from Genius servers.
+
+We extracted the songs by the most popular songs in each genre - pop, rap, rock, country and r&b. Afterwards, we created a varied pool of 150 artists that associated with different music styles and periods, and extracted maximum of 100 samples from each.
+We combined all the data, without repetitions, into one final database. After preforming a cleaning of non-English lyrics, we got our final corpus that contains 8,808 different songs with over all of 60,630 samples, while each sample is a specific sentence from the song's lyrics and its top rated annotation.
+
+### Supported Tasks and Leaderboards
+
+Seq2Seq
+
+### Languages
+
+[En] - English
+
+## Dataset Structure
+
+### Data Fields
+
+We stored each sample in a 'SongInfo' structure with the following attributes: title, genre, annotations and song's meta data. 
+The meta data contains the artist's name, song id in the server, lyrics and statistics such page views.
+
+### Data Splits
+
+train
+train_songs
+
+test
+test_songs
+
+validation
+validation songs
+
+## Split info
+- songs
+- samples
+
+train [0.64 (0.8 * 0.8)], test[0.2], validation [0.16 (0.8 * 0.2)]
+
+## Dataset Creation
+
+### Source Data
+Genius - https://genius.com/
+
+### Annotations
+
+#### Who are the annotators?
+
+top-ranked annotations by users in Genoius websites / Official Genius annotations
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+We are excited about the future of applying attention-based models on task such as meaning generation. 
+We hope this dataset will encourage more NLP researchers to improve the way we understand and enjoy songs, since
+achieving artistic comprehension is another step that progress us to the goal of robust AI. 
+
+### Other Known Limitations
+
+The artists list can be found here.
+
+## Additional Information
+
+### Dataset Curators
+
+This Dataset created by Mor Ventura and Michael Toker.
+
+### Licensing Information
+
+All source of data belongs to Genius.
+
+### Contributions
+
+Thanks to [@venturamor, @tokeron](https://github.com/venturamor/TRBLLmaker-NLP) for adding this dataset.

huggingface_dataset/Dataset_Card/ThePioneer_FictionalAsianBeautyCollection.md

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+---
+license: cc0-1.0
+tags:
+- Raw data for training
+- video
+- art
+size_categories:
+- 1K<n<10K
+pretty_name: AtashiCollection
+---
+
+# 概要
+- 私自身から作成した人工・架空の東アジア系美人（あたし/Atashi）の動画セット。
+ - 名称はnewest順。1～4は約500本、5は約400本の動画。
+ - 無加工の原データ（タグ付けもこちらでは行わず）。画像生成AIのみならず、将来的に動画生成AIの学習原データとしても使えるように想定。
+ - 顔の合成にはFaceApp, Meitu, Faceplayを利用。
+ - Faceplayのビデオ音声をそのまま利用しているため、Audioについては第三者が著作権を保持している可能性がある。ただし、その場合であっても、日本国法ではAudioの学習も合法である。
+ - Visualな側面を切り出したい場合は、どちらにせよAudioは使わないはずなので、実質関係ないとみてよい。
+ - Faceplayの置換漏れフレーム・人物、男性化されたあたしが含まれている可能性があるため、必要であればそのチェックや除去は各自で行うこと。
+- 実写タッチを強化したいが、実在人物を使うことで肖像権がらみの問題が発生することを避けたい人向け。
+
+# About
+- A video set of an artificial and fictional East Asian beauty (Atashi), that was created from myself.
+ - The file name is sorted by newest. No. 1 ~ 4 contains about 500, and no. 5 around 400.
+ - The videos are raw data that haven't been modified (not even tagged). I have uploaded them as is so that it could potentially be trained for a generative AI for videos, as well as those for images.
+ - The face of Atashi was created using FaceApp, Meitu, and Faceplay.
+ - Since the audios originate from faceplay videos, it might be copyrighted by others. However, note that it is legal, at least in Japan, to use it for training an AI.
+ - If you want to use it for visual purposes (images and/or videos), it doesn't really matter because the audios are simply unnecessary.
+ - Since they are uploaded as is, it might contain unswapped frames, person, and Atashi transformed into a male. Make sure to check and remove such data by yourself if necessary.
+- The dataset is targeted for those who do not want to use real personal photos but want to train an AI to generate a more photorealistic style.
+
+# 外見サンプル / Sample on how she looks
+- **このビデオ自体はD-IDで生成したもので、zipには含めていない。が、必要なら直接ダウンロードの上、利用してよい。**
+- **This video (generated by D-ID) itself is not included in the zips. You may download and use it directly if necessary.**
+<video width="480" controls>
+  <source src="https://huggingface.co/datasets/ThePioneer/FictionalAsianBeautyCollection/resolve/main/Sample%20view%20of%20Atashi%20with%20her%20explanation%20about%20herself.mp4" type="video/mp4">
+</video>

huggingface_dataset/Dataset_Card/ami.md

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+---
+pretty_name: AMI Corpus
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+dataset_info:
+- config_name: microphone-single
+  features:
+  - name: word_ids
+    sequence: string
+  - name: word_start_times
+    sequence: float32
+  - name: word_end_times
+    sequence: float32
+  - name: word_speakers
+    sequence: string
+  - name: segment_ids
+    sequence: string
+  - name: segment_start_times
+    sequence: float32
+  - name: segment_end_times
+    sequence: float32
+  - name: segment_speakers
+    sequence: string
+  - name: words
+    sequence: string
+  - name: channels
+    sequence: string
+  - name: file
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  splits:
+  - name: train
+    num_bytes: 42013753
+    num_examples: 134
+  - name: validation
+    num_bytes: 5110497
+    num_examples: 18
+  - name: test
+    num_bytes: 4821283
+    num_examples: 16
+  download_size: 11387715153
+  dataset_size: 51945533
+- config_name: microphone-multi
+  features:
+  - name: word_ids
+    sequence: string
+  - name: word_start_times
+    sequence: float32
+  - name: word_end_times
+    sequence: float32
+  - name: word_speakers
+    sequence: string
+  - name: segment_ids
+    sequence: string
+  - name: segment_start_times
+    sequence: float32
+  - name: segment_end_times
+    sequence: float32
+  - name: segment_speakers
+    sequence: string
+  - name: words
+    sequence: string
+  - name: channels
+    sequence: string
+  - name: file-1-1
+    dtype: string
+  - name: file-1-2
+    dtype: string
+  - name: file-1-3
+    dtype: string
+  - name: file-1-4
+    dtype: string
+  - name: file-1-5
+    dtype: string
+  - name: file-1-6
+    dtype: string
+  - name: file-1-7
+    dtype: string
+  - name: file-1-8
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 42126341
+    num_examples: 134
+  - name: validation
+    num_bytes: 5125645
+    num_examples: 18
+  - name: test
+    num_bytes: 4834751
+    num_examples: 16
+  download_size: 90941506169
+  dataset_size: 52086737
+- config_name: headset-single
+  features:
+  - name: word_ids
+    sequence: string
+  - name: word_start_times
+    sequence: float32
+  - name: word_end_times
+    sequence: float32
+  - name: word_speakers
+    sequence: string
+  - name: segment_ids
+    sequence: string
+  - name: segment_start_times
+    sequence: float32
+  - name: segment_end_times
+    sequence: float32
+  - name: segment_speakers
+    sequence: string
+  - name: words
+    sequence: string
+  - name: channels
+    sequence: string
+  - name: file
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  splits:
+  - name: train
+    num_bytes: 42491091
+    num_examples: 136
+  - name: validation
+    num_bytes: 5110497
+    num_examples: 18
+  - name: test
+    num_bytes: 4821283
+    num_examples: 16
+  download_size: 11505070978
+  dataset_size: 52422871
+- config_name: headset-multi
+  features:
+  - name: word_ids
+    sequence: string
+  - name: word_start_times
+    sequence: float32
+  - name: word_end_times
+    sequence: float32
+  - name: word_speakers
+    sequence: string
+  - name: segment_ids
+    sequence: string
+  - name: segment_start_times
+    sequence: float32
+  - name: segment_end_times
+    sequence: float32
+  - name: segment_speakers
+    sequence: string
+  - name: words
+    sequence: string
+  - name: channels
+    sequence: string
+  - name: file-0
+    dtype: string
+  - name: file-1
+    dtype: string
+  - name: file-2
+    dtype: string
+  - name: file-3
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 42540063
+    num_examples: 136
+  - name: validation
+    num_bytes: 5116989
+    num_examples: 18
+  - name: test
+    num_bytes: 4827055
+    num_examples: 16
+  download_size: 45951596391
+  dataset_size: 52484107
+---
+
+# Dataset Card for AMI Corpus
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Dataset Preprocessing](#dataset-preprocessing)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+
+<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
+  <p><b>Deprecated:</b> This legacy dataset is outdated. Please, use <a href="https://huggingface.co/datasets/edinburghcstr/ami"> edinburghcstr/ami </a> instead.</p>
+</div>
+
+
+## Dataset Description
+
+- **Homepage:** [AMI corpus](https://groups.inf.ed.ac.uk/ami/corpus/)
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
+synchronized to a common timeline. These include close-talking and far-field microphones, individual and
+room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,
+the participants also have unsynchronized pens available to them that record what is written. The meetings
+were recorded in English using three different rooms with different acoustic properties, and include mostly
+non-native speakers.
+
+### Dataset Preprocessing
+
+Individual samples of the AMI dataset contain very large audio files (between 10 and 60 minutes). 
+Such lengths are unfeasible for most speech recognition models. In the following, we show how the
+dataset can effectively be chunked into multiple segments as defined by the dataset creators.
+
+The following function cuts the long audio files into the defined segment lengths:
+
+```python
+import librosa
+import math
+from datasets import load_dataset
+
+SAMPLE_RATE = 16_000
+
+def chunk_audio(batch):
+    new_batch = {
+        "audio": [],
+        "words": [],
+        "speaker": [],
+        "lengths": [],
+        "word_start_times": [],
+        "segment_start_times": [],
+    }
+
+    audio, _ = librosa.load(batch["file"][0], sr=SAMPLE_RATE)
+
+    word_idx = 0
+    num_words = len(batch["words"][0])
+    for segment_idx in range(len(batch["segment_start_times"][0])):
+        words = []
+        word_start_times = []
+        start_time = batch["segment_start_times"][0][segment_idx]
+        end_time = batch["segment_end_times"][0][segment_idx]
+
+        # go back and forth with word_idx since segments overlap with each other
+        while (word_idx > 1) and (start_time < batch["word_end_times"][0][word_idx - 1]):
+            word_idx -= 1
+
+        while word_idx < num_words and (start_time > batch["word_start_times"][0][word_idx]):
+            word_idx += 1
+
+        new_batch["audio"].append(audio[int(start_time * SAMPLE_RATE): int(end_time * SAMPLE_RATE)])
+
+        while word_idx < num_words and batch["word_start_times"][0][word_idx] < end_time:
+            words.append(batch["words"][0][word_idx])
+            word_start_times.append(batch["word_start_times"][0][word_idx])
+            word_idx += 1
+
+        new_batch["lengths"].append(end_time - start_time)
+        new_batch["words"].append(words)
+        new_batch["speaker"].append(batch["segment_speakers"][0][segment_idx])
+        new_batch["word_start_times"].append(word_start_times)
+
+        new_batch["segment_start_times"].append(batch["segment_start_times"][0][segment_idx])
+
+    return new_batch
+    
+ami = load_dataset("ami", "headset-single")
+ami = ami.map(chunk_audio, batched=True, batch_size=1, remove_columns=ami["train"].column_names)
+```
+
+The segmented audio files can still be as long as a minute. To further chunk the data into shorter 
+audio chunks, you can use the following script.
+
+```python
+MAX_LENGTH_IN_SECONDS = 20.0
+
+def chunk_into_max_n_seconds(batch):
+    new_batch = {
+        "audio": [],
+        "text": [],
+    }
+
+    sample_length = batch["lengths"][0]
+    segment_start = batch["segment_start_times"][0]
+
+    if sample_length > MAX_LENGTH_IN_SECONDS:
+        num_chunks_per_sample = math.ceil(sample_length / MAX_LENGTH_IN_SECONDS)
+        avg_chunk_length = sample_length / num_chunks_per_sample
+        num_words = len(batch["words"][0])
+
+        # start chunking by times
+        start_word_idx = end_word_idx = 0
+        chunk_start_time = 0
+        for n in range(num_chunks_per_sample):
+            while (end_word_idx < num_words - 1) and (batch["word_start_times"][0][end_word_idx] < segment_start + (n + 1) * avg_chunk_length):
+                end_word_idx += 1
+
+            chunk_end_time = int((batch["word_start_times"][0][end_word_idx] - segment_start) * SAMPLE_RATE)
+            new_batch["audio"].append(batch["audio"][0][chunk_start_time: chunk_end_time])
+            new_batch["text"].append(" ".join(batch["words"][0][start_word_idx: end_word_idx]))
+
+            chunk_start_time = chunk_end_time
+            start_word_idx = end_word_idx
+    else:
+        new_batch["audio"].append(batch["audio"][0])
+        new_batch["text"].append(" ".join(batch["words"][0]))
+
+    return new_batch
+    
+ami = ami.map(chunk_into_max_n_seconds, batched=True, batch_size=1, remove_columns=ami["train"].column_names, num_proc=64)
+```
+
+A segmented and chunked dataset of the config `"headset-single"`can be found [here](https://huggingface.co/datasets/ami-wav2vec2/ami_single_headset_segmented_and_chunked).
+
+### Supported Tasks and Leaderboards
+
+- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task does not have an active leaderboard at the moment.
+
+- `speaker-diarization`: The dataset can be used to train model for Speaker Diarization (SD). The model is presented with an audio file and asked to predict which speaker spoke at what time.
+
+### Languages
+
+The audio is in English.
+
+## Dataset Structure
+
+### Data Instances 
+
+A typical data point comprises the path to the audio file (or files in the case of 
+the multi-headset or multi-microphone dataset), called `file` and its transcription as 
+a list of words, called `words`. Additional information about the `speakers`, the `word_start_time`, `word_end_time`, `segment_start_time`, `segment_end_time` is given.
+In addition 
+
+and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
+ 
+```
+{'word_ids': ["ES2004a.D.words1", "ES2004a.D.words2", ...],
+ 'word_start_times': [0.3700000047683716, 0.949999988079071, ...],
+ 'word_end_times': [0.949999988079071, 1.5299999713897705, ...],
+ 'word_speakers': ['A', 'A', ...], 
+ 'segment_ids': ["ES2004a.sync.1", "ES2004a.sync.2", ...]
+ 'segment_start_times': [10.944000244140625, 17.618999481201172, ...],
+ 'segment_end_times': [17.618999481201172, 18.722000122070312, ...],
+ 'segment_speakers': ['A', 'B', ...], 
+ 'words', ["hmm", "hmm", ...]
+ 'channels': [0, 0, ..], 
+ 'file': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f"
+ 'audio': {'path': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f",
+	  	   'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
+	  	   'sampling_rate': 16000},
+}
+```
+
+### Data Fields
+
+- word_ids: a list of the ids of the words
+
+- word_start_times: a list of the start times of when the words were spoken in seconds
+
+- word_end_times: a list of the end times of when the words were spoken in seconds
+
+- word_speakers: a list of speakers one for each word
+
+- segment_ids: a list of the ids of the segments
+
+- segment_start_times: a list of the start times of when the segments start
+
+- segment_end_times: a list of the start times of when the segments ends
+
+- segment_speakers: a list of speakers one for each segment
+
+- words: a list of all the spoken words
+
+- channels: a list of all channels that were used for each word
+
+- file: a path to the audio file
+
+- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+
+### Data Splits
+
+The dataset consists of several configurations, each one having train/validation/test splits:
+
+- headset-single: Close talking audio of single headset. This configuration only includes audio belonging to the headset of the person currently speaking.
+
+- headset-multi (4 channels): Close talking audio of four individual headset. This configuration includes audio belonging to four individual headsets. For each annotation there are 4 audio files 0, 1, 2, 3.
+
+- microphone-single: Far field audio of single microphone. This configuration only includes audio belonging the first microphone, *i.e.* 1-1, of the microphone array.
+
+- microphone-multi (8 channels): Far field audio of microphone array. This configuration includes audio of the first microphone array 1-1, 1-2, ..., 1-8.
+
+In general, `headset-single` and `headset-multi` include significantly less noise than 
+`microphone-single` and `microphone-multi`.
+
+|                             | Train | Valid | Test |
+| -----                       | ------ | ----- | ---- |
+| headset-single | 136 (80h) |  18 (9h) | 16 (9h) |
+| headset-multi (4 channels) | 136 (320h) |  18 (36h) | 16 (36h) |
+| microphone-single | 136 (80h) |  18 (9h) | 16 (9h) |
+| microphone-multi (8 channels) | 136 (640h) |  18 (72h) | 16 (72h) |
+
+Note that each sample contains between 10 and 60 minutes of audio data which makes it 
+impractical for direct transcription. One should make use of the segment and word start times and end times to chunk the samples into smaller samples of manageable size.
+
+## Dataset Creation
+
+All information about the dataset creation can be found 
+[here](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml)
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+CC BY 4.0
+
+### Citation Information
+#### TODO
+
+### Contributions
+
+Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) and [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
+#### TODO

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-jeffdshen__redefine_math2_8shot-jeffdshen__redefine_mat-af4c71-1853163414.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- jeffdshen/redefine_math2_8shot
+eval_info:
+  task: text_zero_shot_classification
+  model: inverse-scaling/opt-66b_eval
+  metrics: []
+  dataset_name: jeffdshen/redefine_math2_8shot
+  dataset_config: jeffdshen--redefine_math2_8shot
+  dataset_split: train
+  col_mapping:
+    text: prompt
+    classes: classes
+    target: answer_index
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: inverse-scaling/opt-66b_eval
+* Dataset: jeffdshen/redefine_math2_8shot
+* Config: jeffdshen--redefine_math2_8shot
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-596cbd-1668659066.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- mathemakitten/winobias_antistereotype_test
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-30b
+  metrics: ['f1', 'perplexity']
+  dataset_name: mathemakitten/winobias_antistereotype_test
+  dataset_config: mathemakitten--winobias_antistereotype_test
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: facebook/opt-30b
+* Dataset: mathemakitten/winobias_antistereotype_test
+* Config: mathemakitten--winobias_antistereotype_test
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@ddcas](https://huggingface.co/ddcas) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-billsum-default-3fec5f-14625987.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- billsum
+eval_info:
+  task: summarization
+  model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
+  metrics: []
+  dataset_name: billsum
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: text
+    target: summary
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
+* Dataset: billsum
+* Config: default
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-launch__gov_report-plain_text-2fa37c-16136227.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- launch/gov_report
+eval_info:
+  task: summarization
+  model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2
+  metrics: ['bertscore']
+  dataset_name: launch/gov_report
+  dataset_config: plain_text
+  dataset_split: test
+  col_mapping:
+    text: document
+    target: summary
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2
+* Dataset: launch/gov_report
+* Config: plain_text
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model.

huggingface_dataset/Dataset_Card/blended_skill_talk.md

@@ -0,0 +1,230 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: BlendedSkillTalk
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- conversational
+task_ids:
+- dialogue-generation
+paperswithcode_id: blended-skill-talk
+dataset_info:
+  features:
+  - name: personas
+    sequence: string
+  - name: additional_context
+    dtype: string
+  - name: previous_utterance
+    sequence: string
+  - name: context
+    dtype: string
+  - name: free_messages
+    sequence: string
+  - name: guided_messages
+    sequence: string
+  - name: suggestions
+    sequence:
+    - name: convai2
+      dtype: string
+    - name: empathetic_dialogues
+      dtype: string
+    - name: wizard_of_wikipedia
+      dtype: string
+  - name: guided_chosen_suggestions
+    sequence: string
+  - name: label_candidates
+    sequence:
+      sequence: string
+  splits:
+  - name: train
+    num_bytes: 10831361
+    num_examples: 4819
+  - name: validation
+    num_bytes: 43961658
+    num_examples: 1009
+  - name: test
+    num_bytes: 44450102
+    num_examples: 980
+  download_size: 38101408
+  dataset_size: 99243121
+---
+
+# Dataset Card for "blended_skill_talk"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://parl.ai/projects/bst/](https://parl.ai/projects/bst/)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 36.34 MB
+- **Size of the generated dataset:** 14.38 MB
+- **Total amount of disk used:** 50.71 MB
+
+### Dataset Summary
+
+A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### default
+
+- **Size of downloaded dataset files:** 36.34 MB
+- **Size of the generated dataset:** 14.38 MB
+- **Total amount of disk used:** 50.71 MB
+
+An example of 'train' looks as follows.
+```
+{
+  'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'],
+  'additional_context': 'Backstreet Boys',
+  'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys!  Have you ever seen them performing live?', "No,I listen to their music a lot,  mainly the unbreakable which  is the Backstreet Boys' sixth studio album. "],
+  'context': 'wizard_of_wikipedia',
+  'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "],
+  'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both.  By the way, what is k-pop?'],
+  'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']},
+  'guided_chosen_suggestions': ['convai2', '', ''],
+  'label_candidates': []}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `personas`: a `list` of `string` features.
+- `additional_context`: a `string` feature.
+- `previous_utterance`: a `list` of `string` features.
+- `context`: a `string` feature.
+- `free_messages`: a `list` of `string` features.
+- `guided_messgaes`: a `list` of `string` features.
+- `suggestions`: a dictionary feature containing:
+  - `convai2`: a `string` feature.
+  - `empathetic_dialogues`: a `string` feature.
+  - `wizard_of_wikipedia`: a `string` feature.
+- `guided_chosen_suggestions`: a `list` of `string` features.
+- `label_candidates`: a `list` of `lists` of `string` features.
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default| 4819|      1009| 980|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@misc{smith2020evaluating,
+    title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
+    author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
+    year={2020},
+    eprint={2004.08449},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

huggingface_dataset/Dataset_Card/codesue_kelly.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- sv
+language_creators:
+- expert-generated
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: kelly
+size_categories:
+- 1K<n<10K
+source_datasets: []
+tags:
+- lexicon
+- swedish
+- CEFR
+task_categories:
+- text-classification
+task_ids:
+- text-scoring
+---
+
+# Dataset Card for Kelly
+
+Keywords for Language Learning for Young and adults alike
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://spraakbanken.gu.se/en/resources/kelly
+- **Paper:** https://link.springer.com/article/10.1007/s10579-013-9251-2
+
+### Dataset Summary
+
+The Swedish Kelly list is a freely available frequency-based vocabulary list
+that comprises general-purpose language of modern Swedish. The list was
+generated from a large web-acquired corpus (SweWaC) of 114 million words
+dating from the 2010s. It is adapted to the needs of language learners and
+contains 8,425 most frequent lemmas that cover 80% of SweWaC.
+
+### Languages
+
+Swedish (sv-SE)
+
+## Dataset Structure
+
+### Data Instances
+
+Here is a sample of the data:
+
+```python
+{
+    'id': 190,
+    'raw_frequency': 117835.0,
+    'relative_frequency': 1033.61,
+    'cefr_level': 'A1',
+    'source': 'SweWaC',
+    'marker': 'en',
+    'lemma': 'dag',
+    'pos': 'noun-en',
+    'examples': 'e.g. god dag'
+}
+```
+
+This can be understood as:
+
+> The common noun "dag" ("day") has a rank of 190 in the list. It was used 117,835
+times in SweWaC, meaning it occured 1033.61 times per million words. This word
+is among the most important vocabulary words for Swedish language learners and
+should be learned at the A1 CEFR level. An example usage of this word is the
+phrase "god dag" ("good day").
+
+
+### Data Fields
+
+- `id`: The row number for the data entry, starting at 1. Generally corresponds
+  to the rank of the word.
+- `raw_frequency`: The raw frequency of the word.
+- `relative_frequency`: The relative frequency of the word measured in
+  number of occurences per million words.
+- `cefr_level`: The CEFR level (A1, A2, B1, B2, C1, C2) of the word.
+- `source`: Whether the word came from SweWaC, translation lists (T2), or
+  was manually added (manual).
+- `marker`: The grammatical marker of the word, if any, such as an article or
+  infinitive marker.
+- `lemma`: The lemma of the word, sometimes provided with its spelling or
+  stylistic variants.
+- `pos`: The word's part-of-speech.
+- `examples`: Usage examples and comments. Only available for some of the words.
+
+Manual entries were prepended to the list, giving them a higher rank than they
+might otherwise have had. For example, the manual entry "Göteborg ("Gothenberg")
+has a rank of 20, while the first non-manual entry "och" ("and") has a rank of
+87. However, a conjunction and common stopword is far more likely to occur than
+the name of a city.
+
+### Data Splits
+
+There is a single split, `train`.
+
+## Dataset Creation
+
+Please refer to the article [Corpus-based approaches for the creation of a frequency
+based vocabulary list in the EU project KELLY – issues on reliability, validity and
+coverage](https://gup.ub.gu.se/publication/148533?lang=en) for information about how
+the original dataset was created and considerations for using the data.
+
+**The following changes have been made to the original dataset**:
+
+- Changed header names.
+- Normalized the large web-acquired corpus name to "SweWac" in the `source` field.
+- Set the relative frequency of manual entries to null rather than 1000000.
+
+
+## Additional Information
+
+### Licensing Information
+
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0)
+
+### Citation Information
+
+Please cite the authors if you use this dataset in your work:
+
+```bibtex
+@article{Kilgarriff2013,
+  doi = {10.1007/s10579-013-9251-2},
+  url = {https://doi.org/10.1007/s10579-013-9251-2},
+  year = {2013},
+  month = sep,
+  publisher = {Springer Science and Business Media {LLC}},
+  volume = {48},
+  number = {1},
+  pages = {121--163},
+  author = {Adam Kilgarriff and Frieda Charalabopoulou and Maria Gavrilidou and Janne Bondi Johannessen and Saussan Khalil and Sofie Johansson Kokkinakis and Robert Lew and Serge Sharoff and Ravikiran Vadlapudi and Elena Volodina},
+  title = {Corpus-based vocabulary lists for language learners for nine languages},
+  journal = {Language Resources and Evaluation}
+}
+```
+
+### Contributions
+
+Thanks to [@spraakbanken](https://github.com/spraakbanken) for creating this dataset
+and to [@codesue](https://github.com/codesue) for adding it.

huggingface_dataset/Dataset_Card/deepklarity_top-flutter-packages.md

@@ -0,0 +1,18 @@
+---
+license: cc
+---
+
+**Top Flutter Packages Dataset**
+Flutter is an open source framework by Google for building beautiful, natively compiled, multi-platform applications from a single codebase. It is gaining quite a bit of popularity because of ability to code in a single language and have it running on Android/iOS and web as well.
+
+This dataset contains a snapshot of Top 5000+ flutter/dart packages hosted on [Flutter package repository](https://pub.dev/)
+
+The dataset was scraped in `July-2022`. 
+
+We aim to use this dataset to perform analysis and identify trends and get a bird's eye view of the rapidly evolving flutter ecosystem.
+
+#### Mantainers:
+- [Kondrolla Dinesh Reddy](https://twitter.com/KondrollaR)
+- [Keshaw Soni](https://twitter.com/SoniKeshaw)
+- [Somya Gautam](http://linkedin.in/in/somya-gautam)
+

huggingface_dataset/Dataset_Card/id_newspapers_2018.md

@@ -0,0 +1,189 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- id
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- language-modeling
+- masked-language-modeling
+paperswithcode_id: null
+pretty_name: Indonesian Newspapers 2018
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: date
+    dtype: string
+  - name: title
+    dtype: string
+  - name: content
+    dtype: string
+  config_name: id_newspapers_2018
+  splits:
+  - name: train
+    num_bytes: 1116031922
+    num_examples: 499164
+  download_size: 446018349
+  dataset_size: 1116031922
+---
+
+# Dataset Card for Indonesian Newspapers 2018
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel)
+- **Repository:** [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel)
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:** [feryandi.n@gmail.com](mailto:feryandi.n@gmail.com),
+[cahya.wirawan@gmail.com](mailto:cahya.wirawan@gmail.com)
+
+### Dataset Summary
+
+The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers: Detik, Kompas, Tempo,
+CNN Indonesia, Sindo, Republika and Poskota. The articles are dated between 1st January 2018 and 20th August 2018
+(with few exceptions dated earlier). The size of uncompressed 500K json files (newspapers-json.tgz) is around 2.2GB,
+and the cleaned uncompressed in a big text file (newspapers.txt.gz) is about 1GB. The original source in Google Drive
+contains also a dataset in html format which include raw data (pictures, css, javascript, ...)
+from the online news website. A copy of the original dataset is available at
+https://cloud.uncool.ai/index.php/s/mfYEAgKQoY3ebbM
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+Indonesian
+
+## Dataset Structure
+```
+{
+  'id': 'string',
+  'url': 'string',
+  'date': 'string',
+  'title': 'string',
+  'content': 'string'
+}
+```
+### Data Instances
+
+An instance from the dataset is
+
+```
+{'id': '0',
+ 'url': 'https://www.cnnindonesia.com/olahraga/20161221234219-156-181385/lorenzo-ingin-samai-rekor-rossi-dan-stoner',
+ 'date': '2016-12-22 07:00:00',
+ 'title': 'Lorenzo Ingin Samai Rekor Rossi dan Stoner',
+ 'content': 'Jakarta, CNN Indonesia -- Setelah bergabung dengan Ducati, Jorge Lorenzo berharap bisa masuk dalam jajaran pebalap yang mampu jadi juara dunia kelas utama dengan dua pabrikan berbeda. Pujian Max Biaggi untuk Valentino Rossi Jorge Lorenzo Hadir dalam Ucapan Selamat Natal Yamaha Iannone: Saya Sering Jatuh Karena Ingin yang Terbaik Sepanjang sejarah, hanya ada lima pebalap yang mampu jadi juara kelas utama (500cc/MotoGP) dengan dua pabrikan berbeda, yaitu Geoff Duke, Giacomo Agostini, Eddie Lawson, Valentino Rossi, dan Casey Stoner. Lorenzo ingin bergabung dalam jajaran legenda tersebut. “Fakta ini sangat penting bagi saya karena hanya ada lima pebalap yang mampu menang dengan dua pabrikan berbeda dalam sejarah balap motor.” “Kedatangan saya ke Ducati juga menghadirkan tantangan yang sangat menarik karena hampir tak ada yang bisa menang dengan Ducati sebelumnya, kecuali Casey Stoner. Hal itu jadi motivasi yang sangat bagus bagi saya,” tutur Lorenzo seperti dikutip dari Crash Lorenzo saat ini diliputi rasa penasaran yang besar untuk menunggang sepeda motor Desmosedici yang dipakai tim Ducati karena ia baru sekali menjajal motor tersebut pada sesi tes di Valencia, usai MotoGP musim 2016 berakhir. “Saya sangat tertarik dengan Ducati arena saya hanya memiliki kesempatan mencoba motor itu di Valencia dua hari setelah musim berakhir. Setelah itu saya tak boleh lagi menjajalnya hingga akhir Januari mendatang. Jadi saya menjalani penantian selama dua bulan yang panjang,” kata pebalap asal Spanyol ini. Dengan kondisi tersebut, maka Lorenzo memanfaatkan waktu yang ada untuk liburan dan melepaskan penat. “Setidaknya apa yang terjadi pada saya saat ini sangat bagus karena saya jadi memiliki waktu bebas dan sedikit liburan.” “Namun tentunya saya tak akan larut dalam liburan karena saya harus lebih bersiap, terutama dalam kondisi fisik dibandingkan sebelumnya, karena saya akan menunggangi motor yang sulit dikendarai,” ucap Lorenzo. Selama sembilan musim bersama Yamaha, Lorenzo sendiri sudah tiga kali jadi juara dunia, yaitu pada 2010, 2012, dan 2015. (kid)'}
+```
+
+### Data Fields
+- `id`: id of the sample
+- `url`: the url to the original article
+- `date`: the publishing date of the article
+- `title`: the title of the article
+- `content`: the content of the article
+
+### Data Splits
+
+The dataset contains train set of 499164 samples.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. The dataset is shared for the sole purpose of aiding open scientific research in Bahasa Indonesia (computing or linguistics), and can only be used for that purpose. The ownership of each article within the dataset belongs to the respective newspaper from which it was extracted; and the maintainer of the repository does not claim ownership of any of the content within it. If you think, by any means, that this dataset breaches any established copyrights; please contact the repository maintainer.
+
+### Citation Information
+
+[N/A]
+
+### Contributions
+
+Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.

huggingface_dataset/Dataset_Card/midas_kdd.md

@@ -0,0 +1,118 @@
+## Dataset Summary
+
+A dataset for benchmarking keyphrase extraction and generation techniques from abstracts of english scientific papers. For more details about the dataset please refer the original paper - [https://aclanthology.org/D14-1150.pdf](https://aclanthology.org/D14-1150.pdf)
+Original source of the data - []()
+
+
+## Dataset Structure
+
+
+### Data Fields
+
+- **id**: unique identifier of the document.
+- **document**: Whitespace separated list of words in the document.
+- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
+- **extractive_keyphrases**: List of all the present keyphrases.
+- **abstractive_keyphrase**: List of all the absent keyphrases.
+
+
+### Data Splits
+
+|Split| #datapoints  |
+|--|--|
+| Test | 755 |
+
+- Percentage of keyphrases that are named entities: 56.99% (named entities detected using scispacy - en-core-sci-lg model)
+- Percentage of keyphrases that are noun phrases: 54.99% (noun phrases detected using spacy en-core-web-lg after removing determiners)
+
+## Usage
+
+### Full Dataset
+
+```python
+from datasets import load_dataset
+
+# get entire dataset
+dataset = load_dataset("midas/kdd", "raw")
+
+# sample from the test split
+print("Sample from test dataset split")
+test_sample = dataset["test"][0]
+print("Fields in the sample: ", [key for key in test_sample.keys()])
+print("Tokenized Document: ", test_sample["document"])
+print("Document BIO Tags: ", test_sample["doc_bio_tags"])
+print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
+print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
+print("\n-----------\n")
+```
+**Output**
+
+```bash
+Sample from test data split
+Fields in the sample:  ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
+Tokenized Document:  ['Discovering', 'roll-up', 'dependencies']
+Document BIO Tags:  ['O', 'O', 'O']
+Extractive/present Keyphrases:  []
+Abstractive/absent Keyphrases:  ['logical design']
+
+-----------
+
+```
+
+### Keyphrase Extraction
+```python
+from datasets import load_dataset
+
+# get the dataset only for keyphrase extraction
+dataset = load_dataset("midas/kdd", "extraction")
+
+print("Samples for Keyphrase Extraction")
+
+# sample from the test split
+print("Sample from test data split")
+test_sample = dataset["test"][0]
+print("Fields in the sample: ", [key for key in test_sample.keys()])
+print("Tokenized Document: ", test_sample["document"])
+print("Document BIO Tags: ", test_sample["doc_bio_tags"])
+print("\n-----------\n")
+```
+
+### Keyphrase Generation
+```python
+# get the dataset only for keyphrase generation
+dataset = load_dataset("midas/kdd", "generation")
+
+print("Samples for Keyphrase Generation")
+
+# sample from the test split
+print("Sample from test data split")
+test_sample = dataset["test"][0]
+print("Fields in the sample: ", [key for key in test_sample.keys()])
+print("Tokenized Document: ", test_sample["document"])
+print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
+print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
+print("\n-----------\n")
+```
+
+## Citation Information
+```
+@inproceedings{caragea-etal-2014-citation,
+    title = "Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach",
+    author = "Caragea, Cornelia  and
+      Bulgarov, Florin Adrian  and
+      Godea, Andreea  and
+      Das Gollapalli, Sujatha",
+    booktitle = "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP})",
+    month = oct,
+    year = "2014",
+    address = "Doha, Qatar",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D14-1150",
+    doi = "10.3115/v1/D14-1150",
+    pages = "1435--1446",
+
+}
+```
+
+## Contributions
+Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset

huggingface_dataset/Dataset_Card/pain_AASL.md

@@ -0,0 +1,61 @@
+---
+license: cc-by-sa-4.0
+task_categories:
+- image-segmentation
+language:
+- ar
+pretty_name: RGB Arabic Alphabets Sign Language Dataset
+size_categories:
+- 1K<n<10K
+---
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Repository:** https://www.kaggle.com/datasets/muhammadalbrham/rgb-arabic-alphabets-sign-language-dataset
+- **Paper:** https://arxiv.org/abs/2301.11932
+- **Point of Contact:** muhammadal-brham@ieee.org
+
+### Dataset Summary
+
+RGB Arabic Alphabet Sign Language (AASL) dataset comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset.
+
+### Supported Tasks and Leaderboards
+
+- Image Classification
+
+### Languages
+
+- Arabic
+
+## Dataset Structure
+
+### Data Splits
+
+- All images for now
+
+
+### Licensing Information
+
+https://creativecommons.org/licenses/by-sa/4.0/
+
+### Citation Information
+```
+@misc{https://doi.org/10.48550/arxiv.2301.11932,
+  doi = {10.48550/ARXIV.2301.11932},
+  
+  url = {https://arxiv.org/abs/2301.11932},
+  
+  author = {Al-Barham, Muhammad and Alsharkawi, Adham and Al-Yaman, Musa and Al-Fetyani, Mohammad and Elnagar, Ashraf and SaAleek, Ahmad Abu and Al-Odat, Mohammad},
+  
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {RGB Arabic Alphabets Sign Language Dataset},
+  
+  publisher = {arXiv},
+  
+  year = {2023},
+  
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```

huggingface_dataset/Dataset_Card/selfishark_hf-issues-dataset-with-comments.md

@@ -0,0 +1,15 @@
+### Dataset Summary
+
+GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.
+
+### Supported Tasks and Leaderboards
+
+For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
+
+- `task-category-tag`: The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* [metric name](https://huggingface.co/metrics/metric_name). The ([model name](https://huggingface.co/model_name) or [model class](https://huggingface.co/transformers/model_doc/model_class.html)) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at [leaderboard url]() and ranks models based on [metric name](https://huggingface.co/metrics/metric_name) while also reporting [other metric name](https://huggingface.co/metrics/other_metric_name).
+
+### Languages
+
+Provide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...
+
+When relevant, please provide [BCP-47 codes](https://tools.ietf.org/html/bcp47), which consist of a [primary language subtag](https://tools.ietf.org/html/bcp47#section-2.2.1), with a [script subtag](https://tools.ietf.org/html/bcp47#section-2.2.3) and/or [region subtag](https://tools.ietf.org/html/bcp47#section-2.2.4) if available.

huggingface_dataset/Dataset_Card/toloka_WSDMCup2023.md

@@ -0,0 +1,156 @@
+---
+annotations_creators:
+- crowdsourced
+language:
+- en
+language_creators:
+- crowdsourced
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: WSDMCup2023
+size_categories:
+- 10K<n<100K
+source_datasets: []
+tags:
+- toloka
+task_categories:
+- visual-question-answering
+task_ids:
+- visual-question-answering
+dataset_info:
+  features:
+  - name: image
+    dtype: string
+  - name: width
+    dtype: int64
+  - name: height
+    dtype: int64
+  - name: left
+    dtype: int64
+  - name: top
+    dtype: int64
+  - name: right
+    dtype: int64
+  - name: bottom
+    dtype: int64
+  - name: question
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 38990
+  - name: train_sample
+    num_examples: 1000
+  - name: test_public
+    num_examples: 1705
+  - name: test_private
+    num_examples: 4504
+  config_name: wsdmcup2023
+---
+
+# Dataset Card for WSDMCup2023
+
+## Dataset Description
+
+- **Homepage:** [Toloka Visual Question Answering Challenge](https://toloka.ai/challenges/wsdm2023)
+- **Repository:** [WSDM Cup 2023 Starter Pack](https://github.com/Toloka/WSDMCup2023)
+- **Paper:** 
+- **Leaderboard:** [CodaLab Competition Leaderboard](https://codalab.lisn.upsaclay.fr/competitions/7434#results)
+- **Point of Contact:** research@toloka.ai
+
+| Question | Image and Answer |
+| --- | --- |
+| What do you use to hit the ball? | <img src="https://tlk-infra-front.azureedge.net/portal-static/images/wsdm2023/tennis/x2/image.webp" width="228" alt="What do you use to hit the ball?"> |
+| What do people use for cutting? | <img src="https://tlk-infra-front.azureedge.net/portal-static/images/wsdm2023/scissors/x2/image.webp" width="228" alt="What do people use for cutting?"> |
+| What do we use to support the immune system and get vitamin C? | <img src="https://tlk-infra-front.azureedge.net/portal-static/images/wsdm2023/juice/x2/image.webp" width="228" alt="What do we use to support the immune system and get vitamin C?"> |
+
+### Dataset Summary
+
+The WSDMCup2023 Dataset consists of images associated with textual questions.
+One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing
+the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in
+Context dataset, [MS COCO](https://cocodataset.org/). All data labeling was performed on the [Toloka crowdsourcing platform](https://toloka.ai/).
+
+Our dataset has 45,199 instances split among three subsets: train (38,990 instances), public test (1,705 instances),
+and private test (4,504 instances). The entire train dataset was available for everyone since the start of the challenge.
+The public test dataset was available since the evaluation phase of the competition, but without any ground truth labels.
+After the end of the competition, public and private sets were released.
+
+## Dataset Citation
+
+Please cite the challenge results or dataset description as follows.
+
+- Ustalov D., Pavlichenko N., Likhobaba D., and Smirnova A. [WSDM Cup 2023 Challenge on Visual Question Answering](http://ceur-ws.org/Vol-3357/invited1.pdf). *Proceedings of the 4th Crowd Science Workshop on Collaboration of Humans and Learning Algorithms for Data Labeling.* Singapore, 2023, pp.&nbsp;1&ndash;7.
+
+```bibtex
+@inproceedings{TolokaWSDMCup2023,
+  author    = {Ustalov, Dmitry and Pavlichenko, Nikita and Likhobaba, Daniil and Smirnova, Alisa},
+  title     = {{WSDM~Cup 2023 Challenge on Visual Question Answering}},
+  year      = {2023},
+  booktitle = {Proceedings of the 4th Crowd Science Workshop on Collaboration of Humans and Learning Algorithms for Data Labeling},
+  pages     = {1--7},
+  address   = {Singapore},
+  issn      = {1613-0073},
+  url       = {http://ceur-ws.org/Vol-3357/invited1.pdf},
+  language  = {english},
+}
+```
+
+### Supported Tasks and Leaderboards
+
+The Visual Question Answering.
+
+### Language
+
+English
+
+## Dataset Structure
+
+### Data Instances
+A data instance contains a url to the picture, information about the image size - width and height,
+information about ground truth bounding box - left top and right bottom dots, 
+contains the question related to the picture.
+image,width,height,left,top,right,bottom,question
+```
+{'image': https://toloka-cdn.azureedge.net/wsdmcup2023/000000000013.jpg,
+'width': 640,
+'height': 427,
+'left': 129,
+'top': 192,
+'right': 155,
+'bottom': 212,
+'question': What does it use to breath?}
+```
+### Data Fields
+
+* image: contains url to the image
+* width: value in pixels of image width
+* heigth: value in pixels of image height
+* left: the x coordinate in pixels to determin left-top dot of bounding box
+* top: the y coordinate in pixels to determin left-top dot of bounding box
+* right: the x coordinate in pixels to determin right-bottom dot of bounding box
+* bottom: the y coordinate in pixels to determin right-bottom dot of bounding box
+* question: a question related to the picture
+
+### Data Splits
+There are four splits in the data: train, train_sample, test_public, test_private. 'train' split contains the full pull for model training.
+'train-sample' split contains the part of 'train' split. 'test_public' split contains public data to test the model. 
+'test_private' split contains private data for final model test.
+
+### Source Data
+
+The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in
+Context dataset, [MS COCO](https://cocodataset.org/).
+
+### Annotations
+
+All data labeling was performed on the [Toloka crowdsourcing platform](https://toloka.ai/).
+
+Only annotators who self-reported the knowledge of English had access to the annotation task.
+
+### Citation Information
+
+* Competition: https://toloka.ai/challenges/wsdm2023
+* CodaLab: https://codalab.lisn.upsaclay.fr/competitions/7434
+* Dataset: https://doi.org/10.5281/zenodo.7057740

huggingface_dataset/Dataset_Card/uoe-nlp_multi3-nlu.md

@@ -0,0 +1,155 @@
+---
+language:
+- multilingual
+license:
+- cc-by-4.0 
+multilinguality:
+- multilingual
+source_datasets:
+- nluplusplus
+task_categories:
+- text-classification
+pretty_name: multi3-nlu
+
+---
+
+# Dataset Card for Multi<sup>3</sup>NLU++
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contact](#contact)
+  
+## Dataset Description
+
+- **Paper:** [arXiv](https://arxiv.org/abs/2212.10455)
+
+
+### Dataset Summary
+Please note the dataset is not being loaded currently in the dataset loader - but you can access the raw files of the dataset in the folder where the dataset gets downloaded. We will fix this ASAP. 
+
+Multi<sup>3</sup>NLU++ consists of 3080 utterances per language representing challenges in building multilingual multi-intent multi-domain task-oriented dialogue systems. The domains include banking and hotels. There are 62 unique intents. 
+
+### Supported Tasks and Leaderboards
+
+- multi-label intent detection
+- slot filling
+- cross-lingual language understanding for task-oriented dialogue
+
+### Languages
+
+The dataset covers four language pairs in addition to the source dataset in English: 
+Spanish, Turkish, Marathi, Amharic
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance contains the following features: _text_, _intents_, _uid_, _lang_, and ocassionally _slots_ and _values_
+
+See the [Multi<sup>3</sup>NLU++ corpus viewer](https://huggingface.co/datasets/uoe-nlp/multi3-nlu/viewer/uoe-nlp--multi3-nlu/train) to explore more examples.
+
+An example from the Multi<sup>3</sup>NLU++ looks like the following:
+```
+{
+        "text": "माझे उद्याचे रिझर्वेशन मला रद्द का करता येणार नाही?",
+        "intents": [
+            "why",
+            "booking",
+            "cancel_close_leave_freeze",
+            "wrong_notworking_notshowing"
+        ],
+        "slots": {
+            "date_from": {
+                "text": "उद्याचे",
+                "span": [
+                    5,
+                    12
+                ],
+                "value": {
+                    "day": 16,
+                    "month": 3,
+                    "year": 2022
+                }
+            }
+        },
+        "uid": "hotel_1_1",
+        "lang": "mr"
+
+}
+```
+
+### Data Fields
+
+- 'text': a string containing the utterance for which the intent needs to be detected
+- 'intents': the corresponding intent labels
+- 'uid': unique identifier per language
+- 'lang': the language of the dataset 
+- 'slots': annotation of the span that needs to be extracted for value extraction with its label and _value_
+
+
+### Data Splits
+
+The experiments are done on different k-fold validation setups. The dataset has multiple types of data splits. Please see Section 4 of the paper. 
+
+## Dataset Creation
+
+### Curation Rationale
+Existing task-oriented dialogue datasets are 1) predominantly limited to detecting a single intent, 2) focused on a single domain, and 3) include a small set of slot types. Furthermore, the success of task-oriented dialogue is 4) often evaluated on a small set of higher-resource languages (i.e., typically English) which does not test how generalisable systems are to the diverse range of the world's languages.
+Our proposed dataset addresses all these limitations
+
+
+### Source Data
+
+
+#### Initial Data Collection and Normalization
+Please see Section 3 of the paper 
+
+#### Who are the source language producers?
+The source language producers are authors of [NLU++ dataset](https://arxiv.org/abs/2204.13021). The dataset was professionally translated into our chosen four languages. We used Blend Express and Proz.com to recruit these translators. 
+
+
+### Personal and Sensitive Information
+
+None. Names are fictional
+
+
+
+### Discussion of Biases
+
+We have carefully vetted the examples to exclude the problematic examples.
+
+### Other Known Limitations
+The dataset comprises utterances extracted from real dialogues between users and conversational agents as well as synthetic human-authored utterances constructed with the aim of introducing additional combinations of intents and slots. The utterances therefore lack the wider context that would be present in a complete dialogue. As such the dataset cannot be used to evaluate systems with respect to discourse-level phenomena present in dialogue.
+
+## Additional Information
+N/A
+
+### Licensing Information
+
+The dataset is Creative Commons Attribution 4.0 International (cc-by-4.0) 
+
+### Citation Information
+
+Coming soon
+
+### Contact
+[Nikita Moghe](mailto:nikita.moghe@ed.ac.uk) and [Evgeniia Razumovskaia](er563@cam.ac.uk) and [Liane Guillou](mailto:lguillou@ed.ac.uk)
+
+Dataset card based on [Allociné](https://huggingface.co/datasets/allocine)