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The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges. Each phrase is labelled as either negative, somewhat negative, neutral, somewhat positive or positive. The corpus with all 5 labels is referred to as SST-5 or SST fine-grained. Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.

The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges. Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.

The SST-5, also known as the Stanford Sentiment Treebank with 5 labels, is a dataset used for sentiment analysis. The SST-5 dataset consists of 11,855 single sentences extracted from movie reviews¹. It includes a total of 215,154 unique phrases from parse trees, each annotated by 3 human judges¹. Each phrase is labeled as either negative, somewhat negative, neutral, somewhat positive, or positive. This is why it's referred to as SST-5 or SST fine-grained.

CrossTask dataset contains instructional videos, collected for 83 different tasks. For each task an ordered list of steps with manual descriptions is provided. The dataset is divided in two parts: 18 primary and 65 related tasks. Videos for the primary tasks are collected manually and provided with annotations for temporal step boundaries. Videos for the related tasks are collected automatically and don't have annotations. Source: CrossTask Image Source: https://arxiv.org/pdf/1903.08225v2.pdf

license: apache-2.0 tags: human-feedback size_categories: 100K<n<1M pretty_name: OpenAssistant Conversations # OpenAssistant Conversations Dataset (OASST1) ## Dataset Description - Homepage: https://www.open-assistant.io/ - Repository: https://github.com/LAION-AI/Open-Assistant - Paper: https://arxiv.org/abs/2304.07327 ### Dataset Summary In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers. Please refer to our paper for further details. ### Dataset Structure This dataset contains message trees. Each message tree has an initial prompt message as the root node, which can have multiple child messages as replies, and these child messages can have multiple replies. All messages have a role property: this can either be "assistant" or "prompter". The roles in conversation threads from prompt to leaf node strictly alternate between "prompter" and "assistant". This version of the dataset contains data collected on the open-assistant.io website until April 12 2023. ### JSON Example: Message For readability, the following JSON examples are shown formatted with indentation on multiple lines. Objects are stored without indentation (on single lines) in the actual jsonl files. json { "message_id": "218440fd-5317-4355-91dc-d001416df62b", "parent_id": "13592dfb-a6f9-4748-a92c-32b34e239bb4", "user_id": "8e95461f-5e94-4d8b-a2fb-d4717ce973e4", "text": "It was the winter of 2035, and artificial intelligence (..)", "role": "assistant", "lang": "en", "review_count": 3, "review_result": true, "deleted": false, "rank": 0, "synthetic": true, "model_name": "oasst-sft-0_3000,max_new_tokens=400 (..)", "labels": { "spam": { "value": 0.0, "count": 3 }, "lang_mismatch": { "value": 0.0, "count": 3 }, "pii": { "value": 0.0, "count": 3 }, "not_appropriate": { "value": 0.0, "count": 3 }, "hate_speech": { "value": 0.0, "count": 3 }, "sexual_content": { "value": 0.0, "count": 3 }, "quality": { "value": 0.416, "count": 3 }, "toxicity": { "value": 0.16, "count": 3 }, "humor": { "value": 0.0, "count": 3 }, "creativity": { "value": 0.33, "count": 3 }, "violence": { "value": 0.16, "count": 3 } } } ### JSON Example: Conversation Tree For readability, only a subset of the message properties is shown here. json { "message_tree_id": "14fbb664-a620-45ce-bee4-7c519b16a793", "tree_state": "ready_for_export", "prompt": { "message_id": "14fbb664-a620-45ce-bee4-7c519b16a793", "text": "Why can't we divide by 0? (..)", "role": "prompter", "lang": "en", "replies": [ { "message_id": "894d30b6-56b4-4605-a504-89dd15d4d1c8", "text": "The reason we cannot divide by zero is because (..)", "role": "assistant", "lang": "en", "replies": [ // ... ] }, { "message_id": "84d0913b-0fd9-4508-8ef5-205626a7039d", "text": "The reason that the result of a division by zero is (..)", "role": "assistant", "lang": "en", "replies": [ { "message_id": "3352725e-f424-4e3b-a627-b6db831bdbaa", "text": "Math is confusing. Like those weird Irrational (..)", "role": "prompter", "lang": "en", "replies": [ { "message_id": "f46207ca-3149-46e9-a466-9163d4ce499c", "text": "Irrational numbers are simply numbers (..)", "role": "assistant", "lang": "en", "replies": [] }, // ... ] } ] } ] } } Please refer to oasst-data for details about the data structure and Python code to read and write jsonl files containing oasst data objects. If you would like to explore the dataset yourself you can find a getting-started notebook in the notebooks/openassistant-oasst1 folder of the LAION-AI/Open-Assistant github repository. ## Main Dataset Files Conversation data is provided either as nested messages in trees (extension .trees.jsonl.gz) or as a flat list (table) of messages (extension .messages.jsonl.gz). ### Ready For Export Trees 2023-04-12_oasst_ready.trees.jsonl.gz 10,364 trees with 88,838 total messages 2023-04-12_oasst_ready.messages.jsonl.gz 88,838 messages Trees in ready_for_export state without spam and deleted messages including message labels. The oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training. ### All Trees 2023-04-12_oasst_all.trees.jsonl.gz 66,497 trees with 161,443 total messages 2023-04-12_oasst_all.messages.jsonl.gz 161,443 messages All trees, including those in states prompt_lottery_waiting (trees that consist of only one message, namely the initial prompt), aborted_low_grade (trees that stopped growing because the messages had low quality), and halted_by_moderator. ### Supplemental Exports: Spam & Prompts 2023-04-12_oasst_spam.messages.jsonl.gz These are messages which were deleted or have a negative review result ("review_result": false). Besides low quality, a frequent reason for message deletion is a wrong language tag. 2023-04-12_oasst_prompts.messages.jsonl.gz These are all the kept initial prompt messages with positive review result (no spam) of trees in ready_for_export or prompt_lottery_waiting state. ### Using the Huggingface Datasets While HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees. Nevertheless, we make all messages which can also be found in the file 2023-04-12_oasst_ready.trees.jsonl.gz available in parquet as train/validation splits. These are directly loadable by Huggingface Datasets. To load the oasst1 train & validation splits use: python from datasets import load_dataset ds = load_dataset("OpenAssistant/oasst1") train = ds['train'] # len(train)=84437 (95%) val = ds['validation'] # len(val)=4401 (5%) The messages appear in depth-first order of the message trees. Full conversation trees can be reconstructed from the flat messages table by using the parent_id and message_id properties to identify the parent-child relationship of messages. The message_tree_id and tree_state properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state. ### Languages OpenAssistant Conversations incorporates 35 different languages with a distribution of messages as follows: Languages with over 1000 messages - English: 71956 - Spanish: 43061 - Russian: 9089 - German: 5279 - Chinese: 4962 - French: 4251 - Thai: 3042 - Portuguese (Brazil): 2969 - Catalan: 2260 - Korean: 1553 - Ukrainian: 1352 - Italian: 1320 - Japanese: 1018 <details> <summary><b>Languages with under 1000 messages</b></summary> <ul> <li>Vietnamese: 952</li> <li>Basque: 947</li> <li>Polish: 886</li> <li>Hungarian: 811</li> <li>Arabic: 666</li> <li>Dutch: 628</li> <li>Swedish: 512</li> <li>Turkish: 454</li> <li>Finnish: 386</li> <li>Czech: 372</li> <li>Danish: 358</li> <li>Galician: 339</li> <li>Hebrew: 255</li> <li>Romanian: 200</li> <li>Norwegian Bokmål: 133</li> <li>Indonesian: 115</li> <li>Bulgarian: 95</li> <li>Bengali: 82</li> <li>Persian: 72</li> <li>Greek: 66</li> <li>Esperanto: 59</li> <li>Slovak: 19</li> </ul> </details> ## Contact - Discord Open Assistant Discord Server - GitHub: LAION-AI/Open-Assistant - E-Mail: open-assistant@laion.ai

The ManySStuBs4J corpus is a collection of simple fixes to Java bugs, designed for evaluating program repair techniques. We collect all bug-fixing changes using the SZZ heuristic, and then filter these to obtain a data set of small bug fix changes. These are single statement fixes, classified where possible into one of 16 syntactic templates which we call SStuBs. The dataset contains simple statement bugs mined from open-source Java projects hosted in GitHub. There are two variants of the dataset. One mined from the 100 Java Maven Projects and one mined from the top 1000 Java Projects. The dataest contains 153,652 single statement bugfix changes mined from 1,000 popular open-source Java projects, annotated by whether they match any of a set of 16 bug templates, inspired by state-of-the-art program repair techniques.

The PLAsTiCC dataset is a collection of simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge. This diverse dataset contains 14 types of astronomical time-varying objects, simulated using the expected instrument characteristics and survey strategy of the upcoming Legacy Survey of Space and Time [LSST 79] conducted at the Vera C. Rubin Observatory. The dataset includes two overall categories of time-series objects: transients, which are short-lived events such as supernovae, and variable sources, which are objects with fluctuating brightness such as pulsating stars. Specifically, the dataset includes the following transients: type Ia supernovae (SNIa), SNIax, SNIa-91bg, SNIbc, SNII, superluminous supernovae (SLSN), tidal disruption events (TDE), and single lens microlensing events (µLens-Single); and the following variable objects: active galactic nuclei (AGN), Mira variables, eclipsing binary systems (EB), and RR Lyrae (RRL). The dataset contains a collection of 3,492,890 time-series, providing in each case several fields: time of observation, flux, flux error, and filter in which the flux is measured. Observations in 6 filters are included (u,g,r,i,z,Y). In addition, each time series is associated with a class representing the type of object, and a redshift for that object.

SST-5 is the Stanford Sentiment Treebank 5-way classification dataset (positive, somewhat positive, neutral, somewhat negative, negative). To create SST-3 (positive, neutral, negative), the 'somewhat positive' class was merged and treated as 'positive'. Similarly, the 'somewhat negative' class was merged and treated as 'negative'.

Ulm-TSST is a dataset continuous emotion (valence and arousal) prediction and `physiological-emotion' prediction. It consists of a multimodal richly annotated dataset of self-reported, and external dimensional ratings of emotion and mental well-being. After a brief period of preparation the subjects are asked to give an oral presentation, within a job-interview setting. Ulm-TSST includes biological recordings, such as Electrocardiogram (ECG), Electrodermal Activity (EDA), Respiration, and Heart Rate (BPM) as well as continuous arousal and valence annotations. With 105 participants (69.5% female) aged between 18 and 39 years, a total of 10 hours were accumulated.

The sStoryCloze refers to the Spoken StoryCloze benchmark, which is a spoken version of the StoryCloze dataset. The StoryCloze dataset consists of five-sentence commonsense stories, where the task is to predict the ending of a story given the first four sentences and two possible endings, one of which is the correct ending and the other is a distractor. The sStoryCloze evaluates the model's capabilities to capture fine-grained causal and temporal commonsense relations in spoken language. It assesses the model's ability to generate coherent and contextually appropriate continuations given a spoken prompt. The dataset is used to evaluate the performance of SpeechLMs in understanding and generating spoken narratives.

The GlassTemp dataset is collected from Polyinfo. It uses monomers as polymer graphs to predict the property of glass transition temperature. The glass transition temperature of the material itself denotes the temperature range over which this glass transition takes place.

We provide two image stacks where each contains 20 sections from serial section Transmission Electron Microscopy (ssTEM) of the Drosophila melanogaster third instar larva ventral nerve cord. Both stacks measure approx. 4.7 x 4.7 x 1 microns with a resolution of 4.6 x 4.6 nm/pixel and section thickness of 45-50 nm. In addition to the raw image data, we provide for the first stack a dense labeling of neuron membranes (including orientation and junction), mitochondria, synapses and glia/extracellular space. The first stack serves as a training dataset, and a second stack of the same dimension can be used as a test dataset. Source: https://figshare.com/articles/dataset/Segmented_anisotropic_ssTEM_dataset_of_neural_tissue/856713 Image source: https://figshare.com/articles/dataset/Segmented_anisotropic_ssTEM_dataset_of_neural_tissue/856713

The Rendered SST2 dataset is a dataset released by OpenAI, that measures the optical character recognition capability of visual representations. It uses sentences from the Stanford Sentiment Treebank dataset and renders them into images, with black texts on a white background, in a 448×448 resolution.

These are the summary crosstabular data of the 2024 ISPSOS survey on which the paper, "Automation from the Worker's Perspective" is based.

This is a dataset for 3-way sentiment classification of reviews (negative, neutral, positive). It is a merge of Stanford Sentiment Treebank (SST-3) and DynaSent Rounds 1 and 2, licensed under Apache 2.0 and Creative Commons Attribution 4.0 respectively. The SST-3, DynaSent R1, and DynaSent R2 datasets were randomly mixed to form a new dataset with 102,097 Train examples, 5,421 Validation examples, and 6,530 Test examples. See Table 1 for the distribution of labels within this merged dataset. ### Table 1: Label Distribution for the Merged Dataset | Split | Negative | Neutral | Positive | |------------|----------|---------|----------| | Train | 21,910 | 49,148 | 31,039 | | Validation | 1,868 | 1,669 | 1,884 | | Test | 2,352 | 1,829 | 2,349 | ### Table 2: Contribution of Sources to the Merged Dataset | Dataset | Samples | Percent (%) | |-------------------|---------|-------------| | DynaSent R1 Train | 80,488 | 78.83 | | DynaSent R2 Train | 13,065 | 12.80 | | SST-3 Train | 8,544 | 8.37 | | Total | 102,097 | 100.00 | ### Source Datasets SST-5 is the Stanford Sentiment Treebank 5-way classification (positive, somewhat positive, neutral, somewhat negative, negative). To create SST-3 (positive, neutral, negative), the 'somewhat positive' class was merged and treated as 'positive'. Similarly, the 'somewhat negative class' was merged and treated as 'negative'. DynaSent is a sentiment analysis dataset and dynamic benchmark with three classification labels: positive, negative, and neutral. The dataset was created in two rounds. First, a RoBERTa model was fine-tuned on a variety of datasets including SST-3, IMBD, and Yelp. They then extracted challenging sentences that fooled the model, and validated them with humans. For Round 2, a new RoBERTa model was trained on similar (but different) data, including the Round 1 dataset. The Dynabench platform was then used to create sentences written by workers that fooled the model. It’s worth noting that the source datasets all have class imbalances. SST-3 positive and negative are about twice the number of neutral. In DynaSent R1, the neutral are more than three times the negative. And in DynaSent R2, the positive are more than double the neutral. Although this imbalance may be by design for DynaSent (to focus on the more challenging neutral class), it still represents an imbalanced dataset. The risk is that the model will learn mostly the dominant class. Merging the data helps mitigate this imbalance. Although there is still a majority of neutral examples in the training dataset, the neutral to negative ratio in DynaSent R1 is 3.21, and this is improved to 2.24 in the merged dataset. Another potential issue is that the models will learn the dominant dataset, which is DynaSent R1. See Table 2 for a breakdown of the sources contributing to the Merged dataset.