Omkar2118/datasets huggingface dataset:
RTE3-FR

RTE3-FR dataset is the French translation of the Textual Entailment English dataset used in the RTE-3 Challenge (https://nlp.stanford.edu/RTE3-pilot).

Like its English counterpart, the French RTE-3 dataset is composed of a development set and a test set, each containing 800 T/H pairs.

The dataset is annotated for a 3-way task with the following labels: entailment (0), neutral (1), contradiction (2).

RTE3-FR is available both in XML and TSV format.

Introducing paper: New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French
GQNLI-FR

GQNLI-FR is a manually translated French version of the GQNLI challenge dataset, originally written in English.

GQNLI is an evaluation corpus that is aimed for testing language model's generalized quantifier reasoning ability. The dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.

Introducing paper: New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French
Probeable Problems ICER 2024

* Functionally correct (ok) and incorrect (buggy) solutions to five Probleable Problems: http://arxiv.org/abs/2405.15123
* The ok solutions correspond to attempts that successfully probed *all* ambiguities in the given specification; the buggy solutions represent attempts that addressed these ambiguities partially
* A more nuanced analysis (beyond ok/buggy) of these attempts may reveal greater insights

Introducing paper: Probeable Problems for Beginner-level Programming-with-AI Contests
AndroidWorld

AndroidWorld is an environment for building and benchmarking autonomous computer control agents.

It runs on a live Android emulator and contains a highly reproducible benchmark of 116 hand-crafted tasks across 20 apps, which are dynamically instantiated with randomly-generated parameters to create millions of unique task variations.

In addition to the built-in tasks, AndroidWorld also supports the popular web benchmark, MiniWoB++ from Liu et al..

Key features of AndroidWorld include:

📝 116 diverse tasks across 20 real-world apps
🎲 Dynamic task instantiation for millions of unique variations
🏆 Durable reward signals for reliable evaluation
🌐 Open environment with access to millions of Android apps and websites
💾 Lightweight footprint (2 GB memory, 8 GB disk)
🔧 Extensible design to easily add new tasks and benchmarks
🖥️ Integration with MiniWoB++ web-based tasks

Introducing paper: AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents
CulturaX
Full name: CulturaX

We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for large language model (LLM) development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. We employ MinHash at document level to achieve fuzzy deduplication for the datasets in different languages. Our data cleaning framework includes diverse criteria and threshold selections, guided by extensive data samples, ensuring comprehensive noise filtering in various aspects. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs.

Our dataset combines the most recent iteration of mC4 (version 3.1.0) [1] with all accessible OSCAR corpora up to the present year, including 20.19, 21.09, 22.01, and 23.01 [2]. After deep cleaning and deduplication, CulturaX involves 16TB data in the parquet format (expanding to 27TB when unpacked). More than a half of our dataset is dedicated to non-English languages to significantly boost the data size and enhance the feasibility of training models in multilingual scenarios.

To obtain perplexity scores for data cleaning, we train a SentencePiece tokenizer and 5-gram Kneser-Ney language models as provided in the KenLM library [3] using the 20230501 dumps of Wikipedia. Our KenLM models are also released in HuggingFace: https://huggingface.co/uonlp/kenlm.

Details for the dataset can be found in our technical paper: https://arxiv.org/abs/2309.09400 and https://huggingface.co/datasets/uonlp/CulturaX

Introducing paper: CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
LMOT
Full name: Low-light Multi-object Tracking Dataset

The **Low-light Multi-object Tracking Dataset (LMOT)** is a large-scale dataset that focuses on multi-object tracking in dark scenes. It consists of two parts: 1) The low-light and well-lit videos captured by our dual-camera system. 2) The real world low-light videos captured by a simple camera, to evaluate the generalization in real night scenarios. The videos are provided in both RAW format and sRGB format. After careful annotation, we collect 32 video sequences (2.3\times MOT17), over 35K frames (3.1 \times MOT17) and over 815K bounding boxes (2.8 \times MOT17).

Introducing paper: Multi-Object Tracking in the Dark
DARK FACE
Full name: DARK FACE: Face Detection in Low Light Condition

DARK FACE dataset provides 6,000 real-world low light images captured during the nighttime, at teaching buildings, streets, bridges, overpasses, parks etc., all labeled with bounding boxes for of human face, as the main training and/or validation sets. We also provide 9,000 unlabeled low-light images collected from the same setting. Additionally, we provided a unique set of 789 paired low-light/normal-light images captured in controllable real lighting conditions (but unnecessarily containing faces), which can be used as parts of the training data at the participants' discretization. There will be a hold-out testing set of 4,000 low-light images, with human face bounding boxes annotated.
MultiBypass140

*BernBypass70* is a dataset consisting of 70 surgical videos of LRYGB at Inselspital, Bern University Hospital, Switzerland. The videos were recorded at a resolution of 720 × 576 at 25 fps. 

*StrasBypass70*, extending the Bypass40 dataset, is a collection of 70 videos of LRYGB surgeries performed by surgeons at the University Hospital of Strasbourg, France. The videos were recorded at a resolution of 854 × 480 or 1920 × 1080 resolution at 25 fps and were uniformly edited to a resolution of 854 × 480. 

*MultiBypass140* is the combined dataset of 140 videos from Bern and Strasbourg medical centers. All videos have been anonymized by blacking out the potentially identifying frames outside the patient’s body.

Introducing paper: Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery
MIMIC-ED-Assist

To support the machine learning (ML) community in developing a time-cost-effective diagnostic assistant, we collaborate with ED clinicians to curate a benchmark, called MIMIC-ED-Assist, that is derived from MIMIC-IV and MIMIC-ED. MIMIC-ED-Assist is designed to test the ability of AI systems to provide both accurate and time-cost saving laboratory recommendations. Our benchmark consists of two prediction targets identified by our clinical collaborators to reflect patient risk: critical outcomes, which include patient death and ICU transfer, and lengthened ED stay, defined as ED LOS exceeding 24 hours. Accurately identifying patients at high risks of these outcomes reduces time-cost by allowing clinicians to perform timely interventions and efficiently allocate resources. MIMIC-ED-Assist mirrors real-world ED practices by grouping individual laboratory tests into commonly performed groups, e.g., complete blood count (CBC). MIMIC-ED-Assist then tests AI systems on their ability to recommend the most informative groups to make accurate diagnostic suggestions while minimizing the total time required to perform these tests, thereby reducing LOS.

Introducing paper: ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
Human-Animal-Cartoon
Full name: Human-Animal-Cartoon

Human-Animal-Cartoon (HAC) dataset consists of seven actions (‘sleeping’, ‘watching tv’, ‘eating’, ‘drinking’, ‘swimming’, ‘running’, and ‘opening door’) performed by humans, animals, and cartoon figures, forming three different domains. 3381 video clips are collected from the internet with around 1000 for each domain and three modalities are provided in the dataset: video, audio, and optical flow.

Introducing paper: SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
Surgical Scene Graph Generation

The training subset consists of 15 robotic nephrectomy procedures captured on the da Vinci X or Xi system. There are 149 frames per video sequence, and the dimension of each frame is 1280x1024. Segmentation annotations are provided with 10 different classes, including instruments, kidneys, and other objects in the surgical scenario. The main differences with the 2017 instrument segmentation dataset  are annotation of kidney parenchyma, surgical objects such as suturing needles, Suturing thread clips, and additional instruments. We annotated the graphical representation of the interaction between the surgical instruments and the defective tissue in the surgical scene with the help of our clinical expertise with the da Vinci Xi robotic system. We also delineate the bounding box to identify all the surgical objects. Kidney and instruments are represented as nodes and active edges annotated as the interaction class in the graph. In total, 12 kinds of interactions were identified to generate the scene graph representation. The identified interactions are grasping, retraction, tissue manipulation, tool manipulation, cutting, cauterization, suction, looping, suturing, clipping, staple, ultrasound sensing. We split the newly annotated dataset into 12/3 video sequences (1788/449 frames) for training and cross-validation.

Introducing paper: Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
Instrument-Tissue Interaction Graph Generation

The training subset consists of 15 robotic nephrectomy procedures captured on the da Vinci X or Xi system. There are 149 frames per video sequence, and the dimension of each frame is 1280x1024. Segmentation annotations are provided with 10 different classes, including instruments, kidneys, and other objects in the surgical scenario. The main differences with the 2017 instrument segmentation dataset  are annotation of kidney parenchyma, surgical objects such as suturing needles, Suturing thread clips, and additional instruments. We annotated the graphical representation of the interaction between the surgical instruments and the defective tissue in the surgical scene with the help of our clinical expertise with the da Vinci Xi robotic system. We also delineate the bounding box to identify all the surgical objects. Kidney and instruments are represented as nodes and active edges annotated as the interaction class in the graph. In total, 12 kinds of interactions were identified to generate the scene graph representation. The identified interactions are grasping, retraction, tissue manipulation, tool manipulation, cutting, cauterization, suction, looping, suturing, clipping, staple, ultrasound sensing. We split the newly annotated dataset into 12/3 video sequences (1788/449 frames) for training and cross-validation.

Introducing paper: Learning and Reasoning with the Graph Structure Representation in Robotic Surgery