PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit Posts
- URL: http://arxiv.org/abs/2407.04541v2
- Date: Sun, 24 Nov 2024 09:40:34 GMT
- Title: PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit Posts
- Abstract summary: PoPreRo is the first dataset for Popularity Prediction of Romanian posts collected from Reddit.
PoPreRo dataset includes a varied compilation of post samples from five distinct subreddits of Romania, totaling 28,107 data samples.
- Score: 23.024346740949948
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce PoPreRo, the first dataset for Popularity Prediction of Romanian posts collected from Reddit. The PoPreRo dataset includes a varied compilation of post samples from five distinct subreddits of Romania, totaling 28,107 data samples. Along with our novel dataset, we introduce a set of competitive models to be used as baselines for future research. Interestingly, the top-scoring model achieves an accuracy of 61.35% and a macro F1 score of 60.60% on the test set, indicating that the popularity prediction task on PoPreRo is very challenging. Further investigations based on few-shot prompting the Falcon-7B Large Language Model also point in the same direction. We thus believe that PoPreRo is a valuable resource that can be used to evaluate models on predicting the popularity of social media posts in Romanian. We release our dataset at https://github.com/ana-rogoz/PoPreRo.
Related papers
- Scaling Open-Ended Reasoning to Predict the Future [56.672065928345525]
We train language models to make predictions on open-ended forecasting questions.<n>To scale up training data, we synthesize novel forecasting questions from global events reported in daily news.<n>We find calibration improvements from forecasting training generalize across popular benchmarks.
arXiv Detail & Related papers (2025-12-31T18:59:51Z) - Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval [49.1574468325115]
We introduce Amharic-specific dense retrieval models based on pre-trained Amharic BERT and RoBERTa backbones.<n>Our proposed RoBERTa-Base-Amharic-Embed model (110M parameters) achieves a 17.6% relative improvement in MRR@10.<n>More compact variants, such as RoBERTa-Medium-Amharic-Embed (42M) remain competitive while being over 13x smaller.
arXiv Detail & Related papers (2025-05-25T23:06:20Z) - DataDecide: How to Predict Best Pretraining Data with Small Experiments [67.95896457895404]
We release models, data, and evaluations in DataDecide -- the most extensive open suite of models over differences in data and scale.
We conduct controlled pretraining experiments across 25 corpora with differing sources, deduplication, and filtering up to 100B tokens, model sizes up to 1B parameters, and 3 random seeds.
arXiv Detail & Related papers (2025-04-15T17:02:15Z) - GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation [90.53485251837235]
Time series foundation models excel in zero-shot forecasting, handling diverse tasks without explicit training.
GIFT-Eval is a pioneering benchmark aimed at promoting evaluation across diverse datasets.
GIFT-Eval encompasses 23 datasets over 144,000 time series and 177 million data points.
arXiv Detail & Related papers (2024-10-14T11:29:38Z) - RoQLlama: A Lightweight Romanian Adapted Language Model [2.1892046440619626]
We tackle the problem of reduced computing resources by using QLoRA for training.
We release RoQLlama-7b, a quantized LLM, which shows equal or improved results compared to its full-sized counterpart.
We introduce a novel Romanian dataset, namely RoMedQA, which contains single-choice medical questions in Romanian.
arXiv Detail & Related papers (2024-10-05T19:14:11Z) - Limits to Predicting Online Speech Using Large Language Models [20.215414802169967]
Recent work suggests that the predictive information contained in posts written by a user's peers can surpass that of the user's own posts.
We collect a corpus of 6.25M posts from more than five thousand X (previously Twitter) users and their peers.
Across the board, we find that the predictability of social media posts remains low, comparable to predicting financial news without context.
arXiv Detail & Related papers (2024-07-08T09:50:49Z) - HistNERo: Historical Named Entity Recognition for the Romanian Language [1.5924012820163407]
HistNERo is the first Romanian corpus for Named Entity Recognition in historical newspapers.
The dataset contains 323k tokens of text, covering more than half of the 19th century.
Eight native Romanian speakers annotated the dataset with five named entities.
arXiv Detail & Related papers (2024-04-30T19:05:22Z) - Pushing the Limits of Pre-training for Time Series Forecasting in the
CloudOps Domain [54.67888148566323]
We introduce three large-scale time series forecasting datasets from the cloud operations domain.
We show it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size.
Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method.
arXiv Detail & Related papers (2023-10-08T08:09:51Z) - RoDia: A New Dataset for Romanian Dialect Identification from Speech [28.458975585210947]
RoDia is the first dataset for Romanian dialect identification from speech.
Includes speech samples from five distinct regions of Romania, covering both urban and rural environments.
Top scoring model achieves a macro F1 score of 59.83% and a micro F1 score of 62.08%, indicating that the task is challenging.
arXiv Detail & Related papers (2023-09-06T21:56:24Z) - Forecasting Future World Events with Neural Networks [68.43460909545063]
Autocast is a dataset containing thousands of forecasting questions and an accompanying news corpus.
The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts.
We test language models on our forecasting task and find that performance is far below a human expert baseline.
arXiv Detail & Related papers (2022-06-30T17:59:14Z) - Czech Dataset for Cross-lingual Subjectivity Classification [13.70633147306388]
We introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from movie reviews and descriptions.
Two annotators annotated the dataset reaching 0.83 of the Cohen's kappa inter-annotator agreement.
We fine-tune five pre-trained BERT-like models to set a monolingual baseline for the new dataset and we achieve 93.56% of accuracy.
arXiv Detail & Related papers (2022-04-29T07:31:46Z) - Progressive End-to-End Object Detection in Crowded Scenes [96.92416613336096]
Previous query-based detectors suffer from two drawbacks: first, multiple predictions will be inferred for a single object, typically in crowded scenes; second, the performance saturates as the depth of the decoding stage increases.
We propose a progressive predicting method to address the above issues. Specifically, we first select accepted queries to generate true positive predictions, then refine the rest noisy queries according to the previously accepted predictions.
Experiments show that our method can significantly boost the performance of query-based detectors in crowded scenes.
arXiv Detail & Related papers (2022-03-15T06:12:00Z) - Domain-matched Pre-training Tasks for Dense Retrieval [68.07140087626637]
Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks.
We show that, with the right pre-training setup, this barrier can be overcome.
We demonstrate this by pre-training large bi-encoder models on 1) a recently released set of 65 million synthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations.
arXiv Detail & Related papers (2021-07-28T19:13:00Z) - Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
Reasoning over visual data is a desirable capability for robotics and vision-based applications.
In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent.
Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
arXiv Detail & Related papers (2020-02-20T18:50:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.