Search Results for "parallel corpus"

Step3-VL-10B is an open-source multimodal foundation model developed by StepFun AI that pushes the boundaries of what compact models can achieve by combining visual and language understanding in a single architecture. Despite having only about 10 billion parameters, it delivers performance that rivals or even surpasses much larger models (10×–20× larger) on a wide range of multimodal benchmarks covering reasoning, perception, and complex tasks, positioning it as one of the most powerful...

The English-Vietnamese Bilingual Corpus (EVBCorpus) is a collection of English and Vietnamese parallel translations and bitexts.

We present OSMAN (Open Source Metric for Measuring Arabic Narratives) - a novel open source Arabic readability metric and tool. The open source Java tool allows users to calculate readability for Arabic text (with and without diacritics). The tool provides methods to split the text into words and sentence, count syllables, Faseeh letters, hard and complex words in addition to adding diacritics (vocalise text). This makes the tool useful for researchers and educators working with Arabic text....

Various tools for creating annotated parallel corpora including pre-trained tagging and parsing models for various languages, sentence alignment tools and word alignment tools. Uplug also includes a web-based interface for interactive sentence and word alignment and scripts for indexing and querying parallel corpora using the Corpus Work Bench CWB. Download 'uplug-main' first and then add other packages.

The project attempts to develop a parallel-corpus-based hybrid high quality English-Khmer-English automatic translation system based on statistical analysis and enhanced with part-of-speech analysis.

Automatic Machine Translation from English to Khmer project is the first effort in Natural Language Processing field for translating English to Khmer (Cambodian) language. This project uses Domy CE, an open source SMT toolkit, for training parallel corpus and web technologies such as Python, Apache2, HTML, XML, and XSLT for developing web-based application. This project is developed by Ms. Kim Sokphyrum (DU) and Ms. Suos Samak (Jamia), under Supervision of Mr. Javier Sola, a Program Manager at Open Institute (OI), Cambodia, Dr. Vasudha Bhatnagar, an Assistant professor and a Head of Computer Science at University of Delhi (DU), New Delhi, India. and Dr. ...

...CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. ...

Sanchay is a collection of tools and APIs for language researchers. It has some implementations of NLP algorithms, some flexible APIs, several user friendly annotation interfaces and Sanchay Query Language for language resources.

This is a project implemented to build a parallel text search system with the main emphasize being placed on searching for the provided query within the shortest possible time. The query will be searched within the GigaWord corpus.

...It leverages Multi-head Latent Attention to compress the KV cache into latent vectors, dramatically reducing memory demand and improving inference speed at scale. The model also employs Multi-Token Prediction, enabling multi-step token generation in a single pass for up to 40% faster output through speculative and parallel decoding techniques. Its training corpus incorporates ten languages, enriched with books, academic sources, code datasets, mathematical tasks, and more than 5.5 trillion tokens of high-quality synthetic data. This combination significantly boosts reasoning, coding, and multilingual performance across modern benchmarks. Designed for high-performance deployment, GigaChat 3 Ultra supports major inference engines and offers optimized BF16 and FP8 execution paths for cluster-grade hardware.