# Academic Text Processing

A citation intent classification model fine-tuned based on Qwen2.5-14B-Instruct, specializing in citation analysis tasks in scientific literature.

A specialized model for scientific paper abstract generation fine-tuned based on the PEGASUS architecture

cde-small-v1 is a small sentence embedding model based on transformer architecture, excelling in multiple text classification, clustering, and retrieval tasks.

A T5-small fine-tuned model for generating scientific paper abstracts, capable of extracting key information from research papers.

A Kazakh language model based on the RoBERTa architecture, trained from scratch using RobertaForMaskedLM, suitable for Kazakh text processing tasks.

Venusaur is a sentence embedding model developed based on the Mihaiii/Bulbasaur foundation model, focusing on sentence similarity and feature extraction tasks.

A summarization model fine-tuned using QLoRa technology on the scitldr dataset based on Meta-Llama-3-8B

A 7B-parameter sentence embedding model based on the Qwen1.5 architecture, focusing on sentence similarity calculation and multi-task evaluation

A summarization generation model fine-tuned based on Mistral-7B-Instruct-v0.2, suitable for scientific literature summarization tasks

GTE-small is a compact general-purpose text embedding model suitable for various natural language processing tasks, including sentence similarity calculation, text classification, and retrieval.

GTE-Large is a powerful sentence transformer model focused on sentence similarity and text embedding tasks, excelling in multiple benchmark tests.

A Korean text summarization model based on the T5 architecture, specifically optimized for Korean text to generate concise and accurate summaries.

A LongT5-XL model fine-tuned on the BookSum dataset, specifically designed for long-text summarization, capable of generating SparkNotes-like summaries.

MyanBERTa is a Burmese pre-trained language model based on the BERT architecture, pre-trained on a Burmese dataset containing 5,992,299 sentences.

Stanza is a precise and efficient multilingual text analysis toolkit that provides text analysis capabilities for Latin.

BERT model pre-trained on English mathematical texts, specializing in mathematical language understanding tasks

BERT model pretrained on Japanese Wikipedia, employing character-level tokenization and whole word masking strategy, suitable for Japanese natural language processing tasks

A text summarization model based on the LED architecture, specifically designed for generating summaries of technical, academic, and narrative long texts.

BERT large model pretrained on Japanese Wikipedia, utilizing Unidic dictionary tokenization and whole word masking strategy