# Context Understanding

This is a lightweight text ranking model based on TinyLlama, specifically designed for classifying and reordering context-question pairs.

A Persian Q&A specialized version fine-tuned based on Llama3, capable of accurately answering Persian questions in context

WestLake-7B-v2 is a 7-billion-parameter large language model specializing in role-playing and text generation, achieving an average score of 74.68 on the Open LLM Leaderboard.

Fraud detection dialogue model fine-tuned based on Intel/neural-chat-7b-v3-1

Merlyn Education Corpus Q&A v2 is a 13-billion-parameter decoder-style transformer model designed for the education domain. It is fine-tuned from the llama2-13b base model and specifically tailored to provide question answers based on given contexts.

This model is based on the t5-large architecture, fine-tuned on the SQuAD dataset for text generation, used to generate questions and corresponding answers from given contexts.

A model specialized in Arabic Q&A tasks, capable of answering related questions based on context.

A RoBERTa-based question answering model specialized in inferring answer text, span, and confidence scores given a question and context.

This is a Q&A model fine-tuned on the YTU dataset, capable of answering related questions based on provided context.

A BERT-based question answering model fine-tuned on the SQuAD dataset

This is a base model based on the BERT architecture, specifically trained on the SQuAD v2 dataset, suitable for question-answering tasks.

A QA and NER model fine-tuned on the conll2003 dataset based on distilbert-base-uncased-squad2

A versatile large language model capable of handling various natural language processing tasks (inferred information)

This is an extractive question-answering model trained with AutoNLP, capable of extracting answers from given text.

A French Q&A task model fine-tuned based on DistilCamemBERT, optimized for inference speed and reduced computational resource consumption

This is a BERT-based Turkish question-answering model, specifically designed for handling Turkish Q&A tasks.

This is a question generation model fine-tuned on the T5-Base architecture, specifically designed to generate relevant questions from given answers and contexts.

A multi-task model based on DistilBERT for question answering and named entity recognition, fine-tuned on the conll2003 dataset

A versatile large language model capable of handling various natural language tasks

This model is fine-tuned from CamemBERT-base, specifically designed for answer extraction tasks in French texts, trained on SquadFR, FQuAD, and PIAF datasets.

A Q&A model supporting Arabic, capable of answering questions based on context.

A Q&A system model fine-tuned on the SQAC dataset based on the Spanish BERT model