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DrQA is an open-domain question answering system that retrieves relevant documents from a large corpus and extracts specific answers to natural language questions. It is implemented as a neural network system that combines a document retrieval engine with a machine reading comprehension model. The system utilizes a two-stage pipeline architecture. A coarse-grained document retriever uses weighted word vectors to identify potential documents, while a fine-grained machine reading comprehension model identifies and extracts the exact text span containing the answer. The project also includes a
Chinese-BERT-wwm is a pre-trained transformer model and encoder designed for Chinese natural language processing. It converts Chinese text into dense vector representations to be used across various natural language processing applications. The model utilizes a whole word masking strategy during pre-training, masking entire words rather than individual characters. This approach is designed to improve the capture of semantic meaning and language structure within Chinese datasets. The project covers a range of downstream tasks including text classification, sequence labeling, and reading compr
bert4keras is a lightweight reimplementation of the BERT transformer architecture for the Keras deep learning framework. It serves as a natural language processing toolkit and transformer model library used for text classification, sequence labeling, and semantic embedding extraction. The framework includes a sequence-to-sequence model system for question answering and text generation, as well as a model inference server to deploy trained transformers as web APIs for real-time predictions. Capabilities cover a broad range of natural language understanding tasks, including reading comprehensi
This project is a natural language processing framework focused on a generalized autoregressive pretrainer designed for unsupervised language representation. It implements a language model that combines permutation-based training with a Transformer-XL backbone to function as a long-context text processor. The system is distinguished by its ability to handle text sequences that exceed standard length limits through the use of segment-level recurrence and relative positional encoding. It scales high-performance pretraining across multiple GPUs and TPU clusters using distributed training impleme
The main features of ymcui/cmrc2018 are: Reading Comprehension, Reading Comprehension Benchmarks.
Projects with overlapping indexed features include: facebookresearch/drqa — DrQA is an open-domain question answering system that retrieves relevant documents from a large corpus and extracts… bojone/bert4keras — bert4keras is a lightweight reimplementation of the BERT transformer architecture for the Keras deep learning… zihangdai/xlnet — This project is a natural language processing framework focused on a generalized autoregressive pretrainer designed… ymcui/chinese-bert-wwm — Chinese-BERT-wwm is a pre-trained transformer model and encoder designed for Chinese natural language processing. It… baidu/dureader — Baseline Systems of DuReader Dataset. benywon/reco.