# Multi-task NLP

FinanceConnect-13B is a 13B-parameter large language model specialized in the financial domain, supporting natural language processing tasks such as summarization, classification, and translation.

FinanceConnect-13B is a 13B-parameter large language model specialized in the financial domain, supporting natural language processing tasks such as summarization, classification, and translation.

stella_en_400M_v5_cpu is a model that performs excellently in multiple natural language processing tasks, especially in tasks such as classification, retrieval, clustering, and semantic text similarity.

LUXIA-21.4B-Alignment is a large language model with 21.4 billion parameters, demonstrating outstanding performance across various natural language processing tasks.

An instruction fine-tuned model based on Llama3-8B, trained on over 2,750 datasets, suitable for various NLP tasks

State-of-the-art multi-task joint parsing BERT model for Modern Hebrew, supporting five tasks: prefix segmentation, morphological disambiguation, lexical analysis, syntactic parsing, and named entity recognition

A zero-shot classification model based on DeBERTa-v3, supporting multi-task natural language inference (MNLI), FEVER, and ANLI tasks, converted to ONNX format for compatibility with Transformers.js

spaCy's medium-sized English processing pipeline, optimized for CPU, including POS tagging, dependency parsing, named entity recognition, etc.

Chinese ELECTRA is a pre-trained model developed by the Harbin Institute of Technology-iFLYTEK Joint Lab (HFL) based on the ELECTRA model released by Google and Stanford University. It features a small parameter size and high performance.

LUKE is a Transformer-based pre-trained model specifically designed for words and entities, providing deep contextual representations through entity-aware self-attention mechanisms.

Stanza is an efficient toolkit for German text grammar analysis, providing syntactic parsing and entity recognition capabilities.

AraBERT is an Arabic pre-trained language model based on Google's BERT architecture, specifically designed for Arabic natural language understanding tasks.

LUKE is a Transformer-based pre-trained model specifically designed for words and entities, providing deep contextual representations through entity-aware self-attention mechanisms.

A Transformer-based English NLP pipeline model providing high-performance named entity recognition, part-of-speech tagging, and dependency parsing.

Stanza is a toolkit for grammatical analysis of multiple human languages, offering functionalities from raw text processing to syntactic parsing and entity recognition.

Chinese natural language processing pipeline based on Transformer, including part-of-speech tagging, dependency parsing, named entity recognition, etc.

Large English language processing model provided by spaCy, optimized for CPU, featuring a complete natural language processing pipeline