elsa66666

elsa66666's Stars

• hiyouga/LLaMA-Factory

  Unified Efficient Fine-Tuning of 100+ LLMs (ACL 2024)

  Language:Python36.1k4.4k

• elsa66666/elsashaw.github.io

  Language:HTML1

• usail-hkust/LLMTSCS

  Official code for article "LLMLight: Large Language Models as Traffic Signal Control Agents".

  Language:Python16722

• yuanzhoulvpi2017/SentenceEmbedding

  Language:Jupyter Notebook9211

• XplainMind/LLMindCraft

  Shaping Language Models with Cognitive Insights

  Language:Python123

• llmware-ai/llmware

  Unified framework for building enterprise RAG pipelines with small, specialized models

  Language:Python8.3k1.5k

• SteveKGYang/MentalLLaMA

  This repository introduces MentaLLaMA, the first open-source instruction following large language model for interpretable mental health analysis.

  Language:Python22623

• The-FinAI/PIXIU

  This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).

  Language:Jupyter Notebook56769

• JBoRu/StructGPT

  The code and data for "StructGPT: A general framework for Large Language Model to Reason on Structured Data"

  Language:Python10040

• UKPLab/kg2text

  Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (authors' implementation for the TACL20 paper)

  Language:Python9421

• qikalin/ConGLR

  Language:Python84

• zjukg/MorsE

  [Paper][SIGIR2022] Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

  Language:Python437

• LARS-research/RED-GNN

  Knowledge Graph Reasoning with Relational Digraph. WebConf 2022

  Language:Python5913

• jhshen95/LASS

  Code repo for COLING 2022 paper "Joint Language Semantic and Structure Embedding for Knowledge Graph Completion"

  Language:Python531

• MIRALab-USTC/KG-TACT

  The code of paper Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs. Jiajun Chen, Huarui He, Feng Wu, Jie Wang. AAAI 2021.

  Language:Python417

• fastnlp/CPT

  CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

  Language:Python48273

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python136k27.3k

• kkteru/grail

  Inductive relation prediction by subgraph reasoning, ICML'20

  Language:Python22857

• xwhan/DeepPath

  code and docs for my EMNLP paper "DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning"

  Language:Python538135

• cheungdaven/QuatE

  Pytorch implementation of Quaternion Knowledge Graph Embeddings.

  Language:Python10821

• Limingyang1/The-code-of-paper-Incorporating-Attributes-Semantics-into-Knowledge-Graph-Embeddings-

  More and more works have focused on incorporating different kinds of literals into Knowledge Graph to promote the performance of knowledge embedding. These literals contain numeric literals, text literals, image literals and so on. These additional descriptions are connected to the entities through certain attributes. To incorporate numeric literals, some methods combine the embeddings of literals part with the traditional part - embeddings of entities. However, in the construction of literals embeddings, these existing methods consider the differences of these attributes: one dimension represents one attribute. But they ignore semantic meanings of attributes themselves. In this paper, we propose two methods to incorporate attributes semantics into knowledge graph embeddings from two perspectives: LiteralE-AN and literalE-AT. They concatenate with the embeddings of numeric literals by different ways. Furthermore, their extension model LiteralE-C is also proposed have a more comprehensive representation of attributes semantics. In an empirical study over two standard datasets FB15k and FB15k-237, we evaluate our models for link prediction. We demonstrate they show effective way to improve LiteralE and achieve the state-of-the-art results. In ablation experiments, we find combined models do better than their singular counterparts in most cases.

  Language:Python51

• migalkin/StarE

  EMNLP 2020: Message Passing for Hyper-Relational Knowledge Graphs

  Language:Python8718

• bhumikabhatia/mental-health-chatbot-Miklal

  A mental health chatbot built using NLP techniques that provides responses from verified psychologists.

  Language:Jupyter Notebook3618

• aespresso/a_journey_into_math_of_ml

  汉语自然语言处理视频教程-开源学习资料

  Language:Jupyter Notebook1.7k701

• PlayPurEo/ML-and-DL

  self-learning

  Language:Python544225

• santi-pdp/segan_pytorch

  Speech Enhancement Generative Adversarial Network in PyTorch

  Language:Python382110

• funcwj/voice-filter

  A unofficial Pytorch implementation of Google's VoiceFilter

  Language:Python9824

• Telecommunication-Telemedia-Assessment/Lombard-Speech-database

  Lombard speech database for German language

  21

• kf99916/TimelineTableViewCell

  Simple timeline view implemented by UITableViewCell

  Language:Swift1.3k118

• xmartlabs/XLPagerTabStrip

  Android PagerTabStrip for iOS.

  Language:Swift7k1.3k