Xia Cui's starred repositories
haystack
:mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
MIUA2024.github.io
MIUA2024 Website
tweetnlp
TweetNLP for all the NLP enthusiasts working on Twitter! The Python library tweetnlp provides a collection of useful tools to analyze/understand tweets such as sentiment analysis, emoji prediction, and named entity recognition, powered by state-of-the-art language models specialised on Twitter.
nova
NOVA is a tool for annotating and analyzing behaviours in social interactions. It supports Annotators using Machine Learning already during the coding process. Further it features both, discrete labels and continuous scores and a visuzalization of streams recorded with the SSI Framework.
Cost-Sensitive_Bert_and_Transformers
Transformers for Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data
stanford_alpaca
Code and documentation to train Stanford's Alpaca models, and generate the data.
awesome-human-label-variation
A curated list of awesome datasets with human label variation (un-aggregated labels) in Natural Language Processing and Computer Vision, accompanying The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation (EMNLP 2022)
Text_Classification
Text Classification Algorithms: A Survey
gridspace-stanford-harper-valley
The Gridspace-Stanford Harper Valley speech dataset. Created in support of CS224S.
counsel-chat
This repository holds the code for working with data from counselchat.com
sentiment-predictor-for-stress-detection
Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.
OpenPrompt
An Open-Source Framework for Prompt-Learning.
mlm-scoring
Python library & examples for Masked Language Model Scoring (ACL 2020)
transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
google-research
Google Research