利用预训练BERT进行真假判定,情感分析等
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Updated
May 2, 2021 - Jupyter Notebook
利用预训练BERT进行真假判定,情感分析等
Notebooks for ML Tasks w/ Scikit and LLMs using Cohere, HuggingFace, LangChain, and OpenAI
This repository contains notebook of NLP, CV and ML
Machine Learning Notebooks
Snippets of nlp frameworks
This repository is dedicated to the exploration and utilization of open models, which have emerged as powerful and versatile alternatives to closed models, often surpassing them in various domains. These models have become a go-to choose for many developers and researchers due to their superior performance.
HugChat-AI-Notebook is an API-based chatbot application that uses HuggingFace Python library to translate languages and generate notes.
My personal BERT validation notebook.
Conda environment for transformers notebook
notebooks to finetune `bert-small-amharic`, `bert-mini-amharic`, and `xlm-roberta-base` models using an Amharic text classification dataset and the transformers library
This repo contains notebooks used in Deep RL Course
A customization of the original notebook available on hugging face.
A notebook for a medium article about text classification with Hugging Face DistilBert and Tensorflow 2.0
This repository contains 3 notebooks used in a Kaggle competition on token classification together with results and obeservations
This is an adaption of the notebook , which is provided as part of a class by Hugging Face
Fine-tune BERT for sentiment analysis. I have done text preprocessing (special tokens, padding, and attention masks) and build a Sentiment Classifier using the amazing Transformers library by Hugging Face! I have train my model on kaggle notebook on gpu. The model give the accuracy of 95.14% on validation dataset.
Part of M.Sc. Computer Science project. Demo Jupyter notebook/Python scripts to download a pretrained language model from huggingface and finetune it according to own topic domain and needs.
This notebook showcases a prototype for a retrieval-augmented generation approach in question-answering. The implementation includes demonstrations using an offline language model (LLM) from Hugging Face and the OpenAI GPT-3.5 API.
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