Tensorflow, Pytorch, Huggingface Transformer, Fastai, etc. tutorial Colab Notebooks.
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Updated
Dec 20, 2022 - Jupyter Notebook
Tensorflow, Pytorch, Huggingface Transformer, Fastai, etc. tutorial Colab Notebooks.
Concepts and examples on using and training LLMs
Transformer LIbrary Docker Stacks
This notebook leverages Transfer Learning Algorithms and standard NLP procedures to summarize a given paragraph meaningfully.
利用预训练BERT进行真假判定,情感分析等
Pre-Training and Fine-Tuning transformer models using PyTorch and the Hugging Face Transformers library. Whether you're delving into pre-training with custom datasets or fine-tuning for specific classification tasks, these notebooks offer explanations and code for implementation.
Training Notebooks
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language. It involves the development and implementation of algorithms and models capable of understanding, interpreting, and generating human-like language
compilation of notebooks
A serie of notebooks exploring GPT-Neo
Exploratory notebooks and utils related to the "Feedback Prize - Evaluating Student Writing" Kaggle competition.
A collection of Jupyter notebook experiments and applications centered around Generative AI with LLMs.
This repository contains my notebooks for the Kaggle challenge "Real or Not? NLP with Disaster Tweets"
A notebook for a medium article about text classification with Hugging Face DistilBert and Tensorflow 2.0
🗨️ This repository contains a collection of notebooks and resources for various NLP tasks using different architectures and frameworks.
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.
My shot at improving upon the ML literature results for the FNC dataset (2017). I take a two stage approach to the classification problem that beats the 2nd and 3rd place all time leaders. Paper describing my approach in detail and notebooks provided in this repo.
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