My personal BERT validation notebook.

Snippets of nlp frameworks

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.

Notebook (Demonstration) for training Distilbert on Glue, and uploading model to Huggingface.

A notebook for a medium article about text classification with Hugging Face DistilBert and Tensorflow 2.0

利用预训练BERT进行真假判定，情感分析等

A jupyter notebook showing how to finetune the vision transformer on a facial expression dataset (FER-2013)

This repository contains notebook of NLP, CV and ML

Hugging face notebooks.

Learning PyTorch through the D2L book. A series of notebooks for the same

A customization of the original notebook available on hugging face.

This is an adaption of the notebook , which is provided as part of a class by Hugging Face

Tensorflow, Pytorch, Huggingface Transformer, Fastai, etc. tutorial Colab Notebooks.

This repo contains notebooks used in Deep RL Course

A tutorial that guides users through the process of fine-tuning a stable diffusion model using HuggingFace's diffusers library. The tutorial includes advice on suitable hardware requirements, data preparation using the BLIP Flowers Dataset and a Python notebook, and detailed instructions for fine-tuning the model.

Machine Learning Notebooks

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 repository hosts the files and notebooks for my Food Classification App. The application is built using Gradio and deployed on Hugging Face. Explore the code and documentation to understand the implementation and feel free to contribute or use it for your own projects.

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.