Trinh Q. Tran's repositories
transformer
This is a PyTorch implementation of the Transformer model in the paper Attention is All You Need
finetune_gpj
Fine-tune GPTJ for multi-task
image_captioning
Image Captioning with Encoder as Efficientnet and Decoder as Decoder of Transformer combined with the attention mechanism.
graduation_thesis
Image Captioning uses the model with Encoder as CNN VGG-16 and Decoder as LSTM combined with Attention mechanism.
object_detection
Unbiased Teacher for Semi-Supervised Object Detection
Aspect-Based-Sentiment-Analysis
Aspect Based Sentiment Analysis
category_classification
This is the model that categorizes which category a product_id belongs to. Using Efficientnet model combined with BERT.
huggingface-transformers-examples
Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2, ALBERT, BERT, DitilBERT, RoBERTa, XLNet... GPT and GPT-2 are trained or fine-tuned using a causal language modeling (CLM) loss while ALBERT, BERT, DistilBERT and RoBERTa are trained or fine-tuned using a masked language modeling (MLM) loss.
rotation_classification
Use Efficientnet Model to classify images into 4 classes rotated 90 degrees, rotated 180 degrees, rotated 270 degrees and not rotated.
TranQuocTrinh
Config files for my GitHub profile.