Multitask Learning Transformers
A simple recipe for training and inferencing Transformer architecture for Multi-Task Learning on custom datasets. You can find two approaches for achieving this in this repo.
Install / Use
npx skills add shahrukhx01/multitask-learning-transformersInstalls into whichever agent you are using.
README
multitask-learning-transformers
A simple recipe for training and inferencing Transformer architecture for Multi-Task Learning on custom datasets. You can find two approaches for achieving this in this repo.
Colab Notebook
Trained Huggingface Model
Install depedencies
pip install -r requirements.txt
Run training
python3 main.py \
--model_name_or_path='roberta-base' \
--per_device_train_batch_size=8 \
--output_dir=output --num_train_epochs=1
Single Encoder Multiple Output Heads
A multi-task model in the age of BERT works by having a shared BERT-style encoder transformer, and different task heads for each task.
Shared Encoder
Separate models for each task, but we make them share same encoder.
References: Multi-task Training with Transformers+NLP
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