Social Media NLP package for PyTorch & PyTorch Lightning.
A Social Media Natural Language Processing package for PyTorch & PyTorch Lightning.
Key Features • About Me • How To Use • Examples
PyTorch Gleam builds upon PyTorch Lightning for the specific use-case of Natural Language Processing on Social Media, such as Twitter. PyTorch Gleam strives to make Social Media NLP research easier to understand, use, and extend. Gleam contains models I use in my research, from fine-tuning a BERT-based model with Lexical, Emotion, and Semantic information in a Graph Attention Network for stance identification towards COVID-19 misinformation, to using Information Retrieval systems to identify new types of misinformation on Twitter.
My name is Maxwell Weinzierl, and I am a Natural Language Processing researcher at the Human Technology Research Institute (HLTRI) at the University of Texas at Dallas. I am currently working on my PhD, which focuses on COVID-19 and HPV vaccine misinformation, trust, and more on Social Media platforms such as Twitter. I have built PyTorch Gleam to enable easy reproducibility for my published research, and for my own quick iterations on research ideas.
How To Use
Step 0: Install
Simple installation from PyPI
pip install pytorch-gleam
You may need to install CUDA drivers and other versions of PyTorch. See PyTorch and PyTorch Lightning for installation help.
Step 1: Create Experiment
configs folder with a YAML experiment file. Gleam utilizes PyTorch Lightning's CLI tools
to configure experiments from YAML files, which enables researchers to clearly look back
and identify both hyper-parameters and model code used in their experiments.
This example is from COVID-19 vaccine misinformation stance identification:
seed_everything: 0 model: class_path: pytorch_gleam.modeling.models.MultiClassFrameLanguageModel init_args: learning_rate: 5e-4 pre_model_name: digitalepidemiologylab/covid-twitter-bert-v2 label_map: No Stance: 0 Accept: 1 Reject: 2 threshold: class_path: pytorch_gleam.modeling.thresholds.MultiClassThresholdModule metric: class_path: pytorch_gleam.modeling.metrics.F1PRMultiClassMetric init_args: mode: macro num_classes: 3 trainer: max_epochs: 10 accumulate_grad_batches: 4 check_val_every_n_epoch: 1 deterministic: true num_sanity_val_steps: 1 checkpoint_callback: false callbacks: - class_path: pytorch_gleam.callbacks.FitCheckpointCallback data: class_path: pytorch_gleam.data.datasets.MultiClassFrameDataModule init_args: batch_size: 8 max_seq_len: 128 label_name: misinfo label_map: No Stance: 0 Accept: 1 Reject: 2 tokenizer_name: digitalepidemiologylab/covid-twitter-bert-v2 num_workers: 8 frame_path: - covid19/misinfo.json train_path: - covid19/stance-train.jsonl val_path: - covid19/stance-dev.jsonl test_path: - covid19/stance-test.jsonl
Documentation on available
will be provided soon.
Details about how to set up YAML experiment files are provided by PyTorch Lightning's documentation.
Annotations for this example are provided in the VaccineLies repository under covid19 as the CoVaxLies collection: CoVaxLies. You will need to download the tweet texts from the tweet ids from the Twitter API.
Step 2: Run Experiment
models folder for your saved TensorBoard logs and model weights.
Determine the GPU ID for the GPU you would like to utilize (multi-gpu supported) and provide the ID in a list, with
a comma at the end if it is a single GPU ID. You can also just specify an integer, such as
1, and PyTorch Lightning
will try to find a single free GPU automatically.
Run the following command to start training:
gleam fit \ --config configs/covid-stance.yaml \ --trainer.gpus 1 \ --trainer.default_root_dir models/covid-stance
Your model will train, with TensorBoard logging all metrics, and a checkpoint will be saved upon completion.
Step 3: Evaluate Experiment
You can easily evaluate your system on a test collection as follows:
gleam test \ --config configs/covid-stance.yaml \ --trainer.gpus 1 \ --trainer.default_root_dir models/covid-stance
These are a work-in-progress, as my original research code is a bit messy, but they will be updated soon!
COVID-19 Vaccine Misinformation Detection on Twitter
COVID-19 Vaccine Misinformation Stance Identification on Twitter
COVID-19 Misinformation Stance Identification on Twitter
Vaccine Misinformation Transfer Learning
Vaccine Hesitancy Profiling on Twitter
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