nlp-from-scratch-assignment for 11-711, Advanced NLP course at Carnegie Mellon University.

To run the model training and evaluation pipeline, cd into the root folder of the repository and run:

python code/model_pipeline/sciner.py --model-checkpoint KISTI-AI/scideberta-cs --lr 5e-5 --epochs 10 --weight_decay 1e-6 --batch_size 8

This objective is to perform recognition of scientific entities in research papers. This repository contains all required components to do the task:

1. Scraping scripts to fetch research paper PDFs at code/data_collection
2. Parsing scripts to parse the PDFs at code/data_collection
3. A collection of 32 manually annotated papers (gold-standard) at data/annotated a. Scripts to split and create train and dev sets are located at data/created_data_train_test_splits b. Held out test set is located at data/test
4. Model training pipeline that achieves 0.626 F1 on the held-out set.

advanced-nlp-f22-hw2
├── code
│ ├── data_collection
│ │ ├── annotation_scripts
│ │ │ ├── bert.conll
│ │ │ ├── bert_copy.json
│ │ │ ├── bert_edited.json
│ │ │ ├── bert.json
│ │ │ ├── bert_min.json
│ │ │ ├── generate_mapping.py
│ │ │ ├── lda_edited.json
│ │ │ ├── lda.json
│ │ │ ├── mapping.json
│ │ │ ├── reverse_mapping.json
│ │ │ └── rule_based.py
│ │ ├── constants.py
│ │ ├── example_scipdf_output.json
│ │ ├── labelstudio_collector.py
│ │ ├── parser.py
│ │ ├── pdf_urls.txt
│ │ ├── random_pdf.py
│ │ ├── sampled_urls.txt
│ │ ├── scrape_all.py
│ │ └── scrape.py
│ ├── model_pipeline
│ │ ├── constants.py
│ │ ├── dataloader.py
│ │ ├── pipeline.py
│ │ └── sciner.py
│ └── notebooks
│ ├── ANLP_NER_BertyBoy.ipynb
│ ├── ANLP_NER_deberta.ipynb
│ ├── ANLP_NER_SCIBERT.ipynb
│ ├── ANLP_NER_scideberta.ipynb
│ └── NER_Model_Pipeline.ipynb
├── data
│ ├── annotated
| | (our annotated conll files by paper as exported from Label Studio)
│ ├── created_data_train_test_splits
| | (our annotations split into train.conll and test.conll)
│ ├── parsed_pdfs
| | (txt files of parsed papers)
│ ├── summary_of_parsed_files.json
│ ├── test
| | (held out test set files and our model predictions on them)
├── github.txt
├── LICENSE
├── README.md
├── requirements.txt
└── setup.sh\