| import pickle | |
| import numpy as np | |
| from evaluation.encoders import Model | |
| from evaluation.eval_datasets import SimpleDataset | |
| from evaluation.evaluator import Evaluator | |
| import argparse | |
| from tqdm import tqdm | |
| import json | |
| def read_data(file_path): | |
| task_data = json.load(open(file_path, "r")) | |
| task_data = list(task_data.values()) | |
| return task_data | |
| class S2ANDEvaluator: | |
| def __init__(self, data_dir: str, model: Model, batch_size: int = 16): | |
| blocks = ["arnetminer", "inspire", "kisti", "pubmed", "qian", "zbmath"] | |
| self.data_dir = data_dir | |
| self.evaluators = [ | |
| Evaluator(block, f"{data_dir}/{block}/{block}_papers.json", SimpleDataset, model, batch_size, [], | |
| "paper_id", process_fn=read_data) for block in blocks] | |
| def generate_embeddings(self, suffix: str): | |
| for evaluator in tqdm(self.evaluators): | |
| print(evaluator.name) | |
| results = evaluator.generate_embeddings() | |
| paper_ids, embs = np.array([str(k) for k in results]), np.array( | |
| [results[k] for k in results]) | |
| pickle.dump((embs, paper_ids), | |
| open(f"{self.data_dir}/{evaluator.name}/{evaluator.name}_{suffix}.pkl", "wb")) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--mtype', help='Model variant to be used (default, pals, adapters, fusion)', default="default") | |
| parser.add_argument('--model', '-m', help='HuggingFace model to be used') | |
| parser.add_argument('--ctrl-tokens', action='store_true', default=False, help='use control codes for tasks') | |
| parser.add_argument('--adapters-dir', help='path to the adapter checkpoints', default=None) | |
| parser.add_argument('--adapters-chkpt', help='hf adapter names keyed on tasks', default=None, type=json.loads) | |
| parser.add_argument('--fusion-dir', help='path to the fusion checkpoints', default=None) | |
| parser.add_argument("--data-dir", help="path to the data directory") | |
| parser.add_argument("--suffix", help="suffix for output embedding files") | |
| args = parser.parse_args() | |
| adapters_load_from = args.adapters_dir if args.adapters_dir else args.adapters_chkpt | |
| model = Model(variant=args.mtype, base_checkpoint=args.model, adapters_load_from=adapters_load_from, | |
| fusion_load_from=args.fusion_dir, use_ctrl_codes=args.ctrl_tokens, | |
| task_id="[PRX]", all_tasks=["[CLF]", "[PRX]", "[RGN]", "[QRY]"]) | |
| evaluator = S2ANDEvaluator(args.data_dir, model) | |
| evaluator.generate_embeddings(args.suffix) | |