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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)
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