scripts/eval/eval_mhop_fever.py [57:85]:
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    parser.add_argument('--model-name', type=str, default='bert-base-uncased')
    parser.add_argument('--gpu', action="store_true")
    parser.add_argument('--shared-encoder', action="store_true")
    parser.add_argument("--save-path", type=str, default="")
    parser.add_argument("--stop-drop", default=0, type=float)
    args = parser.parse_args()
    
    logger.info("Loading data...")
    ds_items = [json.loads(_) for _ in open(args.raw_data).readlines()]

    logger.info("Building index...")
    d = 768
    xb = np.load(args.indexpath).astype('float32')
    print(xb.shape)

    index = faiss.IndexFlatIP(d)
    index.add(xb)
    if args.gpu:
        res = faiss.StandardGpuResources()
        index = faiss.index_cpu_to_gpu(res, 1, index)

    logger.info(f"Loading corpus...")
    id2doc = json.load(open(args.corpus_dict))
    title2doc = {item[0]:item[1] for item in id2doc.values()}
    logger.info(f"Corpus size {len(id2doc)}")
    
    logger.info("Loading trained model...")
    bert_config = AutoConfig.from_pretrained(args.model_name)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
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scripts/eval/eval_single_fever.py [46:74]:
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    parser.add_argument('--model-name', type=str, default='bert-base-uncased')
    parser.add_argument('--gpu', action="store_true")
    parser.add_argument('--shared-encoder', action="store_true")
    parser.add_argument("--save-path", type=str, default="")
    parser.add_argument("--stop-drop", default=0, type=float)
    args = parser.parse_args()
    
    logger.info("Loading data...")
    ds_items = [json.loads(_) for _ in open(args.raw_data).readlines()]

    logger.info("Building index...")
    d = 768
    xb = np.load(args.indexpath).astype('float32')
    print(xb.shape)

    index = faiss.IndexFlatIP(d)
    index.add(xb)
    if args.gpu:
        res = faiss.StandardGpuResources()
        index = faiss.index_cpu_to_gpu(res, 1, index)

    logger.info(f"Loading corpus...")
    id2doc = json.load(open(args.corpus_dict))
    title2doc = {item[0]:item[1] for item in id2doc.values()}
    logger.info(f"Corpus size {len(id2doc)}")
    
    logger.info("Loading trained model...")
    bert_config = AutoConfig.from_pretrained(args.model_name)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
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