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Update src/models/predict_model.py
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src/models/predict_model.py
CHANGED
@@ -6,11 +6,11 @@ from src.models.qa_model import *
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from tqdm.auto import tqdm
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tqdm.pandas()
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df_wiki_windows = pd.read_csv("src/data/processed/wikipedia_20220620_cleaned_v2.csv")
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df_wiki = pd.read_csv("src/data/wikipedia_20220620_short.csv")
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df_wiki.title = df_wiki.title.apply(str)
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entity_dict = json.load(open("src/data/processed/entities.json"))
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new_dict = dict()
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for key, val in entity_dict.items():
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val = val.replace("wiki/", "").replace("_", " ")
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@@ -22,15 +22,15 @@ title2idx = dict([(x.strip(), y) for x, y in zip(df_wiki.title, df_wiki.index.va
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qa_model = QAEnsembleModel("nguyenvulebinh/vi-mrc-large", ["src/models/qa_model_robust.bin"], entity_dict)
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pairwise_model_stage1 = PairwiseModel("nguyenvulebinh/vi-mrc-base").half()
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pairwise_model_stage1.load_state_dict(torch.load("src/models/pairwise_v2.bin"))
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pairwise_model_stage1.eval()
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pairwise_model_stage2 = PairwiseModel("nguyenvulebinh/vi-mrc-base").half()
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pairwise_model_stage2.load_state_dict(torch.load("src/models/pairwise_stage2_seed0.bin"))
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bm25_model_stage1 = BM25Gensim("src/models/bm25_stage1/", entity_dict, title2idx)
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bm25_model_stage2_full = BM25Gensim("src/models/bm25_stage2/full_text/", entity_dict, title2idx)
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bm25_model_stage2_title = BM25Gensim("src/models/bm25_stage2/title/", entity_dict, title2idx)
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def get_answer_e2e(question):
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#Bm25 retrieval for top200 candidates
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from tqdm.auto import tqdm
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tqdm.pandas()
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df_wiki_windows = pd.read_csv("/home/user/app/src/data/processed/wikipedia_20220620_cleaned_v2.csv")
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df_wiki = pd.read_csv("/home/user/app/src/data/wikipedia_20220620_short.csv")
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df_wiki.title = df_wiki.title.apply(str)
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entity_dict = json.load(open("/home/user/app/src/data/processed/entities.json"))
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new_dict = dict()
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for key, val in entity_dict.items():
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val = val.replace("wiki/", "").replace("_", " ")
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qa_model = QAEnsembleModel("nguyenvulebinh/vi-mrc-large", ["src/models/qa_model_robust.bin"], entity_dict)
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pairwise_model_stage1 = PairwiseModel("nguyenvulebinh/vi-mrc-base").half()
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pairwise_model_stage1.load_state_dict(torch.load("/home/user/app/src/models/pairwise_v2.bin"))
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pairwise_model_stage1.eval()
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pairwise_model_stage2 = PairwiseModel("nguyenvulebinh/vi-mrc-base").half()
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pairwise_model_stage2.load_state_dict(torch.load("/home/user/app/src/models/pairwise_stage2_seed0.bin"))
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bm25_model_stage1 = BM25Gensim("/home/user/app/src/models/bm25_stage1/", entity_dict, title2idx)
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bm25_model_stage2_full = BM25Gensim("/home/user/app/src/models/bm25_stage2/full_text/", entity_dict, title2idx)
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bm25_model_stage2_title = BM25Gensim("/home/user/app/src/models/bm25_stage2/title/", entity_dict, title2idx)
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def get_answer_e2e(question):
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#Bm25 retrieval for top200 candidates
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