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Runtime error
Update app.py
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app.py
CHANGED
@@ -12,11 +12,14 @@ if choice == "distilbert-cvent":
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model_checkpoint = "vives/distilbert-base-uncased-finetuned-cvent-2019_2022"
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model = AutoModelForMaskedLM.from_pretrained(model_checkpoint, output_hidden_states=True)
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
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elif choice == "finbert":
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model_checkpoint = "ProsusAI/finbert"
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tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
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model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert", output_hidden_states=True)
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text = st.text_input("Enter word or key-phrase")
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exclude_words = st.radio("exclude_words",[True,False], help="Exclude results that contain any words in the query (i.e exclude 'hot coffee' if the query is 'cold coffee')")
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@@ -29,8 +32,8 @@ with st.sidebar:
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if diversify_box:
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k_diversify = st.number_input("Set of key-phrases to diversify from",10,30,20)
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#load kp
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with open(
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kp_dict = pickle.load(handle)
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keys = list(kp_dict.keys())
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@@ -38,7 +41,7 @@ for key in kp_dict.keys():
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kp_dict[key] = kp_dict[key].detach().numpy()
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#load cosine distances of kp dict
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with open(
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cosine_kp = pickle.load(handle)
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def calculate_top_k(out, tokens,text,exclude_text=False,exclude_words=False, k=5):
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model_checkpoint = "vives/distilbert-base-uncased-finetuned-cvent-2019_2022"
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model = AutoModelForMaskedLM.from_pretrained(model_checkpoint, output_hidden_states=True)
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
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kp_dict_checkpoint = "kp_dict_finbert.pickle"
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kp_cosine_checkpoint = "cosine_kp_finbert.pickle"
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elif choice == "finbert":
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model_checkpoint = "ProsusAI/finbert"
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tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
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model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert", output_hidden_states=True)
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kp_dict_checkpoint = "kp_dict_merged.pickle"
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kp_cosine_checkpoint = "cosine_kp.pickle"
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text = st.text_input("Enter word or key-phrase")
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exclude_words = st.radio("exclude_words",[True,False], help="Exclude results that contain any words in the query (i.e exclude 'hot coffee' if the query is 'cold coffee')")
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if diversify_box:
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k_diversify = st.number_input("Set of key-phrases to diversify from",10,30,20)
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#load kp di
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with open(kp_dict_checkpoint,'rb') as handle:
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kp_dict = pickle.load(handle)
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keys = list(kp_dict.keys())
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kp_dict[key] = kp_dict[key].detach().numpy()
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#load cosine distances of kp dict
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with open(kp_cosine_checkpoint,'rb') as handle:
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cosine_kp = pickle.load(handle)
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def calculate_top_k(out, tokens,text,exclude_text=False,exclude_words=False, k=5):
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