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Update app.py
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app.py
CHANGED
@@ -7,6 +7,7 @@ import torch
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model_checkpoint = "vives/distilbert-base-uncased-finetuned-cvent-2022"
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model = AutoModelForMaskedLM.from_pretrained(model_checkpoint,output_hidden_states=True)
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model_base = AutoModelForMaskedLM.from_pretrained("distilbert-base-uncased", output_hidden_states=True)
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
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text1 = st.text_area("Enter first sentence")
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text2 = st.text_area("Enter second sentence")
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@@ -54,5 +55,12 @@ if text1 and text2:
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mean_pooled_base[1:]
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)[0][0]
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-
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st.write(f">>>Similarity for base {base_out}")
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model_checkpoint = "vives/distilbert-base-uncased-finetuned-cvent-2022"
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model = AutoModelForMaskedLM.from_pretrained(model_checkpoint,output_hidden_states=True)
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model_base = AutoModelForMaskedLM.from_pretrained("distilbert-base-uncased", output_hidden_states=True)
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model_2019_2022 = AutoModelForMaskedLM.from_pretrained("vives/distilbert-base-uncased-finetuned-cvent-2019_2022",output_hidden_states=True)
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
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text1 = st.text_area("Enter first sentence")
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text2 = st.text_area("Enter second sentence")
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mean_pooled_base[1:]
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)[0][0]
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outputs_2019_2022 = model_2019_2022(**tokens)
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mean_pooled_2019_2022 = pool_embeddings(outputs_2019_2022,tokens).detach().numpy()
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fine_tuned_out2 = cosine_similarity(
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[mean_pooled_2019_2022[0]],
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mean_pooled_2019_2022[1:]
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)[0][0]
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st.write(f">>>Similarity for fine-tuned (2022) {fine_tuned_out}")
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st.write(f">>>Similarity for fine-tuned (2019-2022) {fine_tuned_out}")
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st.write(f">>>Similarity for base {base_out}")
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