Update README.md
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README.md
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@@ -81,14 +81,14 @@ from sklearn.metrics.pairwise import cosine_similarity
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sentence_1 = ["Der Zug kommt um 9 Uhr in Zürich an."]
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sentence_2 = ["Le train arrive à Lausanne à 9h."]
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#Compute embedding for both
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embedding_1 = generate_sentence_embedding(sentence_1, language="de")
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embedding_2 = generate_sentence_embedding(sentence_2, language="fr")
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#Compute cosine-similarity
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cosine_score = cosine_similarity(embedding_1, embedding_2)
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#Output the score
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print("The cosine score for", sentence_1, "and", sentence_2, "is", cosine_score)
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```
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Output:
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@@ -119,9 +119,9 @@ The fine-tuning script can be accessed [here](Link).
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#### Training Hyperparameters
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Number of epochs: 1
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Learning rate: 1e-5
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Batch size: 512
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## Evaluation
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sentence_1 = ["Der Zug kommt um 9 Uhr in Zürich an."]
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sentence_2 = ["Le train arrive à Lausanne à 9h."]
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# Compute embedding for both
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embedding_1 = generate_sentence_embedding(sentence_1, language="de")
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embedding_2 = generate_sentence_embedding(sentence_2, language="fr")
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# Compute cosine-similarity
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cosine_score = cosine_similarity(embedding_1, embedding_2)
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# Output the score
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print("The cosine score for", sentence_1, "and", sentence_2, "is", cosine_score)
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```
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Output:
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#### Training Hyperparameters
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- Number of epochs: 1
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- Learning rate: 1e-5
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- Batch size: 512
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## Evaluation
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