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README.md
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Output for the model: encoded text
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## Evaluation
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```
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- Cosine-Similarity : Pearson: 0.
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- Manhattan-Distance: Pearson: 0.
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- Euclidean-Distance: Pearson: 0.
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- Dot-Product-Similarity: Pearson: 0.
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```
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#### How to use
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Here is how to use this model to get the features of a given text in *PyTorch*:
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Output for the model: encoded text
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## Evaluation
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```
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- Cosine-Similarity : Pearson: 0.8532 Spearman: 0.8517
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- Manhattan-Distance: Pearson: 0.8289 Spearman: 0.8333
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- Euclidean-Distance: Pearson: 0.8298 Spearman: 0.8340
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- Dot-Product-Similarity: Pearson: 0.8043 Spearman: 0.8063
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```
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#### How to use
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Here is how to use this model to get the features of a given text in *PyTorch*:
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