## 6 Results
### Runtime Comparison  
To illustrate the impact of the proposed models on sentence embedding generation, we show the execution times across various models. These models were benchmarked on an identical dataset comprising 300,000 sentences, which we used for training and validating the proposed models. All models utilized GPU acceleration, except _FastText_, and were executed in batches of 64. For obtaining sentence embeddings from the sBert\({}_{\gamma 68}\) model, the Sentence-Transformers ([[https://www.sbert.net](https://www.sbert.net)]([https://www.sbert.net](https://www.sbert.net)), accessed on 23 October 2023) python package was used.  
Table 5 indicates that the sBert\({}_{\gamma 68}\) model exhibits the longest duration for sentence embedding generation. In contrast, _FastText_ is uniquely efficient, functioning effectively  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **STS12** & **STS13** & **STS14** & **STS15** & **STS16** & **STSB** & **SICKR** & **Average** \\ \hline sBert\({}_{\gamma 68}\) & 0.72 /0.70 & 0.78/0.77 & 0.80/0.77 & 0.82 /0.83 & 0.82 /0.83 & 0.82 /0.83 & 0.87 /0.82 & 0.80 /0.79 \\ sBert\({}_{valued}\) & 0.72 /0.70 & 0.78/0.78 & 0.80/0.77 & 0.82 /0.83 & 0.82 /0.83 & 0.83 /0.84 & 0.87 /0.81 & 0.81 /0.79 \\ sBert\({}_{384}\) & 0.71 /0.70 & 0.76 /0.77 & 0.80/0.77 & 0.82 /0.83 & 0.81 /0.82 & 0.83 /0.83 & 0.87 /0.80 & 0.80 /0.79 \\ \hline FastText\({}_{subset}\) & 0.32 /0.42 & 0.39 /0.41 & 0.46 /0.48 & 0.53 /0.55 & 0.39 /0.44 & 0.61 /0.61 & **0.76**/**0.69** & 0.49 /0.51 \\ \hline FastText\({}_{quadted}\) & 0.29 /0.37 & 0.30 /0.31 & 0.38 /0.41 & 0.42 /0.44 & 0.31 /0.36 & 0.47 /0.47 & 0.64 /0.61 & 0.40 /0.42 \\ Distance Min & **0.47**/**0.48** & **0.55**/**0.55** & **0.54**/**0.51** & **0.59**/**0.59** & **0.54**/**0.56** & 0.50 /0.49 & 0.59 /0.53 & **0.54**/**0.53** \\ \hline FastText\({}_{quadted}\) & 0.33 /0.44 & 0.39 /0.41 & 0.46 /0.48 & 0.52 /0.54 & 0.39 /0.44 & 0.57 /0.58 & 0.74 /0.68 & 0.49 /0.51 \\ Distance and PP Min & **0.55**/**0.57** & **0.56**/**0.58** & **0.61**/**0.58** & **0.66**/**0.66** & **0.62**/**0.63** & **0.63**/**0.62** & **0.76**/0.67 & **0.63**/**0.62** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of Semantic Textual Similarity tasks on updated word embeddings and the original subset FastText word embeddings. Left values represent Pearson Correlation, whereas the right values present Spearman Correlation values.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **CR** & **MPQA** & **SST2** & **MRPC** & **MR** & **SUBJ** & **SST5** & **TREC** \\ \hline sBert\({}_{\gamma 68}\) & 86.86 & 89.32 & 88.74 & 74.07 & 85.05 & 93.97 & 49.19 & 94.0 \\ sBert\({}_{valued}\) & 85.56 & 89.18 & 88.85 & 75.42 & 84.89 & 93.45 & 48.46 & 92.0 \\ sBert\({}_{384}\) & 83.05 & 88.14 & 83.31 & 74.09 & 78.05 & 92.35 & 46.02 & 91.0 \\ \hline FastText\({}_{subset}\) & 76.88 & 87.49 & 80.07 & 71.71 & **75.25** & **98.93** & **42.76** & **78.4** \\ \hline FastText\({}_{quadted}\) & 66.31 & 83.17 & 70.95 & 67.94 & 67.15 & 81.48 & 33.98 & 66.4 \\ Distance Min & 73.22 & 85.3 & 68.75 & 68.23 & 67.61 & 82.85 & 35.93 & 60.4 \\ \hline FastText\({}_{quadted}\) & 74.97 & **88.06** & **80.51** & 70.67 & 73.76 & 89.17 & 41.0 & 77.2 \\ Distance and PP Min & **79.42** & 87.29 & 78.36 & **72.06** & 74.13 & 88.82 & 40.9 & 76.8 \\ \hline
**Word Domain Coverage** & **0.72** & **0.86** & **0.66** & **0.59** & 0.53 & 0.52 & 0.65 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative performance of various models on classification tasks. The _Word Domain Coverage_ row indicates how the classification accuracy correlates with the overlap between the training data from the WMT dataset and the vocabulary of various classification datasets.  
without GPU support. The models that we propose serve as an intermediate solution, offering a compromise between these two in terms of computational resource demands and time efficiency. Moreover, our implementations can be optimized further for better execution times.