## 2 Materials and Methods
### Dataset Description
#### 2.1.2 Data Statistics  
Tables 2 and 3 present an overview of the size, distribution of labels, and language breakdown of the dataset. These statistics apply to both the combined _train + valid_ set and the _test_ set.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Column Name** & **Data Type** & **Column Description** \\ \hline index & int64 & Sequential number identifying the record in the dataset. \\ job\_submission\_id & int64 & Unique identifier of a candidate’s submission for a given job posting. \\ job\_id & int64 & Unique identifier of the job posting. \\ job\_title & string & The descriptive title or name of the job position (e.g., \"Senior Java Developer\"). \\ job\_description & string & Full description of the job, covering responsibilities, requirements, and other details. \\ candidate\_id & int64 & Unique identifier of the candidate (applicant). \\ candidate\_resume & string & Textual content of the candidate’s resume/CV (potentially multilingual). \\ recruitment\_path & string & A text-based representation of the candidate’s progress through different recruiting stages. \\ recruitment\_path\_len & int64 & Number of completed stages in the candidate’s recruitment path. \\  & & The candidate’s final or current stage status (e.g., \\ recruitment\_path\_status & string & passed technical interview, awaiting \\  & & HR interview, etc.). \\  & & The final recruitment outcome category for the \\ label & string & candidate (e.g., soft\_positive, soft\_negative, or positive). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common data structure for both _train + valid_ and _test_ datasets. Each row represents a single job-candidate pair, with labels indicating recruitment outcomes.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Property** & \begin{tabular}{c} **Train + Valid** \\ **(Count)** \\ \end{tabular} & \begin{tabular}{c} **Train + Valid** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Test (Count)** \\ \end{tabular} &
\begin{tabular}{c} **Test (\%)** \\ \end{tabular} \\ \hline Number of Rows & 14,827 & – & 1000 & – \\ Number of Columns & 23 & – & 23 & – \\ \hline Label: soft\_positive & 6630 & 44.72\% & 432 & 43.20\% \\ Label: soft\_negative & 5556 & 37.47\% & 381 & 38.10\% \\ Label: positive & 2641 & 17.81\% & 187 & 18.70\% \\ \hline Total & 14,827 & 100.00\% & 1000 & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Consolidated statistics for the _train + valid_ and _test_ datasets. Alongside the total number of rows and columns, the distribution of the three label categories is shown.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Train + Valid**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-5}
**Language** & **Record Count** & **Percent** & **Record Count** & **Percent** \\ \hline en & 13,019 & 87.81\% & 870 & 87.00\% \\ pl & 1808 & 12.19\% & 130 & 13.00\% \\ \hline Total & 14,827 & 100.00\% & 1000 & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Language statistics for the _train + valid_ and _test_ sets, indicating the proportion of English and Polish texts in candidate_resume or job_description.  
As shown in Table 2, the _train + valid_ partition comprises 14,827 job-candidate pairs, whereas the _test_ set has 1000 pairs. In both subsets, the soft_positive category is the largest (about 44-45%), with soft_negative being the second largest and positive representing roughly 18% of the entries. This distribution reflects the real-world recruitment funnel: many candidates show partial alignment but do not get hired, a notable subset is quickly rejected, and a smaller group eventually receives an offer.  
Table 3 highlights that most job descriptions are in English (over 85%), with Polish being the second most common language. This bilingual nature of the used dataset influenced our choice of a multilingual embedding model.