---
license: mit
task_categories:
- text-classification
tags:
- medical
- biology
---
## Data Selection & Splitting

* **Source:** HRCS 2014, 2018, and 2022 direct award datasets.
* **Quality Filtering:** 
    * Only **human-coded** abstracts were included.
    * Records with abstracts shorter than **75 characters** were removed during preprocessing to ensure the model had sufficient text to learn from.
* **Train/Test Split:** The **Test Set** was isolated using only **2022 data** to provide a modern performance benchmark.

---

## Training Data Deduplication

To prevent the model from over-fitting on near-identical entries, a robust deduplication pipeline was implemented:

1.  **Vectorization:** Character-level **TF-IDF vectors** were generated from training titles using word-boundary character n-grams (length 3–5).
2.  **Similarity Analysis:** Near-duplicate titles were identified using a **Cosine Similarity threshold of more than or equal to 0.85**.
3.  **Clustering:** Records exceeding this threshold were grouped using a **connected-components graph algorithm**.
4.  **Selection:** Only the first occurrence in file order from each group was retained in the training pool.

---

## Leakage Prevention (Train vs. Test)

To ensure the test set provides a truly unseen and honest evaluation, the following steps were taken:

* **Shared Feature Space:** The TF-IDF vectorizer was fit on the **combined** set of training and test titles.
* **Cross-Set Comparison:** Any training record with a **Cosine Similarity threshold of more than or equal to 0.85** to any record in the test set was permanently removed from the training pool.
* **Test Set Integrity:** The test set itself was deduplicated using **exact title matching only** (no fuzzy matching applied).

> [!IMPORTANT]
> **Limitation:** Short, highly generic grant titles (e.g., *"Studentship"*) may have been deduplicated in the training set due to the similarity threshold.