Update README.md

README.md

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 license: cc0-1.0
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-# Affiliation Triplet Dataset for Embeddings
+# Affiliation Triplet Curriculum Dataset
 
-This dataset is designed for training an embedding model using triplet loss to determine if two affiliation strings refer to the same institution. It contains pairs of affiliation strings, labeled as either positive (match) or negative (no match).
+This dataset is designed for training an embedding model using triplet loss. It contains triplets of affiliation strings (`anchor`, `positive`, `negative`) structured to teach a model to recognize when two strings refer to the same institution.
+
+The dataset is sorted from easiest to hardest to facilitate curriculum learning, allowing the model to learn from simple examples before progressing to more challenging ones.
+
+---
 
 ## Dataset Details
 
 ### Data Fields
 
-The dataset consists of the following fields:
+Each row in the dataset is a complete triplet with the following fields:
+
+-   `triplet_id`: (integer) A unique identifier for each triplet in the sequence.
+-   `anchor`: (string) The primary affiliation string.
+-   `positive`: (string) An affiliation string known to be a match for the `anchor`.
+-   `negative`: (string) An affiliation string known not to be a match for the `anchor`.
+-   `difficulty`: (float) A calculated score representing the triplet's difficulty (`positive_dist_ratio - negative_dist_ratio`). Lower (and negative) scores indicate harder triplets.
+-   `positive_dist_ratio`: (float) The fuzzy string similarity score (0-100) between the normalized `anchor` and `positive`.
+-   `negative_dist_ratio`: (float) The fuzzy string similarity score (0-100) between the normalized `anchor` and `negative`.
+-   `negative_type`: (string) The type of negative example, either 'hard' or 'easy'.
+
+---
+
+## Dataset Structure and Curriculum Learning
+
+The dataset is delivered as a single, unsplit file.
 
-- `anchor_affiliation`: (string) The primary affiliation string.
-- `candidate_affiliation`: (string) An affiliation string to be compared against the anchor.
-- `label`: (integer) `1` if the affiliations are a positive match, `0` if they are a negative match.
+It is **not randomly shuffled**. Instead, it is intentionally sorted by the `difficulty` field in descending order (from highest score to lowest). This creates a curriculum:
 
-### Data Splits
+1.  **Easiest Triplets (Top of the file):** These have a high positive similarity and a low negative similarity.
+2.  **Hardest Triplets (Bottom of the file):** These have a low positive similarity and a high negative similarity, often resulting in a negative difficulty score. These examples are crucial for teaching the model to handle nuanced and tricky cases.
+
+---
 
-The dataset is not formally split, but it is generated with a target of approximately 20% positive examples and 80% negative examples. The data is shuffled before being saved.
+## Triplet Generation
 
-### Positive and Negative Examples
+### Positive Examples
 
-**Positive Examples (Label = 1):**
-These are pairs where the `anchor_affiliation` and `candidate_affiliation` are known to belong to the same institution. They are created by taking an anchor affiliation and pairing it with a different known affiliation string that shares the same ROR ID. 
+The `positive` affiliation is a known variant of the `anchor`, both sharing the same ROR ID. To ensure a meaningful learning signal, trivial pairs (e.g., "Google" vs. "google") with near-perfect similarity (>=99%) are filtered out. This forces the model to learn from meaningful variations like abbreviations, acronyms, or different subunit names.
 
-**Negative Examples (Label = 0):**
-Negative examples are used to teach the model what *doesn't* constitute a match. This dataset includes two types of negative examples:
+### Negative Examples
 
-1.  **Hard Negatives:** These are intentionally tricky pairs. They are generated by pairing an affiliation with a known ROR ID assignment, with an affiliation from the set of ROR IDs returned as candidates by the ROR affiliation matching endpoint (which are incorrect but semantically similar). This forces the model to learn subtle but important distinctions between institutions with similar names or in similar locations (e.g., "University of Michigan" vs. "Western Michigan University").
+The dataset includes two types of negative examples to provide a robust training experience. The final dataset is generated with a target ratio of **80% hard negatives** and **20% easy negatives**.
 
-2.  **Random Negatives:** These pairs are generated by selecting two affiliation strings from completely different institutions (different ROR IDs). These are generally easier for the model to distinguish and help it learn the broader feature space of what makes affiliations different.
+1.  **Hard Negatives:** These are intentionally tricky pairs. They are generated by pairing an `anchor` with an affiliation that is semantically similar but incorrect (e.g., "University of Michigan" vs. "Western Michigan University"). This forces the model to learn subtle but important distinctions.
 
-The final dataset is a shuffled combination of these positive, hard negative, and random negative examples.
+2.  **Easy Negatives:** These pairs are generated by selecting an affiliation from a completely different and unrelated institution. These are generally easier for the model to distinguish and help it learn the broad feature space of what makes affiliations different.