---
language:
- en
- multilingual
license: cc0-1.0
task_categories:
- sentence-similarity
- text-classification
tags:
- affiliations
- ror
- triplet-loss
- contrastive-learning
- curriculum-learning
pretty_name: Affiliation Triplets for Embedding Training
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: anchor
    dtype: string
  - name: anchor_ror_id
    dtype: string
  - name: positive
    dtype: string
  - name: positive_ror_id
    dtype: string
  - name: negative
    dtype: string
  - name: negative_ror_id
    dtype: string
  - name: positive_similarity
    dtype: float64
  - name: negative_similarity
    dtype: float64
  - name: difficulty
    dtype: float64
  - name: negative_type
    dtype: string
  - name: triplet_id
    dtype: int64
  splits:
  - name: train
    num_bytes: 495130870
    num_examples: 1083631
  download_size: 221175576
  dataset_size: 495130870
---

# Affiliation Triplets for Contrastive Learning

This dataset contains 1,083,631 triplets (anchor, positive, negative) for training affiliation embedding and reranking models using triplet loss or contrastive learning.

## Dataset Description

Each triplet consists of:
- Anchor: An affiliation string from OpenAlex
- Positive: A different affiliation string for the same organization (same ROR ID)
- Negative: An affiliation string for a different organization

The dataset is sorted by difficulty (descending) to support curriculum learning.

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `triplet_id` | int | Sequential ID (1-indexed), sorted by difficulty |
| `anchor` | string | The anchor affiliation text |
| `anchor_ror_id` | string | ROR ID of the anchor affiliation |
| `positive` | string | Positive affiliation (same org as anchor) |
| `positive_ror_id` | string | ROR ID of positive (same as anchor) |
| `negative` | string | Negative affiliation (different org) |
| `negative_ror_id` | string | ROR ID of negative (different from anchor) |
| `positive_similarity` | float | Cosine similarity between anchor and positive embeddings |
| `negative_similarity` | float | Cosine similarity between anchor and negative embeddings |
| `difficulty` | float | `positive_similarity - negative_similarity` (higher = easier) |
| `negative_type` | string | `"hard"` (from API candidates) or `"easy"` (random) |

## Statistics

| Metric | Value |
|--------|-------|
| Total triplets | 1,083,631 |
| Hard negatives | 592,300 (54.7%) |
| Easy negatives | 491,331 (45.3%) |
| Difficulty range | 0.00 - 1.19 |
| Mean difficulty | 0.26 |
| Mean positive similarity | 0.50 |
| Mean negative similarity | 0.24 |

## Data Pipeline

This dataset was created through a multi-stage pipeline starting from OpenAlex affiliation data.

Ite begins by loading affiliation strings from OpenAlex that have been assigned ROR IDs, along with sample weights indicating how frequently each affiliation appears.

Next, each affiliation undergoes validation against ROR data. We verify that any country mentioned in the affiliation text matches the country in the ROR record, and that the organization's name actually appears somewhere in the affiliation string. This filtering removes assignments that are potentially incorrect.

Validated affiliations then go through a matching stage where we query the ROR affiliation matching API. We confirm that the assigned ROR ID is the same as that assigned in OpenAlex, and we collect the other candidate ROR IDs that the API returned. These candidates represent organizations that could plausibly be confused with the correct one—they become our hard negatives.

With confirmed matches in hand, we generate embeddings for all unique affiliation texts using [SIRIS-Lab/affilgood-dense-retriever](https://huggingface.co/SIRIS-Lab/affilgood-dense-retriever), producing 1024-dimensional vectors that are pre-normalized for computing cosine similarity.

We then constructs the training examples. For each anchor affiliation, we select hard negatives by finding the most similar affiliation from each candidate ROR ID, and easy negatives by randomly sampling from organizations not in the candidate set. For each negative, we find a positive (another affiliation for the same organization) that has higher similarity to the anchor than the negative does to ensure the triplet provides a valid learning signal.

Finally, we sort all triplets by difficulty in descending order and assign sequential IDs. Difficulty is computed as the gap between positive and negative similarity scores. Higher difficulty means the positive is much more similar than the negative (an easy example); lower difficulty means they're close (a hard example).

## Negative Types

- Hard negatives (54.7%): From ROR API candidate results - these are organizations that the API considered similar to the anchor, making them challenging negatives
- Easy negatives (45.3%): Random organizations not in the API candidates - these provide baseline contrast


## Related Datasets

- [cometadata/triplet-loss-affiliation-intermediates](https://huggingface.co/datasets/cometadata/triplet-loss-affiliation-intermediates) - Validated affiliations and confirmed matches
- [cometadata/triplet-loss-affiliation-embeddings](https://huggingface.co/datasets/cometadata/triplet-loss-affiliation-embeddings) - Pre-computed embeddings

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{affiliation_triplets_2025,
  title={Affiliation Triplets for Embedding Training},
  author={CoMetaData},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/cometadata/affiliation-disambiguation-triplets}
}
```

## License

CC0-1.0