README.md

---
license: mit
task_categories:
  - text-retrieval
language:
  - en
size_categories:
  - 10K<n<100K
configs:
  - config_name: begin
    data_files:
      - split: train
        path: begin/dist_100_0_0.json
  - config_name: middle
    data_files:
      - split: train
        path: middle/dist_0_100_0.json
  - config_name: end
    data_files:
      - split: train
        path: end/dist_0_0_100.json
  - config_name: uniform
    data_files:
      - split: train
        path: uniform/dist_33_33_33.json
---

# Position-Bias Training Datasets (main 4 distributions)

Synthetic retrieval training data used to **induce/control position bias** in embedding
models, restricted to the **four main evidence-position distributions** used in the paper.
Filtered at reranker threshold **t030** (signal_gap ≥ 0.30, highest label purity).

Each example is a `(question, positive_doc)` pair. The distributions differ only in **where
the relevant evidence sits inside the positive document** (beginning / middle / end),
mixed at the ratios below.

## Configs (evidence-position distribution)

| config | ratio (begin/middle/end) | evidence location | records |
|---|---|---|---|
| `begin` | 100 / 0 / 0 | evidence at document **beginning** | 40,915 |
| `middle` | 0 / 100 / 0 | evidence in document **middle** | 40,915 |
| `end` | 0 / 0 / 100 | evidence at document **end** | 40,915 |
| `uniform` | 33.3 / 33.3 / 33.3 | evidence **uniformly** across positions | 40,915 |

## Schema

- `question` (string): synthetic query (apply the model's query prompt at encoding time).
- `positive_doc` (string): the relevant document containing the evidence.

## Usage

```python
from datasets import load_dataset
ds = load_dataset("sionic-ai/position-bias-training-datasets", "begin", split="train")
```

## Provenance

Subset of the private dataset `sionic-ai/position-bias-train-030` (13 distributions);
this repo keeps only the 4 main distributions with semantic config names
(`begin`=100_0_0, `middle`=0_100_0, `end`=0_0_100, `uniform`=33_33_33).