---
license: apache-2.0
base_model: answerdotai/ModernBERT-large
language:
- en
tags:
- fact-checking
- context-pruning
- evidence-retrieval
---

# evidenceprune-modernbert-large

evidenceprune is a lightweight context-pruning model fine-tuned for fact checking. Given a
claim and a web page, it marks the sentences a fact-checker would need.

This checkpoint is a 0.4B encoder fine-tuned from ModernBERT-large as a token classifier.

## Usage

```
pip install git+https://github.com/ofbread/evidenceprune
```

```python
from evidenceprune import Pruner

kept = Pruner("ofbread/evidenceprune-modernbert-large").prune(claim, documents)
```


## Input

`Pruner(model, threshold=None, device=None)` loads the model.
`prune(claim, documents, requirements=None, claim_date="", speaker="", cap_windows=16, stop_after_empty=2, doc_ceiling=12000)` reads the pages.

| parameter | type | meaning |
|---|---|---|
| `claim` | str | the claim being checked |
| `documents` | list of `{"text": str, "title": str, "url": str}` | the pages; `title` and `url` are optional but the model was trained with them |
| `claim_date` | str, optional | when the claim was made, `YYYY-MM-DD` |
| `speaker` | str, optional | who made the claim |
| `requirements` | list of str, optional | what must be established to check the claim |
| `threshold` | float, default 0.3458  | a sentence is kept when its P(keep) is at or above this |
| `cap_windows` | int, default 16 | read at most this many windows of 48 sentences (at most 10,000 characters each) per page |
| `stop_after_empty` | int, default 2 | stop reading a page after this many consecutive windows with nothing kept |
| `doc_ceiling` | int, default 12000 | a page contributes at most this many characters |
| `device` | str, optional | `cuda` or `cpu`|

## Output

`prune` returns one result per document, in order:

| field | type | meaning |
|---|---|---|
| `text` | str | the kept sentences, in page order, gaps marked with `[…]` |
| `kept` | list of (start, end) | offsets of each kept sentence in the document's `text` |
| `sentences` | list of str | the same sentences as strings |
| `probs` | dict, sentence index → float | P(keep) for every sentence the model read |
| `title_kept` | bool | the title is read as the page's first sentence |
| `windows_read`, `n_windows` | int | windows the model read, windows in the page |
| `stopped_early` | bool | whether the stop rule ended the read |