metadata
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
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 |