Verified claim · AI-ML · 100% confidence
BART introduced in: Lewis et al. 2019 — denoising sequence-to-sequence pretraining.
Last verified 2026-05-16 · Methodology veritas-v0.1 · f5b422e3255fd7c0
Structured fields
- Subject
- BART
- Predicate
introduced_in- Object
- Lewis et al. 2019 — denoising sequence-to-sequence pretraining
- Confidence
- 100%
- Tags
- bart · facebook-ai · denoising · seq2seq · encoder-decoder · foundational · 2019 · introduced_in
Sources (2)
[1] preprint · arXiv (Lewis, Liu, Goyal, Ghazvininejad, Mohamed, Levy, Stoyanov, Zettlemoyer / Facebook AI) · 2019-10-29
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension“We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes.”
[2] official blog · Hugging Face · 2019-10-29
BART — Hugging Face Transformers documentation
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curl https://sourcescore.org/api/v1/claims/f5b422e3255fd7c0.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/f5b422e3255fd7c0.json");
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// "BART introduced in: Lewis et al. 2019 — denoising sequence-to-sequence pretraining."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/f5b422e3255fd7c0.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "BART introduced in: Lewis et al. 2019 — denoising sequence-to-sequence pretraining."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_bart_fact() -> dict:
"""Fetch the verified SourceScore claim for BART."""
r = httpx.get("https://sourcescore.org/api/v1/claims/f5b422e3255fd7c0.json")
return r.json()