Verified claim · AI-ML · 100% confidence
RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach.
Last verified 2026-05-16 · Methodology veritas-v0.1 · d4fecb26a4c9cdca
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Structured fields
- Subject
- RoBERTa
- Predicate
introduced_in- Object
- Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach
- Confidence
- 100%
- Tags
- roberta · bert · facebook-ai · pretraining · foundational · 2019 · introduced_in
Sources (2)
[1] preprint · arXiv (Liu, Ott, Goyal, Du, Joshi, Chen, Levy, Lewis, Zettlemoyer, Stoyanov / Facebook AI) · 2019-07-26
RoBERTa: A Robustly Optimized BERT Pretraining Approach“We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it.”
[2] official blog · Hugging Face · 2019-07-26
RoBERTa — Hugging Face Transformers documentationHugging Face is rated by SourceScore — see its reliability →
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RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach. — SourceScore Claim d4fecb26a4c9cdca (verified 2026-05-16). https://sourcescore.org/api/v1/claims/d4fecb26a4c9cdca.jsonEmbed this claim
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// "RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/d4fecb26a4c9cdca.json")
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# "RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_roberta_fact() -> dict:
"""Fetch the verified SourceScore claim for RoBERTa."""
r = httpx.get("https://sourcescore.org/api/v1/claims/d4fecb26a4c9cdca.json")
return r.json()