Verified claim · AI-ML · 82% confidence
DeBERTa introduced in paper: DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020).
Last verified 2026-06-19 · Methodology veritas-v0.1 · 7cbe7b535b211862
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018).
100% confidence · shares 2 tags (bert, nlp)
RoBERTa introduced in: Liu et al. 2019 — A Robustly Optimized BERT Pretraining Approach.
100% confidence · shares 2 tags (roberta, bert)
BigBird introduced in paper: Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020).
82% confidence · shares 2 tags (nlp, 2020)
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
100% confidence · shares 1 tag (2020)
GPT-3 parameter count: 175000000000.
100% confidence · shares 1 tag (2020)
Vision Transformer (ViT) introduced in paper: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Dosovitskiy et al., 2020).
100% confidence · shares 1 tag (2020)
Denoising Diffusion Probabilistic Models (DDPM) introduced in paper: Denoising Diffusion Probabilistic Models (Ho, Jain, Abbeel, 2020).
100% confidence · shares 1 tag (2020)
Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013).
100% confidence · shares 1 tag (nlp)
Structured fields
- Subject
- DeBERTa
- Predicate
introduced_in_paper- Object
- DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020)
- Confidence
- 82%
- Tags
- deberta · disentangled-attention · enhanced-mask-decoder · bert · roberta · nlp · 2020
Sources (2)
[1] preprint · arXiv (Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen) · 2020-06-05
DeBERTa: Decoding-enhanced BERT with Disentangled Attention“In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques.”
[2] docs · Hugging Face
DeBERTa: Decoding-enhanced BERT with Disentangled Attention (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
DeBERTa introduced in paper: DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020). — SourceScore Claim 7cbe7b535b211862 (verified 2026-06-19). https://sourcescore.org/api/v1/claims/7cbe7b535b211862.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the claim record + top cited source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/7cbe7b535b211862/" width="100%" height="360" frameborder="0" loading="lazy" title="DeBERTa introduced in paper: DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020)."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "DeBERTa introduced in paper: DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020)."?
SourceScore records this assertion with 82% legacy editorial confidence as of 2026-06-19, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 2 cited source record(s), excerpts, and live evidence below.
What is the evidence for "DeBERTa introduced in paper: DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020)."?
The record lists 2 cited source(s): arXiv (Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen), Hugging Face. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/7cbe7b535b211862.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-06-19 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/7cbe7b535b211862.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
cURL
curl https://sourcescore.org/api/v1/claims/7cbe7b535b211862.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/7cbe7b535b211862.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "DeBERTa introduced in paper: DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/7cbe7b535b211862.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "DeBERTa introduced in paper: DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_deberta_fact() -> dict:
"""Fetch the verified SourceScore claim for DeBERTa."""
r = httpx.get("https://sourcescore.org/api/v1/claims/7cbe7b535b211862.json")
return r.json()