Verified claim · AI-ML · 100% confidence
BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 4c1ee70007dc89c1
Structured fields
- Subject
- BERT (Bidirectional Encoder Representations from Transformers)
- Predicate
introduced_in_paper- Object
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018)
- Confidence
- 100%
- Tags
- bert · foundational · devlin · 2018 · google · nlp
Sources (2)
[1] preprint · arXiv (Devlin, Chang, Lee, Toutanova) · 2018-10-11
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding“We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.”
[2] peer reviewed · Association for Computational Linguistics · 2019-06-02
BERT (NAACL 2019 proceedings)
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BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018). — SourceScore Claim 4c1ee70007dc89c1 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/4c1ee70007dc89c1.jsonEmbed this claim
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Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/4c1ee70007dc89c1.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/4c1ee70007dc89c1.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/4c1ee70007dc89c1.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_bert_bidirectional_encoder_representations_from_transformers_fact() -> dict:
"""Fetch the verified SourceScore claim for BERT (Bidirectional Encoder Representations from Transformers)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/4c1ee70007dc89c1.json")
return r.json()