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
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.
Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013).
100% confidence · shares 3 tags (foundational, google, nlp)
SentencePiece tokenizer introduced in paper: SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (Kudo & Richardson, 2018).
100% confidence · shares 3 tags (google, foundational, 2018)
ALBERT introduced in paper: ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (Lan et al., 2019).
82% confidence · shares 3 tags (bert, google, foundational)
T5 (Text-to-Text Transfer Transformer) introduced in paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Raffel et al., 2019).
100% confidence · shares 2 tags (foundational, google)
Sparsely-Gated Mixture-of-Experts (MoE) introduced in paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017).
100% confidence · shares 2 tags (foundational, google)
Switch Transformer introduced in paper: Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (Fedus et al., 2021).
100% confidence · shares 2 tags (foundational, google)
Chain-of-Thought prompting introduced in paper: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022).
100% confidence · shares 2 tags (foundational, google)
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 2 tags (foundational, google)
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)
Cite this claim
Ready-to-paste citation (Markdown / plain text):
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
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/4c1ee70007dc89c1/" width="100%" height="360" frameborder="0" loading="lazy" title="BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018)."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018)."?
SourceScore records this assertion with 100% legacy editorial confidence as of 2026-05-16, 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 "BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018)."?
The record lists 2 cited source(s): arXiv (Devlin, Chang, Lee, Toutanova), Association for Computational Linguistics. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/4c1ee70007dc89c1.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-16 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/4c1ee70007dc89c1.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/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()