Verified claim · AI-ML · 100% confidence
CLIP (Contrastive Language-Image Pretraining) introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 85a3ca745eaf4ee0
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Structured fields
- Subject
- CLIP (Contrastive Language-Image Pretraining)
- Predicate
introduced_in_paper- Object
- Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)
- Confidence
- 100%
- Tags
- clip · multimodal · vision · radford · 2021 · openai
Sources (2)
[1] preprint · arXiv (Radford et al., OpenAI) · 2021-02-26
Learning Transferable Visual Models From Natural Language Supervision“We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet.”
[2] official blog · OpenAI · 2021-01-05
CLIP: Connecting text and imagesOpenAI is rated by SourceScore — see its reliability →
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cURL
curl https://sourcescore.org/api/v1/claims/85a3ca745eaf4ee0.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/85a3ca745eaf4ee0.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "CLIP (Contrastive Language-Image Pretraining) introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/85a3ca745eaf4ee0.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "CLIP (Contrastive Language-Image Pretraining) introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_clip_contrastive_language_image_pretraining_fact() -> dict:
"""Fetch the verified SourceScore claim for CLIP (Contrastive Language-Image Pretraining)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/85a3ca745eaf4ee0.json")
return r.json()