XLM-R
E435876
XLM-R is a multilingual transformer-based language model (XLM-RoBERTa) designed for cross-lingual understanding and natural language processing across many languages.
All labels observed (3)
| Label | Occurrences |
|---|---|
| XLM-R canonical | 1 |
| XLM-R-base | 1 |
| XLM-R-large | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4389203 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: XLM-R Context triple: [Hugging Face Transformers, supportsModelType, XLM-R]
-
A.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
B.
AllenNLP
AllenNLP is an open-source natural language processing research library built on PyTorch, designed to facilitate the development and evaluation of state-of-the-art NLP models.
-
C.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
-
D.
MLU
MLU is the IATA airport code for Monroe Regional Airport, a public airport serving Monroe, Louisiana, in the United States.
-
E.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: XLM-R Target entity description: XLM-R is a multilingual transformer-based language model (XLM-RoBERTa) designed for cross-lingual understanding and natural language processing across many languages.
-
A.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
B.
AllenNLP
AllenNLP is an open-source natural language processing research library built on PyTorch, designed to facilitate the development and evaluation of state-of-the-art NLP models.
-
C.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
-
D.
MLU
MLU is the IATA airport code for Monroe Regional Airport, a public airport serving Monroe, Louisiana, in the United States.
-
E.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
XLM-RoBERTa architecture
ⓘ
masked language model ⓘ multilingual language model ⓘ transformer-based model ⓘ |
| architecture | Transformer ⓘ |
| basedOn | RoBERTa ⓘ |
| compatibleWith | Hugging Face Transformers ⓘ |
| designedFor |
cross-lingual generalization
ⓘ
multilingual NLP ⓘ |
| developedBy |
Facebook AI
ⓘ
linked to:
Meta AI
Meta AI ⓘ |
| family |
RoBERTa family
ⓘ
linked to:
RoBERTa
XLM family ⓘ |
| handlesScriptTypes |
Arabic
ⓘ
CJK ⓘ Cyrillic ⓘ Devanagari ⓘ
linked to:
Devanagari script
Latin ⓘ |
| hasEncoderLayersApprox |
12 (base variant)
ⓘ
24 (large variant) ⓘ |
| inputType | text ⓘ |
| languageModelType | encoder-only transformer ⓘ |
| pretrainingDataType |
CommonCrawl
ⓘ
linked to:
Common Crawl
|
| pretrainingObjective | masked language modeling ⓘ |
| primaryUseContext |
production NLP systems
ⓘ
research ⓘ |
| releasedAs | open-source model ⓘ |
| releasedByOrganizationType | industry research lab ⓘ |
| supportsLanguages | multilingual ⓘ |
| supportsLanguagesCountApprox |
100+
ⓘ
over 100 languages ⓘ |
| supportsTask |
cross-lingual transfer learning
ⓘ
cross-lingual understanding ⓘ multilingual representation learning ⓘ named entity recognition ⓘ natural language processing ⓘ question answering ⓘ sentence embedding ⓘ sequence labeling ⓘ text classification ⓘ token-level classification ⓘ zero-shot cross-lingual transfer ⓘ |
| tokenizationMethod | SentencePiece ⓘ |
| usesPositionalEncoding | true ⓘ |
| usesSelfAttention | true ⓘ |
| usesSubwordTokenization | true ⓘ |
| variant |
XLM-R-base
ⓘ
linked to:
XLM-R
XLM-R-large ⓘ
linked to:
XLM-R
|
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: XLM-R Description of subject: XLM-R is a multilingual transformer-based language model (XLM-RoBERTa) designed for cross-lingual understanding and natural language processing across many languages.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.