RoBERTa
E435864
RoBERTa is a robustly optimized transformer-based language model developed by Facebook AI that improves upon BERT through enhanced training strategies and larger-scale data.
All labels observed (1)
| Label | Occurrences |
|---|---|
| RoBERTa canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4389189 — 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: RoBERTa Context triple: [Hugging Face Transformers, supportsModelType, RoBERTa]
-
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.
GPT-2
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
C.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
-
D.
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.
-
E.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
- 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: RoBERTa Target entity description: RoBERTa is a robustly optimized transformer-based language model developed by Facebook AI that improves upon BERT through enhanced training strategies and larger-scale data.
-
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.
GPT-2
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
C.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
-
D.
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.
-
E.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
language model
ⓘ
masked language model ⓘ neural network model ⓘ transformer-based model ⓘ |
| architecture | Transformer NERFINISHED ⓘ |
| availableOn | Hugging Face Transformers NERFINISHED ⓘ |
| basedOn | BERT NERFINISHED ⓘ |
| caseSensitivity | cased ⓘ |
| developer |
Facebook AI
NERFINISHED
ⓘ
Meta AI NERFINISHED ⓘ |
| hasVariant |
RoBERTa-base
NERFINISHED
ⓘ
RoBERTa-large NERFINISHED ⓘ |
| improvementTechnique |
dynamic masking
ⓘ
larger mini-batches ⓘ longer training ⓘ training on more data ⓘ |
| improvesUpon | BERT NERFINISHED ⓘ |
| language | English ⓘ |
| license | MIT License ⓘ |
| openSource | true ⓘ |
| optimizationGoal | robust optimization of BERT pretraining ⓘ |
| paperAuthorsInclude |
Danqi Chen
NERFINISHED
ⓘ
Jingfei Du NERFINISHED ⓘ Luke Zettlemoyer NERFINISHED ⓘ Mandar Joshi NERFINISHED ⓘ Mike Lewis NERFINISHED ⓘ Myle Ott NERFINISHED ⓘ Naman Goyal NERFINISHED ⓘ Omer Levy NERFINISHED ⓘ Veselin Stoyanov NERFINISHED ⓘ Yinhan Liu NERFINISHED ⓘ |
| paperTitle | RoBERTa: A Robustly Optimized BERT Pretraining Approach NERFINISHED ⓘ |
| pretrainingObjective | masked language modeling ⓘ |
| pretrainingType | self-supervised learning ⓘ |
| publicationYear | 2019 ⓘ |
| supports |
natural language inference
ⓘ
question answering ⓘ sequence labeling ⓘ text classification ⓘ textual entailment ⓘ token classification ⓘ |
| tokenizerType | byte-level BPE ⓘ |
| trainingDataScale | larger than BERT ⓘ |
| trainingDataSource |
BookCorpus
NERFINISHED
ⓘ
CC-News ⓘ English Wikipedia NERFINISHED ⓘ OpenWebText NERFINISHED ⓘ Stories corpus ⓘ |
| usesNextSentencePrediction | false ⓘ |
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: RoBERTa Description of subject: RoBERTa is a robustly optimized transformer-based language model developed by Facebook AI that improves upon BERT through enhanced training strategies and larger-scale data.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.