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 (7)
| Label | Occurrences |
|---|---|
| RoBERTa canonical | 7 |
| RoBERTa family | 1 |
| RoBERTa-base | 1 |
| RoBERTa-large | 1 |
| RoBERTa: A Robustly Optimized BERT Pretraining Approach | 1 |
| RobertaForCausalLM | 1 |
| RobertaModel | 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 ⓘ |
| availableOn | Hugging Face Transformers ⓘ |
| basedOn | BERT ⓘ |
| caseSensitivity | cased ⓘ |
| developer |
Facebook AI
ⓘ
linked to:
Meta AI
Meta AI ⓘ |
| hasVariant |
RoBERTa-base
ⓘ
linked to:
RoBERTa
RoBERTa-large ⓘ
linked to:
RoBERTa
|
| improvementTechnique |
dynamic masking
ⓘ
larger mini-batches ⓘ longer training ⓘ training on more data ⓘ |
| improvesUpon | BERT ⓘ |
| language | English ⓘ |
| license | MIT License ⓘ |
| openSource | true ⓘ |
| optimizationGoal | robust optimization of BERT pretraining ⓘ |
| paperAuthorsInclude |
Danqi Chen
ⓘ
Jingfei Du ⓘ Luke Zettlemoyer ⓘ Mandar Joshi ⓘ Mike Lewis ⓘ Myle Ott ⓘ Naman Goyal ⓘ Omer Levy ⓘ Veselin Stoyanov ⓘ Yinhan Liu ⓘ |
| paperTitle |
RoBERTa: A Robustly Optimized BERT Pretraining Approach
ⓘ
linked to:
RoBERTa
|
| 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
ⓘ
CC-News ⓘ English Wikipedia ⓘ OpenWebText ⓘ
linked to:
WebText dataset
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 (13)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: RoBERTa
linked to: RoBERTa
linked to: RoBERTa
linked to: RoBERTa
linked to: RoBERTa