trainingObjective
P12747
predicate
Indicates the goal or target outcome that a training process is designed to achieve.
All labels observed (13)
| Label | Occurrences |
|---|---|
| trainingObjective canonical | 84 |
| optimizationObjective | 19 |
| trainingGoal | 12 |
| lossFunction | 10 |
| hasTrainingObjective | 8 |
| usesTrainingObjective | 8 |
| pretrainingObjective | 6 |
| learningObjective | 5 |
| trainingTarget | 5 |
| VAEObjective | 1 |
| goalOfTraining | 1 |
| supportsTrainingObjective | 1 |
| trainingCriterion | 1 |
Description generation (PDg)
The one-sentence description above was generated by prompting gpt-5.1 with the predicate name and this instruction.
Instruction
Given a predicate that represents a relationship or action between entities, generate a one-sentence description explaining its meaning. # Instructions Focus on describing the relationship, not the entities themselves. # Response Format Begin the description with \' Indicates...\'
Input
Predicate: trainingObjective
Generated description
Indicates the goal or target outcome that a training process is designed to achieve.
Sample triples (161)
| Subject | Object |
|---|---|
|
"Principles of Microeconomics"
surface form:
Principles of Microeconomics
|
develop analytical skills in microeconomics via predicate surface "learningObjective" ⓘ |
|
"Principles of Microeconomics"
surface form:
Principles of Microeconomics
|
prepare students for intermediate microeconomics courses via predicate surface "learningObjective" ⓘ |
| New Mutants | next generation of X-Men via predicate surface "trainingGoal" ⓘ |
|
Yokosuka MXY8 Akigusa glider trainer
surface form:
Yokosuka MXY8 Akigusa
|
safe approach and landing practice for rocket interceptor pilots ⓘ |
| Jump School | train personnel to conduct static-line parachute jumps ⓘ |
| Jump School | prepare soldiers for airborne unit assignment ⓘ |
| Jump Week | to demonstrate safe exit, flight, and landing techniques ⓘ |
| Jump Week | to build confidence in parachute equipment ⓘ |
| Very Deep Convolutional Networks for Large-Scale Image Recognition | softmax cross-entropy via predicate surface "lossFunction" ⓘ |
| Peru national under-17 football team | youth player development ⓘ |
|
SAC
surface form:
Soft Actor-Critic
|
expected return via predicate surface "optimizationObjective" ⓘ |
|
SAC
surface form:
Soft Actor-Critic
|
policy entropy via predicate surface "optimizationObjective" ⓘ |
| RetinaNet | focal loss via predicate surface "optimizationObjective" ⓘ |
| KeypointRCNN | classification loss ⓘ |
| KeypointRCNN | bounding box regression loss ⓘ |
| KeypointRCNN | keypoint localization loss ⓘ |
| Sentara Center for Simulation and Immersive Learning | improve clinical competence via predicate surface "hasTrainingObjective" ⓘ |
| Sentara Center for Simulation and Immersive Learning | enhance patient safety via predicate surface "hasTrainingObjective" ⓘ |
| GPT-Neo | next token prediction ⓘ |
| RoBERTa | masked language modeling via predicate surface "pretrainingObjective" ⓘ |
| DistilBERT | masked language modeling ⓘ |
| DistilBERT | distillation from BERT ⓘ |
| T5 | span corruption via predicate surface "pretrainingObjective" ⓘ |
| T5 | denoising autoencoding via predicate surface "pretrainingObjective" ⓘ |
| BART | denoising autoencoding ⓘ |
| BART | sequence-to-sequence language modeling ⓘ |
| DeBERTa | masked language modeling ⓘ |
| DeBERTa | replaced token detection (for some versions) ⓘ |
| LLaMA | causal language modeling ⓘ |
| OPT | causal language modeling ⓘ |
| Bloom | causal language modeling ⓘ |
| XLM-R | masked language modeling via predicate surface "pretrainingObjective" ⓘ |
| mBART | denoising autoencoding ⓘ |
| mBART | reconstructing original text from noisy input ⓘ |
| Longformer | masked language modeling ⓘ |
| LayoutLM | masked language modeling via predicate surface "optimizationObjective" ⓘ |
| LayoutLM | multi-task learning for document understanding via predicate surface "optimizationObjective" ⓘ |
| EncoderDecoderModel | cross-entropy loss via predicate surface "supportsTrainingObjective" ⓘ |
| Language Models are Unsupervised Multitask Learners | next-token prediction ⓘ |
| Transformer encoder-only | masked language modeling loss ⓘ |
| Transformer encoder-only | classification loss ⓘ |
| Transformer encoder-only | contrastive loss ⓘ |
| Transformer encoder-only | metric learning loss ⓘ |
| BERT | masked language modeling via predicate surface "pretrainingObjective" ⓘ |
| BERT | next sentence prediction via predicate surface "pretrainingObjective" ⓘ |
| GPT | next token prediction via predicate surface "usesTrainingObjective" ⓘ |
| GPT-1 | next-token prediction ⓘ |
| Cascade-Correlation learning architecture | reduce network error by adding hidden units ⓘ |
| Learning Transferable Architectures for Scalable Image Recognition | maximize validation accuracy on CIFAR-10 via predicate surface "optimizationObjective" ⓘ |
| Learning Transferable Architectures for Scalable Image Recognition | improve accuracy-computation trade-off on ImageNet via predicate surface "optimizationObjective" ⓘ |