usedForcontainsinverseOfrdfs:labelcreatedByassignedFromusedByinstantiatedWithpairssimilarToOther subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. “X motherOf this subject” — and answer questions the forward facts can't. Grouped by predicate.
inverseOfInverse of(2)ex:dataset['train']ex:train_loaderusesUses(2)ex:DataLoaderex:training-phasecalledWithCalled With(1)ex:TrainercreatesCreates(1)ex:dataset-splitdatasetDataset(1)ex:train_loaderhasParticipantHas Participant(1)ex:training_processhasTrainDatasetHas Train Dataset(1)ex:traineroutputVariableOutput Variable(1)ex:train-dataset-instantiationrequiresRequires(1)ex:training-configurationtakesParameterTakes Parameter(1)ex:trainer_initializationtrainedOnTrained on(1)ex:trainerusedAsUsed As(1)ex:tokenized_datasetsusesDatasetUses Dataset(1)ex:train_loaderThe long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Related to | Test Dataset | [9] |
| Is Assigned | Query Dataset Result | [9] |
| Is Train Dataset of | Trainer | [11] |
| Is a | Query Dataset | [10] |
| Used in Phase | Training Phase | [6] |
| Paired With | Test Dataset | [6] |
| Contains Data From | Train Df | [6] |
| Referenced But Not Defined | true | [13] |
| Derived From | Dataset | [1] |
| Accesses Key | train | [1] |
| Is Used by | Trainer | [12] |
| Distinct From | Test Dataset | [7] |
| Is Paired With | Train Labels | [3] |
| Is Created Using | Tensor Dataset | [3] |
| Used by | Train Loader | [4] |
| Is Created As | Tensor Dataset | [4] |
| Is Variable | Tensor Dataset | [5] |
| Holds | 2400 | [8] |
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
doc:beam/04edfc72-1f93-4ce7-b6df-887c9a5f1db3from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments, DataCollatorWithPadding, ) from datasets import load_dataset, DatasetDict # Load the model and tokenizer model_na…
doc:beam/2e15bda3-1327-4a52-84cc-730203563e58labels = tokenizer(examples['reformulated'], max_length=512, padding='max_length', truncation=True, return_tensors='pt')['input_ids'] model_inputs['labels'] = labels return model_inputs tokenized_datasets = dataset.map(preproce…
doc:beam/23009db1-c526-4b01-963c-b2c7b2736c5bcombined_inputs = torch.cat([inputs, combined_user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) - train_size train_combined_inputs, val_combi…
doc:beam/212294fd-6444-48ea-90be-0ccd48cb9cc3combined_inputs = torch.cat([inputs, user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) - train_size train_combined_inputs, val_combined_input…
doc:beam/9344edde-d6af-464f-9e96-394ef09895b9# Concatenate existing inputs with user behavior data combined_inputs = torch.cat([inputs, user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) -…
doc:beam/14cf4eab-a053-4cf0-b374-9022e5e69c19model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(df['label'].unique())) tokenizer = AutoTokenizer.from_pretrained(model_name) # Tokenize the data train_encodings = tokenizer(train_df['query'].tolist(), …
doc:beam/2d4011b7-fd19-414d-88f5-084c1fba93b1training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', logging…
doc:beam/56ec773d-331c-4612-b327-318a1a96426f```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset # Example data preparation inputs = torch.randn(3000, 128) # Example input data labels = torch.randn(3000, 1) …
doc:beam/974a068f-3f5b-4b96-b53c-9e0c612e3beetest_encodings = tokenize_data(tokenizer, test_df['query']) # Create datasets train_dataset = QueryDataset(train_encodings, train_df['label'].tolist()) test_dataset = QueryDataset(test_encodings, test_df['label'].tolist()) …
doc:beam/befe5288-0889-4495-85bd-a24c2feddb5d# Define training arguments training_args = TrainingArguments( output_dir=f'./results/{model_name}', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_s…
doc:beam/aaa2ab69-d393-49d6-b565-40f47c0bccb9errors.append(doc) return errors errors = analyze_tokenization_errors(documents, tokenizer) print(f"Tokenization Errors: {errors}") # Fine-tune the model on your specific dataset # This involves preparing a labeled dataset…
doc:beam/f0656b10-4efe-4bd0-9005-6e894f93f6b4train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=tokenizer, data_collator=DataCollatorWithPadding(tokenizer), ) # Fine-tune the model trainer.train() # Define the feedback analysis logic def analyze_feedba…
doc:beam/09c69473-903c-475d-98c1-a87aeedbce93output_dir='./results', num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', logging_steps=10, evaluation_strategy="s…
doc:beam/6c3b0310-9572-42f3-a33f-3f41bc304470logging_steps=10, evaluation_strategy='epoch', save_total_limit=2, ) # Define the trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset['train'], eval_dataset=dataset['test'], dat…
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