(vocab_path)

(model, dataloader, device, pred_pos="last_token", pad_idx=0)

(facts, sample_size, fact_type)

(combined_facts, output_file)

(facts, fact_type)

(p: str)

( results: Dict[str, Any], out_png: str, out_pdf: str, title: str = "", )

(facts)

(s: str)

(facts, output_file)

(self, text)

(self, text)

(self, text)

(self, text)

(self, tokens)

(self, tokens)

(self, tokens)

(self, tokens)

(self, vocab, data)

(self, file_path, vocab)

(obj)

(num_entities, num_relations, out_degree=10, seed=42)

(atomic_facts, n, max_facts=15750)

(input_lengths: torch.Tensor, T: int)

(*fact_lists)

(results: Dict[str, Any], model_order: List[str])

(model)

(batch, max_len=25)

(model: RecurrentGPT2Block, input_ids: torch.Tensor)

( model: RecurrentGPT2Block, dataloader, iters: int, device: torch.device, split_name: str

(model, dataloader, device, pad_idx=0)

( model, dataloader, device, max_recurrence, pred_pos="last_token",

(fact)

( self, input_ids, attention_mask, input_lengths,

(self, input_ids, attention_mask, input_lengths, pred_pos="last_token", H0=3.0, H1=12.0,

(file_path)

(path: str)

( checkpoint_path: str, config: GPT2Config, iters: int, device: torch.device, pos_type: st

(vocab_path)

()

(attention_mask: torch.Tensor, dtype: torch.dtype)

()

()

()

()

(facts, num_examples=5)

(path: str, results: Dict[str, Any])

(train_facts, test_facts, hop, output_dir=data_dir)

(seed: int)

(facts, train_size=15000, test_size=750)

(split_name: str)

(model, dataloader, test_dataloader, args, test_type, start_epoch=0, start_time=time.time(), h

(model, dataloader, valid_dataloader, test_dataloader, args)

(self, idx)

(self, idx)

(self, idx)

(self, idx)

(self, data_dir, file, vocab, all_splits=None)

(self, config, num_iterations, positional_embedding_type='learned', input_injection=False, c_

(self, file_path, vocab, split=None)

(self, config, num_iterations)

(self)

(self)

(self)

(self)

(entities)

(arr, ratio_or_count)

(batch, max_len=25)

(batch)

(batch)

(model, dataloader, device, pred_pos="last_token", max_recurrence=16, pad_idx=0)

(self, input_ids, attention_mask=None)

(self, input_ids, attention_mask=None)

(arr, ratio_or_count)