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Tokenizer Call has 58 facts recorded in Dontopedia across 15 references, with 11 live disagreements.
Mostly:has argument(9), has parameter(8), rdf:type(6)
hasArgumentreturnspasseshasParameterparameterrdfs:labelsetsparameterDefaultValuekeywordArgumenthasKeywordArgumentpassesParameterOther 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.
is_set_inIs Set in(3)ex:max_length_128ex:padding_trueex:truncation_trueparameterOfParameter of(3)ex:paddingex:return_tensorsex:truncationcallsMethodCalls Method(2)ex:batch_tokenizationex:tokenizationcreatedByCreated by(2)ex:input_tensorsex:synonym_inputsreturnedByReturned by(2)ex:inputsex:tokenizer_outputassignedByAssigned by(1)ex:inputsisResultOfIs Result of(1)ex:inputsis_used_byIs Used by(1)ex:texts_parameterusedInUsed in(1)ex:f_string_formattingusesUses(1)ex:tokenize_queriesThe 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 |
|---|---|---|
| Specifies Return Type | Pt Tensors | [6] |
| Parameter Value | pt | [11] |
| Has Return Type | Tensor Dict | [7] |
| Precedes | Model Call | [15] |
| Uses | Keyword Arguments | [15] |
| Calls Method | Tokenizer. Call | [1] |
| Function | Get Embeddings | [2] |
| Is Invoked by | Getitem | [8] |
| Produces | tokenized_inputs | [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/cc213d9b-9051-49f2-ac29-2090be7dfaeamodel = T5ForConditionalGeneration.from_pretrained('./fine_tuned_model') def reformulate_query(query): inputs = tokenizer(f"reformulate: {query}", return_tensors="pt", max_length=512, truncation=True) outputs = model.generate(input…
doc:beam/83e14383-c855-4a1f-8c2c-fe0e2d17e86creformulated_query = query end_time = time.time() return reformulated_query, end_time - start_time # Define a function to process queries in batches def process_queries_in_batches(queries, batch_size=100): results = [] …
doc:beam/465dcb64-9710-4e90-8651-452b28528272def __init__(self, texts, tokenizer): self.texts = texts self.tokenizer = tokenizer def __len__(self): return len(self.texts) def __getitem__(self, idx): inputs = self.tokenizer(self.tex…
doc:beam/9a2ef1d1-902f-4f34-a8d8-8a2fc7318193self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) def __call__(self, text): try: # Tokenize the text inputs = self.tokenizer(text, return_tensors='pt') # Generate the re…
doc:beam/57e2ea52-f5cb-4239-bf9f-3147a3b2efbctokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') def get_context_aware_synonyms(word, context_sentence): inputs = tokenizer(context_sentence, return_tensors='pt', pad…
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