generate

self.field_constraints = field_constraints def generate(self): data_model = pd.DataFrame(columns=self.fields) # Add relationships between fields for relationship in self.relationships: …

self.fields = fields self.relationships = relationships def generate(self): data_model = pd.DataFrame(columns=self.fields) # Add relationships between fields for relationship in self.rel…

data_model[field] = data_model[field].astype(bool) return data_model # Example usage fields = ['field1', 'field2', 'field3', 'field4', 'field5', 'field6', 'field7', 'field8', 'field9'] relationships = […

[Turn 1144] User: I'm designing a system for proposing 7 index fields to reduce search times by 15%, and I want to make sure my design is compatible with the existing system. Can you help me review my data modeling? I've got a list of field…

self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small") # Use a smaller model self.tokenizer = AutoTokenizer.from_pretrained("t5-small") def retrieve(self, query): # Tokenize the query inputs = s…

model = 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…

inputs = tokenizer(query, return_tensors="pt") # Get the reformulated query start_time = time.time() outputs = model.generate(**inputs) end_time = time.time() # Return the reformulated query return toke…

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 = [] for i in range(0, len(queries), batch_size): batch…

model_name = "t5-small" model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` #### 2. Define the Reformulation Function Next, define the reformulation function that leverages t…