tokenize

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…

# Load the LLM model and tokenizer model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") tokenizer = AutoTokenizer.from_pretrained("t5-base") # Define a function to generate answers def generate_answer(question): # Tokenize the ques…

completed_percentage = 0.7 # 70% remaining_percentage = 1 - completed_percentage # Calculate the total effort required for 100% of the work total_effort = effort_spent / completed_percentage # Calculate the remaining effort remaining_eff…

tokens = self.tokenizer.convert_ids_to_tokens(inputs['input_ids'][0]) return tokens def search(self, query): tokens = self.tokenize(query) # Perform search using the tokens return tokens # I…

3. **Efficient Tokenization and Processing**: - The `process_text_chunk` function encapsulates the tokenization, processing, and decoding steps for a single chunk. ### Profiling and Bottleneck Identification To further optimize, you ca…

#### Tokenizer Service ```python from flask import Flask, request, jsonify app = Flask(__name__) @app.route('/tokenize', methods=['POST']) def tokenize(): query = request.json['query'] tokens = re.split(r'\s+', query) return …

dict_df = pd.read_csv(dictionary_path) dictionary = {row['incorrect']: row['correct'] for _, row in dict_df.iterrows()} return dictionary # Tokenization def tokenize(text): return text.split() # Dictionary Lookup def dicti…

def reformulate_query(query): # Tokenize the query inputs = tokenizer(query, return_tensors="pt") # Get the reformulated query start_time = time.time() outputs = model.generate(**inputs) end_time = time.time() …

1. **Run the Profiling Code**: Execute the profiling code to identify the bottleneck. 2. **Analyze Results**: Review the profiling results to understand where the time is being spent. 3. **Optimize**: Based on the analysis, make targeted op…