list of tokens

``` ### Summary By refactoring the code to use a set for lookups and building a new string from a list of tokens, you can significantly improve performance. Additionally, consider batch processing and parallel processing techniques for la…

- The segments are returned as a list of token lists. 5. **Caching**: - Use a dictionary (`self.cache`) to store and reuse previously computed contexts based on the token count. ### Example Usage - **Adding Tokens**: Tokens are add…

### Step 2: Modify the Code to Use BM25 Here's an example of how you can integrate BM25 into your proof of concept: ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import recall_scor…

2. **Iterate Over Tokens**: We iterate over each token using a `for` loop. 3. **Calculate Context Window Indices**: For each token, we calculate the start and end indices for the context window, ensuring they stay within the bounds of the t…

nlp = spacy.load("en_core_web_sm") except OSError as e: print(f"Error loading spaCy model: {e}") nlp = None # Set nlp to None if loading fails # Example query queries = ["This is an example query", "Another example query"] # …

- Define a function `tokenize_queries` that takes a list of queries and tokenizes each one. - Use a `try-except` block inside the loop to handle potential errors during tokenization. - If `nlp` is `None` (indicating the model faile…

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…

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Load spaCy model nlp = spacy.load('en_core_web_sm') def tokenize_text(text): try: doc = nlp(text) tokens = [token.text for t…