List of Corrected Token Strings

model = BertForMaskedLM.from_pretrained('bert-base-uncased') def find_closest_match(word, dictionary, threshold=2): """ Find the closest match in the dictionary using the specified threshold. """ min_distance = float('inf')…

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…

# Tokenize input text tokens = input_text.split() # Apply correction rules corrected_tokens = [correct_token(token) for token in tokens] return ' '.join(corrected_tokens) def correct_token(token): # Define correctio…

### Suggestions for Improvement 1. **Robust Tokenization**: - Use a more sophisticated tokenization method to handle punctuation and special characters. 2. **Enhanced Correction Rules**: - Implement more comprehensive correction rul…

# Ensure NLTK resources are downloaded nltk.download('punkt') # Example dictionary of valid words dictionary = {'hello', 'world', 'example', 'test', 'correction'} def levenshtein_distance(token1, token2): """Calculate Levenshtein dist…

To provide latency statistics, you can use a profiling tool or logging mechanism to measure the time taken for each operation. Here's an example using Python's `time` module: ```python import time start_time = time.time() corrected_text =…