kin-tokenizer 3.3.2

Kinyarwanda tokenizer for encoding and decoding Kinyarwanda language text

Kin-Tokenizer

kin-tokenizer is a Python library designed for tokenizing Kinyarwanda language text, it can also tokenizer other languages like English, French or similar languages but with low compression rate since the tokenizer was trained on Kinyarwanda text only. There is an option for training your own custom tokenizer using defined function or method. It is covered in the section of training your own tokenizer. Hence, you can use that way to train your own tokenizer using dataset for a different language. It is based on the Byte Pair Encoding (BPE) algorithm.

It can both encode and decode text in Kinyarwanda. The metric used to measure the accuracy of this tokenizer is the compression rate and ability to encode and decode texts

Compression rate is the ratio of the total original number of characters in the text to the number of tokens in the encoded text.

Example:

For the sentence: "Nagiye gusura abanyeshuri."

• The sentence has 26 characters.
• Suppose the sentence is tokenized into the following tokens: [23, 45, 67, 89, 23, 123, 44, 22, 55, 22, 45].
• The total number of tokens is 11.

$$ \text{Compression Rate} = \frac{26}{11} $$

So, the compression rate is 2.36X (where X indicates that the number is approximate).

Special Tokens

<|PAD|> is a special token for padding, it is represented by 0 in the vocab

<|EOS|> is a special token for indicating the end of sequence, it is represented by vocab_size - 1 in the vocab

Kin-Tokenizer 3.3

Version 3.3 has a vocabulary size of 20,257. It was the first version used for testing but had poor accuracy in tokenizing text. The compression rate was low due to a bug in the algorithm and was trained on a dataset with over 445 million characters.

Kin-Tokenizer 3.3.1

Version 3.3.1 features a vocabulary size of 4001 with improved words or subwords. It was trained on 1 million Kinyarwanda characters and shows a significantly improved compression rate compared to the previous version. The bug from the previous algorithm has been fixed.

Installation

You can install the package using pip:

    pip install kin-tokenizer

Basis Usage

   from kin_tokenizer import KinTokenizer  # Importing Tokenizer class

   # Creating an instance of tokenizer
   tokenizer = KinTokenizer()

   # Loading the state of the tokenizer (pretrained tokenizer)
   tokenizer.load()

   # Encoding
   text = """
   Nuko Semugeshi akunda uwo mutwe w'abatwa, bitwaga Ishabi, awugira intore ze. Bukeye
bataha biyereka ingabo; dore ko hambere nta mihamirizo yindi yabaga mu Rwanda; guhamiriza
byaje vuba biturutse i Burundi. Ubwo bataha Semugeshi n'abatware be barabitegereza basanga
ari abahanga bose, ariko Waga akaba umuhanga w'imena muri bo; nyamara muri ubwo buhanga
bwe akagiramo intege nke ku mpamvu yo kunanuka, yari afite uruti ruke."""

   tokens = tokenizer.encode(text)
   print(tokens)

   # Calculating compression rate
   text_len = len(text)
   tokens_len = len(tokens)

   compression_rate = text_len / tokens_len
   print(f"Compression rate: {compression_rate:.2f}X")


   # Creating sequences from tokens encoded from text
   x_seq, y_seq = create_sequences(tokens, seq_len=20)

   # Decoding
   decoded_text = tokenizer.decode(tokens)
   print(decoded_text)

   # Decoding one sequence from created sequences
   print(f"Decoded sequence:\n {tokenizer.decode(x_seq[0])}")

   # Printing the vocab size
   print(tokenizer.vocab_size)

   # Print vocabulary (first 200 items)
   count = 0
   for k, v in tokenizer.vocab.items():
       print("{} : {}".format(k, v))
       count += 1
       if count > 200:
           break

Training Your Own Tokenizer

You can also train your own tokenizer using the utils module, which provides two functions: a training function and a function for creating sequences after encoding your text. N.B: Your chosen vocab_size will be met depening on the amount of data you have used for training. The vocab_size is a hyperparameter to be adjusted for better vocabularies in your vocab, and also the size of your dataset and diversity matters. The vocab is initialized by count of 256 from 1-255 unicode characters and 0 for <|PAD|> and 1 for special token <|EOS|> which marks the end of squence and it will be assigned the last token in the vocabulary.

    from kin_tokenizer import KinTokenizer
    from kin_tokenizer.utils import train_kin_tokenizer, create_sequences

    # Training the tokenizer
    tokenizer = train_kin_tokenizer(training_text, vocab_size=512, save=True, tokenizer_path=SAVE_PATH_ROOT)


    # Encoding text using custom trained tokenizer
    tokens = tokenizer.encode(text)

    # Creating sequences
    x_seq, y_seq = create_sequences(tokens, seq_len=20)

Contributing

The project is still being updated and contributions are welcome. You can contribute by:

• Reporting bugs
• Suggesting features
• Writing or improving documentation
• Submitting pull requests