What are Character-based Language Models?
Character-based language models are a type of language model that generates text one character at a time, as opposed to word-based models, which generate text one word at a time. Character-based models have the advantage of being able to handle out-of-vocabulary words and generate novel words by combining existing character sequences. However, they typically require a larger model size and more computational resources compared to word-based models, as they need to learn longer-term dependencies between characters.
Example of a Character-based Language Model in Python
Here’s a simple example of training a character-based RNN language model using the Keras library in Python:
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.utils import to_categorical
# Load and preprocess data
text = "your_text_here"
chars = sorted(list(set(text)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
# Prepare input and output sequences
maxlen = 40
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) - maxlen, step):
sentences.append(text[i : i + maxlen])
next_chars.append(text[i + maxlen])
# Vectorize input and output sequences
x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
for t, char in enumerate(sentence):
x[i, t, char_indices[char]] = 1
y[i, char_indices[next_chars[i]]] = 1
# Build the model
model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(chars))))
model.add(Dense(len(chars), activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
# Train the model
model.fit(x, y, batch_size=128, epochs=60)
This example demonstrates how to load text data, preprocess it, and train a simple LSTM-based character-level language model using Keras.
