For a small dataset, you might want to consider using a Syntactic Language Model (SLM) instead of a Large Language Model (LLM) since SLMs typically require less data and computational resources to train.
One possible dataset you could use for training an SLM is the Penn Treebank (PTB) dataset, which is a widely used dataset for natural language processing tasks. The PTB dataset contains a corpus of text from Wall Street Journal articles, and it is often used for tasks such as language modeling, part-of-speech tagging, and parsing.
To train an SLM on the PTB dataset, you can follow these general steps:
- Prepare the dataset: The PTB dataset is already preprocessed and split into training, validation, and test sets. You can download the dataset from the official website (https://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz) and extract the files.
- Preprocess the data: You will need to preprocess the data by tokenizing the text and converting it into a format that can be used for training. You can use the following code to tokenize the text and create a vocabulary:
import codecs
import os
def read_data(filename):
with codecs.open(filename, 'r', 'utf-8') as f:
return [line.strip() for line in f.readlines()]
def create_vocab(data):
vocab = set()
for sentence in data:
for word in sentence.split():
vocab.add(word)
return sorted(list(vocab))
train_data = read_data('ptb.train.txt')
valid_data = read_data('ptb.valid.txt')
test_data = read_data('ptb.test.txt')
vocab = create_vocab(train_data)
- Define the model architecture: You can use a simple recurrent neural network (RNN) architecture for your SLM. Here’s an example of how you can define the model using the TensorFlow library:
import tensorflow as tf
class SLM(tf.keras.Model):
def __init__(self, vocab_size, embedding_size, hidden_size, num_layers):
super().__init__()
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_size)
self.rnn = tf.keras.layers.LSTM(hidden_size, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform',
units=num_layers)
self.dense = tf.keras.layers.Dense(vocab_size)
def call(self, x, hidden):
x = self.embedding(x)
x, state = self.rnn(x, initial_state=hidden)
x = self.dense(x)
return x, state
def initialize_hidden_state(self, batch_size):
return (tf.zeros((self.rnn.units, batch_size)),
tf.zeros((self.rnn.units, batch_size)))
- Train the model: You can use the following code to train the model:
model = SLM(len(vocab), embedding_size=128, hidden_size=128, num_layers=2)
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
@tf.function
def train_step(inp, targ, state):
with tf.GradientTape() as tape:
x, state = model(inp, state)
loss = loss_object(targ, x)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
return loss, state
def train(model, data, epochs, batch_size):
state = model.initialize_hidden_state(batch_size)
for epoch in range(epochs):
for i in range(0, len(data), batch_size):
end = i + batch_size
batch_data = data[i:end]
batch_inp = [vocab.index(word) for word in batch_data]
batch_targ = [vocab.index(word) for word in batch_data[1:]]
loss, state = train_step(tf.constant(batch_inp), tf.constant(batch_targ), state)
print("Epoch: {:>3} - Loss: {:.4f}".format(epoch+1, loss))
train(model, train_data, epochs=10, batch_size=32)
- Evaluate the model: You can use the following code to evaluate the model on the validation and test sets:
def evaluate(model, data, batch_size):
state = model.initialize_hidden_state(batch_size)
total_loss = 0.0
for i in range(0, len(data), batch_size):
end = i + batch_size
batch_data = data[i:end]
batch_inp = [vocab.index(word) for word in batch_data]
batch_targ = [vocab.index(word) for word in batch_data[1:]]
loss, state = train_step(tf.constant(batch_inp), tf.constant(batch_targ), state)
total_loss += loss
return total_loss / (len(data) / batch_size)
print("Validation Loss: {:.4f}".format(evaluate(model, valid_data, batch_size=32)))
print("Test Loss: {:.4f}".format(evaluate(model, test_data, batch_size=32)))
I hope this helps you get started with training a Syntactic Language Model on the Penn Treebank dataset! Let me know if you have any further questions.