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Next-Token-Prediction.ipynb
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%% Cell type:markdown id:0f901b18 tags:
# Next-Token-Prediction
This is based on the following blog posts:
* Predicting Next Word — NLP & Deep Learning: https://medium.com/@vijay2340025/predicting-next-word-nlp-deep-learning-85010d966671
* How ChatGPT Works: The Model Behind The Bot: https://towardsdatascience.com/how-chatgpt-works-the-models-behind-the-bot-1ce5fca96286
%% Cell type:code id:b2471e25 tags:
``` python
import nltk
import pandas as pd
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
```
%% Cell type:code id:c0febc54 tags:
``` python
nltk.download('punkt')
```
%% Cell type:code id:8a781aa1 tags:
``` python
dataset = """
Is Antwerp a city?,
Is Antwerp a municipality?,
Is Antwerp in Belgium?,
What is Antwerp?,
What is the population of the city of Antwerp?,
Where is the city of Antwerp?,
Why is Antwerp important to fashion?,
Antwerp is to the east of what river?,
How many municipalities does Antwerp have?,
"""
```
%% Cell type:code id:39255d82 tags:
``` python
def get_all_possible_sequences(text):
seq = []
words = nltk.word_tokenize(text)
total_words = len(words)
for i in range(1, total_words):
for j in range(1, len(words)-i+1):
arr = words[j-1:j+i]
seq.append((arr[:-1], arr[-1]))
return seq
def build_vocabulary(docs):
vocabulary = []
for doc in docs:
for w in nltk.word_tokenize(doc):
if w not in vocabulary:
vocabulary.append(w)
vocabulary.append('UNK')
return vocabulary
```
%% Cell type:code id:edb54d0d tags:
``` python
docs = []
for row in dataset.split(","):
docs.append(row.lower())
lst = []
for doc in docs:
tmp_lst = get_all_possible_sequences(doc)
lst = lst + tmp_lst
vocabulary = build_vocabulary(docs)
id2word = {idx: w for (idx, w) in enumerate(vocabulary)}
word2id = {w: idx for (idx, w) in enumerate(vocabulary)}
def seq2id(arr):
return torch.tensor([word2id[i] for i in arr])
def get_max_seq():
return len(list(set([len(i[0]) for i in lst])))
MAX_SEQ_LEN = get_max_seq()
def get_padded_x(data):
new_data = F.pad(input=data.view(1,-1), pad=(0, MAX_SEQ_LEN-data.shape[0], 0, 0), mode='constant', value=word2id['UNK'])
return new_data
def get_xy_vector(arr):
x = seq2id(arr[0])
y = seq2id([arr[1]])
return x, y
```
%% Cell type:code id:431fd558 tags:
``` python
class NextWordModel(nn.Module):
""" Prediction of Next word based on the MAX_SEQ_LEN Sequence """
def __init__(self, embedding_dim, hidden_dim, vocab_size):
super(NextWordModel, self).__init__()
self.hidden_dim = hidden_dim
self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)
self.gru = nn.GRU(embedding_dim * MAX_SEQ_LEN, hidden_dim)
self.linear = nn.Linear(hidden_dim, vocab_size)
def forward(self, sentence):
embeds = self.word_embeddings(sentence)
lstm_out, _ = self.gru(embeds.view(1, 1, -1))
x = self.linear(lstm_out.view(1, -1))
return x
```
%% Cell type:code id:d891422e tags:
``` python
if torch.cuda.is_available():
dev = "cuda:0"
else:
dev = "cpu"
print(f'Running on {dev}')
# set the model to be copied on GPU
device = torch.device(dev)
```
%% Cell type:code id:7c231c92 tags:
``` python
EMBEDDING_DIM = 10
NO_OF_EPOCHS = 300
HIDDEN_DIM = len(vocabulary)
model = NextWordModel(EMBEDDING_DIM, HIDDEN_DIM, len(vocabulary))
loss_function = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)
model.to(device)
for epoch in range(NO_OF_EPOCHS):
running_loss = 0.0
i = 0
for data in lst:
model.zero_grad()
x, y = get_xy_vector(data)
# convert to max seq length with padding
x = get_padded_x(x)
x = x.to(device)
y = y.to(device)
predicted = model(x)
loss = loss_function(predicted, y)
loss.backward()
optimizer.step()
running_loss += loss
i += 1
if i % 100 == 0:
#print(f'Loss at iteration {i} and epoch {epoch} is {running_loss / 100}')
running_loss = 0
print('Finished')
```
%% Cell type:code id:9d0ff01d tags:
``` python
with torch.no_grad():
print('Type something here . . .')
while True:
inp = input("")
inp = inp.strip()
if inp == "q":
break
tokens = nltk.word_tokenize(inp.lower())
x = seq2id(tokens)
x = get_padded_x(x)
x = x.to(device)
predicted = model(x).to(device)
predicted = predicted[0].cpu().numpy()
print(f'Answer: {inp} {id2word[np.argmax(predicted)]} ')
```
%% Cell type:code id:c1e828f0 tags:
``` python
```
......
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