This report documents the implementation of a minimal GPT-like model (MinGPT) trained on the arXiv dataset. The model is built using PyTorch and leverages the Hugging Face transformers library for tokenization. The dataset is fetched using the arxiv Python package, and the model is trained on abstracts from arXiv papers. The goal of this project is to demonstrate the feasibility of training a small-scale transformer-based language model on a domain-specific dataset (arXiv) and to generate coherent text based on a given prompt.
Transformer-based models, such as GPT (Generative Pre-trained Transformer), have revolutionized natural language processing (NLP) by achieving state-of-the-art results in various tasks. These models are trained on large corpora of text and can generate human-like text. However, training such models from scratch requires significant computational resources. In this project, we implement a minimal version of GPT (MinGPT) and train it on a subset of the arXiv dataset to demonstrate the process of training a transformer-based language model.
- Fetch and preprocess the arXiv dataset.
- Implement a minimal GPT-like model using PyTorch.
- Train the model on arXiv abstracts.
- Generate text based on a given prompt.
The arXiv dataset contains metadata and abstracts of scholarly papers across various fields. For this project, we focus on the abstracts of papers related to "machine learning." The dataset is fetched using the arxiv Python package.
The fetch_arxiv_data function queries the arXiv API to retrieve abstracts of papers related to a specific query (e.g., "machine learning"). The abstracts are saved to a text file for further processing.
import arxiv
def fetch_arxiv_data(query="machine learning", max_results=1000):
search = arxiv.Search(query=query, max_results=max_results, sort_by=arxiv.SortCriterion.SubmittedDate)
papers = [result.summary for result in search.results()]
return papers
arxiv_data = fetch_arxiv_data()
with open("arxiv_data.txt", "w", encoding="utf-8") as f:
f.write("\n".join(arxiv_data))A Byte-Pair Encoding (BPE) tokenizer is trained on the arXiv dataset using the tokenizers library. The tokenizer is saved for later use.
The tokenizer is trained on the arXiv abstracts with a vocabulary size of 50,000 tokens. Special tokens such as <s>, <pad>, </s>, <unk>, and <mask> are added to the vocabulary.
import os
from tokenizers import ByteLevelBPETokenizer
# Create the directory if it doesn't exist
os.makedirs("tokenizer", exist_ok=True)
# Train a Byte-Pair Encoding (BPE) tokenizer
tokenizer = ByteLevelBPETokenizer()
tokenizer.train(["arxiv_data.txt"], vocab_size=50_000, min_frequency=2, special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"])
# Save tokenizer
tokenizer.save_model("tokenizer")The MinGPT model is a simplified version of the GPT architecture, consisting of multiple transformer blocks. Each block includes multi-head self-attention and a feed-forward neural network.
The TransformerBlock class implements a single transformer block with multi-head self-attention, layer normalization, and a feed-forward network.
import torch
import torch.nn as nn
class TransformerBlock(nn.Module):
def __init__(self, embed_size, heads, dropout, forward_expansion):
super().__init__()
self.attention = nn.MultiheadAttention(embed_dim=embed_size, num_heads=heads)
self.norm1 = nn.LayerNorm(embed_size)
self.norm2 = nn.LayerNorm(embed_size)
self.feed_forward = nn.Sequential(
nn.Linear(embed_size, forward_expansion * embed_size),
nn.ReLU(),
nn.Linear(forward_expansion * embed_size, embed_size)
)
self.dropout = nn.Dropout(dropout)
def forward(self, value, key, query, mask):
attn_output, _ = self.attention(query, key, value, attn_mask=mask)
x = self.norm1(attn_output + query)
forward_out = self.feed_forward(x)
return self.norm2(forward_out + x)The GPT class implements the MinGPT model, which consists of multiple transformer blocks, token embeddings, positional embeddings, and a final linear layer for token prediction.
class GPT(nn.Module):
def __init__(self, vocab_size, embed_size=256, num_layers=8, heads=8, dropout=0.1, forward_expansion=4):
super().__init__()
self.embed_size = embed_size
self.word_embedding = nn.Embedding(vocab_size, embed_size)
self.position_embedding = nn.Embedding(512, embed_size)
self.layers = nn.ModuleList([TransformerBlock(embed_size, heads, dropout, forward_expansion) for _ in range(num_layers)])
self.fc_out = nn.Linear(embed_size, vocab_size)
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask=None):
N, seq_length = x.shape
positions = torch.arange(0, seq_length).expand(N, seq_length).to(x.device)
out = self.dropout(self.word_embedding(x) + self.position_embedding(positions))
for layer in self.layers:
out = layer(out, out, out, mask)
return self.fc_out(out)The model is trained using the arXiv dataset. The training loop uses the AdamW optimizer and cross-entropy loss.
The train_loop function implements the training process. The model is trained for a specified number of epochs, and the loss is logged for each batch.
import torch.optim as optim
from tqdm import tqdm
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
VOCAB_SIZE = tokenizer.vocab_size
BATCH_SIZE = 32
SEQ_LENGTH = 128
EPOCHS = 5
model = GPT(vocab_size=VOCAB_SIZE).to(device)
optimizer = optim.AdamW(model.parameters(), lr=3e-4)
loss_fn = nn.CrossEntropyLoss()
def train_loop(data, model, optimizer, loss_fn, epochs):
model.train()
for epoch in range(epochs):
loop = tqdm(range(0, len(data), BATCH_SIZE), leave=True)
for i in loop:
batch = torch.tensor(data[i: i + BATCH_SIZE], dtype=torch.long).to(device)
target = batch[:, 1:].contiguous()
input_data = batch[:, :-1].contiguous()
optimizer.zero_grad()
output = model(input_data)
loss = loss_fn(output.view(-1, VOCAB_SIZE), target.view(-1))
loss.backward()
optimizer.step()
loop.set_description(f"Epoch {epoch+1}/{EPOCHS}")
loop.set_postfix(loss=loss.item())
train_loop(tokenized_data["input_ids"], model, optimizer, loss_fn, EPOCHS)After training, the model can generate text based on a given prompt.
The generate_text function generates text by iteratively predicting the next token based on the previous tokens.
import torch
def generate_text(model, tokenizer, start_text, max_len=100):
model.eval()
tokens = tokenizer.encode(start_text, return_tensors="pt").to(device)
for _ in range(max_len):
with torch.no_grad():
output = model(tokens)
logits = output.logits if hasattr(output, "logits") else output
next_token = torch.argmax(logits[:, -1, :], dim=-1).unsqueeze(0)
tokens = torch.cat([tokens, next_token], dim=1)
return tokenizer.decode(tokens.squeeze().detach().cpu().tolist())
# Example usage:
print(generate_text(model, tokenizer, "In this paper, we propose a novel approach to"))The model was trained for 5 epochs, and the training loss decreased steadily over time. The final loss was approximately 3.19.
The model was able to generate text based on a given prompt. For example, when prompted with "In this paper, we propose a novel approach to," the model generated the following text:
In this paper, we propose a novel approach to
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While the generated text is not entirely coherent, it demonstrates that the model has learned some patterns from the arXiv dataset.
In this project, we implemented a minimal GPT-like model (MinGPT) and trained it on the arXiv dataset. The model was able to generate text based on a given prompt, although the results were not entirely coherent. This demonstrates the feasibility of training a small-scale transformer-based language model on a domain-specific dataset.
- Larger Dataset: Train the model on a larger dataset to improve text generation quality.
- Hyperparameter Tuning: Experiment with different hyperparameters (e.g., learning rate, number of layers) to optimize model performance.
- Fine-Tuning: Fine-tune the model on specific arXiv categories (e.g.,
cs.CLfor computation and language) to generate more domain-specific text.
This report provides a detailed overview of the MinGPT implementation and training process. The code and methodology can be extended for further experimentation and improvement.