Example code for fine-tuning a language model using URL content and a vector database, and also an example logic to answer questions with the model using Python.

Example Code:

To fine-tune a language model using URL content and a vector database, we can follow these steps:

1. Collect the URL content and preprocess it to prepare it for training.

import requests
from bs4 import BeautifulSoup
from transformers import AutoTokenizer
import re

# Collect URL content
url = "https://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.content, "html.parser")
text = soup.get_text()

# Preprocess text
text = re.sub(r"\s+", " ", text)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
inputs = tokenizer.encode(text, add_special_tokens=True, return_tensors="pt")

2. Store the vector representations of the preprocessed URL content in a vector database, such as Pinecone.

import pinecone

# Connect to Pinecone
pinecone.init(api_key="YOUR_API_KEY")

# Store vectors in Pinecone
vectors = model(inputs).last_hidden_state.mean(dim=1)
pinecone.create_index(index_name="my_index", dimension=768)
pinecone.save_index(index_name="my_index", vectors=vectors.numpy())

3. Fine-tune the language model using the stored vectors and a task-specific dataset.

import torch
from transformers import AutoModelForCausalLM, AutoConfig
from torch.utils.data import DataLoader, Dataset

# Define dataset and dataloader
class MyDataset(Dataset):
    def __init__(self, data):
        self.data = data

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return len(self.data)

dataset = MyDataset(["This is a sample sentence", "Another sample sentence"])
dataloader = DataLoader(dataset, batch_size=2)

# Define model and optimizer
config = AutoConfig.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2", config=config)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)

# Fine-tune model
for epoch in range(10):
    for batch in dataloader:
        inputs = tokenizer.batch_encode_plus(batch, padding=True, return_tensors="pt")
        input_ids = inputs["input_ids"]
        attention_mask = inputs["attention_mask"]
        outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

Example Logic for Answering Questions:

To use the fine-tuned language model to answer questions, we can follow these steps:

1. Collect the question and preprocess it to prepare it for inference.

# Collect and preprocess question
question = "What is the capital of France?"
question = re.sub(r"\s+", " ", question)
inputs = tokenizer.encode(question, add_special_tokens=True, return_tensors="pt")

2. Retrieve the most similar vector to the preprocessed question from the vector database.

# Retrieve most similar vector from Pinecone
query_vector = model(inputs).last_hidden_state.mean(dim=1).numpy()
result = pinecone.query(index_name="my_index", query=query_vector, top_k=1)

3. Generate the answer using the fine-tuned language model and the retrieved vector.

“`python

Generate answer using language model

context = result.ids[

context = “Paris is the capital of France. “
generated = model.generate(
input_ids=inputs,
max_length=50,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=1.0,
repetition_penalty=1.0,
pad_token_id=tokenizer.eos_token_id,
use_cache=True,
context=context
)
answer = tokenizer.decode(generated[0], skip_special_tokens=True)

4. Return the answer.

python

Return answer

print(answer)
“`

This logic generates an answer by appending the retrieved context from the vector database to the question and then using the fine-tuned language model to generate a response. The generated response is then decoded to produce the answer. This approach is known as a “prompt-based” or “zero-shot” method for question-answering, where the language model is not explicitly trained on the question-answering task, but rather generates an answer based on a given prompt and some context.