How to implement summarization using tokenizer in django

Basic concept of encoder and decoder

In the context of Natural Language Processing (NLP), the concepts of encoder and decoder are typically associated with sequence-to-sequence models, such as the popular Transformer model. These models are commonly used for tasks like machine translation, text summarization, and question-answering.

Encoder:

1. The encoder is responsible for processing the input sequence and converting it into a fixed-size representation called the context vector or hidden state.
2. It consists of several layers of self-attention mechanisms and feed-forward neural networks.
3. The input sequence is typically tokenized, embedded, and passed through multiple encoder layers to capture the contextual information.
4. The final hidden state or context vector produced by the encoder carries the encoded representation of the input sequence's meaning and captures the relevant information to be used by the decoder.

5. The encoder's role is to understand the input sequence and encode its semantics into a fixed-size representation.
   Decoder:

6. The decoder takes the encoded representation (context vector) generated by the encoder and generates the output sequence.

7. It also consists of several layers of self-attention mechanisms and feed-forward neural networks.

8. At each time step, the decoder takes the previously generated token and the context vector as input to predict the next token in the sequence.

9. The decoder's initial hidden state is usually initialized with the context vector generated by the encoder.

10. The decoder uses an autoregressive approach, where it predicts one token at a time conditioned on the previous tokens and the context vector.

11. The decoding process continues until an end-of-sequence token or a maximum length is reached.

12. The decoder's role is to generate a meaningful output sequence based on the encoded representation and the previously generated tokens.
    The encoder-decoder architecture allows for mapping an input sequence to an output sequence of different lengths. The encoder captures the input's meaning and converts it into a fixed-size representation, while the decoder uses this representation to generate the desired output sequence.

EXAMPLE

The line summary_ids = model.generate(inputs, max_length=150, num_beams=4, early_stopping=True) is responsible for generating the summary using the pre-trained model. Let's break down this line and explain each part with an example:

summary_ids = model.generate(inputs, max_length=150, num_beams=4, early_stopping=True)

model.generate: This method is used to generate the summary based on the input. It takes the encoded inputs as the input to the model and generates the summary.
inputs: The encoded inputs that we obtained from the previous step using the tokenizer.
max_length=150: This argument specifies the maximum length of the generated summary. The generated summary will be truncated if it exceeds this length.
num_beams=4: This argument specifies the number of beams to use during beam search. Beam search is a technique used in sequence generation tasks to explore multiple possibilities and generate better-quality outputs.
early_stopping=True: This argument specifies whether to stop the generation process when all beams have reached the end token. Setting it to True ensures that the summary is not excessively long.
Example:
Let's say we have the encoded inputs obtained from the tokenizer step:

inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)

We'll use these inputs to generate the summary:

summary_ids = model.generate(inputs, max_length=150, num_beams=4, early_stopping=True)

The summary_ids variable will contain the generated summary as a sequence of token IDs. To convert it back to human-readable text, we can use the tokenizer's decode method:

summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

The summary_text will contain the generated summary as a string:

"An example summary of the input text."

Note that the actual generated summary may vary depending on the model and the input text used.

Decoder Example

The line summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

is used to decode the generated summary_ids into human-readable text. Let's break down this line and explain each part with an example:

summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

summary_ids[0]: The summary_ids variable contains the generated summary as a sequence of token IDs. We access the first element summary_ids[0] because we typically generate only one summary.
tokenizer.decode: This method is used to convert the token IDs back into text using the tokenizer.
skip_special_tokens=True: This argument specifies whether to skip special tokens such as [CLS], [SEP], and [PAD] during decoding. Setting it to True ensures that only the relevant text is included in the decoded summary.
Example:
Let's assume that summary_ids contains the generated summary as a sequence of token IDs:

summary_ids = [101, 2054, 2572, 2011, 1037, 2562, 7592, 1012, 102]

We'll use the tokenizer to decode the token IDs into text:

summary = tokenizer.decode(summary_ids, skip_special_tokens=True)

The summary variable will contain the decoded summary as a string:

"A sample summary of the input text."

Note that the actual generated summary may vary depending on the model and the input text used.

Implement Summarization in django

Step 1: Install required packages
Make sure you have the required packages installed. You'll need torch and transformers.

Step 2: Create a Django view
In your Django project, create a view that handles the text summarization.

from django.http import JsonResponse
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

def text_summarization(request):
    # Load the pre-trained tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained("t5-base")
    model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

    # Get the input text from the request
    input_text = request.GET.get('text', '')

    # Tokenize the input text
    inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)

    # Generate the summary
    summary_ids = model.generate(inputs, max_length=150, num_beams=4, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

    # Prepare the response
    response_data = {
        'input_text': input_text,
        'summary': summary
    }

    return JsonResponse(response_data)

Step 3: Configure the URL pattern
In your Django project's urls.py, configure the URL pattern for the text summarization view.

from django.urls import path
from .views import text_summarization

urlpatterns = [
    path('text-summarization/', text_summarization, name='text-summarization'),
]

Step 4: Test the text summarization endpoint
You can now test the text summarization endpoint by making a request to http://localhost:8000/text-summarization/ with the text parameter containing the input text.

For example, if you're using curl:

$ curl -X GET "http://localhost:8000/text-summarization/?text=This%20is%20the%20input%20text%20to%20summarize."

Output:

{
    "input_text": "This is the input text to summarize.",
    "summary": "This is the summarized text."
}

This example demonstrates a basic text summarization pipeline using a tokenizer in Django. It loads a pre-trained tokenizer and model, tokenizes the input text, generates the summary, and returns the summary as a JSON response