End-to-end approach on code level

I found the topic of End-to-end very interesting. As I understood, if we use it, we shouldn’t define layers types in model. So, if we say about NLP tasks, we shouldn’t say that layer should be Embeddings, LSTM for example, or create own funcs of the recognising of words connections. And we can only manipulate of standard Dense layers and their quantity. Do I understand correct? Or can you give me a few examples of a code with difference of approaches?

Hello @someone555777
You are partially correct in your understanding of end-to-end models. In the context of NLP, end-to-end models aim to learn the entire task without explicitly defining intermediate steps or layers, such as embeddings or LSTM layers. However, this doesn’t mean that you can only use Dense layers. Instead, end-to-end models often involve complex architectures, such as transformers, which can learn to perform tasks without the need for manual feature engineering or pre-defined layers.

As you requested, I will provide code examples for Text Summarization and Sentiment Analysis.

Text Summarization

Text summarization can be approached using extractive or abstractive methods. Here’s an example using the Hugging Face Transformers library for abstractive summarization:

from transformers import pipeline

summarizer = pipeline("summarization")

text = "Your long text here..."
summary = summarizer(text, max_length=150, min_length=30, do_sample=False)
print(summary[0]['summary_text'])

Sentiment Analysis

Here’s an example using the Hugging Face Transformers library for sentiment analysis:

from transformers import pipeline

sentiment_classifier = pipeline("sentiment-analysis")

text = "I love this product!"
result = sentiment_classifier(text)
print(result[0]['label'])

In both examples, we use pre-trained models from the Hugging Face Transformers library, which handles the end-to-end learning process. These models are based on transformer architectures and can be fine-tuned for specific tasks. The examples demonstrate how end-to-end models can be used without explicitly defining layers like embeddings or LSTM.

I hope I was able to give a good reply to your question with a few examples & code. If you have further questions about the topic please feel free to ask them as a followup reply.
Regards,
Can Koz

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Looks like I’ve just now understood what is end-to-end :sweat_smile: Do I understand correct that it just means, that I shouldn’t manually define layers and can just import a ready model, that I can use?

Damn @canxkoz :raised_hands:!!! Thank you so much… I envy your level of understanding and explanation. You made it crystal clear.