AI - NLP (4) Comparing AI Models for NLP Tasks - Deep Learning vs. Transformer Applications

The process of handling language in AI typically involves stages like data collection, preprocessing, embedding, modeling, training, and prediction. Comparing these steps across previous examples, we can summarize as follows:

1. Data Collection and Preparation

• First Code (Deep-Learning-for-NLP-Tasks)
  • Data Preparation: Uses simple example data (sentences and sentiment labels).
  • Label Encoding: Converts text labels to numbers using LabelEncoder.
• Second Code (Neural-Network-for-Text-Classification)
  • Data Preparation: Uses a simple dataset consisting of texts and labels, similar to the first example.
  • Label Encoding: Labels are already provided as numbers, so no additional encoding is required.
• Third Code (Text-Generation-with-Pretrained-Transformer-Models)
  • Data Preparation: Uses a single sentence as the input for prediction.
  • Label Encoding: Not needed, as the model is already pre-trained and only performs inference.

2. Preprocessing

• First Code
  • Tokenization: Uses AutoTokenizer to tokenize text into a format that BERT can understand.
  • Padding and Truncation: Sets a maximum length to ensure uniform token sequence lengths.
• Second Code
  • Tokenization: Uses Tokenizer to convert text into numerical sequences.
  • Padding: Uses pad_sequences to standardize sequence lengths, generating simple word index-based sequences.
• Third Code
  • Tokenization: Uses AutoTokenizer to tokenize input text and convert it into a format compatible with BERT.
  • Padding and Truncation: Similar to the first code, sequences are padded and truncated to a maximum length.

3. Embedding and Feature Extraction

• First Code
  • Embedding: Utilizes the embedding layer of a pre-trained BERT model to vectorize the contextual meaning of the text.
• Second Code
  • Embedding: Uses an Embedding layer to embed words into 16-dimensional vectors, with weights learned during training.
• Third Code
  • Embedding: Employs BERT’s pre-trained embeddings, automatically applied during the inference process.

4. Modeling

• First Code
  • Model: Uses a BERT-based sequence classification model, adjusted via fine-tuning to perform sentiment classification.
• Second Code
  • Model: A simple neural network with a sequential structure of Embedding -> GlobalAveragePooling1D -> Dense layers.
• Third Code
  • Model: Directly uses the BERT model for inference without additional training.

5. Training

• First Code
  • Training Process: Trains the BERT model using training and validation data for 3 epochs.
• Second Code
  • Training Process: Trains the neural network model for 10 epochs, with a relatively simple and fast training process.
• Third Code
  • Training Process: No training; uses a pre-trained model purely for prediction.

6. Prediction and Output

• First Code
  • Prediction: Predicts sentiment (positive/negative) for input text and outputs the result.
• Second Code
  • Prediction: Predicts sentiment for new text, classifying as positive or negative based on a 0.5 threshold.
• Third Code
  • Prediction: Quickly predicts sentiment (positive/negative) of the input sentence using the pre-trained model.

Summary

• First Code (Deep-Learning-for-NLP-Tasks) demonstrates the application of deep learning models, particularly pre-trained BERT, in NLP tasks.
• Second Code (Neural-Network-for-Text-Classification) describes the use of a simple neural network model for text classification tasks.
• Third Code (Text-Generation-with-Pretrained-Transformer-Models) provides an example of using pre-trained Transformer models for quick sentiment prediction without training.

This summary highlights the steps involved in NLP processing with AI, from data preparation to prediction, and clearly illustrates the differences in approach and model complexity across each example.