How can I assess the impact of keyword positional?

In the context of data science, here are step-by-step explanations given:-

Baseline model training

You can train a baseline transformer-based model even without keyword positional encoding on the review of the movie dataset.

You can record the performance metrics such as precision and recall.

Implementation of keyword positional encoding

You can define a list of keywords such as “excellent”, “terrible” etc.You can modify the input embedding layer of the transformer to incorporate the keyword positional encoding.

Import torch

Import torch.nn as nn

Class KeywordPositionalEncoding(nn.Module):

    Def __init__(self, keywords, embedding_dim, max_len=512):

        Super(KeywordPositionalEncoding, self).__init__()

        Self.keywords = keywords

        Self.embedding_dim = embedding_dim

        Self.max_len = max_len

        Self.keyword_weights = {kw: I for I, kw in enumerate(keywords)}

    Def forward(self, tokens):

        Positions = torch.arange(0, self.max_len).unsqueeze(1).float()

        Position_encoding = positions / (10000 ** (2 * (positions // 2) / self.embedding_dim))

        # Encode keywords with additional positional information

        Keyword_positions = torch.zeros_like(tokens, dtype=torch.float)

        For idx, token in enumerate(tokens):

            If token in self.keyword_weights:

                Keyword_positions[idx] = self.keyword_weights[token]

        # Add keyword positional information to the standard positional encoding

        Return position_encoding + keyword_positions

# Usage in transformer

Embedding_dim = 768  # Assuming transformer embedding size is 768

Keyword_encoding = KeywordPositionalEncoding(keywords, embedding_dim)

Training of model with keyword positional encoding

You can train the modified transformer model by using the same dataset and also the training Configuration as the baseline model.

You can save the trained model for evaluation.

Evaluation and comparison

You can compare the performance of the model on the test set by using the same metrics as the baseline model.

You can use paired statistical tests such as paired tests to compare the performance of the model.

From scipy.stats import ttest_rel

  Baseline_scores = [0.85, 0.84, 0.86]  # Example scores for baseline model

Keyword_scores = [0.88, 0.87, 0.89] # Example scores for model with keyword encoding

# Conduct paired t-test

T_stat, p_value = ttest_rel(keyword_scores, baseline_scores) Print(f”T-statistic: {t_stat}, P-value: {p_value}”)Analyzing the attention Weights

You can visualize the attention Weights to see if the model with the keyword positional encoding is focusing more on the specific keywords.

You can also use the tools such as “bertviz” for attention visualization.

  From bertviz import head_view

# Assuming you have a function to get attention weights

Attention_weights = get_attention_weights(model_with_keyword_encoding, input_tokens)

Head_view(attention_weights, input_tokens)

Feature importance analysis

You can perform a feature importance analysis to understand the impact of keywords on the performance of the models.

You can use techniques such as SHAP (Shapely additives explanation) for this particular purpose.

Import shap

Explainer = shap.Explainer(model_with_keyword_encoding, data=X_test)

Shap_values = explainer(X_test)

Shap.summary_plot(shap_values, X_test)