README.md

metadata

pretty_name: IMDb Sentiment Analysis - DistilBERT Feature Cache
dataset_info:
  features:
    - name: train_feat
      dtype: float16
      shape:
        - 25000
        - 128
        - 768
    - name: test_feat
      dtype: float16
      shape:
        - 25000
        - 128
        - 768
    - name: train_mask
      dtype: bool
      shape:
        - 25000
        - 128
    - name: test_mask
      dtype: bool
      shape:
        - 25000
        - 128
configs:
  - config_name: default
    data_files:
      - split: train
        path: train_feat.npy
      - split: test
        path: test_feat.npy
tags:
  - sentiment-analysis
  - movie-reviews
  - embeddings
  - features
  - distilbert
  - rnn
  - py-torch

IMDb Movie Reviews - DistilBERT Contextual Embedding Cache

This dataset contains pre-extracted contextual embedding features of the standard IMDb Movie Reviews dataset (Sentiment Analysis). The features were extracted using a frozen DistilBERT (distilbert-base-uncased) encoder.

By caching these high-dimensional embeddings, you can train downstream classifiers (like LSTMs, GRUs, Attention heads, or custom Transformers) in seconds on a local GPU or CPU, bypassing the massive computation overhead of running transformer inference on every epoch.

📊 Dataset Specification

All features are saved as memory-mapped Numpy binary files (.npy), allowing them to be loaded on consumer hardware with low memory footprint via np.memmap.

File	Shape	Dtype	Size on Disk	Description
train_feat.npy	(25000, 128, 768)	float16	4.58 GB	Frozen DistilBERT last-hidden-state embeddings for the IMDb Train Split
train_mask.npy	(25000, 128)	bool	3.05 MB	Attention mask for the training split
test_feat.npy	(25000, 128, 768)	float16	4.58 GB	Frozen DistilBERT last-hidden-state embeddings for the IMDb Test Split
test_mask.npy	(25000, 128)	bool	3.05 MB	Attention mask for the test split

Extraction Details

• Base Model: distilbert-base-uncased (frozen, Hugging Face transformers)
• Tokenization: Contextual embeddings extracted from the last hidden state (768 dimensions).
• Sequence Length (max_len): 128 tokens (truncated/padded).
• Precision: float16 (RAM-safe memmap layout).

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🏆 Project Benchmarks & Leaderboard

This project involved an iterative design of multiple classification models. Below is the complete leaderboard of all runs, showing that the overall project reached a peak performance of 91.42% Accuracy (SOTA-level for custom LSTMs):

Version	Model Architecture	Key Techniques	Trainable Params	Best Test Accuracy	Best Test F1
v16	3-Layer LSTM (Pretrained)	ULMFiT-style LM Pretraining (50K Unsupervised) + Gradual Unfreeze	8.4M	91.42%	91.58%
v18	Bidirectional LM (Pretrained)	Forward+Backward LM pretrain + combined classifier + FGM	-	91.15%	-
v9	3-Layer BiLSTM + Attn	Vocab=20K, embed=128, hidden=256, max_len=300	12.5M	90.13%	-
v13	AWD-LSTM + Multi-Head Attn	Variational Dropout + Recurrent Weight Dropout	12.5M	90.12%	90.19%
v15	BiLSTM + MHA + Concat Pool	Adversarial Training (FGM, epsilon=0.5)	6.2M	90.12%	90.18%
v17	DistilBERT (Frozen) + BiLSTM	Contextual Feature Extractor (This Cache)	4.7M	86.86%	86.99%
v5	BiLSTM + Attention	Single-layer LSTM + Dot-product Attention	1.3M	88.38%	-
v2	BiLSTM + Grad Clip	Bidirectional + Gradient clipping max_norm=1.0	1.3M	81.39%	79.86%

Why v17 (BERT Feature Cache) achieved 86.86%

The feature cache provided here (v17) uses a completely frozen DistilBERT encoder. Because the weights of the DistilBERT model are not fine-tuned on the IMDb reviews (to save GPU memory and prevent overfitting during training), it acts as a general feature extractor. Combined with a BiLSTM + Multi-Head Attention classifier head, it reaches a solid 86.86% Accuracy in less than 45 seconds of training per epoch.

To reach 91%+, language model pre-training (such as in v16 and v18) on the 50,000 unsupervised IMDb reviews, or fully fine-tuning the transformer layers, is required.

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📈 v17 Classifier Training Curves & Confusion Matrix

📊 Training Curves

🎯 Confusion Matrix

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🛠️ Usage Example

You can load these files directly into PyTorch Dataset using memory mapping, which reads pages dynamically from disk without loading the entire 9.2 GB into RAM:

import numpy as np
import torch
from torch.utils.data import Dataset

class IMDbFeatureDataset(Dataset):
    def __init__(self, feat_path, mask_path, labels, seq_len=128, bert_dim=768):
        self.labels = labels
        self.n = len(labels)
        # Load as memory-mapped array (RAM-safe)
        self.features = np.memmap(feat_path, dtype=np.float16, mode='r',
                                  shape=(self.n, seq_len, bert_dim))
        self.masks = np.memmap(mask_path, dtype=np.bool_, mode='r',
                               shape=(self.n, seq_len))

    def __len__(self):
        return self.n

    def __getitem__(self, idx):
        x = torch.from_numpy(self.features[idx].astype(np.float32)) # Convert to fp32
        mask = torch.from_numpy(self.masks[idx])
        y = torch.tensor(self.labels[idx], dtype=torch.float32)
        return x, mask, y