README.md

metadata

language:
  - en
library_name: transformers
tags:
  - ensemble
  - text-classification
  - sentiment-analysis
  - imdb
license: apache-2.0
datasets:
  - imdb
metrics:
  - accuracy
  - f1
pipeline_tag: text-classification
base_model:
  - bert-base-uncased
model-index:
  - name: BERT IMDb Ensemble for Sentiment Analysis
    results:
      - task:
          type: text-classification
          name: Sentiment Classification
        dataset:
          name: IMDb
          type: imdb
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.939
          - name: F1
            type: f1
            value: 0.939

BERT IMDb Ensemble for Sentiment Analysis 🎬🎭

Model description

This is an ensemble of 3 BERT-base-uncased models fine-tuned on the IMDb dataset for binary sentiment classification (positive vs. negative reviews).
Each model was trained with a different random seed, and predictions are combined using weighted or unweighted averaging for more robust performance.

• Base model: bert-base-uncased
• Task: Sentiment classification (binary: 0 = negative, 1 = positive)
• Ensembling strategy: Weighted logits averaging

---

Training procedure

• Dataset: IMDb (train/test split from Hugging Face datasets)

• Preprocessing:

  • Tokenization with bert-base-uncased
  • Truncation at 512 tokens

• Hyperparameters:

  • Epochs: 2
  • Batch size: 8
  • Optimizer: AdamW (default in Trainer)
  • FP16: Enabled
  • Seeds: [42, 123, 999]

---

Evaluation results

Across the three models, results are very consistent:

Model (Seed)	Epochs	Val. Accuracy	Val. Macro F1
42	2	93.74%	0.9374
123	2	93.84%	0.9383
999	2	93.98%	0.9398

Ensemble performance (weighted example [0.2, 0.2, 0.6]) improves stability and helps reduce variance across seeds.

---

How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("ByteMeHarder-404/bert-imdb-ensemble")
model = AutoModelForSequenceClassification.from_pretrained("ByteMeHarder-404/bert-imdb-ensemble")

inputs = tokenizer("This movie was an absolute masterpiece!", return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)

print(probs)  # tensor([[0.01, 0.99]]) -> positive sentiment