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wakaflocka17/
ensemble-majority-voting-imdb

Name: ensemble-majority-voting-imdb
Availability: InStock

text-classification

stanfordnlp/imdb

EleutherAI/gpt-neo-2.7B

finetune

apache-2.0

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📝 Model Card: ensemble-majority-voting-imdb

🔍 Introduction

The wakaflocka17/ensemble-majority-voting-imdb model is a majority-voting ensemble of three fine-tuned sentiment classifiers (bert-imdb-finetuned, bart-imdb-finetuned, gptneo-imdb-finetuned) on the IMDb dataset. Each model votes on the sentiment label and the ensemble returns the label with the most votes, improving overall accuracy.

📊 Evaluation Metrics

Metric	Value
Accuracy	0.93296
Precision	0.9559
Recall	0.9078
F1-score	0.9312

⚙️ Training Parameters

Parameter	Values
Models in ensemble	`bert_base_uncased`, `bart_base`, `gpt_neo_2_7b`
Repo for ensemble	`models/ensemble_majority_voting`
Batch size (eval)	64

🚀 Example of use in Colab

Installing dependencies

!pip install --upgrade transformers huggingface_hub

(Optional) Authentication for private models

python

from huggingface_hub import login
login(token="hf_yourhftoken")

Loading models and creating ensemble pipeline

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
from collections import Counter

# List of fine-tuned model repo IDs
model_ids = [
    "wakaflocka17/bert-imdb-finetuned",
    "wakaflocka17/bart-imdb-finetuned",
    "wakaflocka17/gptneo-imdb-finetuned"
]

Load pipelines

python

pipelines = []
for repo_id in model_ids:
    tokenizer = AutoTokenizer.from_pretrained(repo_id)
    model = AutoModelForSequenceClassification.from_pretrained(repo_id)
    model.config.id2label = {0: 'NEGATIVE', 1: 'POSITIVE'}
    pipelines.append(TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=False))

Ensemble prediction function

python

def ensemble_predict(text):
    votes = []
    # Collect each model's vote along with its name
    for model_id, pipe in zip(model_ids, pipelines):
        label = pipe(text)[0]['label']
        votes.append({
            "model": model_id,   # or model_id.split("/")[-1] for just the short name
            "label": label
        })
    # Determine majority label
    majority_label = Counter([v["label"] for v in votes]).most_common(1)[0][0]
    return {
        "ensemble_label": majority_label,
        "individual_votes": votes
    }

Inference on a text example

python

testo = "This movie was absolutely fantastic—wonderful performances and a gripping story!"
result = ensemble_predict(testo)
print(result)
# Example output:
# {
#   'ensemble_label': 'POSITIVE',
#   'individual_votes': [
#       {'model': 'wakaflocka17/bert-imdb-finetuned', 'label': 'POSITIVE'},
#       {'model': 'wakaflocka17/bart-imdb-finetuned', 'label': 'NEGATIVE'},
#       {'model': 'wakaflocka17/gptneo-imdb-finetuned', 'label': 'POSITIVE'}
#   ]
# }

📖 How to cite

If you use this model in your work, you can cite it as:

latex

@misc{Sentiment-Project,
  author       = {Francesco Congiu},
  title        = {Sentiment Analysis with Pretrained, Fine-tuned and Ensemble Transformer Models},
  howpublished = {\url{https://github.com/wakaflocka17/DLA_LLMSANALYSIS}},
  year         = {2025}
}

🔗 Reference Repository

All the file structure and script examples can be found at: https://github.com/wakaflocka17/DLA_LLMSANALYSIS/tree/main