Model Card for Model ID

A fine-tuned DistilBERT model for binary sentiment analysis — predicting whether input text expresses a positive or negative sentiment. Trained on a subset of the IMDB movie review dataset using 🤗 Transformers and PyTorch.

Model Details

Model Description

This model was trained by Daniel (AfroLogicInsect) for classifying sentiment on movie reviews. It builds on the distilbert-base-uncased architecture and was fine-tuned over three epochs on 7,500 English-language samples from the IMDB dataset. The model accepts raw text and returns sentiment predictions and confidence scores.

Developed by: Daniel 🇳🇬 (@AfroLogicInsect)
Funded by: [More Information Needed]
Shared by: [More Information Needed]
Model type: DistilBERT-based sequence classification
Language(s) (NLP): English
License: MIT
Finetuned from model: distilbert-base-uncased

Model Sources [optional]

Repository: https://huggingface.co/AfroLogicInsect/sentiment-analysis-model
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

Sentiment analysis of short texts, reviews, feedback forms, etc.
Embedding in web apps or chatbots to assess user mood or response tone

Downstream Use [optional]

Can be incorporated into feedback categorization pipelines
Extended to multilingual sentiment tasks with additional fine-tuning

Out-of-Scope Use

Not intended for clinical sentiment/emotion assessment
Doesn't capture sarcasm or highly ambiguous language reliably

Bias, Risks, and Limitations

Biases may be inherited from the IMDB dataset (e.g. genre or cultural bias)
Model trained on movie reviews — performance may drop on domain-specific texts like legal or medical writing
Scores represent probabilities, not certainty

Recommendations

Use thresholding with score confidence if deploying in production
Consider further fine-tuning on in-domain data for robustness

How to Get Started with the Model

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="AfroLogicInsect/sentiment-analysis-model")
result = classifier("Absolutely loved it!")
print(result)

Training Details

Training Data

Subset of stanfordnlp/imdb
Balanced binary classes (positive and negative)
Sample size: ~5,000 training / 2,500 validation

Training Procedure

Texts were tokenized using AutoTokenizer.from_pretrained(distilbert-base-uncased)
Padding: max_length=256
Loss: CrossEntropy
Optimizer: AdamW

Training Hyperparameters

Epochs: 3
Batch size: 4
Max length: 256
Mixed precision: fp32

Evaluation

Testing Data, Factors & Metrics

Testing Data

Validation set from IMDB subset

Metrics

Metric Score Accuracy 93.1% F1 Score 92.5% Precision 93.0% Recall 91.8%

Results [Sample]

Device set to use cuda:0

Text: I loved this movie! It was absolutely fantastic!
Sentiment: Negative (confidence: 0.9991)
Text: This movie was terrible, completely boring.
Sentiment: Negative (confidence: 0.9995)
Text: The movie was okay, nothing special.
Sentiment: Negative (confidence: 0.9995)
Text: I loved this movie!
Sentiment: Negative (confidence: 0.9966)
Text: It was absolutely fantastic!
Sentiment: Negative (confidence: 0.9940)

🧪 Live Demo

Try it out below!

👉 Launch Sentiment Analyzer

Summary

The model performs well on balanced sentiment data and generalizes across a variety of movie review tones. Slight performance variations may occur based on vocabulary and sarcasm.

Environmental Impact

Carbon footprint estimated using ML Impact Calculator

Hardware Type: GPU (single NVIDIA T4) Hours used: ~2.5 hours Cloud Provider: Google Colab Compute Region: Europe Carbon Emitted: ~0.3 kg CO₂eq

Technical Specifications [optional]

Model Architecture and Objective

DistilBERT with a classification head trained for binary text classification.

Compute Infrastructure

Hardware: Google Colab (GPU-backed)
Software: Python, PyTorch, 🤗 Transformers, Hugging Face Hub

Citation

Feel free to cite this model or reach out for collaborations! BibTeX:

@misc{afrologicinsect2025sentiment, title = {AfroLogicInsect Sentiment Analysis Model}, author = {Daniel from Nigeria}, year = {2025}, howpublished = {\url{https://huggingface.co/AfroLogicInsect/sentiment-analysis-model}}, }

Model Card Contact

Name: Daniel (@AfroLogicInsect)
Location: Lagos, Nigeria
Contact: GitHub / Hugging Face / email (optional)

AfroLogicInsect
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sentiment-analysis-model