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AfroLogicInsect/sentiment-analysis-model

sourceHugging Facemitupdated 1y agoView on Hugging Face
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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]

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  • —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

{python}
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)