AfroLogicInsect/sentiment-analysis-model_v2

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_v2
• 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: ~15,000 training / 1,500 validation

Training Hyperparameters

Training arguments

training_args = TrainingArguments( output_dir = "./sentiment-model-v2", num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, learning_rate=2e-5, # Explicit learning rate warmup_steps=100, # Reduced warmup weight_decay=0.01, logging_dir="./logs", logging_steps=50, eval_strategy="steps", eval_steps=200, # < 500: More frequent evaluation save_strategy="steps", save_steps=200, # match eval_steps load_best_model_at_end=True, metric_for_best_model="f1", greater_is_better=True, seed=42, # Reproducibility dataloader_drop_last=False, #remove_unused_columns=False, )

Create trainer

trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=val_dataset, tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, )

Evaluation

Testing Data, Factors & Metrics

Testing Data

• Validation set from IMDB subset

Metrics

Step Training Loss Validation Loss Accuracy F1 Precision Recall 200 0.391100 0.344377 0.850000 0.863554 0.791991 0.949333 400 0.299000 0.304345 0.876000 0.865994 0.942006 0.801333 600 0.301700 0.298436 0.881333 0.888331 0.838863 0.944000 800 0.280700 0.260090 0.893333 0.897698 0.862408 0.936000 1000 0.173100 0.288142 0.899333 0.897766 0.911967 0.884000 1200 0.203700 0.263154 0.904667 0.905486 0.897772 0.913333 1400 0.186100 0.275240 0.904000 0.901370 0.926761 0.877333 1600 0.130400 0.291926 0.904667 0.903313 0.916324 0.890667 1800 0.158900 0.304814 0.908000 0.908488 0.903694 0.913333 2000 0.087900 0.332357 0.904000 0.905263 0.893506 0.917333 2200 0.119300 0.339073 0.908667 0.910399 0.893453 0.928000 2400 0.178100 0.366023 0.903333 0.905660 0.884371 0.928000 2600 0.072100 0.372015 0.909333 0.908356 0.918256 0.898667 2800 0.097700 0.368600 0.906667 0.908016 0.895078 0.921333

Final evaluation results: { 'eval_loss': 0.3390733003616333, 'eval_accuracy': 0.9086666666666666, 'eval_f1': 0.9103989535644212, 'eval_precision': 0.8934531450577664, 'eval_recall': 0.928, 'eval_runtime': 9.9181, 'eval_samples_per_second': 151.239, 'eval_steps_per_second': 9.478, 'epoch': 3.0 }

Results [Sample]

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TESTING FIXED MODEL

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Testing fixed model... Text Expected Predicted Confidence Match

I absolutely loved this movie! It was fantastic! positive positive 0.9959 ✓ This movie was terrible and boring. negative negative 0.9969 ✓ Amazing acting and great story! positive positive 0.9959 ✓ Worst film I've ever seen. negative negative 0.9950 ✓ Incredible cinematography and soundtrack. positive positive 0.9950 ✓ Complete waste of time and money. negative negative 0.9957 ✓ The movie was okay, nothing special. neutral negative 0.9915 N/A I enjoyed most of it. positive positive 0.9912 ✓ Pretty disappointing overall. negative negative 0.9936 ✓ Masterpiece of cinema! positive positive 0.9939 ✓

Overall Accuracy: 100.0% (9/9)

🧪 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

BibTeX:

[@misc{afrologicinsect2025sentiment, title = {AfroLogicInsect Sentiment Analysis Model}, author = {Akan Daniel}, year = {2025}, howpublished = {\url{https://huggingface.co/AfroLogicInsect/sentiment-analysis-model_v2}}, }]

Model Card Contact

• Name: Daniel (@AfroLogicInsect)
• Location: Lagos, Nigeria
• Contact: GitHub / Hugging Face / email (danielamahtoday@gmail.com)