| | --- |
| | library_name: transformers |
| | tags: |
| | - sentiment-analysis |
| | - distilbert |
| | - text-classification |
| | - nlp |
| | - imdb |
| | - binary-classification |
| | license: mit |
| | datasets: |
| | - stanfordnlp/imdb |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | base_model: |
| | - distilbert/distilbert-base-uncased |
| | --- |
| | |
| | # 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] |
| |
|
| | <!-- Provide the basic links for the model. --> |
| |
|
| | - **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](https://huggingface.co/spaces/AfroLogicInsect/sentiment-analysis-model-gradio) |
| |
|
| |
|
| | #### 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](https://mlco2.github.io/impact#compute) |
| |
|
| | 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) |
