| --- |
| 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 |
|
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| ### Model Description |
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| 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. --> |
|
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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 |
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| ### Downstream Use [optional] |
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| - Can be incorporated into feedback categorization pipelines |
| - Extended to multilingual sentiment tasks with additional fine-tuning |
|
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| ### Out-of-Scope Use |
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| - Not intended for clinical sentiment/emotion assessment |
| - Doesn't capture sarcasm or highly ambiguous language reliably |
|
|
| ## Bias, Risks, and Limitations |
|
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| - 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 |
|
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| - 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 |
|
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| ### Training Data |
|
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| - Subset of stanfordnlp/imdb |
| - Balanced binary classes (positive and negative) |
| - Sample size: ~5,000 training / 2,500 validation |
|
|
| ### Training Procedure |
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| - Texts were tokenized using AutoTokenizer.from_pretrained(distilbert-base-uncased) |
| - Padding: max_length=256 |
| - Loss: CrossEntropy |
| - Optimizer: AdamW |
|
|
| #### Training Hyperparameters |
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| - Epochs: 3 |
| - Batch size: 4 |
| - Max length: 256 |
| - Mixed precision: fp32 |
|
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|
|
| ## Evaluation |
|
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| ### Testing Data, Factors & Metrics |
|
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| #### Testing Data |
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| - Validation set from IMDB subset |
|
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| #### Metrics |
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| 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) |
|
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| - Text: This movie was terrible, completely boring. |
| - Sentiment: Negative (confidence: 0.9995) |
|
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| - 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 |
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| Try it out below! |
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| 👉 [Launch Sentiment Analyzer](https://huggingface.co/spaces/AfroLogicInsect/sentiment-analysis-model-gradio) |
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|
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| #### Summary |
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| 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. |
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| ## Environmental Impact |
|
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| Carbon footprint estimated using [ML Impact Calculator](https://mlco2.github.io/impact#compute) |
|
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| Hardware Type: GPU (single NVIDIA T4) |
| Hours used: ~2.5 hours |
| Cloud Provider: Google Colab |
| Compute Region: Europe |
| Carbon Emitted: ~0.3 kg CO₂eq |
|
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| ## Technical Specifications [optional] |
|
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| ### Model Architecture and Objective |
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| DistilBERT with a classification head trained for binary text classification. |
|
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| ### 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) |
