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license: mit
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---
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license: mit
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language:
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- en
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pipeline_tag: text-classification
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tags:
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- open-source
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- binary-classification
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- sst-2
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- distilbert
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- sentiment-analysis
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---
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# Model Card: Sentiment Classifier (DistilBERT - SST-2)
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## Overview
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This model is a fine-tuned version of `distilbert-base-uncased` on the SST-2 dataset, designed for **binary sentiment classification**: labeling text as either *positive* or *negative*.
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It’s fast, compact, and suitable for real-time inference tasks such as social media monitoring, customer feedback triage, and lightweight embedded NLP.
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---
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## Use Cases
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- Detecting sentiment in tweets, reviews, or comments
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- Routing customer support tickets by tone
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- Analyzing product sentiment in e-commerce or app stores
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- Monitoring brand perception over time
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---
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## Example
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```text
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Input: "This new update is amazing — so much faster!"
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Output: Positive
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Input: "This feature is broken and support isn't helping."
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Output: Negative
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---
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## Strengths
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- Extremely lightweight: good for mobile and low-latency use
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- Fine-tuned on a benchmark sentiment dataset (SST-2)
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- Strong out-of-the-box performance for informal English
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---
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## Limitations
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- Binary only (positive/negative) — no neutral or nuanced emotion
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- Trained on English movie reviews — may misinterpret sarcasm, cultural tone, or domain-specific feedback
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- Not ideal for clinical, legal, or safety-critical sentiment tasks
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---
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## Model Details
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- Architecture: DistilBERT
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- Base model: `distilbert-base-uncased`
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- Fine-tuning dataset: SST-2 (Stanford Sentiment Treebank)
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- Max input: 512 tokens
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- Classes: `Positive`, `Negative`
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---
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## License
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MIT License — free to use, adapt, and deploy commercially.
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---
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## Authorship Note
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This model card was written by [Sarah Mancinho](https://huggingface.co/Sarah-h-h) as part of a public AI/LLM contribution series on Hugging Face.
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Original model: [`distilbert-base-uncased-finetuned-sst-2-english`](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)
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---
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## Citation
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