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