| --- |
| license: apache-2.0 |
| pipeline_tag: text-classification |
| tags: |
| - sentiment-analysis |
| - text-classification |
| - from-scratch |
| - transformer |
| - sst2 |
| datasets: |
| - stanfordnlp/sst2 |
| metrics: |
| - accuracy |
| --- |
| |
| # Vibe Check — a sentiment classifier trained from scratch |
|
|
| **Vibe Check** is a compact (~2M parameter) Transformer text classifier that predicts whether a |
| sentence is **Positive** or **Negative**. It was built **entirely from scratch** — my own word |
| tokenizer and my own model architecture, trained from **random initialization** (no pretrained |
| weights, no fine-tuning) on the public **Stanford Sentiment Treebank (SST-2)** dataset. |
|
|
| ## Highlights |
| - **From scratch:** custom tokenizer + 2-layer Transformer encoder + mean-pooling classifier head. |
| - **~2.0M parameters**, trained from random init on ~67k public sentences. |
| - **~82% validation accuracy** on SST-2 (small model, no pretrained embeddings). |
| - Runs on CPU in milliseconds. |
|
|
| ## Not a fine-tune |
| Unlike many models on the Hub, this is **not** a fine-tune of a large pretrained model — every weight |
| was learned from zero on public data. Training code, tokenizer, and config are included. |
|
|
| ## Usage |
| ```python |
| # see vibe.py in this repo for the model definition + predict_vibe() |
| from vibe import predict_vibe |
| print(predict_vibe("I absolutely love this!")) # {'label': 'Positive', 'confidence': 98.5} |
| ``` |
|
|
| ## Data & license |
| Trained on **[SST-2](https://huggingface.co/datasets/stanfordnlp/sst2)** (public research dataset). |
| Released under **Apache-2.0**. For entertainment/educational use; a small model, so expect ~1 in 5 |
| predictions to be wrong on hard/ambiguous text. |
|
|
