---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
tags:
- prompt-compression
- token-classification
- tiny-model
base_model: google/bert_uncased_L-2_H-128_A-2
---

# Saccade Tiny

Saccade Tiny is a small experimental token classifier that predicts whether
each word in a prompt should be **KEEP** or **DROP** before LLM inference.

## Architecture

- Base model: `google/bert_uncased_L-2_H-128_A-2`
- Task: binary token classification
- Labels: `KEEP`, `DROP`

## Training data

This first version was trained primarily on synthetic examples. Clean
instructions were modified with injected filler and redundant wording.
Original words were labelled `KEEP`; injected words were labelled `DROP`.

## Held-out evaluation

- Accuracy: 1.0000
- KEEP precision: 1.0000
- KEEP recall: 1.0000
- KEEP F1: 1.0000

## Intended use

Research and demonstrations involving conservative filler removal from English
prompts.

## Limitations

The model was trained mainly on synthetic data and may remove meaningful
context. It has not yet demonstrated a general improvement in downstream LLM
accuracy. Do not use it for safety-critical, legal, medical, or financial text.