metadata
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.