---
license: mit
datasets:
- shorecode/summary-collection-200k-rows
language:
- en
base_model:
- google/t5-efficient-tiny-nh8
library_name: transformers
tags:
- summary
- summarizer
widget:
- text: Model training
  output:
    url: Screenshot_20251104_204645.png
metrics:
- f1
- rouge
- extractiveness
model-index:
- name: t5-efficient-tiny-summarizer-general-purpose-v2
  results:
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: f1 Score
      type: f1 Score
      value: 0.29
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: Faithfullness (facebook/bart-large-cnn)
      type: facebook/bart-large-cnn
      value: 1.71
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: Summarization Compression
      type: Lighteval extractiveness
      value: 7.52
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: Summarization Coverage
      type: Lighteval extractiveness
      value: 0.96
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: Summarization Density
      type: Lighteval extractiveness
      value: 8.68
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: rougeL precision
      type: Lighteval
      value: 0.59
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: rougeL recall
      type: Lighteval
      value: 0.31
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: rougeL fmeasure
      type: Lighteval
      value: 0.41
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: rouge1 precision
      type: Lighteval
      value: 0.63
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: rouge1 recall
      type: Lighteval
      value: 0.33
  - task:
      type: Summarization
    dataset:
      name: shorecode/summary-collection-60k-rows
      type: shorecode/summary-collection-60k-rows
    metrics:
    - name: rouge1 fmeasure
      type: Lighteval
      value: 0.44
---

# This model was built to shorten text that is injected into LLM prompts to reduce API calling costs
Very high compression (7x+) meaning the text is 7 times smaller when sent to your LLM provider!
Recommended kwargs:
- num_beams=2 || 3
- no_repeat_ngram_size=2
- min_length=20
- max_new_tokens=500 || as high as you can tolerate, 4x500 = 2000 characters. anything after 2000 is clipped
<Gallery />

https://api.wandb.ai/links/shorecode-shorecode-llc/6udfudmr