Transformers
PyTorch
ONNX
Safetensors
English
t5
text2text-generation
summary
summarizer
Eval Results (legacy)
text-generation-inference
Instructions to use shorecode/t5-efficient-tiny-summarizer-general-purpose-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shorecode/t5-efficient-tiny-summarizer-general-purpose-v3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shorecode/t5-efficient-tiny-summarizer-general-purpose-v3") model = AutoModelForSeq2SeqLM.from_pretrained("shorecode/t5-efficient-tiny-summarizer-general-purpose-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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 |