Transformers
PyTorch
TensorFlow
ONNX
Safetensors
English
t5
text2text-generation
summary
summarizer
Eval Results (legacy)
text-generation-inference
Instructions to use shorecode/t5-efficient-tiny-summarizer-general-purpose-v2 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-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shorecode/t5-efficient-tiny-summarizer-general-purpose-v2") model = AutoModelForSeq2SeqLM.from_pretrained("shorecode/t5-efficient-tiny-summarizer-general-purpose-v2") - 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_20251030_222933.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: 0.323 | |
| - 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: 2.56 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: Summarization Compression | |
| type: Lighteval extractiveness | |
| value: 6.91 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: Summarization Coverage | |
| type: Lighteval extractiveness | |
| value: 0.89 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: Summarization Density | |
| type: Lighteval extractiveness | |
| value: 4.95 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: rougeL precision | |
| type: Lighteval | |
| value: 0.51 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: rougeL recall | |
| type: Lighteval | |
| value: 0.13 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: rougeL fmeasure | |
| type: Lighteval | |
| value: 0.20 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: rouge1 precision | |
| type: Lighteval | |
| value: 0.71 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: rouge1 recall | |
| type: Lighteval | |
| value: 0.18 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| name: shorecode/summary-collection-60k-rows | |
| type: shorecode/summary-collection-60k-rows | |
| metrics: | |
| - name: rouge1 fmeasure | |
| type: Lighteval | |
| value: 0.28 | |
| # Deprecated | |
| Please visit https://huggingface.co/shorecode/t5-efficient-tiny-summarizer-general-purpose-v3 for an improved version | |
| # 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! | |
| <Gallery /> | |
| https://api.wandb.ai/links/shorecode-shorecode-llc/nqr415rk |