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!
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Model tree for shorecode/t5-efficient-tiny-summarizer-general-purpose-v2
Base model
google/t5-efficient-tiny-nh8Dataset used to train shorecode/t5-efficient-tiny-summarizer-general-purpose-v2
Evaluation results
- f1 Score on shorecode/summary-collection-60k-rowsself-reported0.323
- Faithfullness (facebook/bart-large-cnn) on shorecode/summary-collection-60k-rowsself-reported2.560
- Summarization Compression on shorecode/summary-collection-60k-rowsself-reported6.910
- Summarization Coverage on shorecode/summary-collection-60k-rowsself-reported0.890
- Summarization Density on shorecode/summary-collection-60k-rowsself-reported4.950
- rougeL precision on shorecode/summary-collection-60k-rowsself-reported0.510
- rougeL recall on shorecode/summary-collection-60k-rowsself-reported0.130
- rougeL fmeasure on shorecode/summary-collection-60k-rowsself-reported0.200
# 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")