flan-t5
Collection
A set flant-t5 fine tuned models for practicing
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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
flan-t5-small-summary-peft
Model Details
Model Description
Enhanced Dialogue Summarization Model using Parameter-Efficient Fine-Tuning (PEFT) with LoRA adapters on google/flan-t5-small. Achieves improved summary quality while training only 0.16% of parameters.
- Developed by: Paul
- Model type: Seq2Seq LM with LoRA adapters
- Language(s): English
- License: Apache 2.0 (inherited from base model)
- Finetuned from: google/flan-t5-small
- Training Efficiency: 94% parameter reduction vs full fine-tuning.
Model Sources
- Repository: [Your HF Repo Link]
- Paper: DialogSum Paper
- Demo: [Gradio Space Link]
Uses
Direct Use
Optimized for dialogue summarization tasks in customer service, meeting transcripts, and conversational analysis.
Downstream Use
- Conversational AI systems
- Dialogue content indexing
- Customer interaction analytics
Out-of-Scope Use
- Medical/legal document analysis
- Multilingual summarization
- Real-time low-latency applications
Bias & Limitations
While LoRA maintains similar bias profiles to full fine-tuning, users should:
⚠️ Validate outputs for sensitive domains
⚠️ Test with diverse dialogue samples
⚠️ Monitor for hallucination in summaries
Quick Start
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Model tree for gnokit/flan-t5-small-summary-peft
Base model
google/flan-t5-small