Summarization
PEFT
Safetensors
Transformers
English
lora
abstractive-summarization
Generated from Trainer
Instructions to use lityops/Abstractive-Style-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lityops/Abstractive-Style-Summarizer with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base") model = PeftModel.from_pretrained(base_model, "lityops/Abstractive-Style-Summarizer") - Transformers
How to use lityops/Abstractive-Style-Summarizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="lityops/Abstractive-Style-Summarizer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lityops/Abstractive-Style-Summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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base_model: google/flan-t5-base
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library_name: peft
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tags:
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model-index:
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- name: Abstractive Style Summarizer
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results: []
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# Abstractive Style Summarizer
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language: en
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base_model: google/flan-t5-base
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library_name: peft
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tags:
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model-index:
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- name: Abstractive Style Summarizer
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results: []
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datasets:
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- xsum
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- cnn_dailymail
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- multi_news
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license: mit
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# Abstractive Style Summarizer
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