Instructions to use ayuliasw/summarization-t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayuliasw/summarization-t5 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="ayuliasw/summarization-t5")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ayuliasw/summarization-t5") model = AutoModelForSeq2SeqLM.from_pretrained("ayuliasw/summarization-t5") - Notebooks
- Google Colab
- Kaggle
Model Card: Fine-Tuned T5 Base for Text Summarization
Model Description
The Fine-Tuned T5 Base is a variant of the T5 transformer model, designed for the task of text summarization.
Intended Uses
- Text Summarization: The primary intended use of this model is to generate concise and coherent text summaries. It is well-suited for applications that involve summarizing lengthy documents, news articles, and textual content.
Training Stats
- Evaluation Loss: 0.07698195427656174
- Evaluation Rouge Score: 0.8044771688726501 (rouge-1), 0.720724349217967 (rouge-2), 0.785232990839545 (rouge-l)
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