Instructions to use jaesani/large_eng_summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaesani/large_eng_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="jaesani/large_eng_summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jaesani/large_eng_summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("jaesani/large_eng_summarizer") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -27,7 +27,7 @@ Transformer-based architecture, likely BERT or GPT derived.
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Fine-tuned for English text summarization tasks.
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Training Data
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Trained on a
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The model is fine-tuned to understand and summarize general content, suitable for a wide range of domains.
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Performance
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Fine-tuned for English text summarization tasks.
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Training Data
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Trained on a npc-engine/light-batch-summarize-dialogue.
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The model is fine-tuned to understand and summarize general content, suitable for a wide range of domains.
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Performance
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