Instructions to use Lin0He/text-summary-gpt2-short with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lin0He/text-summary-gpt2-short 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="Lin0He/text-summary-gpt2-short")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Lin0He/text-summary-gpt2-short") model = AutoModelForCausalLM.from_pretrained("Lin0He/text-summary-gpt2-short", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update pipeline.py
Browse files- pipeline.py +2 -2
pipeline.py
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@@ -79,7 +79,7 @@ class PreTrainedPipeline():
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def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
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# process input
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# process input text
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prediction = model_infer( self.model, self.tokenizer,
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return {"text": prediction}
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def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
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# process input
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inputs = data.pop("inputs", data)
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# process input text
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prediction = model_infer( self.model, self.tokenizer, inputs['text']+"TL;DR")
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return {"text": prediction}
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