Summarization
Transformers
PyTorch
English
pegasus
text2text-generation
seq2seq
Eval Results (legacy)
Instructions to use tuner007/pegasus_summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuner007/pegasus_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="tuner007/pegasus_summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tuner007/pegasus_summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("tuner007/pegasus_summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -26,7 +26,7 @@ model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_dev
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def get_response(input_text):
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batch = tokenizer([input_text],truncation=True,padding='longest',max_length=1024, return_tensors="pt").to(torch_device)
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gen_out = model.generate(**batch,max_length=128,num_beams=
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output_text = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
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return output_text
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```
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def get_response(input_text):
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batch = tokenizer([input_text],truncation=True,padding='longest',max_length=1024, return_tensors="pt").to(torch_device)
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gen_out = model.generate(**batch,max_length=128,num_beams=5, num_return_sequences=1, temperature=1.5)
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output_text = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
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return output_text
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```
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