Summarization
Transformers
PyTorch
TensorFlow
Vietnamese
t5
text2text-generation
text-generation-inference
Instructions to use polieste/fastAbs_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use polieste/fastAbs_large 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="polieste/fastAbs_large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("polieste/fastAbs_large") model = AutoModelForSeq2SeqLM.from_pretrained("polieste/fastAbs_large") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -28,7 +28,7 @@ encoding = tokenizer(text, return_tensors="pt")
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input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
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outputs = model.generate(
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input_ids=input_ids, attention_mask=attention_masks,
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max_length=
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early_stopping=True
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)
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for output in outputs:
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input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
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outputs = model.generate(
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input_ids=input_ids, attention_mask=attention_masks,
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max_length=512,
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early_stopping=True
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)
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for output in outputs:
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