Instructions to use yuvraj/xSumm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yuvraj/xSumm 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="yuvraj/xSumm")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm") model = AutoModelForSeq2SeqLM.from_pretrained("yuvraj/xSumm") - Notebooks
- Google Colab
- Kaggle
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Model description
β BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article β
How to use
β PyTorch model available β
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
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tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm")
model = AutoModelWithLMHead.from_pretrained("yuvraj/xSumm")
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xsumm = pipeline('summarization', model=model, tokenizer=tokenizer)
xsumm("<text to be summarized>")
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## Limitations and bias
Trained on a small fraction of the xsumm training dataset
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