# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm")
model = AutoModelForSeq2SeqLM.from_pretrained("yuvraj/xSumm")Quick Links
Model description
BartForConditionalGenerationModel for extreme summarization- creates a one line abstractive summary of a given article
How to use
PyTorch model available
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("yuvraj/xSumm")
model = AutoModelWithLMHead.from_pretrained("yuvraj/xSumm")
xsumm = pipeline('summarization', model=model, tokenizer=tokenizer)
xsumm("<text to be summarized>")
## Limitations and bias
Trained on a small fraction of the xsumm training dataset
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# 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")