kmfoda/booksum
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How to use pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15 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="pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15")
model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15", device_map="auto")This model is a fine-tuned version of pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13 on the kmfoda/booksum dataset.
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The following hyperparameters were used during training: