# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sumedh/biomedical_text_summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("sumedh/biomedical_text_summarization")Quick Links
This model was created for text summarization for clinical text.
Check the index for evaluation scores on the ROUGE metric.
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Dataset used to train sumedh/biomedical_text_summarization
Evaluation results
- ROUGE-1self-reported39.409
- ROUGE-2self-reported12.812
- ROUGE-Lself-reported21.919
- ROUGE-LSUMself-reported35.243
- lossself-reported2.200
- gen_lenself-reported133.854
# 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="sumedh/biomedical_text_summarization")