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t5-large-finetuned-xsum-cnn

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t5-large-summarization
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metadata
language:
  - en
license: mit
tags:
  - summarization
  - t5-large-summarization
  - pipeline:summarization
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
model-index:
  - name: sysresearch101/t5-large-finetuned-xsum-cnn
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: xsum
          type: xsum
          config: default
          split: test
        metrics:
          - type: rouge
            value: 36.7656
            name: ROUGE-1
            verified: true
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          - type: rouge
            value: 14.6898
            name: ROUGE-2
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          - type: rouge
            value: 30.0646
            name: ROUGE-L
            verified: true
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          - type: rouge
            value: 30.0563
            name: ROUGE-LSUM
            verified: true
            verifyToken: >-
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          - type: loss
            value: 1.6373405456542969
            name: loss
            verified: true
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          - type: gen_len
            value: 18.6054
            name: gen_len
            verified: true
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datasets:
  - abisee/cnn_dailymail
  - EdinburghNLP/xsum
base_model:
  - google-t5/t5-large

T5-Large Fine-tuned on the combined XSum + CNN/DailyMail Datasets

Task: Abstractive Summarization (English)
Base Model: google-t5/t5-large
License: MIT

Overview

This model is a T5-Large checkpoint fine-tuned jointly on XSum and CNN/DailyMail datasets. It produces concise, abstractive summaries and has been widely adopted as a baseline in summarization research.

Performance ~ On XSum test set

Metric Score
ROUGE-1 36.77
ROUGE-2 14.69
ROUGE-L 30.06
Loss 1.64
Avg. Length 18.6 tokens

Usage

Quick Start 

from transformers import pipeline

summarizer = pipeline("summarization", model="sysresearch101/t5-large-finetuned-xsum-cnn")

article = "Your article text here..."
summary = summarizer(article, max_length=80, min_length=20, do_sample=False)
print(summary[0]['summary_text'])

Advanced Usage 

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("sysresearch101/t5-large-finetuned-xsum-cnn")
model = AutoModelForSeq2SeqLM.from_pretrained("sysresearch101/t5-large-finetuned-xsum-cnn")

inputs = tokenizer("summarize: " + article, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(
    **inputs,
    max_length=80,
    min_length=20,
    num_beams=4,
    no_repeat_ngram_size=2,
    length_penalty=1.0,
    repetition_penalty=2.5,
    use_cache=True,
    early_stopping=True,
    do_sample = True,
    temperature = 0.8,
    top_k = 50,
    top_p = 0.95
)

summary = tokenizer.decode(outputs[0], skip_special_tokens=True)

Training Data

  • XSum: BBC articles with single-sentence summaries
  • CNN/DailyMail: News articles with multi-sentence summaries

Intended Use

  • Primary: Summarization.
  • Secondary: Educational demonstrations, reproducible baselines, Research benchmarking, academic studies on summarization

Limitations

  • Optimized for English news text; performance may vary on other domains
  • Tends to produce very concise summaries (18-20 tokens average)
  • No built-in fact-checking or content filtering

Citation 

@misc{stept2023_t5_large_xsum_cnn_summarization,
  author = {Shlomo Stept (sysresearch101)},
  title = {T5-Large Fine-tuned on XSum + CNN/DailyMail for Abstractive Summarization},
  year = {2023},
  publisher = {Hugging Face},
  url = {https://huggingface.co/sysresearch101/t5-large-finetuned-xsum-cnn}
}

Papers Using This Model

Contact

Created by Shlomo Stept (ORCID: 0009-0009-3185-589X) DARMIS AI