Summarization
Transformers
Safetensors
English
bart
text2text-generation
keyword-extraction
scientific-text
sft
Instructions to use YanJo199/scientific-summarizer-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YanJo199/scientific-summarizer-v1 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="YanJo199/scientific-summarizer-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("YanJo199/scientific-summarizer-v1") model = AutoModelForSeq2SeqLM.from_pretrained("YanJo199/scientific-summarizer-v1") - Notebooks
- Google Colab
- Kaggle
scientific-summarizer-v1
Starter model repository for scientific summarization experiments.
Status
Fine-tuned checkpoint uploaded: v0.2 (trained on sft_review_queue_500.jsonl, 1 epoch, batch size 2).
Pilot checkpoint from 10-row run is also available in repo history.
Base model plan
Recommended first baseline: facebook/bart-large-cnn
Dataset
YanJo199/scientific-papers-sft-v1- Start with
pilot/sft_review_queue_10.jsonlfor smoke tests - Scale to
sft_review_queue_500.jsonlafter validation
Training objective
Supervised fine-tuning for the summary task.
Evaluation
Latest evaluation (February 25, 2026):
- Validation file:
data/sft_valid_v6_summary.jsonl - Samples:
100(summary task) - ROUGE-1:
0.4309 - ROUGE-2:
0.3134 - ROUGE-L:
0.3586 - ROUGE-Lsum:
0.3584
Next release target
Run full validation (all rows) and publish a tuned v0.3 checkpoint with updated training settings.
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