armanc/scientific_papers
Updated • 3.77k • 175
How to use juliosocher/bart-large-cnn-finetuned-scientific-articles 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="juliosocher/bart-large-cnn-finetuned-scientific-articles") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("juliosocher/bart-large-cnn-finetuned-scientific-articles")
model = AutoModelForSeq2SeqLM.from_pretrained("juliosocher/bart-large-cnn-finetuned-scientific-articles")This model is a fine-tuned version of facebook/bart-large-cnn on the scientific_papers dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 3.3695 | 1.0 | 56 | 2.8464 | 32.1056 | 10.3835 | 18.7541 | 29.2623 |
| 2.7639 | 2.0 | 112 | 2.6667 | 31.2657 | 10.758 | 18.9862 | 28.3279 |
| 2.5169 | 3.0 | 168 | 2.6219 | 33.226 | 11.4766 | 19.5923 | 30.0664 |
| 2.2985 | 4.0 | 224 | 2.6029 | 32.8562 | 11.5606 | 19.8616 | 29.7606 |
| 2.0851 | 5.0 | 280 | 2.6456 | 33.8477 | 11.8866 | 20.1038 | 30.5011 |
Base model
facebook/bart-large-cnn