EdinburghNLP/xsum
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How to use ryusangwon/bart-xsum with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ryusangwon/bart-xsum")
model = AutoModelForSeq2SeqLM.from_pretrained("ryusangwon/bart-xsum")This model is a fine-tuned version of facebook/bart-large on the xsum dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9566 | 0.08 | 500 | 1.7765 |
| 1.9288 | 0.16 | 1000 | 1.7549 |
| 1.8561 | 0.24 | 1500 | 1.7462 |
| 1.7802 | 0.31 | 2000 | 1.6921 |
| 1.8444 | 0.39 | 2500 | 1.6699 |
| 1.8145 | 0.47 | 3000 | 1.6525 |
| 1.7736 | 0.55 | 3500 | 1.6313 |
| 1.7259 | 0.63 | 4000 | 1.6234 |
| 1.7028 | 0.71 | 4500 | 1.6217 |
| 1.7235 | 0.78 | 5000 | 1.5750 |
| 1.6534 | 0.86 | 5500 | 1.5749 |
| 1.6392 | 0.94 | 6000 | 1.5537 |