EdinburghNLP/xsum
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How to use vineetsharma/xsum-t5-small with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("vineetsharma/xsum-t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("vineetsharma/xsum-t5-small", device_map="auto")This model is a fine-tuned version of t5-small 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.9158 | 0.16 | 2000 | 2.5725 | 26.6629 | 6.6436 | 20.8032 | 20.7995 | 18.7886 |
| 2.7868 | 0.31 | 4000 | 2.5286 | 27.3979 | 7.1077 | 21.4451 | 21.4487 | 18.8045 |
| 2.756 | 0.47 | 6000 | 2.5058 | 27.8049 | 7.4383 | 21.8465 | 21.8479 | 18.8179 |
| 2.7388 | 0.63 | 8000 | 2.4903 | 28.1541 | 7.6412 | 22.1566 | 22.1572 | 18.8265 |
| 2.7208 | 0.78 | 10000 | 2.4819 | 28.2559 | 7.6877 | 22.2086 | 22.2118 | 18.8268 |
| 2.7175 | 0.94 | 12000 | 2.4789 | 28.3309 | 7.7568 | 22.2948 | 22.2942 | 18.824 |
Base model
google-t5/t5-small