din0s/asqa
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How to use irenepap/t5-base-asqa-ob with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("irenepap/t5-base-asqa-ob")
model = AutoModelForSeq2SeqLM.from_pretrained("irenepap/t5-base-asqa-ob")This model is a fine-tuned version of t5-base on the ASQA dataset with context (open book). 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 | Rougelsum |
|---|---|---|---|---|
| 2.5404 | 1.0 | 710 | 1.8160 | 13.0967 |
| 2.0048 | 2.0 | 1420 | 1.7752 | 13.2823 |
| 1.9116 | 3.0 | 2130 | 1.7574 | 13.3068 |
| 1.8722 | 4.0 | 2840 | 1.7469 | 13.3896 |
| 1.8298 | 5.0 | 3550 | 1.7395 | 13.4231 |
| 1.8397 | 6.0 | 4260 | 1.7347 | 13.5553 |
| 1.7575 | 7.0 | 4970 | 1.7303 | 13.5613 |
| 1.7433 | 8.0 | 5680 | 1.7266 | 13.5253 |
| 1.7502 | 9.0 | 6390 | 1.7254 | 13.5391 |
| 1.731 | 10.0 | 7100 | 1.7233 | 13.4958 |
| 1.6788 | 11.0 | 7810 | 1.7250 | 13.5977 |
| 1.6793 | 12.0 | 8520 | 1.7243 | 13.5956 |
| 1.6531 | 13.0 | 9230 | 1.7255 | 13.6186 |
| 1.683 | 14.0 | 9940 | 1.7259 | 13.6567 |
| 1.6348 | 15.0 | 10650 | 1.7256 | 13.6463 |