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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- rouge
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model-index:
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- name: t5-small-asqa-ob
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# t5-small-asqa-ob
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This model is a fine-tuned version of [google/t5-small-ssm-nq](https://huggingface.co/google/t5-small-ssm-nq) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.9381
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- Rouge1: 0.1633
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- Rouge2: 0.0907
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- Rougel: 0.1394
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- Rougelsum: 0.1393
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 50
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
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| 3.8212 | 1.0 | 710 | 2.7920 | 0.1248 | 0.0624 | 0.1064 | 0.1063 |
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| 3.0559 | 2.0 | 1420 | 2.5937 | 0.1319 | 0.0715 | 0.1139 | 0.1138 |
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| 2.568 | 3.0 | 2130 | 2.4971 | 0.1398 | 0.0754 | 0.1206 | 0.1204 |
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| 2.384 | 4.0 | 2840 | 2.5024 | 0.1473 | 0.0817 | 0.1273 | 0.1271 |
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| 2.1599 | 5.0 | 3550 | 2.4947 | 0.1498 | 0.0824 | 0.1288 | 0.1287 |
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| 2.0444 | 6.0 | 4260 | 2.5305 | 0.1502 | 0.0837 | 0.1291 | 0.1290 |
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| 1.9219 | 7.0 | 4970 | 2.5486 | 0.1599 | 0.0890 | 0.1376 | 0.1373 |
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| 1.7532 | 8.0 | 5680 | 2.5772 | 0.1647 | 0.0914 | 0.1413 | 0.1411 |
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| 1.6895 | 9.0 | 6390 | 2.6346 | 0.1630 | 0.0911 | 0.1397 | 0.1395 |
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| 1.5751 | 10.0 | 7100 | 2.6650 | 0.1700 | 0.0944 | 0.1450 | 0.1449 |
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| 1.4616 | 11.0 | 7810 | 2.6705 | 0.1571 | 0.0874 | 0.1348 | 0.1346 |
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| 1.3923 | 12.0 | 8520 | 2.7767 | 0.1695 | 0.0951 | 0.1453 | 0.1450 |
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| 1.3043 | 13.0 | 9230 | 2.8091 | 0.1704 | 0.0943 | 0.1460 | 0.1457 |
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| 1.2868 | 14.0 | 9940 | 2.8390 | 0.1553 | 0.0854 | 0.1327 | 0.1324 |
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| 1.176 | 15.0 | 10650 | 2.9381 | 0.1633 | 0.0907 | 0.1394 | 0.1393 |
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### Framework versions
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- Transformers 4.23.0.dev0
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- Pytorch 1.12.1+cu102
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- Datasets 2.5.1
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- Tokenizers 0.12.1
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