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---
license: apache-2.0
tags:
- generated_from_trainer
datasets: din0s/asqa
metrics:
- rouge
base_model: google/t5-small-ssm-nq
model-index:
- name: t5-small-asqa-ob
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-asqa-ob
This model is a fine-tuned version of [google/t5-small-ssm-nq](https://huggingface.co/google/t5-small-ssm-nq) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset without context (closed book).
It achieves the following results on the evaluation set:
- Loss: 2.8099
- Rouge1: 0.1493
- Rouge2: 0.0837
- Rougel: 0.1272
- Rougelsum: 0.1270
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.8208 | 1.0 | 710 | 2.7856 | 0.1267 | 0.0644 | 0.1086 | 0.1084 |
| 3.0532 | 2.0 | 1420 | 2.6247 | 0.1321 | 0.0721 | 0.1145 | 0.1144 |
| 2.5656 | 3.0 | 2130 | 2.5062 | 0.1399 | 0.0773 | 0.1213 | 0.1213 |
| 2.3806 | 4.0 | 2840 | 2.5004 | 0.1431 | 0.0805 | 0.1243 | 0.1241 |
| 2.157 | 5.0 | 3550 | 2.5008 | 0.1455 | 0.0808 | 0.1255 | 0.1254 |
| 2.0458 | 6.0 | 4260 | 2.5313 | 0.1510 | 0.0846 | 0.1303 | 0.1301 |
| 1.914 | 7.0 | 4970 | 2.5298 | 0.1585 | 0.0885 | 0.1361 | 0.1358 |
| 1.7479 | 8.0 | 5680 | 2.5832 | 0.1508 | 0.0844 | 0.1292 | 0.1291 |
| 1.6875 | 9.0 | 6390 | 2.5928 | 0.1493 | 0.0834 | 0.1281 | 0.1279 |
| 1.574 | 10.0 | 7100 | 2.6364 | 0.1591 | 0.0885 | 0.1364 | 0.1363 |
| 1.4554 | 11.0 | 7810 | 2.6978 | 0.1513 | 0.0849 | 0.1295 | 0.1295 |
| 1.3909 | 12.0 | 8520 | 2.8099 | 0.1493 | 0.0837 | 0.1272 | 0.1270 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.5.1
- Tokenizers 0.12.1
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