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---
base_model: UBC-NLP/AraT5v2-base-1024
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: results_arat5_wiki
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. -->
# results_arat5_wiki
This model is a fine-tuned version of [UBC-NLP/AraT5v2-base-1024](https://huggingface.co./UBC-NLP/AraT5v2-base-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4401
- Rouge1: 0.0905
- Rouge2: 0.0
- Rougel: 0.0915
- Rougelsum: 0.0912
- Gen Len: 19.0
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 7.7921 | 0.4757 | 500 | 6.2870 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 5.9839 | 0.9515 | 1000 | 5.5934 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 5.4311 | 1.4272 | 1500 | 5.0896 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 5.1245 | 1.9029 | 2000 | 4.7004 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 4.7258 | 2.3787 | 2500 | 4.3347 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 4.5072 | 2.8544 | 3000 | 4.0503 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 4.2388 | 3.3302 | 3500 | 3.8321 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 4.0817 | 3.8059 | 4000 | 3.6509 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.8843 | 4.2816 | 4500 | 3.4451 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.7958 | 4.7574 | 5000 | 3.3071 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.6397 | 5.2331 | 5500 | 3.1619 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.5658 | 5.7088 | 6000 | 3.0068 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.4171 | 6.1846 | 6500 | 2.9459 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.2697 | 6.6603 | 7000 | 2.8074 | 0.0842 | 0.0 | 0.0849 | 0.0844 | 19.0 |
| 3.3168 | 7.1361 | 7500 | 2.7153 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.1594 | 7.6118 | 8000 | 2.6676 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.0928 | 8.0875 | 8500 | 2.5849 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.0318 | 8.5633 | 9000 | 2.5152 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
| 3.0392 | 9.0390 | 9500 | 2.4849 | 0.0902 | 0.0 | 0.0911 | 0.0908 | 19.0 |
| 2.9917 | 9.5147 | 10000 | 2.4569 | 0.0768 | 0.0001 | 0.0774 | 0.0768 | 19.0 |
| 2.9281 | 9.9905 | 10500 | 2.4401 | 0.0905 | 0.0 | 0.0915 | 0.0912 | 19.0 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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