File size: 2,712 Bytes
9886da0 949723d 9886da0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
---
tags:
- summarization
- Arat5-base
- abstractive summarization
- ar
- xlsum
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: AraT5-base-title-generation-finetune-ar-xlsum
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. -->
# AraT5-base-title-generation-finetune-ar-xlsum
This model is a fine-tuned version of [UBC-NLP/AraT5-base-title-generation](https://huggingface.co./UBC-NLP/AraT5-base-title-generation) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2837
- Rouge-1: 32.46
- Rouge-2: 15.15
- Rouge-l: 28.38
- Gen Len: 18.48
- Bertscore: 74.24
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 5.815 | 1.0 | 293 | 4.7437 | 27.05 | 10.49 | 23.56 | 18.03 | 72.56 |
| 5.0818 | 2.0 | 586 | 4.5004 | 28.92 | 11.97 | 25.09 | 18.61 | 73.08 |
| 4.7855 | 3.0 | 879 | 4.3910 | 29.66 | 12.57 | 25.79 | 18.58 | 73.3 |
| 4.588 | 4.0 | 1172 | 4.3469 | 30.22 | 13.05 | 26.36 | 18.59 | 73.61 |
| 4.4388 | 5.0 | 1465 | 4.3226 | 30.88 | 13.81 | 27.01 | 18.65 | 73.78 |
| 4.3162 | 6.0 | 1758 | 4.2990 | 30.9 | 13.6 | 26.92 | 18.68 | 73.78 |
| 4.2178 | 7.0 | 2051 | 4.2869 | 31.35 | 14.01 | 27.41 | 18.57 | 73.96 |
| 4.1387 | 8.0 | 2344 | 4.2794 | 31.28 | 13.98 | 27.34 | 18.6 | 73.87 |
| 4.0787 | 9.0 | 2637 | 4.2806 | 31.45 | 14.17 | 27.46 | 18.66 | 73.97 |
| 4.0371 | 10.0 | 2930 | 4.2837 | 31.55 | 14.19 | 27.52 | 18.65 | 74.0 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|