File size: 2,593 Bytes
0c12d11 |
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 |
---
base_model: silmi224/finetune-led-35000
tags:
- summarization
- generated_from_trainer
model-index:
- name: led-risalah_data_v9
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. -->
# led-risalah_data_v9
This model is a fine-tuned version of [silmi224/finetune-led-35000](https://huggingface.co/silmi224/finetune-led-35000) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6418
- Rouge1 Precision: 0.6262
- Rouge1 Recall: 0.3099
- Rouge1 Fmeasure: 0.4143
## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.2018 | 0.9714 | 17 | 1.4789 | 0.5782 | 0.2807 | 0.3761 |
| 1.0123 | 2.0 | 35 | 1.4305 | 0.5931 | 0.2892 | 0.3876 |
| 0.8845 | 2.9714 | 52 | 1.4693 | 0.6327 | 0.3088 | 0.4148 |
| 0.705 | 4.0 | 70 | 1.4903 | 0.6263 | 0.3052 | 0.4096 |
| 0.6323 | 4.9714 | 87 | 1.5086 | 0.6167 | 0.3052 | 0.4075 |
| 0.5926 | 6.0 | 105 | 1.5386 | 0.6238 | 0.3031 | 0.4072 |
| 0.5149 | 6.9714 | 122 | 1.5742 | 0.6308 | 0.3035 | 0.4096 |
| 0.4324 | 8.0 | 140 | 1.6112 | 0.6188 | 0.3083 | 0.411 |
| 0.3748 | 8.9714 | 157 | 1.6382 | 0.6262 | 0.3097 | 0.4138 |
| 0.4033 | 9.7143 | 170 | 1.6418 | 0.6262 | 0.3099 | 0.4143 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|