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---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: LLM_Teached_Pegasus_FS
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. -->
# LLM_Teached_Pegasus_FS
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6167
- Rouge1: 0.4649
- Rouge2: 0.2096
- Rougel: 0.3686
- Rougelsum: 0.3688
- Gen Len: 30.6191
- Precision: 0.9102
- Recall: 0.9083
- F1: 0.9091
## 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: 2e-05
- train_batch_size: 24
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| No log | 1.0 | 208 | 1.8075 | 0.411 | 0.1689 | 0.3152 | 0.3155 | 29.9091 | 0.901 | 0.897 | 0.8988 |
| No log | 2.0 | 417 | 1.7312 | 0.4379 | 0.1893 | 0.3442 | 0.3446 | 29.9073 | 0.9059 | 0.9024 | 0.904 |
| 2.0112 | 3.0 | 625 | 1.6987 | 0.4475 | 0.1978 | 0.352 | 0.3525 | 30.0173 | 0.9075 | 0.9039 | 0.9055 |
| 2.0112 | 4.0 | 834 | 1.6768 | 0.4514 | 0.1981 | 0.357 | 0.3573 | 30.0618 | 0.9082 | 0.9047 | 0.9063 |
| 1.7647 | 5.0 | 1042 | 1.6617 | 0.4537 | 0.2003 | 0.3592 | 0.3595 | 30.3264 | 0.9084 | 0.9055 | 0.9068 |
| 1.7647 | 6.0 | 1251 | 1.6502 | 0.4554 | 0.2021 | 0.3607 | 0.361 | 30.0827 | 0.9089 | 0.9057 | 0.9072 |
| 1.7647 | 7.0 | 1459 | 1.6416 | 0.4592 | 0.2052 | 0.3639 | 0.3641 | 30.0218 | 0.9099 | 0.9064 | 0.908 |
| 1.6948 | 8.0 | 1668 | 1.6360 | 0.4612 | 0.2054 | 0.3649 | 0.365 | 30.7827 | 0.909 | 0.9074 | 0.9081 |
| 1.6948 | 9.0 | 1876 | 1.6302 | 0.4621 | 0.2062 | 0.3645 | 0.3647 | 30.6291 | 0.9095 | 0.9074 | 0.9083 |
| 1.6501 | 10.0 | 2085 | 1.6265 | 0.4606 | 0.2051 | 0.3651 | 0.3655 | 30.4818 | 0.9095 | 0.9073 | 0.9083 |
| 1.6501 | 11.0 | 2293 | 1.6230 | 0.4625 | 0.2073 | 0.3658 | 0.366 | 30.8064 | 0.9097 | 0.908 | 0.9087 |
| 1.6222 | 12.0 | 2502 | 1.6205 | 0.4644 | 0.2082 | 0.3674 | 0.3679 | 30.5527 | 0.9103 | 0.9081 | 0.909 |
| 1.6222 | 13.0 | 2710 | 1.6188 | 0.4648 | 0.2087 | 0.3681 | 0.3683 | 30.8055 | 0.9101 | 0.9083 | 0.909 |
| 1.6222 | 14.0 | 2919 | 1.6172 | 0.4654 | 0.2097 | 0.3685 | 0.3689 | 30.6709 | 0.9104 | 0.9084 | 0.9093 |
| 1.6048 | 15.0 | 3127 | 1.6169 | 0.465 | 0.21 | 0.3693 | 0.3697 | 30.6309 | 0.9104 | 0.9084 | 0.9093 |
| 1.6048 | 15.96 | 3328 | 1.6167 | 0.4649 | 0.2096 | 0.3686 | 0.3688 | 30.6191 | 0.9102 | 0.9083 | 0.9091 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
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