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---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: LLM_Teached_Bart
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_Bart
This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3237
- Rouge1: 0.4756
- Rouge2: 0.203
- Rougel: 0.3677
- Rougelsum: 0.3678
- Gen Len: 41.4318
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.6644 | 1.0 | 1250 | 1.6972 | 0.4687 | 0.2036 | 0.3619 | 0.362 | 43.4245 |
| 1.3035 | 2.0 | 2500 | 1.6463 | 0.4762 | 0.2104 | 0.3746 | 0.3747 | 39.5091 |
| 1.0206 | 3.0 | 3750 | 1.7278 | 0.476 | 0.2117 | 0.3743 | 0.3746 | 38.9555 |
| 0.8224 | 4.0 | 5000 | 1.8642 | 0.477 | 0.2094 | 0.3724 | 0.3723 | 40.5182 |
| 0.654 | 5.0 | 6250 | 1.9480 | 0.4757 | 0.2083 | 0.3717 | 0.3716 | 39.8736 |
| 0.5302 | 6.0 | 7500 | 2.1332 | 0.4773 | 0.2062 | 0.37 | 0.3699 | 40.8309 |
| 0.4364 | 7.0 | 8750 | 2.2474 | 0.4749 | 0.2008 | 0.3648 | 0.3648 | 42.0391 |
| 0.3782 | 8.0 | 10000 | 2.3237 | 0.4756 | 0.203 | 0.3677 | 0.3678 | 41.4318 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
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