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---
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: prophetnet_summarization_pretrained
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.4982
---
<!-- 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. -->
# prophetnet_summarization_pretrained
This model is a fine-tuned version of [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3683
- Rouge1: 0.4982
- Rouge2: 0.2267
- Rougel: 0.2983
- Rougelsum: 0.2985
- Gen Len: 139.3831
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| No log | 1.0 | 124 | 2.5178 | 0.4894 | 0.2223 | 0.2903 | 0.2903 | 139.8105 |
| No log | 2.0 | 248 | 2.4170 | 0.4973 | 0.2279 | 0.2975 | 0.297 | 140.6492 |
| No log | 3.0 | 372 | 2.3895 | 0.4964 | 0.2282 | 0.2984 | 0.2981 | 138.5323 |
| No log | 4.0 | 496 | 2.3683 | 0.4982 | 0.2267 | 0.2983 | 0.2985 | 139.3831 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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