|
--- |
|
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 |
|
|
