
<!-- 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

{"tags": ["generated_from_trainer"], "datasets": ["billsum"], "metrics": ["rouge"], "model-index": [{"name": "prophetnet_summarization_pretrained", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "billsum", "type": "billsum", "config": "default", "split": "ca_test", "args": "default"}, "metrics": [{"type": "rouge", "value": 0.4982, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard prophetnet text2text-generation generated_from_trainer dataset:billsum model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #prophetnet #text2text-generation #generated_from_trainer #dataset-billsum #model-index #autotrain_compatible #endpoints_compatible #region-us 

prophetnet\_summarization\_pretrained
=====================================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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 ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #prophetnet #text2text-generation #generated_from_trainer #dataset-billsum #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3