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# BART_pretrained_on_billsum_finetuned_on_small_SCOTUS_extracted_dataset

This model is a fine-tuned version of [bheshaj/bart-large-billsum-epochs20](https://huggingface.co/bheshaj/bart-large-billsum-epochs20) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6264
- Rouge1: 0.1228
- Rouge2: 0.0388
- Rougel: 0.0995
- Rougelsum: 0.1001
- Gen Len: 19.1037

## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 0.98  | 10   | 4.5839          | 0.107  | 0.0259 | 0.0896 | 0.0896    | 20.0    |
| No log        | 1.98  | 20   | 4.2717          | 0.1329 | 0.0325 | 0.1086 | 0.1086    | 19.7378 |
| No log        | 2.98  | 30   | 4.1413          | 0.1351 | 0.0333 | 0.1102 | 0.1105    | 19.7134 |
| No log        | 3.98  | 40   | 4.0582          | 0.1406 | 0.0406 | 0.1151 | 0.115     | 19.5976 |
| No log        | 4.98  | 50   | 3.9916          | 0.1418 | 0.0407 | 0.1144 | 0.1144    | 19.6829 |
| No log        | 5.98  | 60   | 3.9373          | 0.1244 | 0.0356 | 0.1    | 0.1001    | 18.7134 |
| No log        | 6.98  | 70   | 3.8867          | 0.1184 | 0.0322 | 0.0967 | 0.0972    | 18.4146 |
| No log        | 7.98  | 80   | 3.8363          | 0.122  | 0.0325 | 0.0979 | 0.0981    | 18.4451 |
| No log        | 8.98  | 90   | 3.7884          | 0.1183 | 0.0324 | 0.096  | 0.0967    | 18.8902 |
| No log        | 9.98  | 100  | 3.7625          | 0.1153 | 0.0318 | 0.0939 | 0.094     | 18.8049 |
| No log        | 10.98 | 110  | 3.7269          | 0.1063 | 0.0296 | 0.0882 | 0.0882    | 18.2378 |
| No log        | 11.98 | 120  | 3.7023          | 0.1194 | 0.0314 | 0.0977 | 0.0981    | 19.1646 |
| No log        | 12.98 | 130  | 3.6839          | 0.1275 | 0.0371 | 0.1053 | 0.105     | 19.5488 |
| No log        | 13.98 | 140  | 3.6693          | 0.1237 | 0.0354 | 0.0998 | 0.1004    | 19.2561 |
| No log        | 14.98 | 150  | 3.6578          | 0.1246 | 0.0371 | 0.1022 | 0.1023    | 19.1951 |
| No log        | 15.98 | 160  | 3.6447          | 0.1299 | 0.0398 | 0.1062 | 0.1062    | 19.4329 |
| No log        | 16.98 | 170  | 3.6366          | 0.1247 | 0.0355 | 0.1004 | 0.1007    | 19.1829 |
| No log        | 17.98 | 180  | 3.6319          | 0.123  | 0.038  | 0.0994 | 0.1       | 19.0122 |
| No log        | 18.98 | 190  | 3.6279          | 0.1225 | 0.0386 | 0.0994 | 0.1002    | 19.122  |
| No log        | 19.98 | 200  | 3.6264          | 0.1228 | 0.0388 | 0.0995 | 0.1001    | 19.1037 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "BART_pretrained_on_billsum_finetuned_on_small_SCOTUS_extracted_dataset", "results": []}]}
transformers pytorch bart text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

BART\_pretrained\_on\_billsum\_finetuned\_on\_small\_SCOTUS\_extracted\_dataset
===============================================================================


This model is a fine-tuned version of bheshaj/bart-large-billsum-epochs20 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 3.6264
* Rouge1: 0.1228
* Rouge2: 0.0388
* Rougel: 0.0995
* Rougelsum: 0.1001
* Gen Len: 19.1037


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
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #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
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #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
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2