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# distilbart-cnn-6-6-finetuned-summscreen-10-epochs

This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the SummScreen dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4962
- Rouge1: 26.3499
- Rouge2: 7.3999
- Rougel: 18.6087
- Rougelsum: 23.17
- Gen Len: 49.8609

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.1229        | 1.0   | 3673  | 3.1271          | 26.6959 | 7.4401 | 18.8303 | 23.7132   | 49.9763 |
| 2.8872        | 2.0   | 7346  | 3.0482          | 26.6447 | 7.5599 | 18.5921 | 23.2786   | 49.8195 |
| 2.5733        | 3.0   | 11019 | 3.0292          | 27.425  | 7.9963 | 19.3544 | 24.1281   | 49.8757 |
| 2.3886        | 4.0   | 14692 | 3.0625          | 27.1291 | 7.5541 | 18.9375 | 23.8729   | 49.8905 |
| 2.215         | 5.0   | 18365 | 3.1118          | 27.1773 | 7.551  | 19.0524 | 24.1015   | 49.9142 |
| 2.0377        | 6.0   | 22038 | 3.2086          | 27.2237 | 7.8821 | 19.2136 | 24.0477   | 49.784  |
| 1.9358        | 7.0   | 25711 | 3.3405          | 26.7555 | 7.6628 | 18.8609 | 23.5264   | 49.8343 |
| 1.8292        | 8.0   | 29384 | 3.4124          | 26.7741 | 7.4529 | 18.9276 | 23.5827   | 49.8757 |
| 1.7702        | 9.0   | 33057 | 3.4457          | 26.6281 | 7.4415 | 18.7932 | 23.4608   | 49.8639 |
| 1.7443        | 10.0  | 36730 | 3.4962          | 26.3499 | 7.3999 | 18.6087 | 23.17     | 49.8609 |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "widget": [{"text": "DOCTOR: We have to get to Triton, destroy all the Morpheus machines. End this. This is how we get home?\nCLARA: I've never been so pleased to see\n(Sandmen move between them and the Tardis. More approach from behind.)\nDOCTOR: Nagata!\n(He turns Nagata round and uses her helmet schematic projector on a copper sheet.)\nCLARA: Doctor, quickly!\n(The Doctor taps the projection of the anti-grav shield generators.)\nNAGATA: What did you just do?\nDOCTOR: Self-destructed the grav-shields.\nNAGATA: What?\n(The spacestation tilts. The women cry out.)\nDOCTOR: It's working!\n(Clara uses her key to open the Tardis door. The Sandmen are stationary, falling to pieces.)\nDOCTOR: Neptune's gravity is pulling them apart, bit by bit! It doesn't make sense. None of this makes any sense.\n(They run inside the Tardis and it dematerialises.)"}, {"text": "Michael: I'm starting my own paper company. \nAndy: No way!? \nMichael: Yeah. \nAndy: In this climate? \nMichael: Yeah. In all climates. It's going to be worldwide. And I'm looking for some talented salesmen to join me. That's where you come in. \nAndy: Ehh... [in accent] well it's a very intriguing concept, isn't it? Um... hmmm..\n[makes weird noises to stall, Dwight enters] Michael is starting his own paper company. What do you think about that? \nDwight: Your own paper company. \nMichael: Can you believe it? Well, we'll see, we'll see. It's just a, just a nugget of an idea right now so \nDwight: Right... \nMichael: Potential, lots of potential. yes. \nDwight: What a courageous venture. \nMichael: It's... it's very courageous, very exciting. Um... \nDwight: Location is hard for me, with the farm and the responsibilities... \nMichael: That's what I was thinking, with the farm, so... You getting to wherever I'm gonna put my thing. \nDwight: Okay. So yeah. \nMichael: So think about it. Lets put a pin in it for now. \nDwight: You know, I would love to put a pin in that."}, {"text": "Penny: This is great. What\u2019s the occasion? \nLeonard: No occasion. You know, things have been a little weird between us, so I wanted to throw together a fun night just for you. \nPenny: That is so sweet. \nLeonard: I got all your favourites. Beer, wings, sliders. We can watch the football game. I even painted my stomach. \nPenny: Go Sports? \nLeonard: Well, in case you were in the mood for baseball, I didn\u2019t want to look ridiculous. \nPenny: This is awesome. I love it! \nLeonard: Good, I\u2019m glad. \nPenny: Gosh, I worked my ass off today. This is exactly what I needed. \nLeonard: Great. Just relax and enjoy. Tonight is all about you. \nPenny: Ah, thank you! \nLeonard: So, where exactly are we in this relationship? \nPenny: Oh, come on. I just told you I had a hard day. \nLeonard: You\u2019re right, I\u2019m sorry. Let\u2019s watch the game. \nPenny: Great."}], "model-index": [{"name": "distilbart-cnn-6-6-finetuned-summscreen-10-epochs", "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 

distilbart-cnn-6-6-finetuned-summscreen-10-epochs
=================================================


This model is a fine-tuned version of sshleifer/distilbart-cnn-6-6 on the SummScreen dataset.
It achieves the following results on the evaluation set:


* Loss: 3.4962
* Rouge1: 26.3499
* Rouge2: 7.3999
* Rougel: 18.6087
* Rougelsum: 23.17
* Gen Len: 49.8609


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: 1e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1
* Datasets 2.9.0
* 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: 1e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1
* Datasets 2.9.0
* 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: 1e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2