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# bart-cnn-pubmed-arxiv-pubmed-v3-e100

This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1806
- Rouge1: 59.4159
- Rouge2: 48.867
- Rougel: 51.9013
- Rougelsum: 58.3382
- Gen Len: 142.0

## 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: 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.2541        | 1.0   | 795   | 0.9350          | 52.5594 | 32.6314 | 35.2302 | 50.1767   | 142.0    |
| 0.7018        | 2.0   | 1590  | 0.8022          | 53.4804 | 35.4649 | 37.1673 | 51.2428   | 142.0    |
| 0.5266        | 3.0   | 2385  | 0.7752          | 52.9462 | 34.3697 | 36.611  | 50.6922   | 142.0    |
| 0.3475        | 4.0   | 3180  | 0.7771          | 53.4605 | 35.4738 | 38.5714 | 51.3798   | 142.0    |
| 0.2691        | 5.0   | 3975  | 0.7424          | 54.1132 | 35.7289 | 39.2653 | 51.6822   | 141.4259 |
| 0.182         | 6.0   | 4770  | 0.8037          | 53.7969 | 35.7324 | 38.4764 | 51.4929   | 141.7778 |
| 0.1446        | 7.0   | 5565  | 0.7686          | 55.0274 | 38.7813 | 42.6251 | 52.9847   | 142.0    |
| 0.1191        | 8.0   | 6360  | 0.7807          | 55.4651 | 38.6537 | 41.2746 | 53.578    | 141.8704 |
| 0.0976        | 9.0   | 7155  | 0.8045          | 55.2843 | 40.2358 | 42.8464 | 54.0957   | 142.0    |
| 0.0882        | 10.0  | 7950  | 0.8533          | 56.8288 | 41.6714 | 44.3961 | 54.9406   | 142.0    |
| 0.0721        | 11.0  | 8745  | 0.8962          | 55.3187 | 40.1599 | 43.2103 | 54.1964   | 142.0    |
| 0.0597        | 12.0  | 9540  | 0.8653          | 55.5706 | 40.2321 | 44.0075 | 53.9883   | 142.0    |
| 0.054         | 13.0  | 10335 | 0.8566          | 55.6622 | 40.0252 | 42.6907 | 54.0548   | 142.0    |
| 0.0476        | 14.0  | 11130 | 0.8900          | 57.5046 | 43.6309 | 46.449  | 55.9909   | 142.0    |
| 0.0432        | 15.0  | 11925 | 0.9149          | 55.604  | 39.9591 | 43.1729 | 54.3703   | 142.0    |
| 0.0403        | 16.0  | 12720 | 0.9258          | 55.1275 | 39.6566 | 42.3852 | 53.7656   | 142.0    |
| 0.0351        | 17.0  | 13515 | 0.9184          | 58.2352 | 44.6109 | 47.3863 | 56.9529   | 142.0    |
| 0.032         | 18.0  | 14310 | 0.9275          | 55.9687 | 41.2482 | 44.0076 | 54.0707   | 142.0    |
| 0.0313        | 19.0  | 15105 | 0.9635          | 56.3574 | 41.2113 | 44.8358 | 54.6279   | 142.0    |
| 0.0258        | 20.0  | 15900 | 0.9478          | 57.8445 | 44.297  | 46.8836 | 56.2003   | 142.0    |
| 0.0277        | 21.0  | 16695 | 0.9363          | 58.4823 | 46.0943 | 48.7817 | 57.5883   | 141.6667 |
| 0.0219        | 22.0  | 17490 | 0.9705          | 57.6022 | 43.9147 | 47.3054 | 56.3866   | 142.0    |
| 0.0231        | 23.0  | 18285 | 0.9857          | 56.5809 | 42.9124 | 46.789  | 55.3897   | 142.0    |
| 0.021         | 24.0  | 19080 | 1.0155          | 56.9745 | 43.8859 | 46.6109 | 55.708    | 142.0    |
| 0.02          | 25.0  | 19875 | 1.0095          | 57.9702 | 45.1809 | 48.2856 | 56.6941   | 142.0    |
| 0.0175        | 26.0  | 20670 | 0.9634          | 57.7023 | 45.1577 | 48.2398 | 56.5282   | 142.0    |
| 0.0161        | 27.0  | 21465 | 1.0197          | 58.739  | 46.3307 | 49.2328 | 57.5778   | 142.0    |
| 0.0186        | 28.0  | 22260 | 0.9790          | 56.1661 | 42.9731 | 45.8654 | 54.4365   | 142.0    |
| 0.0145        | 29.0  | 23055 | 0.9883          | 55.8554 | 41.7405 | 45.177  | 54.478    | 142.0    |
| 0.013         | 30.0  | 23850 | 0.9977          | 55.5831 | 41.2429 | 44.8063 | 53.886    | 142.0    |
| 0.0131        | 31.0  | 24645 | 0.9765          | 57.4478 | 44.8905 | 48.1376 | 56.102    | 141.463  |
| 0.0118        | 32.0  | 25440 | 1.0000          | 58.4282 | 46.6557 | 49.4122 | 57.1979   | 142.0    |
| 0.0117        | 33.0  | 26235 | 0.9924          | 57.1995 | 44.4177 | 47.6248 | 56.0251   | 141.2407 |
| 0.011         | 34.0  | 27030 | 1.0698          | 57.8918 | 45.925  | 49.0505 | 56.9352   | 142.0    |
| 0.0093        | 35.0  | 27825 | 1.0297          | 57.7003 | 45.4556 | 47.9919 | 56.5134   | 141.8148 |
| 0.0112        | 36.0  | 28620 | 1.0429          | 58.4039 | 46.6401 | 49.3897 | 57.4753   | 142.0    |
| 0.0101        | 37.0  | 29415 | 1.0761          | 59.2768 | 47.5384 | 50.2152 | 57.9493   | 142.0    |
| 0.0095        | 38.0  | 30210 | 1.0254          | 58.6205 | 47.246  | 50.87   | 57.7829   | 142.0    |
| 0.0087        | 39.0  | 31005 | 1.0216          | 57.7667 | 44.7762 | 48.067  | 56.6006   | 142.0    |
| 0.0082        | 40.0  | 31800 | 1.0587          | 58.4703 | 45.8371 | 48.5321 | 57.2036   | 142.0    |
| 0.0075        | 41.0  | 32595 | 1.0621          | 58.5629 | 46.8885 | 49.5943 | 57.4579   | 142.0    |
| 0.0079        | 42.0  | 33390 | 1.0845          | 57.664  | 45.5954 | 48.408  | 56.661    | 141.9815 |
| 0.0076        | 43.0  | 34185 | 1.0705          | 58.1776 | 46.0435 | 49.3126 | 57.138    | 142.0    |
| 0.0074        | 44.0  | 34980 | 1.0636          | 58.1022 | 46.4877 | 48.7985 | 56.9073   | 142.0    |
| 0.007         | 45.0  | 35775 | 1.0810          | 57.8251 | 44.8767 | 47.8991 | 56.5977   | 142.0    |
| 0.0057        | 46.0  | 36570 | 1.0560          | 58.5086 | 46.3448 | 49.2576 | 57.4386   | 142.0    |
| 0.0062        | 47.0  | 37365 | 1.0903          | 58.8772 | 47.2886 | 49.9502 | 57.611    | 142.0    |
| 0.0058        | 48.0  | 38160 | 1.0847          | 59.4672 | 48.3847 | 51.602  | 58.4588   | 142.0    |
| 0.0061        | 49.0  | 38955 | 1.0798          | 59.5308 | 48.0396 | 50.8641 | 58.5016   | 142.0    |
| 0.0062        | 50.0  | 39750 | 1.0795          | 59.5026 | 48.5319 | 51.7426 | 58.7111   | 142.0    |
| 0.0051        | 51.0  | 40545 | 1.0842          | 57.7941 | 46.1198 | 48.7341 | 56.7164   | 142.0    |
| 0.0057        | 52.0  | 41340 | 1.0777          | 58.6131 | 46.3924 | 49.0787 | 57.1278   | 142.0    |
| 0.0039        | 53.0  | 42135 | 1.1133          | 57.6447 | 45.6699 | 48.5207 | 56.6447   | 142.0    |
| 0.0038        | 54.0  | 42930 | 1.0714          | 58.1462 | 46.4616 | 49.273  | 57.2771   | 142.0    |
| 0.004         | 55.0  | 43725 | 1.0852          | 58.6577 | 47.2095 | 50.4702 | 57.7724   | 142.0    |
| 0.0044        | 56.0  | 44520 | 1.1152          | 59.0564 | 47.1621 | 50.2807 | 58.3122   | 142.0    |
| 0.0042        | 57.0  | 45315 | 1.0831          | 58.1767 | 46.8127 | 49.9166 | 57.1833   | 142.0    |
| 0.0038        | 58.0  | 46110 | 1.1156          | 57.8515 | 46.3229 | 48.6843 | 56.7218   | 142.0    |
| 0.0038        | 59.0  | 46905 | 1.1105          | 57.9332 | 45.8354 | 49.27   | 57.1209   | 142.0    |
| 0.0034        | 60.0  | 47700 | 1.1104          | 60.0207 | 49.2067 | 51.8751 | 58.9484   | 142.0    |
| 0.0028        | 61.0  | 48495 | 1.1533          | 58.3432 | 46.8835 | 50.2868 | 57.5427   | 141.6111 |
| 0.0026        | 62.0  | 49290 | 1.1441          | 58.6838 | 46.9472 | 49.9524 | 57.5287   | 142.0    |
| 0.0028        | 63.0  | 50085 | 1.1232          | 58.0202 | 45.5855 | 48.6554 | 56.8368   | 141.9444 |
| 0.0037        | 64.0  | 50880 | 1.1520          | 58.3905 | 47.0348 | 49.8478 | 57.3665   | 142.0    |
| 0.0029        | 65.0  | 51675 | 1.1358          | 59.231  | 48.7251 | 51.6138 | 58.5718   | 142.0    |
| 0.0026        | 66.0  | 52470 | 1.1559          | 58.9482 | 47.2137 | 49.4299 | 57.7235   | 142.0    |
| 0.0025        | 67.0  | 53265 | 1.1272          | 59.3333 | 47.7419 | 50.7018 | 58.326    | 142.0    |
| 0.0026        | 68.0  | 54060 | 1.1613          | 58.6404 | 47.3218 | 50.255  | 57.4646   | 142.0    |
| 0.0015        | 69.0  | 54855 | 1.1575          | 58.7927 | 47.7018 | 50.695  | 57.796    | 142.0    |
| 0.0018        | 70.0  | 55650 | 1.1463          | 58.9455 | 47.2691 | 50.176  | 57.9997   | 142.0    |
| 0.0023        | 71.0  | 56445 | 1.1622          | 58.5943 | 46.9325 | 49.4159 | 57.2131   | 142.0    |
| 0.0024        | 72.0  | 57240 | 1.1258          | 58.2779 | 47.4119 | 49.9836 | 57.4867   | 142.0    |
| 0.0019        | 73.0  | 58035 | 1.1333          | 58.9185 | 47.5755 | 50.0765 | 57.8661   | 142.0    |
| 0.0017        | 74.0  | 58830 | 1.1469          | 60.5037 | 49.4508 | 52.2863 | 59.6675   | 141.963  |
| 0.0017        | 75.0  | 59625 | 1.1349          | 59.4264 | 47.4554 | 50.0383 | 58.3103   | 142.0    |
| 0.0025        | 76.0  | 60420 | 1.1215          | 58.2795 | 46.9852 | 49.5787 | 57.4501   | 142.0    |
| 0.0012        | 77.0  | 61215 | 1.1272          | 58.2248 | 47.0914 | 50.2569 | 57.1888   | 142.0    |
| 0.001         | 78.0  | 62010 | 1.1648          | 59.3808 | 48.4901 | 51.118  | 58.6251   | 142.0    |
| 0.0011        | 79.0  | 62805 | 1.1433          | 58.8697 | 47.6232 | 50.0226 | 57.6299   | 142.0    |
| 0.001         | 80.0  | 63600 | 1.1486          | 59.0608 | 47.1931 | 50.1354 | 57.8687   | 142.0    |
| 0.0011        | 81.0  | 64395 | 1.1695          | 58.341  | 47.0306 | 49.9269 | 57.339    | 142.0    |
| 0.001         | 82.0  | 65190 | 1.1589          | 58.9283 | 48.4586 | 51.2319 | 57.9485   | 142.0    |
| 0.0009        | 83.0  | 65985 | 1.1868          | 59.1377 | 48.2469 | 50.8486 | 58.1111   | 142.0    |
| 0.001         | 84.0  | 66780 | 1.1664          | 58.7706 | 47.5868 | 50.5937 | 57.7824   | 142.0    |
| 0.0009        | 85.0  | 67575 | 1.1719          | 57.8121 | 45.5997 | 48.2442 | 56.5272   | 142.0    |
| 0.0006        | 86.0  | 68370 | 1.1662          | 58.5204 | 47.5947 | 50.1839 | 57.6431   | 142.0    |
| 0.0007        | 87.0  | 69165 | 1.1668          | 59.2416 | 48.2985 | 51.0347 | 58.2794   | 142.0    |
| 0.0007        | 88.0  | 69960 | 1.1619          | 58.6933 | 47.5716 | 50.6785 | 57.5726   | 142.0    |
| 0.0003        | 89.0  | 70755 | 1.1765          | 59.2853 | 48.6451 | 51.3017 | 58.2603   | 142.0    |
| 0.0005        | 90.0  | 71550 | 1.1766          | 59.248  | 48.5642 | 50.9843 | 58.1706   | 142.0    |
| 0.0005        | 91.0  | 72345 | 1.1983          | 59.0009 | 48.311  | 51.0192 | 57.9822   | 142.0    |
| 0.0006        | 92.0  | 73140 | 1.1721          | 59.1248 | 49.0902 | 51.9937 | 58.2288   | 142.0    |
| 0.0003        | 93.0  | 73935 | 1.1799          | 58.2448 | 47.4011 | 49.987  | 57.515    | 142.0    |
| 0.0005        | 94.0  | 74730 | 1.1900          | 59.931  | 49.6663 | 52.3233 | 58.962    | 142.0    |
| 0.0004        | 95.0  | 75525 | 1.1868          | 59.5898 | 49.0004 | 51.4835 | 58.6463   | 142.0    |
| 0.0093        | 96.0  | 76320 | 1.1831          | 59.9405 | 49.83   | 52.4355 | 59.0702   | 142.0    |
| 0.0004        | 97.0  | 77115 | 1.1841          | 59.7379 | 49.5435 | 52.5255 | 58.8526   | 142.0    |
| 0.0004        | 98.0  | 77910 | 1.1790          | 59.5515 | 49.0724 | 51.9888 | 58.5488   | 142.0    |
| 0.0003        | 99.0  | 78705 | 1.1786          | 59.7712 | 49.0557 | 51.8137 | 58.7144   | 142.0    |
| 0.0002        | 100.0 | 79500 | 1.1806          | 59.4159 | 48.867  | 51.9013 | 58.3382   | 142.0    |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-v3-e100", "results": []}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bart-cnn-pubmed-arxiv-pubmed-v3-e100
====================================


This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1806
* Rouge1: 59.4159
* Rouge2: 48.867
* Rougel: 51.9013
* Rougelsum: 58.3382
* Gen Len: 142.0


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: 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: 100
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 100
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1