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finetuned-BART-UK-financial-summarization-multiple-references
nadzma/finetuned-BART-UK-financial-summarization-multiple-references
finetuned-BART-UK-financial-summarization-multiple-references is a machine learning model from nadzma. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- bart
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jan 24, 2022
- SHA
- c86eb39c
Model card
From the Hugging Face model README
<!-- 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. -->
finetuned-BART-UK-financial-summarization-multiple-references
This model is a fine-tuned version of facebook/bart-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8651
- Rouge1: 30.2768
- Rouge2: 16.5597
- Rougel: 23.9264
- Rougelsum: 29.9211
- Gen Len: 114.7128
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 3.0082 | 0.1 | 1000 | 2.4917 | 13.4643 | 8.5691 | 12.4617 | 13.3171 | 20.0 |
| 2.4695 | 0.2 | 2000 | 2.3554 | 12.3599 | 8.0996 | 11.4208 | 12.2321 | 20.0 |
| 2.2945 | 0.3 | 3000 | 2.2914 | 12.6496 | 8.3592 | 11.7265 | 12.5178 | 19.8696 |
| 2.4193 | 0.41 | 4000 | 2.2373 | 13.1184 | 8.7248 | 12.2447 | 13.019 | 20.0 |
| 2.2386 | 0.51 | 5000 | 2.2263 | 11.7267 | 7.5929 | 10.8553 | 11.5917 | 18.264 |
| 2.2384 | 0.61 | 6000 | 2.2009 | 13.6181 | 9.1253 | 12.6438 | 13.4864 | 20.0 |
| 2.1491 | 0.71 | 7000 | 2.1802 | 12.7595 | 8.2607 | 11.8995 | 12.6552 | 20.0 |
| 2.1001 | 0.81 | 8000 | 2.1458 | 12.6461 | 8.2799 | 11.7226 | 12.5094 | 19.9408 |
| 2.0731 | 0.91 | 9000 | 2.0940 | 13.098 | 8.5378 | 12.0868 | 13.0062 | 19.8944 |
| 2.0307 | 1.01 | 10000 | 2.0865 | 12.9732 | 8.5554 | 12.0287 | 12.8468 | 19.9712 |
| 1.8416 | 1.12 | 11000 | 2.0670 | 13.1366 | 8.6049 | 12.2127 | 13.0122 | 19.9184 |
| 1.9564 | 1.22 | 12000 | 2.0494 | 12.664 | 8.4708 | 11.8496 | 12.5582 | 19.9856 |
| 1.8846 | 1.32 | 13000 | 2.0231 | 13.3412 | 8.9207 | 12.4697 | 13.2275 | 19.9904 |
| 1.8347 | 1.42 | 14000 | 2.0383 | 12.5847 | 8.3611 | 11.6671 | 12.4601 | 20.0 |
| 1.8773 | 1.52 | 15000 | 1.9892 | 12.6655 | 8.2128 | 11.7945 | 12.5593 | 19.9592 |
| 1.8015 | 1.62 | 16000 | 1.9959 | 13.0598 | 8.7329 | 12.1663 | 12.9552 | 19.9736 |
| 1.8119 | 1.72 | 17000 | 1.9758 | 12.7897 | 8.4912 | 11.8756 | 12.6557 | 20.0 |
| 1.8025 | 1.83 | 18000 | 1.9764 | 13.2535 | 8.8228 | 12.3222 | 13.1407 | 19.948 |
| 1.8417 | 1.93 | 19000 | 1.9613 | 12.5747 | 8.3244 | 11.6604 | 12.4882 | 19.9712 |
| 1.6609 | 2.03 | 20000 | 1.9648 | 12.7294 | 8.4078 | 11.8369 | 12.6136 | 20.0 |
| 1.5885 | 2.13 | 21000 | 1.9464 | 13.2406 | 8.9054 | 12.4006 | 13.1491 | 19.9688 |
| 1.6082 | 2.23 | 22000 | 1.9442 | 13.0069 | 8.6038 | 12.1138 | 12.9006 | 20.0 |
| 1.5773 | 2.33 | 23000 | 1.9340 | 12.6373 | 8.3122 | 11.7935 | 12.5309 | 20.0 |
| 1.6535 | 2.43 | 24000 | 1.9432 | 13.1702 | 8.8258 | 12.2924 | 13.0442 | 19.9712 |
| 1.5923 | 2.53 | 25000 | 1.9226 | 13.2556 | 8.9101 | 12.3433 | 13.136 | 19.9648 |
| 1.4997 | 2.64 | 26000 | 1.9128 | 13.0711 | 8.6794 | 12.1325 | 12.9455 | 19.9904 |
| 1.574 | 2.74 | 27000 | 1.9093 | 13.1309 | 8.7958 | 12.2906 | 13.0341 | 19.9904 |
| 1.6254 | 2.84 | 28000 | 1.8991 | 12.8586 | 8.5662 | 12.0449 | 12.7786 | 19.9832 |
| 1.6427 | 2.94 | 29000 | 1.8905 | 12.7339 | 8.5642 | 11.9439 | 12.6131 | 19.996 |
| 1.3442 | 3.04 | 30000 | 1.9000 | 12.818 | 8.5827 | 11.9443 | 12.6917 | 20.0 |
| 1.466 | 3.14 | 31000 | 1.8990 | 12.9079 | 8.6233 | 12.0834 | 12.7858 | 19.9864 |
| 1.4822 | 3.24 | 32000 | 1.8937 | 13.2885 | 9.061 | 12.4811 | 13.1978 | 19.952 |
| 1.4399 | 3.35 | 33000 | 1.8927 | 13.0858 | 8.8237 | 12.2749 | 12.974 | 19.9624 |
| 1.3888 | 3.45 | 34000 | 1.8776 | 12.955 | 8.7827 | 12.1243 | 12.8524 | 19.9864 |
| 1.4031 | 3.55 | 35000 | 1.8816 | 12.8846 | 8.6634 | 11.9851 | 12.775 | 19.9904 |
| 1.4458 | 3.65 | 36000 | 1.8732 | 12.8441 | 8.5444 | 11.9473 | 12.728 | 19.9784 |
| 1.401 | 3.75 | 37000 | 1.8734 | 12.8671 | 8.616 | 12.0224 | 12.7432 | 19.9568 |
| 1.3798 | 3.85 | 38000 | 1.8664 | 12.7505 | 8.5224 | 11.8733 | 12.6269 | 19.9904 |
| 1.4413 | 3.95 | 39000 | 1.8656 | 12.8364 | 8.609 | 11.9612 | 12.7237 | 19.9904 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.7.0
- Datasets 1.17.0
- Tokenizers 0.11.0
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Jan 24, 2022