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finetuned-BART-UK-financial-summarization
nadzma/finetuned-BART-UK-financial-summarization
finetuned-BART-UK-financial-summarization 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 19, 2022
- SHA
- 3b4f642d
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
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: 1.8235
- Rouge1: 43.0491
- Rouge2: 31.1442
- Rougel: 37.3232
- Rougelsum: 39.8716
- Gen Len: 121.2402
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 |
|---|---|---|---|---|---|---|---|---|
| 2.2823 | 0.34 | 1000 | 2.1138 | 13.157 | 8.2441 | 11.8879 | 12.1511 | 20.0 |
| 1.8952 | 0.68 | 2000 | 1.9969 | 13.4743 | 8.9624 | 12.214 | 12.605 | 20.0 |
| 1.7594 | 1.02 | 3000 | 1.9643 | 14.0421 | 9.6907 | 12.9115 | 13.141 | 20.0 |
| 1.5823 | 1.36 | 4000 | 1.9357 | 14.717 | 10.5958 | 13.6169 | 13.9246 | 20.0 |
| 1.4926 | 1.7 | 5000 | 1.8886 | 14.7609 | 10.8361 | 13.7826 | 14.0178 | 19.9777 |
| 1.5201 | 2.04 | 6000 | 1.9262 | 14.9849 | 11.0313 | 14.0855 | 14.3163 | 20.0 |
| 1.3739 | 2.38 | 7000 | 1.8690 | 15.3066 | 11.2232 | 14.2604 | 14.6091 | 20.0 |
| 1.3481 | 2.72 | 8000 | 1.8463 | 15.0157 | 11.0947 | 14.0939 | 14.3456 | 20.0 |
| 1.2094 | 3.06 | 9000 | 1.8442 | 15.0272 | 10.8948 | 13.9635 | 14.3162 | 20.0 |
| 1.0402 | 3.4 | 10000 | 1.8370 | 15.1651 | 11.2822 | 14.2048 | 14.4765 | 19.9888 |
| 1.1247 | 3.74 | 11000 | 1.8335 | 15.271 | 11.3492 | 14.3258 | 14.5952 | 20.0 |
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 19, 2022