Instructions to use learn3r/bart_large_gov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use learn3r/bart_large_gov with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("learn3r/bart_large_gov") model = AutoModelForSeq2SeqLM.from_pretrained("learn3r/bart_large_gov") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: facebook/bart-large | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - learn3r/gov_report_memsum_oracle | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: bart_large_gov | |
| results: | |
| - task: | |
| name: Summarization | |
| type: summarization | |
| dataset: | |
| name: learn3r/gov_report_memsum_oracle | |
| type: learn3r/gov_report_memsum_oracle | |
| metrics: | |
| - name: Rouge1 | |
| type: rouge | |
| value: 71.9948 | |
| <!-- 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. --> | |
| # bart_large_gov | |
| This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the learn3r/gov_report_memsum_oracle dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.4266 | |
| - Rouge1: 71.9948 | |
| - Rouge2: 41.0084 | |
| - Rougel: 38.0938 | |
| - Rougelsum: 69.4488 | |
| - Gen Len: 751.0288 | |
| ## 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: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 16 | |
| - total_train_batch_size: 128 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 20.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | |
| | 1.7352 | 1.0 | 136 | 1.5224 | 72.0472 | 41.3267 | 36.4817 | 69.4011 | 685.9300 | | |
| | 1.6874 | 1.99 | 272 | 1.4779 | 71.7737 | 40.8546 | 36.8472 | 69.2034 | 699.4866 | | |
| | 1.5695 | 3.0 | 409 | 1.4583 | 72.2243 | 41.372 | 37.8382 | 69.6295 | 695.0977 | | |
| | 1.4951 | 3.99 | 545 | 1.4495 | 71.5808 | 40.5556 | 37.152 | 69.0536 | 753.5967 | | |
| | 1.496 | 5.0 | 682 | 1.4386 | 72.1271 | 41.1645 | 38.4096 | 69.6176 | 700.2160 | | |
| | 1.4258 | 6.0 | 818 | 1.4374 | 71.9975 | 41.0013 | 37.9947 | 69.449 | 743.7068 | | |
| | 1.4301 | 7.0 | 955 | 1.4296 | 71.8896 | 40.8303 | 38.346 | 69.357 | 724.5062 | | |
| | 1.4015 | 8.0 | 1091 | 1.4313 | 72.0031 | 40.9229 | 38.2581 | 69.4154 | 731.2685 | | |
| | 1.391 | 8.99 | 1227 | 1.4266 | 71.9948 | 41.0084 | 38.0938 | 69.4488 | 751.0288 | | |
| | 1.3642 | 10.0 | 1364 | 1.4287 | 71.9115 | 40.8683 | 38.1602 | 69.3514 | 756.9568 | | |
| | 1.3516 | 10.99 | 1500 | 1.4289 | 72.3822 | 41.5074 | 38.8088 | 69.8232 | 719.2798 | | |
| | 1.3243 | 12.0 | 1637 | 1.4301 | 71.83 | 40.764 | 38.1124 | 69.2767 | 749.9475 | | |
| | 1.3582 | 12.99 | 1773 | 1.4283 | 71.9495 | 40.9556 | 38.4201 | 69.4394 | 736.6698 | | |
| | 1.3149 | 14.0 | 1910 | 1.4298 | 71.9599 | 40.8875 | 38.2722 | 69.4209 | 753.3230 | | |
| | 1.288 | 15.0 | 2046 | 1.4326 | 72.1615 | 41.1549 | 38.611 | 69.5977 | 744.8858 | | |
| | 1.2937 | 16.0 | 2183 | 1.4315 | 71.9783 | 40.9073 | 38.4263 | 69.4109 | 755.5340 | | |
| | 1.258 | 17.0 | 2319 | 1.4328 | 72.0298 | 40.931 | 38.4845 | 69.4823 | 734.6399 | | |
| | 1.2617 | 17.99 | 2455 | 1.4336 | 71.9488 | 40.8816 | 38.4521 | 69.4151 | 744.7068 | | |
| | 1.2864 | 19.0 | 2592 | 1.4346 | 72.1334 | 40.9965 | 38.5682 | 69.5666 | 744.2449 | | |
| | 1.2936 | 19.94 | 2720 | 1.4351 | 72.0397 | 40.9431 | 38.4161 | 69.5028 | 744.4588 | | |
| ### Framework versions | |
| - Transformers 4.37.0.dev0 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.15.0 | |