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bart-large-cnn-billsum
This model is a fine-tuned version of facebook/bart-large-cnn on the billsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.7658
- Rouge1: 0.5014
- Rouge2: 0.2463
- Rougel: 0.3189
- Rougelsum: 0.3752
- Gen Len: 125.5645
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 248 | 1.8112 | 0.4809 | 0.2299 | 0.3067 | 0.3716 | 113.1371 |
| No log | 2.0 | 496 | 1.7501 | 0.5089 | 0.2484 | 0.325 | 0.3844 | 123.9435 |
| 1.7258 | 3.0 | 744 | 1.7386 | 0.5008 | 0.2412 | 0.3163 | 0.3732 | 127.2056 |
| 1.7258 | 4.0 | 992 | 1.7658 | 0.5014 | 0.2463 | 0.3189 | 0.3752 | 125.5645 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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Dataset used to train StatsGary/bart-large-cnn-billsum
Evaluation results
- Rouge1 on billsumself-reported0.501
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("StatsGary/bart-large-cnn-billsum") model = AutoModelForSeq2SeqLM.from_pretrained("StatsGary/bart-large-cnn-billsum")