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pep_summarization
This model is a fine-tuned version of facebook/bart-large-cnn on the fedora-copr/pep-sum dataset. It achieves the following results on the evaluation set:
- Loss: 0.1242
- Rouge1: 75.3806
- Rouge2: 74.6735
- Rougel: 75.5866
- Rougelsum: 75.5446
- Gen Len: 85.3188
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: 1e-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: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 69 | 0.0957 | 72.6601 | 71.6824 | 72.6858 | 72.4668 | 95.4493 |
| No log | 2.0 | 138 | 0.1345 | 75.0063 | 74.0782 | 75.0597 | 74.8943 | 92.0145 |
| No log | 3.0 | 207 | 0.1412 | 75.3012 | 74.5492 | 75.4246 | 75.324 | 85.4638 |
| No log | 4.0 | 276 | 0.1089 | 74.8426 | 74.0317 | 74.8939 | 74.8128 | 85.0435 |
| No log | 5.0 | 345 | 0.1242 | 75.3806 | 74.6735 | 75.5866 | 75.5446 | 85.3188 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Base model
facebook/bart-large-cnnDataset used to train jpodivin/pep_summarization
Evaluation results
- Rouge1 on fedora-copr/pep-sumself-reported75.381
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jpodivin/pep_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("jpodivin/pep_summarization")