metadata
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: abstractive_summarization
results: []
abstractive_summarization
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0699
- Rouge1: 0.166
- Rouge2: 0.1297
- Rougel: 0.1594
- Rougelsum: 0.1593
- Gen Len: 18.9974
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|---|
| 2.465 | 1.0 | 1658 | 2.1944 | 0.1613 | 0.1244 | 0.1538 | 0.1537 | 18.996 |
| 2.3525 | 2.0 | 3316 | 2.1101 | 0.1646 | 0.128 | 0.1572 | 0.1571 | 18.9974 |
| 2.2844 | 3.0 | 4974 | 2.0779 | 0.1655 | 0.1291 | 0.1587 | 0.1586 | 18.9965 |
| 2.2874 | 4.0 | 6632 | 2.0699 | 0.166 | 0.1297 | 0.1594 | 0.1593 | 18.9974 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1