tech-dialogue-summarization-3
This model is a fine-tuned version of lidiya/bart-large-xsum-samsum on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3468
- Rouge1: 52.6316
- Rouge2: 32.4324
- Rougel: 47.3684
- Rougelsum: 47.3684
- Gen Len: 38.0
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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 1 | 1.4146 | 52.6316 | 32.4324 | 47.3684 | 47.3684 | 38.0 |
| No log | 2.0 | 2 | 1.3652 | 52.6316 | 32.4324 | 47.3684 | 47.3684 | 38.0 |
| No log | 3.0 | 3 | 1.3468 | 52.6316 | 32.4324 | 47.3684 | 47.3684 | 38.0 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Base model
lidiya/bart-large-xsum-samsum