metadata
license: mit
base_model: alexdg19/bert_large_xsum_samsum
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: bert_large_xsum_samsum2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: test
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 0.6112
bert_large_xsum_samsum2
This model is a fine-tuned version of alexdg19/bert_large_xsum_samsum on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.1949
- Rouge1: 0.6112
- Rouge2: 0.3855
- Rougel: 0.5301
- Rougelsum: 0.5296
- Gen Len: 30.5427
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: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 41 | 0.9966 | 0.6323 | 0.416 | 0.5587 | 0.5598 | 26.9573 |
| No log | 2.0 | 82 | 1.0976 | 0.6279 | 0.413 | 0.5569 | 0.5583 | 27.8171 |
| No log | 3.0 | 123 | 1.1576 | 0.6236 | 0.4141 | 0.553 | 0.5537 | 29.5183 |
| No log | 4.0 | 164 | 1.1998 | 0.6148 | 0.3948 | 0.5402 | 0.541 | 30.5061 |
| No log | 5.0 | 205 | 1.1949 | 0.6112 | 0.3855 | 0.5301 | 0.5296 | 30.5427 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1