metadata
base_model: >-
/home/co-ou1/rds/hpc-work/transformers/examples/pytorch/summarization/longt5_xl_gov_report_bp_10/checkpoint-477
tags:
- generated_from_trainer
datasets:
- learn3r/gov_report_memsum_oracle
metrics:
- rouge
model-index:
- name: longt5_xl_gov_report_bp_10_continue
results:
- task:
name: Summarization
type: summarization
dataset:
name: learn3r/gov_report_memsum_oracle
type: learn3r/gov_report_memsum_oracle
metrics:
- name: Rouge1
type: rouge
value: 71.9439
longt5_xl_gov_report_bp_10_continue
This model is a fine-tuned version of /home/co-ou1/rds/hpc-work/transformers/examples/pytorch/summarization/longt5_xl_gov_report_bp_10/checkpoint-477 on the learn3r/gov_report_memsum_oracle dataset. It achieves the following results on the evaluation set:
- Loss: 1.4878
- Rouge1: 71.9439
- Rouge2: 43.7031
- Rougel: 41.8301
- Rougelsum: 69.1853
- Gen Len: 833.0319
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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 4.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.6226 | 1.0 | 68 | 1.4878 | 71.9439 | 43.7031 | 41.8301 | 69.1853 | 833.0319 |
| 0.4983 | 1.99 | 136 | 1.5908 | 70.6191 | 43.2627 | 42.581 | 68.0871 | 627.5031 |
| 0.4175 | 2.99 | 204 | 1.6407 | 71.6704 | 43.1655 | 41.9746 | 68.992 | 737.4352 |
| 0.3958 | 3.99 | 272 | 1.8739 | 70.7685 | 42.5122 | 41.7454 | 68.0785 | 671.4938 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3