---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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](https://huggingface.co.//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