---
base_model: longt5_xl_summ_screen_memsum_bp_20/checkpoint-140
tags:
- generated_from_trainer
datasets:
- learn3r/summ_screen_fd_memsum_bp
metrics:
- rouge
model-index:
- name: longt5_xl_summ_screen_memsum_bp_30
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: learn3r/summ_screen_fd_memsum_bp
      type: learn3r/summ_screen_fd_memsum_bp
    metrics:
    - name: Rouge1
      type: rouge
      value: 47.1842
---

<!-- 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_summ_screen_memsum_bp_30

This model is a fine-tuned version of [longt5_xl_summ_screen_memsum_bp_20/checkpoint-140](https://huggingface.co/longt5_xl_summ_screen_memsum_bp_20/checkpoint-140) on the learn3r/summ_screen_fd_memsum_bp dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6817
- Rouge1: 47.1842
- Rouge2: 18.22
- Rougel: 28.4626
- Rougelsum: 45.5778
- Gen Len: 308.9083

## 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: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 0.0707        | 0.97  | 14   | 2.7097          | 41.4751 | 15.5831 | 25.1976 | 39.9229   | 453.5296 |
| 0.0608        | 1.95  | 28   | 2.7271          | 45.691  | 17.905  | 27.9519 | 43.8787   | 387.4172 |
| 0.0851        | 2.99  | 43   | 3.0001          | 47.1647 | 17.8993 | 28.7561 | 45.661    | 261.5680 |
| 0.0697        | 3.97  | 57   | 2.9297          | 46.6892 | 17.8922 | 28.0724 | 44.8821   | 365.3047 |
| 0.0296        | 4.94  | 71   | 2.9017          | 44.2702 | 17.7874 | 26.7598 | 42.6857   | 440.6391 |
| 0.0312        | 5.98  | 86   | 3.0489          | 47.7884 | 18.1788 | 28.6688 | 46.0744   | 306.6716 |
| 0.0383        | 6.96  | 100  | 2.6817          | 47.1842 | 18.22   | 28.4626 | 45.5778   | 308.9083 |
| 0.0367        | 8.0   | 115  | 3.0245          | 45.5573 | 17.2161 | 28.0573 | 43.7772   | 227.8550 |
| 0.04          | 8.97  | 129  | 3.2873          | 44.0164 | 17.1682 | 26.4769 | 42.3752   | 429.8757 |
| 0.028         | 9.74  | 140  | 2.9815          | 46.6542 | 17.8515 | 28.146  | 45.0274   | 337.4822 |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3