---
license: apache-2.0
base_model: google/long-t5-tglobal-xl
tags:
- generated_from_trainer
datasets:
- learn3r/summ_screen_memsum_oracle
model-index:
- name: longt5_xl_sfd_memsum_30
  results: []
---

<!-- 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_sfd_memsum_30

This model is a fine-tuned version of [google/long-t5-tglobal-xl](https://huggingface.co/google/long-t5-tglobal-xl) on the learn3r/summ_screen_memsum_oracle dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1322

## 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: 30.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6697        | 0.97  | 14   | 2.4168          |
| 2.2272        | 1.95  | 28   | 2.2644          |
| 1.9024        | 2.99  | 43   | 2.2556          |
| 1.6554        | 3.97  | 57   | 2.4007          |
| 1.3619        | 4.94  | 71   | 2.4233          |
| 1.1577        | 5.98  | 86   | 2.6797          |
| 0.9584        | 6.96  | 100  | 2.8449          |
| 0.7197        | 8.0   | 115  | 3.0255          |
| 0.5756        | 8.97  | 129  | 3.1467          |
| 0.485         | 9.95  | 143  | 3.2976          |
| 0.4027        | 10.99 | 158  | 3.8111          |
| 0.2938        | 11.97 | 172  | 3.7330          |
| 0.2665        | 12.94 | 186  | 4.1417          |
| 0.2019        | 13.98 | 201  | 4.0316          |
| 0.1706        | 14.96 | 215  | 4.1357          |
| 0.1418        | 16.0  | 230  | 4.1022          |
| 0.1286        | 16.97 | 244  | 4.1198          |
| 0.1022        | 17.95 | 258  | 4.1862          |
| 0.1122        | 18.99 | 273  | 4.6386          |
| 0.093         | 19.97 | 287  | 4.6829          |
| 0.0783        | 20.94 | 301  | 4.6637          |
| 0.0698        | 21.98 | 316  | 4.7190          |
| 0.0688        | 22.96 | 330  | 5.0200          |
| 0.0633        | 24.0  | 345  | 4.7576          |
| 0.0609        | 24.97 | 359  | 4.7805          |
| 0.0553        | 25.95 | 373  | 4.7338          |
| 0.0503        | 26.99 | 388  | 5.1409          |
| 0.0471        | 27.97 | 402  | 5.1463          |
| 0.0472        | 28.94 | 416  | 5.1636          |
| 0.0376        | 29.22 | 420  | 5.1322          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0