---
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_40
  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_40

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: 5.2679

## 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: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 40.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5238        | 0.97  | 28   | 2.3147          |
| 2.1298        | 1.98  | 57   | 2.2837          |
| 1.7525        | 2.99  | 86   | 2.3335          |
| 1.2954        | 4.0   | 115  | 2.4995          |
| 1.0518        | 4.97  | 143  | 2.8326          |
| 0.7083        | 5.98  | 172  | 2.9095          |
| 0.5124        | 6.99  | 201  | 3.4108          |
| 0.4503        | 8.0   | 230  | 3.4459          |
| 0.3145        | 8.97  | 258  | 3.5216          |
| 0.2573        | 9.98  | 287  | 4.0127          |
| 0.213         | 10.99 | 316  | 3.9714          |
| 0.1682        | 12.0  | 345  | 3.8427          |
| 0.1396        | 12.97 | 373  | 4.2025          |
| 0.1363        | 13.98 | 402  | 4.4012          |
| 0.1148        | 14.99 | 431  | 4.7174          |
| 0.0907        | 16.0  | 460  | 4.4980          |
| 0.0942        | 16.97 | 488  | 4.7024          |
| 0.0765        | 17.98 | 517  | 4.3482          |
| 0.0799        | 18.99 | 546  | 4.5386          |
| 0.073         | 20.0  | 575  | 4.5889          |
| 0.0825        | 20.97 | 603  | 4.6817          |
| 0.0616        | 21.98 | 632  | 5.0263          |
| 0.0677        | 22.99 | 661  | 4.5804          |
| 0.0571        | 24.0  | 690  | 4.8399          |
| 0.0525        | 24.97 | 718  | 4.9350          |
| 0.081         | 25.98 | 747  | 4.6903          |
| 0.0505        | 26.99 | 776  | 5.0005          |
| 0.0576        | 28.0  | 805  | 5.0196          |
| 0.0448        | 28.97 | 833  | 5.1100          |
| 0.0457        | 29.98 | 862  | 5.0008          |
| 0.0442        | 30.99 | 891  | 5.5093          |
| 0.0391        | 32.0  | 920  | 5.4296          |
| 0.0392        | 32.97 | 948  | 5.2357          |
| 0.0376        | 33.98 | 977  | 5.2266          |
| 0.0381        | 34.99 | 1006 | 5.2630          |
| 0.0339        | 36.0  | 1035 | 5.3532          |
| 0.0377        | 36.97 | 1063 | 5.4443          |
| 0.0336        | 37.98 | 1092 | 5.0809          |
| 0.0316        | 38.96 | 1120 | 5.2679          |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2