---
base_model: /exports/eddie/scratch/s1970716/models/summarization/longt5_xl_gov_memsum_bp_5/checkpoint-1360
tags:
- generated_from_trainer
datasets:
- learn3r/gov_report_memsum_bp
metrics:
- rouge
model-index:
- name: longt5_xl_gov_memsum_bp_10
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: learn3r/gov_report_memsum_bp
      type: learn3r/gov_report_memsum_bp
    metrics:
    - name: Rouge1
      type: rouge
      value: 67.1508
---

<!-- 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_memsum_bp_10

This model is a fine-tuned version of [/exports/eddie/scratch/s1970716/models/summarization/longt5_xl_gov_memsum_bp_5/checkpoint-1360](https://huggingface.co//exports/eddie/scratch/s1970716/models/summarization/longt5_xl_gov_memsum_bp_5/checkpoint-1360) on the learn3r/gov_report_memsum_bp dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1440
- Rouge1: 67.1508
- Rouge2: 38.9594
- Rougel: 36.6571
- Rougelsum: 64.7944
- Gen Len: 712.0165

## 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len   |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:---------:|
| 0.4356        | 1.0   | 272  | 1.1440          | 67.1508 | 38.9594 | 36.6571 | 64.7944   | 712.0165  |
| 0.3485        | 2.0   | 545  | 1.2622          | 66.9296 | 38.7595 | 36.4964 | 64.6309   | 808.4393  |
| 0.2933        | 3.0   | 818  | 1.3804          | 65.3911 | 38.0875 | 35.5935 | 63.1902   | 976.0340  |
| 0.2443        | 4.0   | 1091 | 1.4998          | 67.0929 | 38.0763 | 35.8704 | 64.9022   | 816.7253  |
| 0.2033        | 4.99  | 1360 | 1.5508          | 64.1251 | 36.9601 | 34.8068 | 61.9673   | 1052.8817 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1