---
tags:
- generated_from_trainer
- summarization
- book summary
dataset:
- kmfoda/booksum
metrics:
- rouge
model-index:
- name: long-t5-tglobal-large-booksum-WIP
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: kmfoda/booksum
      type: kmfoda/booksum
      config: kmfoda--booksum
      split: test
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 25.6136
      verified: true
    - name: ROUGE-2
      type: rouge
      value: 2.8652
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 12.4913
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 23.1102
      verified: true
    - name: loss
      type: loss
      value: 5.004334926605225
      verified: true
    - name: gen_len
      type: gen_len
      value: 89.4354
      verified: true
---


# tglobal-large-booksum-WIP

> this is a WIP checkpoint that has been fine-tuned from the vanilla (original) for 10ish epochs. It is **not ready to be used for inference**

This model is a fine-tuned version of [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) on the `kmfoda/booksum` dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9519
- Rouge1: 21.8058
- Rouge2: 2.9343
- Rougel: 10.3717
- Rougelsum: 20.1537
- Gen Len: 106.055

## Model description

Testing fine-tuning only on booksum with 16384/1024 the whole time (vs. previous large WIP checkpoint I made that started from a partially-trained `pubmed` checkpoint)

## Intended uses & limitations

this is a WIP checkpoint that has been fine-tuned from the vanilla (original) for 10ish epochs. It is **not ready to be used for inference**

## Training and evaluation data

This is **only** fine-tuned on booksum (vs. previous large WIP checkpoint I made that started from a partially-trained `pubmed` checkpoint)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 1
- eval_batch_size: 1
- seed: 31060
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 5.0389        | 0.99  | 37   | 219.03  | 5.1884          | 29.995  | 4.4045 | 12.8837 | 27.557    |
| 4.8986        | 1.0   | 75   | 5.1286  | 26.921          | 3.7193  | 11.3605| 25.3492 | 276.005   |
| 4.5928        | 2.0   | 150  | 4.9900  | 26.6667         | 3.7342  | 11.8223| 24.7087 | 178.775   |
| 4.6159        | 3.0   | 225  | 4.9519  | 21.8058         | 2.9343  | 10.3717| 20.1537 | 106.055   |


#### eval in bf16


```
***** eval metrics *****
  epoch                   =        3.0
  eval_gen_len            =    103.075
  eval_loss               =     4.9501
  eval_rouge1             =    21.6345
  eval_rouge2             =      2.877
  eval_rougeL             =     10.386
  eval_rougeLsum          =    20.0148
  eval_runtime            = 0:06:02.75
  eval_samples            =        200
  eval_samples_per_second =      0.551
  eval_steps_per_second   =      0.138
[INFO|trainer.py:2724] 2022-11-27 01:00:
```

### Framework versions

- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1