transformers safetensors longt5 text2text-generation generated_from_trainer synthsumm summarization en dataset:pszemraj/synthsumm base_model:google/long-t5-tglobal-base base_model:finetune:google/long-t5-tglobal-base license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: google/long-t5-tglobal-base
datasets:
- pszemraj/synthsumm
language:
- en
license: apache-2.0
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
- synthsumm
inference:
  parameters:
    max_length: 64
    min_length: 8
    no_repeat_ngram_size: 3
    early_stopping: true
    repetition_penalty: 3.5
    encoder_no_repeat_ngram_size: 4
    num_beams: 3
---

# long-t5-tglobal-base-synthsumm_direct

Fine-tuned on a synthetic dataset of curated long-context text and `GPT-3.5-turbo-1106` summaries spanning multiple domains + "random" long-context examples from pretraining datasets


- Note: this model has **not** been fine-tuned on any other summarization datasets, just the `synthsumm` data

Try it: [gradio demo](https://huggingface.co/spaces/pszemraj/document-summarization) | free [HF inference api](https://gist.github.com/pszemraj/08f527380ed00ef2f2169e220341c489) via `requests`| [.md with example outputs](evals-outputs/GAUNTLET.md) (gauntlet)

## Usage 

It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If interested, you can also use the `textsum` [util repo](https://github.com/pszemraj/textsum) to have most of this abstracted out for you:

```bash
pip install -U textsum
```

```python
from textsum.summarize import Summarizer

model_name = "pszemraj/long-t5-tglobal-base-synthsumm_direct"
summarizer = Summarizer(model_name) # GPU auto-detected
text = "put the text you don't want to read here"
summary = summarizer.summarize_string(text)
print(summary)
```

## Details

This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4378
- Rouge1: 48.0918
- Rouge2: 21.2531
- Rougel: 34.4307
- Rougelsum: 43.0271
- Gen Len: 84.5231


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 26605
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.9183        | 0.38  | 125  | 1.5762          | 38.7221 | 15.0873 | 28.3123 | 34.9655   | 129.2154 |
| 1.8815        | 0.77  | 250  | 1.5230          | 44.3531 | 17.9384 | 31.7417 | 39.5563   | 87.3538  |
| 1.7264        | 1.15  | 375  | 1.4735          | 45.7781 | 20.102  | 33.329  | 41.4737   | 101.9231 |
| 1.8545        | 1.54  | 500  | 1.4505          | 47.0134 | 20.6159 | 33.6118 | 41.6579   | 88.2308  |
| 1.7444        | 1.92  | 625  | 1.4378          | 48.0918 | 21.2531 | 34.4307 | 43.0271   | 84.5231  |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0