transformers pytorch pegasus_x text2text-generation generated_from_trainer summarization en dataset:ccdv/arxiv-summarization arxiv:1804.05685 arxiv:2208.04347 base_model:google/pegasus-x-base base_model:finetune:google/pegasus-x-base license:bigscience-openrail-m model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/pegasus-x-base
datasets:
- ccdv/arxiv-summarization
language:
- en
library_name: transformers
license: bigscience-openrail-m
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: Paper-Summarization-ArXiv
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: ccdv/arxiv-summarization
      type: ccdv/arxiv-summarization
      config: section
      split: test
      args: section
    metrics:
    - type: rouge
      value: 43.2305
      name: ROUGE-1
    - type: rouge
      value: 16.6571
      name: ROUGE-2
    - type: rouge
      value: 24.4315
      name: ROUGE-L
    - type: rouge
      value: 33.9399
      name: ROUGE-LSum
---

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# Paper-Summarization-ArXiv

This model is a fine-tuned version of [google/pegasus-x-base](https://huggingface.co/google/pegasus-x-base) on the arxiv-summarization dataset.


**Base Model**: [Pegasus-x-base (State-of-the-art for Long Context Summarization)](https://huggingface.co/google/pegasus-x-base)

**Finetuning Dataset**: 
- We used **full of ArXiv Dataset (Cohan et al., 2018, NAACL-HLT 2018)** [[PDF]](https://arxiv.org/abs/1804.05685)
  - (Full length is 200,000+)

**GPU**: (RTX A6000) x 1

**Train time**: About 120 hours for 5 epochs

**Test time**: About 8 hours for test dataset.


## Intended uses & limitations

- **Research Paper Summarization**



## Compare to Baseline
- Pegasus-X-base **zero-shot** Performance:
  - R-1 | R-2 | R-L | R-LSUM : 6.2269 | 0.7894 | 4.6905 | 5.4591

- **This model**


  - R-1 | R-2 | R-L | R-LSUM : 43.2305 | 16.6571 | 24.4315 | 33.9399 at 
  ```(python)
  model.generate(input_ids =inputs["input_ids"].to(device),
                              attention_mask=inputs["attention_mask"].to(device),
                              length_penalty=1, num_beams=2, max_length=128*4,min_length=150, no_repeat_ngram_size= 3, top_k=25,top_p=0.95)
    
  ```
  - R-1 | R-2 | R-L | R-LSUM : 40.8486 | 16.3717 | 25.2937 | 33.6923 (refer to **PEGASUS-X's [paper](https://arxiv.org/pdf/2208.04347.pdf)**) at 
  ```(python)
  model.generate(input_ids =inputs["input_ids"].to(device),
                              attention_mask=inputs["attention_mask"].to(device),
                              length_penalty=1, num_beams=1, max_length=128*2,top_p=1)
  ```
  - R-1 | R-2 | R-L | R-LSUM : 38.1317 | 15.0357 | 23.0286 | 30.9938 (**Diverse Beam-Search Decoding**) at
  ```(python)
  model.generate(input_ids =inputs["input_ids"].to(device),
                              attention_mask=inputs["attention_mask"].to(device),
                              num_beam_groups=5,diversity_penalty=1.0,num_beams=5,min_length=150,max_length=128*4)
  ```
  - R-1 | R-2 | R-L | R-LSUM : 43.3017 | 16.6023 | 24.1867 | 33.7019  at
  ```(python)
  model.generate(input_ids =inputs["input_ids"].to(device),
                              attention_mask=inputs["attention_mask"].to(device),
                              length_penalty=1.2, num_beams=4, max_length=128*4,min_length=150, no_repeat_ngram_size= 3, temperature=0.9,top_k=50,top_p=0.92)
   
  ```



## Training procedure

We use huggingface-based environment such as datasets, trainer, etc.


### Training hyperparameters

The following hyperparameters were used during training:

```(python)
learning_rate: 1e-05,
train_batch_size: 1,
eval_batch_size: 1,
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: linear,
lr_scheduler_warmup_steps: 1586,
num_epochs: 5
```


### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6153        | 1.0   | 3172  | 2.1045          |
| 2.202         | 2.0   | 6344  | 2.0511          |
| 2.1547        | 3.0   | 9516  | 2.0282          |
| 2.132         | 4.0   | 12688 | 2.0164          |
| 2.1222        | 5.0   | 15860 | 2.0127          |





### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2