transformers pytorch bart text2text-generation summarization custom_code en dataset:scientific_papers arxiv:2210.15497 autotrain_compatible region:us
---
datasets:
- scientific_papers
language:
- en
metrics:
- rouge
tags:
- summarization
model-index:
- name: ccdv/lsg-bart-base-4096-arxiv
  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. -->

**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**

LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https://github.com/ccdv-ai/convert_checkpoint_to_lsg).

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-4096-arxiv", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-arxiv", trust_remote_code=True)

text = "Replace by what you want."
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=0)
generated_text = pipe(
  text, 
  truncation=True, 
  max_length=64, 
  no_repeat_ngram_size=7,
  num_beams=2,
  early_stopping=True
  )
```

# ccdv/lsg-bart-base-4096-arxiv

This model is a fine-tuned version of [ccdv/lsg-bart-base-4096](https://huggingface.co/ccdv/lsg-bart-base-4096) on the [scientific_papers arxiv](https://huggingface.co/datasets/scientific_papers) dataset. \
It achieves the following results on the test set:

| Length | Sparse Type  | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
|:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
| 4096   | Local        | 256        | 0        | 768        | 46.65 | 18.91 | 26.90 | 42.18 |
| 4096   | Local        | 128        | 0        | 384        | 46.18 | 18.57 | 26.71 | 41.69 |
| 4096   | Pooling      | 128        | 4        | 644        | 46.27 | 18.68 | 26.87 | 41.82 |
| 4096   | Stride       | 128        | 4        | 644        | 46.34 | 18.64 | 26.69 | 41.87 |
| 4096   | Block Stride | 128        | 4        | 644        | 46.23 | 18.62 | 26.62 | 41.80 |
| 4096   | Norm         | 128        | 4        | 644        | 45.96 | 18.46 | 26.52 | 41.51 |
| 4096   | LSH          | 128        | 4        | 644        | 46.19 | 18.72 | 26.89 | 41.76 |

With smaller block size (lower ressources):

| Length | Sparse Type  | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
|:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
| 4096   | Local        | 64         | 0        | 192        | 44.71 | 17.53 | 26.03 | 40.23 |
| 4096   | Local        | 32         | 0        | 96         | 39.67 | 14.34 | 23.81 | 35.00 |
| 4096   | Pooling      | 32         | 4        | 160        | 42.75 | 16.34 | 25.20 | 38.23 |
| 4096   | Stride       | 32         | 4        | 160        | 44.23 | 17.21 | 25.71 | 39.72 |
| 4096   | Block Stride | 32         | 4        | 160        | 44.15 | 17.10 | 25.68 | 39.60 |
| 4096   | Norm         | 32         | 4        | 160        | 42.02 | 15.65 | 24.56 | 37.45 |
| 4096   | LSH          | 32         | 4        | 160        | 42.58 | 16.21 | 25.10 | 38.04 |

## Model description
The model relies on Local-Sparse-Global attention to handle long sequences:
![attn](attn.png)

The model has about ~145 millions parameters (6 encoder layers - 6 decoder layers). \
The model is warm started from BART-base, converted to handle long sequences (encoder only) and fine tuned.

## 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: 8e-05
- train_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6.0

### Generate hyperparameters

The following hyperparameters were used during generation:
- dataset_name: scientific_papers
- dataset_config_name: arxiv
- eval_batch_size: 8
- eval_samples: 6440
- early_stopping: True
- ignore_pad_token_for_loss: True
- length_penalty: 2.0
- max_length: 320
- min_length: 32
- num_beams: 5
- no_repeat_ngram_size: None
- seed: 123

### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.1+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
