---
language:
- en
tags:
- summarization
datasets:
- ccdv/WCEP-10
metrics:
- rouge
model-index:
- name: ccdv/lsg-bart-base-4096-wcep
  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-wcep", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-wcep", 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)
```
# ccdv/lsg-bart-base-4096-wcep

This model is a fine-tuned version of [ccdv/lsg-bart-base-4096](https://huggingface.co/ccdv/lsg-bart-base-4096) on the [ccdv/WCEP-10 roberta](https://huggingface.co/datasets/ccdv/WCEP-10) 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        | 46.02 | 24.23 | 37.38 | 38.72 |
| 4096   | Local        | 128        | 0        | 384        | 45.43 | 23.86 | 36.94 | 38.30 |
| 4096   | Pooling      | 128        | 4        | 644        | 45.36 | 23.61 | 36.75 | 38.06 |
| 4096   | Stride       | 128        | 4        | 644        | 45.87 | 24.31 | 37.41 | 38.70 |
| 4096   | Block Stride | 128        | 4        | 644        | 45.78 | 24.16 | 37.20 | 38.48 |
| 4096   | Norm         | 128        | 4        | 644        | 45.34 | 23.39 | 36.47 | 37.78 |
| 4096   | LSH          | 128        | 4        | 644        | 45.15 | 23.53 | 36.74 | 38.02 |

With smaller block size (lower ressources):

| Length | Sparse Type  | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
|:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
| 4096   | Local        | 64         | 0        | 192        | 44.48 | 22.98 | 36.20 | 37.52 |
| 4096   | Local        | 32         | 0        | 96         | 43.60 | 22.17 | 35.61 | 36.66 |
| 4096   | Pooling      | 32         | 4        | 160        | 43.91 | 22.41 | 35.80 | 36.92 |
| 4096   | Stride       | 32         | 4        | 160        | 44.62 | 23.11 | 36.32 | 37.53 |
| 4096   | Block Stride | 32         | 4        | 160        | 44.47 | 23.02 | 36.28 | 37.46 |
| 4096   | Norm         | 32         | 4        | 160        | 44.45 | 23.03 | 36.10 | 37.33 |
| 4096   | LSH          | 32         | 4        | 160        | 43.87 | 22.50 | 35.75 | 36.93 |

## 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: 10.0

### Generate hyperparameters

The following hyperparameters were used during generation:
- dataset_name: ccdv/WCEP-10
- dataset_config_name: roberta
- eval_batch_size: 8
- eval_samples: 1022
- early_stopping: True
- ignore_pad_token_for_loss: True
- length_penalty: 2.0
- max_length: 64
- min_length: 0
- 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