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---
language:
- en
tags:
- summarization
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: ccdv/lsg-bart-base-4096-multinews
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-multinews", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-multinews", 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-multinews
This model is a fine-tuned version of [ccdv/lsg-bart-base-4096](https://huggingface.co/ccdv/lsg-bart-base-4096) on the [multi_news default](https://huggingface.co/datasets/multi_news) 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 | 47.10 | 18.94 | 25.22 | 43.13 |
| 4096 | Local | 128 | 0 | 384 | 46.73 | 18.79 | 25.13 | 42.76 |
| 4096 | Pooling | 128 | 4 | 644 | 46.83 | 18.87 | 25.23 | 42.86 |
| 4096 | Stride | 128 | 4 | 644 | 46.83 | 18.68 | 24.98 | 42.88 |
| 4096 | Block Stride | 128 | 4 | 644 | 46.83 | 18.72 | 25.06 | 42.88 |
| 4096 | Norm | 128 | 4 | 644 | 46.74 | 18.60 | 24.93 | 42.79 |
| 4096 | LSH | 128 | 4 | 644 | 46.74 | 18.82 | 25.19 | 42.77 |
With smaller block size (lower ressources):
| Length | Sparse Type | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum |
|:------ |:------------ |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
| 4096 | Local | 64 | 0 | 192 | 45.61 | 17.91 | 24.54 | 41.65 |
| 4096 | Local | 32 | 0 | 96 | 43.50 | 16.36 | 23.45 | 39.61 |
| 4096 | Pooling | 32 | 4 | 160 | 44.77 | 17.31 | 24.16 | 40.86 |
| 4096 | Stride | 32 | 4 | 160 | 45.29 | 17.81 | 24.45 | 41.40 |
| 4096 | Block Stride | 32 | 4 | 160 | 45.39 | 17.86 | 24.51 | 41.43 |
| 4096 | Norm | 32 | 4 | 160 | 44.65 | 17.25 | 24.09 | 40.76 |
| 4096 | LSH | 32 | 4 | 160 | 44.44 | 17.20 | 24.00 | 40.57 |
## Model description
The model relies on Local-Sparse-Global attention to handle long sequences:
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: 12.0
### Generate hyperparameters
The following hyperparameters were used during generation:
- dataset_name: multi_news
- dataset_config_name: default
- eval_batch_size: 8
- eval_samples: 5622
- 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
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