---
language:
- en
tags:
- summarization
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: ccdv/lsg-bart-base-16384-pubmed
  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. -->

**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)**

# ccdv/lsg-bart-base-16384-pubmed

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

| Length | Global tokens | Sparse Type | Block Size | Sparsity | Connexions | R1    | R2    | RL    | RLsum |
|:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- |
| 16384  | 64            | -           | 256        | 0        | 768        | 48.29 | 22.53 | 29.35 | 44.55 |


## 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 [ccdv/lsg-bart-base-4096-pubmed](https://huggingface.co./ccdv/lsg-bart-base-4096-pubmed), 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: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Generate hyperparameters

The following hyperparameters were used during generation:
- dataset_name: scientific_papers
- dataset_config_name: pubmed
- eval_batch_size: 2
- early_stopping: True
- ignore_pad_token_for_loss: True
- length_penalty: 2.0
- max_length: 512
- min_length: 128
- num_beams: 5
- num_samples: None
- 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