|
--- |
|
language: |
|
- en |
|
tags: |
|
- summarization |
|
datasets: |
|
- scientific_papers |
|
metrics: |
|
- rouge |
|
base_model: ccdv/lsg-bart-base-4096-pubmed |
|
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. --> |
|
|
|
**Transformers >= 4.23.1**\ |
|
**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-16384-pubmed", trust_remote_code=True) |
|
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-16384-pubmed", 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-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](https://huggingface.co/datasets/scientific_papers) dataset. \ |
|
The model is converted to handle 16384 long sequences and fine-tuned accordingly during 1 epoch. \ |
|
It achieves the following results on the test set: |
|
|
|
| Length | Global tokens | Fine-tuning | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum | |
|
|:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- | |
|
| 16384 | 64 | Full | 256 | 0 | 768 | 48.32 | 22.52 | 29.36 | 44.57 | |
|
| 16384 | 1 | Full | 256 | 0 | 768 | 48.26 | 22.53 | 29.40 | 44.51 | |
|
| 16384 | 64 | Global only | 256 | 0 | 768 | 48.12 | 20.46 | 29.34 | 44.40 | |
|
| 16384 | 1 | None | 256 | 0 | 768 | 48.03 | 22.42 | 29.28 | 44.32 | |
|
|
|
Reference model: |
|
|
|
| Length | Global tokens | Fine-tuning | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum | |
|
|:------ |:------------- |:----------- |:---------- |:-------- | :--------- |:----- |:----- |:----- |:----- | |
|
| 4096 | 1 | - | 256 | 0 | 768 | 47.37 | 21.74 | 28.59 | 43.67 | |
|
|
|
## 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 [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: 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: 1.0 |
|
|
|
### Generate hyperparameters |
|
|
|
The following hyperparameters were used during generation: |
|
- dataset_name: scientific_papers |
|
- dataset_config_name: pubmed |
|
- eval_batch_size: 4 |
|
- eval_samples: 6658 |
|
- early_stopping: True |
|
- ignore_pad_token_for_loss: True |
|
- length_penalty: 2.0 |
|
- max_length: 512 |
|
- min_length: 128 |
|
- 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 |
|
|
