Transformers >= 4.36.1
This model relies on a custom modeling file, you need to add trust_remote_code=True
See #13467
LSG ArXiv paper.
Github/conversion script is available at this link.
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 on the scientific_papers pubmed 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, 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