language:
- en
tags:
- summarization
datasets:
- ccdv/mediasum
metrics:
- rouge
model-index:
- name: ccdv/lsg-bart-base-4096-mediasum
results: []
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-4096-mediasum", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-mediasum", 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-mediasum
This model is a fine-tuned version of ccdv/lsg-bart-base-4096 on the ccdv/mediasum roberta_prepended 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 | 35.16 | 18.13 | 31.54 | 32.20 |
| 4096 | Local | 128 | 0 | 384 | 34.16 | 17.61 | 30.75 | 31.41 |
| 4096 | Pooling | 128 | 4 | 644 | 34.52 | 17.71 | 31.01 | 31.67 |
| 4096 | Stride | 128 | 4 | 644 | 35.05 | 18.11 | 31.47 | 32.13 |
| 4096 | Block Stride | 128 | 4 | 644 | 34.72 | 17.81 | 31.13 | 31.82 |
| 4096 | Norm | 128 | 4 | 644 | 34.75 | 17.86 | 31.10 | 31.77 |
| 4096 | LSH | 128 | 4 | 644 | 34.54 | 17.81 | 31.05 | 31.71 |
With smaller block size (lower ressources):
| Length | Sparse Type | Block Size | Sparsity | Connexions | R1 | R2 | RL | RLsum |
|---|---|---|---|---|---|---|---|---|
| 4096 | Local | 64 | 0 | 192 | 32.55 | 16.66 | 29.36 | 30.00 |
| 4096 | Local | 32 | 0 | 96 | 30.98 | 15.41 | 27.84 | 28.46 |
| 4096 | Pooling | 32 | 4 | 160 | 31.84 | 16.02 | 28.68 | 29.30 |
| 4096 | Stride | 32 | 4 | 160 | 32.67 | 16.68 | 29.47 | 30.10 |
| 4096 | Block Stride | 32 | 4 | 160 | 32.51 | 16.64 | 29.33 | 29.94 |
| 4096 | Norm | 32 | 4 | 160 | 32.44 | 16.48 | 29.20 | 29.79 |
| 4096 | LSH | 32 | 4 | 160 | 31.79 | 16.04 | 28.67 | 29.31 |
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: 6.0
Generate hyperparameters
The following hyperparameters were used during generation:
- dataset_name: ccdv/mediasum
- dataset_config_name: roberta_prepended
- eval_batch_size: 8
- eval_samples: 10000
- early_stopping: True
- ignore_pad_token_for_loss: True
- length_penalty: 2.0
- max_length: 128
- min_length: 3
- 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