模型:

ccdv/lsg-bart-base-4096-multinews

任务:

摘要生成

类库:

PyTorch Transformers

数据集:

multi_news 3Amulti_news

语言:

其他:

bart 文生文 custom_code AutoTrain Compatible

预印本库:

arxiv:2210.15497

模型介绍文件清单

中文

Transformers >= 4.23.1 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-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 on the multi_news default 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

作者:

ccdv

数据集大小:

554.92 MB