lsg-bart-base-4096-wcep Open-source Long Text Summarization Model - Supports Long Sequence Processing of 4096 Tokens

Lsg Bart Base 4096 Wcep

Developed by ccdv

Long text summarization model based on LSG-BART architecture, fine-tuned on the WCEP-10 dataset, supports processing long sequences of up to 4096 tokens

Text Generation

Transformers

English#Long Text Summarization #Local-Sparse-Global Attention #4096 Context Window

Downloads 27

Release Time : 5/25/2022

Model Overview

This model employs a local-sparse-global attention mechanism to handle long sequences, making it suitable for long text summarization tasks. It is based on a modified BART-base architecture and supports efficient processing of long documents.

Model Features

Long Sequence Processing Capability

Supports processing long texts up to 4096 tokens through the local-sparse-global attention mechanism for efficient computation

Multiple Sparse Attention Modes

Provides various sparse attention configuration options including local, pooling, strided, block-strided, normalized, and LSH

Resource Efficiency Optimization

Allows balancing performance and resource consumption by adjusting block size and sparsity

Model Capabilities

Long Text Summarization

Multi-Document Summarization

English Text Processing

Use Cases

Text Summarization

News Article Summarization

Generates concise summaries for long news articles

Achieves a ROUGE-1 score of 46.02 on the WCEP-10 test set

Research Report Condensation

Extracts key information from lengthy research reports

Supports processing long documents up to 4096 tokens

🚀 ccdv/lsg-bart-base-4096-wcep

This model is a fine - tuned version of ccdv/lsg-bart-base-4096 on the ccdv/WCEP-10 roberta dataset, which can be used for text summarization.

🚀 Quick Start

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.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-4096-wcep", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-wcep", 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)

📚 Documentation

This model is a fine - tuned version of ccdv/lsg-bart-base-4096 on the ccdv/WCEP-10 roberta 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	46.02	24.23	37.38	38.72
4096	Local	128	0	384	45.43	23.86	36.94	38.30
4096	Pooling	128	4	644	45.36	23.61	36.75	38.06
4096	Stride	128	4	644	45.87	24.31	37.41	38.70
4096	Block Stride	128	4	644	45.78	24.16	37.20	38.48
4096	Norm	128	4	644	45.34	23.39	36.47	37.78
4096	LSH	128	4	644	45.15	23.53	36.74	38.02

With smaller block size (lower ressources):

Length	Sparse Type	Block Size	Sparsity	Connexions	R1	R2	RL	RLsum
4096	Local	64	0	192	44.48	22.98	36.20	37.52
4096	Local	32	0	96	43.60	22.17	35.61	36.66
4096	Pooling	32	4	160	43.91	22.41	35.80	36.92
4096	Stride	32	4	160	44.62	23.11	36.32	37.53
4096	Block Stride	32	4	160	44.47	23.02	36.28	37.46
4096	Norm	32	4	160	44.45	23.03	36.10	37.33
4096	LSH	32	4	160	43.87	22.50	35.75	36.93

🔧 Technical Details

Model description

The model relies on Local - Sparse - Global attention to handle long sequences: attn

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.

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: 10.0

Generate hyperparameters

The following hyperparameters were used during generation:

dataset_name: ccdv/WCEP-10
dataset_config_name: roberta
eval_batch_size: 8
eval_samples: 1022
early_stopping: True
ignore_pad_token_for_loss: True
length_penalty: 2.0
max_length: 64
min_length: 0
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

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