language:
- en
tags:
- summarization
datasets:
- ccdv/WCEP-10
metrics:
- rouge
model-index:
- name: ccdv/lsg-bart-base-4096-wcep
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-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)
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.
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 |
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: 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