It's recommended to use this model with
beam search decoding
. If interested, you can also use the
textsum
util repo to have most of this abstracted out for you:
pip install -U textsum
from textsum.summarize import Summarizer
model_name = "pszemraj/led-large-book-summary-continued"
summarizer = Summarizer(model_name) # GPU auto-detected
text = "put the text you don't want to read here"
summary = summarizer.summarize_string(text)
print(summary)
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 3e-05
train_batch_size: 4
eval_batch_size: 2
seed: 8191
gradient_accumulation_steps: 16
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 2.0
mixed_precision_training: Native AMP
Runs of pszemraj led-large-book-summary-continued on huggingface.co
32
Total runs
1
24-hour runs
3
3-day runs
12
7-day runs
25
30-day runs
More Information About led-large-book-summary-continued huggingface.co Model
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led-large-book-summary-continued is an open source model from GitHub that offers a free installation service, and any user can find led-large-book-summary-continued on GitHub to install. At the same time, huggingface.co provides the effect of led-large-book-summary-continued install, users can directly use led-large-book-summary-continued installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
led-large-book-summary-continued install url in huggingface.co: