long-t5-tglobal-base-synthsumm_direct

long-t5-tglobal-base-synthsumm_direct

Fine-tuned on a synthetic dataset of curated long-context text and GPT-3.5-turbo-1106 summaries spanning multiple domains + "random" long-context examples from pretraining datasets

• Note: this model has not been fine-tuned on any other summarization datasets, just the synthsumm data

Try it: gradio demo | free HF inference api via requests| .md with example outputs (gauntlet)

Usage

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/long-t5-tglobal-base-synthsumm_direct"
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)

Details

This model is a fine-tuned version of google/long-t5-tglobal-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.4378
• Rouge1: 48.0918
• Rouge2: 21.2531
• Rougel: 34.4307
• Rougelsum: 43.0271
• Gen Len: 84.5231

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 1
• eval_batch_size: 1
• seed: 26605
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: inverse_sqrt
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 2.0

Training results

Framework versions

• Transformers 4.36.0.dev0
• Pytorch 2.1.0
• Datasets 2.15.0
• Tokenizers 0.15.0