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arxiv27k-t5-abst-title-gen
emre/arxiv27k-t5-abst-title-gen
arxiv27k-t5-abst-title-gen is a summarization model from emre. Use it when you need a shorter version of a longer text. It is set up for transformers. The card lists the license as apache-2.0.
This model is a fine-tuned version of mt5-small on the arxiv-abstract-title dataset. It achieves the following results on the evaluation set: - Loss: 1.6002 - Rouge1: 32.8 - Rouge2: 21.9 - Rougel: 34.8 -
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At a glance
- Task
- Summarization
- Library
- transformers
- License
- apache-2.0
- Model type
- mt5
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- May 5, 2023
- SHA
- 6b77d7ca
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Model card
From the Hugging Face model README
arxiv27k-t5-abst-title-gen/
This model is a fine-tuned version of mt5-small on the arxiv-abstract-title dataset. It achieves the following results on the evaluation set:
- Loss: 1.6002
- Rouge1: 32.8
- Rouge2: 21.9
- Rougel: 34.8
Model description
Model has been trained with a colab-pro notebook in 4 hours.
Intended uses & limitations
Can be used for generating journal titles from given abstracts
Training args
model_args = T5Args() model_args.max_seq_length = 256 model_args.train_batch_size = 8 model_args.eval_batch_size = 8 model_args.num_train_epochs = 6 model_args.evaluate_during_training = False model_args.use_multiprocessing = False model_args.fp16 = False model_args.save_steps = 40000 model_args.save_eval_checkpoints = False model_args.save_model_every_epoch = True model_args.output_dir = OUTPUT_DIR model_args.no_cache = True model_args.reprocess_input_data = True model_args.overwrite_output_dir = True model_args.num_return_sequences = 1
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
Contact
[email protected] Davut Emre Taşar
- Task
- Summarization
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- May 5, 2023