
# 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
detasar@gmail.com
Davut Emre Taşar
{"license": "apache-2.0", "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "model-index": [{"name": "arxiv27k-t5-abst-title-gen/", "results": []}]}
transformers pytorch safetensors mt5 text2text-generation generated_from_trainer summarization license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #summarization #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# 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
detasar@URL
Davut Emre Taşar
# 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
detasar@URL
Davut Emre Taşar
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #summarization #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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
detasar@URL
Davut Emre Taşar
passage: TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #summarization #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# 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
detasar@URL
Davut Emre Taşar