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# mt5-summarize-ch_trad

This model is a fine-tuned version of [uer/t5-small-chinese-cluecorpussmall](https://huggingface.co/uer/t5-small-chinese-cluecorpussmall) on the xlsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.2489
- eval_rouge1: 0.1313
- eval_rouge2: 0.0505
- eval_rougeL: 0.1275
- eval_rougeLsum: 0.1272
- eval_gen_len: 128.0
- eval_runtime: 541.5872
- eval_samples_per_second: 8.623
- eval_steps_per_second: 0.27
- epoch: 7.71
- step: 9000

## Model description

More information needed

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "mt5-summarize-ch_trad", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:xlsum autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# mt5-summarize-ch_trad

This model is a fine-tuned version of uer/t5-small-chinese-cluecorpussmall on the xlsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.2489
- eval_rouge1: 0.1313
- eval_rouge2: 0.0505
- eval_rougeL: 0.1275
- eval_rougeLsum: 0.1272
- eval_gen_len: 128.0
- eval_runtime: 541.5872
- eval_samples_per_second: 8.623
- eval_steps_per_second: 0.27
- epoch: 7.71
- step: 9000

## Model description

More information needed

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2

# mt5-summarize-ch_trad

This model is a fine-tuned version of uer/t5-small-chinese-cluecorpussmall on the xlsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.2489
- eval_rouge1: 0.1313
- eval_rouge2: 0.0505
- eval_rougeL: 0.1275
- eval_rougeLsum: 0.1272
- eval_gen_len: 128.0
- eval_runtime: 541.5872
- eval_samples_per_second: 8.623
- eval_steps_per_second: 0.27
- epoch: 7.71
- step: 9000 ## Model description

More information needed ## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # mt5-summarize-ch_trad

This model is a fine-tuned version of uer/t5-small-chinese-cluecorpussmall on the xlsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.2489
- eval_rouge1: 0.1313
- eval_rouge2: 0.0505
- eval_rougeL: 0.1275
- eval_rougeLsum: 0.1272
- eval_gen_len: 128.0
- eval_runtime: 541.5872
- eval_samples_per_second: 8.623
- eval_steps_per_second: 0.27
- epoch: 7.71
- step: 9000 ## Model description

More information needed ## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# mt5-summarize-ch_trad

This model is a fine-tuned version of uer/t5-small-chinese-cluecorpussmall on the xlsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.2489
- eval_rouge1: 0.1313
- eval_rouge2: 0.0505
- eval_rougeL: 0.1275
- eval_rougeLsum: 0.1272
- eval_gen_len: 128.0
- eval_runtime: 541.5872
- eval_samples_per_second: 8.623
- eval_steps_per_second: 0.27
- epoch: 7.71
- step: 9000## Model description

More information needed## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2