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# chinese-bert-wwm-finetuned-product-1

This model is a fine-tuned version of [hfl/chinese-bert-wwm](https://huggingface.co/hfl/chinese-bert-wwm) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0000
- eval_runtime: 10.6737
- eval_samples_per_second: 362.572
- eval_steps_per_second: 5.715
- epoch: 11.61
- step: 18797

## 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: 2e-05
- train_batch_size: 256
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Framework versions

- Transformers 4.17.0
- Pytorch 1.6.0
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "chinese-bert-wwm-finetuned-product-1", "results": []}]}
transformers pytorch tensorboard bert text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# chinese-bert-wwm-finetuned-product-1

This model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0000
- eval_runtime: 10.6737
- eval_samples_per_second: 362.572
- eval_steps_per_second: 5.715
- epoch: 11.61
- step: 18797

## 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: 2e-05
- train_batch_size: 256
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Framework versions

- Transformers 4.17.0
- Pytorch 1.6.0
- Datasets 2.0.0
- Tokenizers 0.11.6

# chinese-bert-wwm-finetuned-product-1

This model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0000
- eval_runtime: 10.6737
- eval_samples_per_second: 362.572
- eval_steps_per_second: 5.715
- epoch: 11.61
- step: 18797 ## 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: 2e-05
- train_batch_size: 256
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20 ### Framework versions

- Transformers 4.17.0
- Pytorch 1.6.0
- Datasets 2.0.0
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # chinese-bert-wwm-finetuned-product-1

This model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0000
- eval_runtime: 10.6737
- eval_samples_per_second: 362.572
- eval_steps_per_second: 5.715
- epoch: 11.61
- step: 18797 ## 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: 2e-05
- train_batch_size: 256
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20 ### Framework versions

- Transformers 4.17.0
- Pytorch 1.6.0
- Datasets 2.0.0
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# chinese-bert-wwm-finetuned-product-1

This model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0000
- eval_runtime: 10.6737
- eval_samples_per_second: 362.572
- eval_steps_per_second: 5.715
- epoch: 11.61
- step: 18797## 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: 2e-05
- train_batch_size: 256
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20### Framework versions

- Transformers 4.17.0
- Pytorch 1.6.0
- Datasets 2.0.0
- Tokenizers 0.11.6