
# mt5-small-nlu-all-crosswoz

This model is a fine-tuned version of [mt5-small](https://huggingface.co/mt5-small) on [CrossWOZ](https://huggingface.co/datasets/ConvLab/crosswoz) both user and system utterances.

Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0

### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
{"language": ["zh"], "license": "apache-2.0", "tags": ["mt5-small", "text2text-generation", "natural language understanding", "conversational system", "task-oriented dialog"], "datasets": ["ConvLab/crosswoz"], "metrics": ["Dialog acts Accuracy", "Dialog acts F1"], "widget": [{"text": "user: \u4f60\u597d\uff0c\u7ed9\u6211\u63a8\u8350\u4e00\u4e2a\u8bc4\u5206\u662f5\u5206\uff0c\u4ef7\u683c\u5728100-200\u5143\u7684\u9152\u5e97\u3002"}, {"text": "system: \u62b1\u6b49\uff0c\u4e3a\u60a8\u641c\u7d22\u4e86\u4e00\u4e9b\u7ecf\u6d4e\u578b\u9152\u5e97\u90fd\u6ca1\u6709\u5065\u8eab\u623f\u3002\u5176\u4ed6\u7c7b\u578b\u7684\u4e00\u4e9b\u9152\u5e97\u884c\u5417\uff1f\u6bd4\u5982\u5317\u4eac\u8d35\u90fd\u5927\u9152\u5e97\u3001\u5317\u4eac\u4eac\u4eea\u5927\u9152\u5e97\u8fd9\u4e9b\u4e5f\u662f\u5f88\u597d\u7684\uff0c\u5c31\u662f\u4ef7\u683c\u9ad8\u4e86\u4e00\u4e9b\u3002"}], "inference": {"parameters": {"max_length": 100}}, "model-index": [{"name": "mt5-small-nlu-all-crosswoz", "results": [{"task": {"type": "text2text-generation", "name": "natural language understanding"}, "dataset": {"name": "CrossWOZ", "type": "ConvLab/crosswoz", "split": "test", "revision": "4a3e56082543ed9eecb9c76ef5eadc1aa0cc5ca0"}, "metrics": [{"type": "Dialog acts Accuracy", "value": 84.0, "name": "Accuracy"}, {"type": "Dialog acts F1", "value": 90.1, "name": "F1"}]}]}]}
transformers pytorch mt5 text2text-generation mt5-small natural language understanding conversational system task-oriented dialog zh dataset:ConvLab/crosswoz license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #mt5-small #natural language understanding #conversational system #task-oriented dialog #zh #dataset-ConvLab/crosswoz #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# mt5-small-nlu-all-crosswoz

This model is a fine-tuned version of mt5-small on CrossWOZ both user and system utterances.

Refer to ConvLab-3 for model description and usage.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0

### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
# mt5-small-nlu-all-crosswoz

This model is a fine-tuned version of mt5-small on CrossWOZ both user and system utterances.

Refer to ConvLab-3 for model description and usage. ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #mt5 #text2text-generation #mt5-small #natural language understanding #conversational system #task-oriented dialog #zh #dataset-ConvLab/crosswoz #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # mt5-small-nlu-all-crosswoz

This model is a fine-tuned version of mt5-small on CrossWOZ both user and system utterances.

Refer to ConvLab-3 for model description and usage. ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #mt5 #text2text-generation #mt5-small #natural language understanding #conversational system #task-oriented dialog #zh #dataset-ConvLab/crosswoz #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# mt5-small-nlu-all-crosswoz

This model is a fine-tuned version of mt5-small on CrossWOZ both user and system utterances.

Refer to ConvLab-3 for model description and usage.## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1