
# mt5-small-dst-crosswoz

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

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: 4
- total_train_batch_size: 64
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0

### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
{"language": ["zh"], "license": "apache-2.0", "tags": ["mt5-small", "text2text-generation", "dialog state tracking", "conversational system", "task-oriented dialog"], "datasets": ["ConvLab/crosswoz"], "metrics": ["Joint Goal Accuracy", "Slot 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\nsystem: \u63a8\u8350\u60a8\u53bb\u5317\u4eac\u5e03\u63d0\u514b\u7cbe\u54c1\u9152\u5e97\u3002\nuser: \u5317\u4eac\u5e03\u63d0\u514b\u7cbe\u54c1\u9152\u5e97\u9152\u5e97\u662f\u4ec0\u4e48\u7c7b\u578b\uff0c\u6709\u5065\u8eab\u623f\u5417\uff1f\nsystem: \u5317\u4eac\u5e03\u63d0\u514b\u7cbe\u54c1\u9152\u5e97\u8bc4\u5206\u662f4.8\u5206\uff0c\u662f\u9ad8\u6863\u578b\u9152\u5e97\uff0c\u6ca1\u6709\u5065\u8eab\u623f\u3002\nuser: \u7ed9\u6211\u63a8\u8350\u4e00\u4e2a\u8bc4\u5206\u57284.5\u5206\u4ee5\u4e0a\uff0c\u6e38\u73a9\u65f6\u95f4\u57282\u5c0f\u65f6 - 3\u5c0f\u65f6\u7684\u514d\u8d39\u666f\u70b9\u3002"}, {"text": "user: \u60a8\u597d\uff0c\u8bf7\u5e2e\u6211\u63a8\u8350\u4e2a4.5\u5206\u4ee5\u4e0a\u7684\u666f\u70b9\u6e38\u73a9\u5457\uff0c\u6700\u597d\u628a\u5468\u8fb9\u6709\u4ec0\u4e48\u9152\u5e97\u544a\u8bc9\u6211\u4e00\u4e0b\u3002\nsystem: \u90a3\u6211\u63a8\u8350\u60a8\u6545\u5bab\uff0c\u5468\u8fb9\u7684\u9152\u5e97\u6709\u5317\u4eac\u5929\u4f26\u738b\u671d\u9152\u5e97, \u5317\u4eac\u9996\u90fd\u5bbe\u9986, \u5317\u4eac\u8d35\u90fd\u5927\u9152\u5e97\u3002\nuser: \u90a3\u8bf7\u5728\u6545\u5bab\u5468\u8fb9\u7684\u9152\u5e97\u91cc\uff0c\u5e2e\u6211\u627e\u4e2a\u8bc4\u5206\u57284.5\u5206\u4ee5\u4e0a\u7684\u5e97\u3002\nsystem: \u5317\u4eac\u8d35\u90fd\u5927\u9152\u5e97\u7b26\u5408\u60a8\u7684\u8981\u6c42\u3002\nuser: \u8bf7\u5e2e\u6211\u547c\u53eb\u4e00\u8f86\u4ece\u6545\u5bab\u5230\u5317\u4eac\u8d35\u90fd\u5927\u9152\u5e97\u7684\u51fa\u79df\u8f66\uff0c\u544a\u8bc9\u6211\u8f66\u578b\u548c\u8f66\u724c\u53f7\u3002"}], "inference": {"parameters": {"max_length": 100}}, "model-index": [{"name": "mt5-small-dst-crosswoz", "results": [{"task": {"type": "text2text-generation", "name": "dialog state tracking"}, "dataset": {"name": "CrossWOZ", "type": "ConvLab/crosswoz", "split": "test", "revision": "4a3e56082543ed9eecb9c76ef5eadc1aa0cc5ca0"}, "metrics": [{"type": "Joint Goal Accuracy", "value": 62.5, "name": "JGA"}, {"type": "Slot F1", "value": 90.4, "name": "Slot F1"}]}]}]}
transformers pytorch mt5 text2text-generation mt5-small dialog state tracking 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 #dialog state tracking #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-dst-crosswoz

This model is a fine-tuned version of mt5-small on CrossWOZ.

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: 4
- total_train_batch_size: 64
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0

### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
# mt5-small-dst-crosswoz

This model is a fine-tuned version of mt5-small on CrossWOZ.

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: 4
- total_train_batch_size: 64
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #mt5 #text2text-generation #mt5-small #dialog state tracking #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-dst-crosswoz

This model is a fine-tuned version of mt5-small on CrossWOZ.

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: 4
- total_train_batch_size: 64
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #mt5 #text2text-generation #mt5-small #dialog state tracking #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-dst-crosswoz

This model is a fine-tuned version of mt5-small on CrossWOZ.

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: 4
- total_train_batch_size: 64
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1