
# diwank/silicone-deberta-pair

`deberta-base`-based dialog acts classifier. Trained on the `balanced` variant of the [silicone-merged](https://huggingface.co/datasets/diwank/silicone-merged) dataset: a simplified merged dialog act data from datasets in the [silicone](https://huggingface.co/datasets/silicone) collection. 

Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. **Outputs one of 11 labels**:

```python
(0, 'acknowledge')
(1, 'answer')
(2, 'backchannel')
(3, 'reply_yes')
(4, 'exclaim')
(5, 'say')
(6, 'reply_no')
(7, 'hold')
(8, 'ask')
(9, 'intent')
(10, 'ask_yes_no')
```

## Example:

```python
from simpletransformers.classification import (
    ClassificationModel, ClassificationArgs
)

model = ClassificationModel("deberta", "diwank/silicone-deberta-pair")
convert_to_label = lambda n: [
    ['acknowledge',
     'answer',
     'backchannel',
     'reply_yes',
     'exclaim',
     'say',
     'reply_no',
     'hold',
     'ask',
     'intent',
     'ask_yes_no'
    ][i] for i in n
]

predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]])
convert_to_label(predictions)  # answer

```


## Report from W&B

https://wandb.ai/diwank/da-silicone-combined/reports/silicone-deberta-pair--VmlldzoxNTczNjE5?accessToken=yj1jz4c365z0y5b3olgzye7qgsl7qv9lxvqhmfhtb6300hql6veqa5xiq1skn8ys
{"license": "mit"}
transformers pytorch tf deberta text-classification license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# diwank/silicone-deberta-pair

'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. 

Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of 11 labels:



## Example:




## Report from W&B

URL
# diwank/silicone-deberta-pair

'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. 

Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of 11 labels: ## Example: ## Report from W&B

URL
TAGS
#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # diwank/silicone-deberta-pair

'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. 

Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of 11 labels: ## Example: ## Report from W&B

URL
passage: TAGS
#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# diwank/silicone-deberta-pair

'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. 

Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of 11 labels:## Example:## Report from W&B

URL