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# main_intent_test

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.

## Model description

Custom data generated labeling text according to these five categories.
Five categories represent the five essential intents of a user for the ACTS scenario.

- Connect : Greetings and introduction with the student
- Pump : Asking the student for information
- Inform : Providing information to the student 
- Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback)
- None : Not related to scenario

Takes a user input of string text and classifies it according to one of five categories.

## Intended uses & limitations


from transformers import pipeline
classifier = pipeline("text-classification",model="mp6kv/main_intent_test")


output = classifier("great job, you're getting it!")

score = output[0]['score']

label = output[0]['label']

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "main_intent_test", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# main_intent_test

This model is a fine-tuned version of roberta-base on an unknown dataset.

## Model description

Custom data generated labeling text according to these five categories.
Five categories represent the five essential intents of a user for the ACTS scenario.

- Connect : Greetings and introduction with the student
- Pump : Asking the student for information
- Inform : Providing information to the student 
- Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback)
- None : Not related to scenario

Takes a user input of string text and classifies it according to one of five categories.

## Intended uses & limitations


from transformers import pipeline
classifier = pipeline("text-classification",model="mp6kv/main_intent_test")


output = classifier("great job, you're getting it!")

score = output[0]['score']

label = output[0]['label']

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6

# main_intent_test

This model is a fine-tuned version of roberta-base on an unknown dataset. ## Model description

Custom data generated labeling text according to these five categories.
Five categories represent the five essential intents of a user for the ACTS scenario.

- Connect : Greetings and introduction with the student
- Pump : Asking the student for information
- Inform : Providing information to the student 
- Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback)
- None : Not related to scenario

Takes a user input of string text and classifies it according to one of five categories. ## Intended uses & limitations


from transformers import pipeline
classifier = pipeline("text-classification",model="mp6kv/main_intent_test")


output = classifier("great job, you're getting it!")

score = output[0]['score']

label = output[0]['label'] ## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # main_intent_test

This model is a fine-tuned version of roberta-base on an unknown dataset. ## Model description

Custom data generated labeling text according to these five categories.
Five categories represent the five essential intents of a user for the ACTS scenario.

- Connect : Greetings and introduction with the student
- Pump : Asking the student for information
- Inform : Providing information to the student 
- Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback)
- None : Not related to scenario

Takes a user input of string text and classifies it according to one of five categories. ## Intended uses & limitations


from transformers import pipeline
classifier = pipeline("text-classification",model="mp6kv/main_intent_test")


output = classifier("great job, you're getting it!")

score = output[0]['score']

label = output[0]['label'] ## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# main_intent_test

This model is a fine-tuned version of roberta-base on an unknown dataset.## Model description

Custom data generated labeling text according to these five categories.
Five categories represent the five essential intents of a user for the ACTS scenario.

- Connect : Greetings and introduction with the student
- Pump : Asking the student for information
- Inform : Providing information to the student 
- Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback)
- None : Not related to scenario

Takes a user input of string text and classifies it according to one of five categories.## Intended uses & limitations


from transformers import pipeline
classifier = pipeline("text-classification",model="mp6kv/main_intent_test")


output = classifier("great job, you're getting it!")

score = output[0]['score']

label = output[0]['label']## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5### Training results### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6