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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