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---
license: mit
tags:
- generated_from_trainer
model-index:
- name: main_intent_test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
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