readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: pump_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. -->
  
  # pump_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 three categories.
  These three categories are the subcategories of Pump - essentially when a user asks a question and expects an answer in response
  
  - Value: a slot value or a calculation
  - Clarification: Asking for further information on a previous answer
  - Testing: Testing for knowledge of facts and definitions
  
  Takes a user input of string text and classifies it according to one of three categories.
  
  ## Intended uses & limitations
  
  
  from transformers import pipeline
  classifier = pipeline("text-classification",model="mp6kv/pump_intent_test")
  
  
  output = classifier("What is the value of the length of the blue object?")
  
  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
  