Edit model card

pump_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 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
Downloads last month
5
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.