---
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