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
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