---
license: unknown
datasets:
- PolyAI/minds14
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- FacebookAI/roberta-base
pipeline_tag: text-classification
model-index:
- name: roBERTa-minds14-en-classifier
  results:
  - task:
      type: text-classification
    dataset:
      name: minds-14
      type: en
    metrics:
    - name: Accuracy
      type: Accuracy
      value: 0.9724
  - task:
      type: text-classification
    dataset:
      name: minds-14
      type: en
    metrics:
    - name: Precision
      type: Precision
      value: 0.9736
  - task:
      type: text-classification
    dataset:
      name: minds-14
      type: en
    metrics:
    - name: Recall
      type: Recall
      value: 0.9724
  - task:
      type: text-classification
    dataset:
      name: minds-14
      type: en
    metrics:
    - name: f1
      type: f1
      value: 0.9724
---
this model based on roberta model that trained with minds-14 dataset, only trained in english version : enUS + enAU + enGB

the intent_classes available:
```python
intent_classes = {
    0: 'abroad',
    1: 'address',
    2: 'app_error',
    3: 'atm_limit',
    4: 'balance',
    5: 'business_loan',
    6: 'card_issues',
    7: 'cash_deposit',
    8: 'direct_debit',
    9: 'freeze',
    10: 'high_value_payment',
    11: 'joint_account',
    12: 'latest_transactions',
    13: 'pay_bill'
}
```
example of using model to classify intent:
```python
>>> from transformers import pipeline

model = "/content/RoBERTa-mind14-classifier-intent"
classifier = pipeline("text-classification", model=model)

text = "hi what's the maximum amount of money I can withdraw from" # Replace with your desired input text

prediction = classifier(text)
prediction
```
example output:
```python
[{'label': 'LABEL_3', 'score': 0.9933607578277588}]
```