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mrm8488
/
distilroberta-finetuned-banking77

Text Classification
Transformers
PyTorch
Safetensors
English
roberta
banking
intent
multiclass
Inference Endpoints
Model card Files Community
2
distilroberta-finetuned-banking77
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---
language: en
tags:
- banking
- intent
- multiclass
datasets:
- banking77
widget:
- text: How long until my transfer goes through?
model-index:
- name: mrm8488/distilroberta-finetuned-banking77
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: banking77
      type: banking77
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8961038961038961
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.9063619413451185
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.8961038961038961
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.906361941345118
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8961038961038963
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.8961038961038961
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.8961038961038961
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8914970725184695
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.8961038961038962
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.8914970725184699
      verified: true
    - name: loss
      type: loss
      value: 0.5607758164405823
      verified: true
---
# distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896

## How to use

```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

ckpt = 'mrm8488/distilroberta-finetuned-banking77'
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = AutoModelForSequenceClassification.from_pretrained(ckpt)

classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')
# Output: [{'label': 'exchange_rate', 'score': 0.8509947657585144}]
```

> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">&hearts;</span> in Spain