# 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
{"language": "en", "tags": ["banking", "intent", "multiclass"], "datasets": ["banking77"], "widget": [{"text": "How long until my transfer goes through?"}]}
transformers pytorch safetensors roberta text-classification banking intent multiclass en dataset:banking77 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #banking #intent #multiclass #en #dataset-banking77 #autotrain_compatible #endpoints_compatible #has_space #region-us 

# distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896

## How to use



> Created by Manuel Romero/@mrm8488 | LinkedIn
> Made with <span style="color: #e25555;">&hearts;</span> in Spain
# distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896 ## How to use



> Created by Manuel Romero/@mrm8488 | LinkedIn
> Made with <span style="color: #e25555;">&hearts;</span> in Spain
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #banking #intent #multiclass #en #dataset-banking77 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896 ## How to use



> Created by Manuel Romero/@mrm8488 | LinkedIn
> Made with <span style="color: #e25555;">&hearts;</span> in Spain
passage: TAGS
#transformers #pytorch #safetensors #roberta #text-classification #banking #intent #multiclass #en #dataset-banking77 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896## How to use



> Created by Manuel Romero/@mrm8488 | LinkedIn
> Made with <span style="color: #e25555;">&hearts;</span> in Spain