---
tags: autotrain
language: en
widget:
- text: "I am still waiting on my card?"
datasets:
- banking77
model-index:
- name: BERT-Banking77
  results:
  - task: 
      name: Text Classification
      type: text-classification
    dataset:
      name: "BANKING77" 
      type: banking77
    metrics:
       - name: Accuracy
         type: accuracy
         value: 92.64
       - name: Macro F1
         type: macro-f1
         value: 92.64
       - name: Weighted F1
         type: weighted-f1
         value: 92.60
co2_eq_emissions: 0.03330651014155927
---
#  `BERT-Banking77` Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 940131041
- CO2 Emissions (in grams): 0.03330651014155927

## Validation Metrics

- Loss: 0.3505457043647766
- Accuracy: 0.9263261296660118
- Macro F1: 0.9268371013605569
- Micro F1: 0.9263261296660118
- Weighted F1: 0.9259954221865809
- Macro Precision: 0.9305746406646502
- Micro Precision: 0.9263261296660118
- Weighted Precision: 0.929031563971418
- Macro Recall: 0.9263724620088746
- Micro Recall: 0.9263261296660118
- Weighted Recall: 0.9263261296660118


## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/philschmid/autotrain-does-it-work-940131041
```

Or Python API:

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/BERT-Banking77'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')
```