---
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: 91.99
- name: Macro F1
type: macro-f1
value: 91.99
- name: Weighted F1
type: weighted-f1
value: 91.99
- task:
type: text-classification
name: Text Classification
dataset:
name: banking77
type: banking77
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.922077922077922
verified: true
- name: Precision Macro
type: precision
value: 0.9256326708783564
verified: true
- name: Precision Micro
type: precision
value: 0.922077922077922
verified: true
- name: Precision Weighted
type: precision
value: 0.9256326708783565
verified: true
- name: Recall Macro
type: recall
value: 0.922077922077922
verified: true
- name: Recall Micro
type: recall
value: 0.922077922077922
verified: true
- name: Recall Weighted
type: recall
value: 0.922077922077922
verified: true
- name: F1 Macro
type: f1
value: 0.9221617304411865
verified: true
- name: F1 Micro
type: f1
value: 0.922077922077922
verified: true
- name: F1 Weighted
type: f1
value: 0.9221617304411867
verified: true
- name: loss
type: loss
value: 0.31692808866500854
verified: true
co2_eq_emissions: 5.632805352029529
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 940131045
- CO2 Emissions (in grams): 5.632805352029529
## Validation Metrics
- Loss: 0.3392622470855713
- Accuracy: 0.9199410609037328
- Macro F1: 0.9199390885956755
- Micro F1: 0.9199410609037327
- Weighted F1: 0.9198140295005729
- Macro Precision: 0.9235531521509113
- Micro Precision: 0.9199410609037328
- Weighted Precision: 0.9228777883152248
- Macro Recall: 0.919570805773292
- Micro Recall: 0.9199410609037328
- Weighted Recall: 0.9199410609037328
## 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-940131045
```
Or Python API:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/DistilBERT-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?')
```