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?')
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Dataset used to train philschmid/BERT-Banking77
Evaluation results
- Accuracy on BANKING77self-reported92.640
- Macro F1 on BANKING77self-reported92.640
- Weighted F1 on BANKING77self-reported92.600
- Accuracy on banking77test set verified0.928
- Precision Macro on banking77test set verified0.931
- Precision Micro on banking77test set verified0.928
- Precision Weighted on banking77test set verified0.931
- Recall Macro on banking77test set verified0.928
- Recall Micro on banking77test set verified0.928
- Recall Weighted on banking77test set verified0.928