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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?')
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Dataset used to train philschmid/DistilBERT-Banking77
Evaluation results
- Accuracy on BANKING77self-reported91.990
- Macro F1 on BANKING77self-reported91.990
- Weighted F1 on BANKING77self-reported91.990
- Accuracy on banking77test set self-reported0.922
- Precision Macro on banking77test set self-reported0.926
- Precision Micro on banking77test set self-reported0.922
- Precision Weighted on banking77test set self-reported0.926
- Recall Macro on banking77test set self-reported0.922
- Recall Micro on banking77test set self-reported0.922
- Recall Weighted on banking77test set self-reported0.922