
# 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?')
```
{"language": "en", "tags": "autotrain", "datasets": ["banking77"], "widget": [{"text": "I am still waiting on my card?"}], "co2_eq_emissions": 5.632805352029529, "model-index": [{"name": "BERT-Banking77", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "BANKING77", "type": "banking77"}, "metrics": [{"type": "accuracy", "value": 91.99, "name": "Accuracy"}, {"type": "macro-f1", "value": 91.99, "name": "Macro F1"}, {"type": "weighted-f1", "value": 91.99, "name": "Weighted F1"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "banking77", "type": "banking77", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.922077922077922, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.9256326708783564, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.922077922077922, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.9256326708783565, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.922077922077922, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.922077922077922, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.922077922077922, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.9221617304411865, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.922077922077922, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.9221617304411867, "name": "F1 Weighted", "verified": true}, {"type": "loss", "value": 0.31692808866500854, "name": "loss", "verified": true}]}]}]}
transformers pytorch distilbert text-classification autotrain en dataset:banking77 model-index co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-banking77 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# 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:



Or Python API:


# 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:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-banking77 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # 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:



Or Python API:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-banking77 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# 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:



Or Python API: