# `RoBERTa-Banking77` trained using autoNLP


- Problem type: Multi-class Classification

## Validation Metrics

- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision: 0.935064935064935
- Weighted Precision: 0.9372295644352717
- Macro Recall: 0.9350649350649349
- Micro Recall: 0.935064935064935
- Weighted Recall: 0.935064935064935


## 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 AutoNLP"}' https://api-inference.huggingface.co/models/philschmid/RoBERTa-Banking77
```

Or Python API:

```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_id = 'philschmid/RoBERTa-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": "autonlp", "datasets": ["banking77"], "widget": [{"text": "I am still waiting on my card?"}], "model-index": [{"name": "RoBERTa-Banking77", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "BANKING77", "type": "banking77"}, "metrics": [{"type": "accuracy", "value": 93.51, "name": "Accuracy"}, {"type": "macro-f1", "value": 93.49, "name": "Macro F1"}, {"type": "weighted-f1", "value": 93.49, "name": "Weighted F1"}]}]}]}
transformers pytorch roberta text-classification autonlp en dataset:banking77 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-banking77 #model-index #autotrain_compatible #endpoints_compatible #region-us 

# 'RoBERTa-Banking77' trained using autoNLP


- Problem type: Multi-class Classification

## Validation Metrics

- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision: 0.935064935064935
- Weighted Precision: 0.9372295644352717
- Macro Recall: 0.9350649350649349
- Micro Recall: 0.935064935064935
- Weighted Recall: 0.935064935064935


## Usage

You can use cURL to access this model:



Or Python API:


# 'RoBERTa-Banking77' trained using autoNLP


- Problem type: Multi-class Classification ## Validation Metrics

- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision: 0.935064935064935
- Weighted Precision: 0.9372295644352717
- Macro Recall: 0.9350649350649349
- Micro Recall: 0.935064935064935
- Weighted Recall: 0.935064935064935 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-banking77 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # 'RoBERTa-Banking77' trained using autoNLP


- Problem type: Multi-class Classification ## Validation Metrics

- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision: 0.935064935064935
- Weighted Precision: 0.9372295644352717
- Macro Recall: 0.9350649350649349
- Micro Recall: 0.935064935064935
- Weighted Recall: 0.935064935064935 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-banking77 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# 'RoBERTa-Banking77' trained using autoNLP


- Problem type: Multi-class Classification## Validation Metrics

- Loss: 0.27382662892341614
- Accuracy: 0.935064935064935
- Macro F1: 0.934939412967268
- Micro F1: 0.935064935064935
- Weighted F1: 0.934939412967268
- Macro Precision: 0.9372295644352715
- Micro Precision: 0.935064935064935
- Weighted Precision: 0.9372295644352717
- Macro Recall: 0.9350649350649349
- Micro Recall: 0.935064935064935
- Weighted Recall: 0.935064935064935## Usage

You can use cURL to access this model:



Or Python API: