Checking or savings account Credit card or prepaid card Credit reporting, credit repair services, or other personal consumer reports Debt collection Money transfer, virtual currency, or money service Mortgage Payday loan, title loan, or personal loan Student loan Vehicle loan or lease
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- vinaydngowda/autonlp-data-case-classify-xlnet
co2_eq_emissions: 19.964760910364927
---

# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 496213536
- CO2 Emissions (in grams): 19.964760910364927

## Validation Metrics

- Loss: 0.7149562835693359
- Accuracy: 0.8092592592592592
- Macro F1: 0.8085189591849891
- Micro F1: 0.8092592592592593
- Weighted F1: 0.8085189591849888
- Macro Precision: 0.8137745564384112
- Micro Precision: 0.8092592592592592
- Weighted Precision: 0.8137745564384112
- Macro Recall: 0.8092592592592592
- Micro Recall: 0.8092592592592592
- Weighted Recall: 0.8092592592592592


## 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/vinaydngowda/autonlp-case-classify-xlnet-496213536
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("vinaydngowda/autonlp-case-classify-xlnet-496213536", use_auth_token=True)

inputs = tokenizer("I love AutoNLP", return_tensors="pt")

outputs = model(**inputs)
```