atmosphere cleanliness food_quality price retention service variety wait_time
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- vreese2414/autotrain-data-test-frank
co2_eq_emissions: 20.85550802376653
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 896929583
- CO2 Emissions (in grams): 20.85550802376653

## Validation Metrics

- Loss: 0.8998094797134399
- Accuracy: 0.717983651226158
- Macro F1: 0.6850466044284794
- Micro F1: 0.717983651226158
- Weighted F1: 0.7093970537930665
- Macro Precision: 0.692166692035814
- Micro Precision: 0.717983651226158
- Weighted Precision: 0.7181745683190863
- Macro Recall: 0.6985625924834511
- Micro Recall: 0.717983651226158
- Weighted Recall: 0.717983651226158


## 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/vreese2414/autotrain-test-frank-896929583
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("vreese2414/autotrain-test-frank-896929583", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("vreese2414/autotrain-test-frank-896929583", use_auth_token=True)

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

outputs = model(**inputs)
```