README.md

---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- madroid/autotrain-data-flex-demo-2
co2_eq_emissions:
  emissions: 0.01231885764237346
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 89717143986
- CO2 Emissions (in grams): 0.0123

## Validation Metrics

- Loss: 0.112
- Accuracy: 0.990
- Macro F1: 0.976
- Micro F1: 0.990
- Weighted F1: 0.990
- Macro Precision: 0.978
- Micro Precision: 0.990
- Weighted Precision: 0.990
- Macro Recall: 0.974
- Micro Recall: 0.990
- Weighted Recall: 0.990


## 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/madroid/autotrain-flex-demo-2-89717143986
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("madroid/autotrain-flex-demo-2-89717143986", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("madroid/autotrain-flex-demo-2-89717143986", use_auth_token=True)

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

outputs = model(**inputs)
```