README.md

---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- hafezd-datasaur/autotrain-data-7206-17163967-caa2-4b1a-89b2-62f2ce6b22b8
co2_eq_emissions:
  emissions: 0.022444083442419303
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 72580138964
- CO2 Emissions (in grams): 0.0224

## Validation Metrics

- Loss: 1.573
- Accuracy: 0.318
- Macro F1: 0.097
- Micro F1: 0.318
- Weighted F1: 0.154
- Macro Precision: 0.064
- Micro Precision: 0.318
- Weighted Precision: 0.101
- Macro Recall: 0.200
- Micro Recall: 0.318
- Weighted Recall: 0.318


## 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/hafezd-datasaur/autotrain-7206-17163967-caa2-4b1a-89b2-62f2ce6b22b8-72580138964
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("hafezd-datasaur/autotrain-7206-17163967-caa2-4b1a-89b2-62f2ce6b22b8-72580138964", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("hafezd-datasaur/autotrain-7206-17163967-caa2-4b1a-89b2-62f2ce6b22b8-72580138964", use_auth_token=True)

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

outputs = model(**inputs)
```