Address Company Job Name OOV Zip
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lucifermorninstar011/autotrain-data-lucifer_multi_auto_all
co2_eq_emissions: 675.6911996033854
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 837626708
- CO2 Emissions (in grams): 675.6911996033854

## Validation Metrics

- Loss: 0.008128546178340912
- Accuracy: 0.9977804696723191
- Macro F1: 0.9942781700973885
- Micro F1: 0.9977804696723191
- Weighted F1: 0.9977851755386459
- Macro Precision: 0.9923939243012706
- Micro Precision: 0.9977804696723191
- Weighted Precision: 0.9977957481683986
- Macro Recall: 0.9961924323977192
- Micro Recall: 0.9977804696723191
- Weighted Recall: 0.9977804696723191


## 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/lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626708
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626708", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626708", use_auth_token=True)

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

outputs = model(**inputs)
```