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: 772.9316141161539
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 837626712
- CO2 Emissions (in grams): 772.9316141161539

## Validation Metrics

- Loss: 0.006297225132584572
- Accuracy: 0.998357670693734
- Macro F1: 0.9947282131241516
- Micro F1: 0.998357670693734
- Weighted F1: 0.9983564218124292
- Macro Precision: 0.9937572688417448
- Micro Precision: 0.998357670693734
- Weighted Precision: 0.9983587534033106
- Macro Recall: 0.9957326552198976
- Micro Recall: 0.998357670693734
- Weighted Recall: 0.998357670693734


## 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-837626712
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626712", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto_all-837626712", use_auth_token=True)

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

outputs = model(**inputs)
```