---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- palakagl/autotrain-data-PersonalAssitant
co2_eq_emissions: 7.025108874009706
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221787
- CO2 Emissions (in grams): 7.025108874009706

## Validation Metrics

- Loss: 0.35467109084129333
- Accuracy: 0.9186046511627907
- Macro F1: 0.9202890631142154
- Micro F1: 0.9186046511627907
- Weighted F1: 0.9185859051606837
- Macro Precision: 0.921802482563032
- Micro Precision: 0.9186046511627907
- Weighted Precision: 0.9210238644296779
- Macro Recall: 0.9218155764486292
- Micro Recall: 0.9186046511627907
- Weighted Recall: 0.9186046511627907


## 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/palakagl/autotrain-PersonalAssitant-717221787
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221787", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221787", use_auth_token=True)

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

outputs = model(**inputs)
```